//Code for Risk models within "Usher-Smith JA, Sharp SJ, Luben R, Griffin SJ. //Development and validation of lifestyle-based models to predict incidence of the most common preventable cancers //Cancer Epidemiology, Biomarkers and Prevention" //1. Set all fixed inputs //1a. RR for each cancer based on literature //Alcohol - RR per gram of alcohol per day gen rr_alcohol_breast = 1.0068 gen rr_alcohol_crc = 0.006993 gen rr_alcohol_oesoph = 1.0129 //Red meat and processed meat - RR per gram of red / processed meat per day gen rr_redmeat_crc = 1.0016 gen rr_procmeat_crc = 1.0033 //Smoking - RR compared to non-smoker gen rr_smoking_lung = 8.96 gen rr_exsmoking_lung = 3.85 gen rr_smoking_oesoph = 2.50 gen rr_exsmoking_oesoph = 2.03 gen rr_smoking_crc = 1.2 gen rr_exsmoking_crc = 1.2 gen rr_smoking_bladder = 3.14 gen rr_exsmoking_bladder = 1.83 gen rr_smoking_kidney = 1.52 gen rr_exsmoking_kidney = 1.25 //Fruit - RR per gram of fruit per day gen rr_fruit_oesoph = 0.994 gen rr_fruit_lung = 0.999 gen rr_veg_oesoph = 0.9972 //Physical activity - RR per MET-hr per week (1 MET-hr = 10 mins moderate activity) gen rr_pa_breast = 0.997 gen rr_pa_crc = 0.9940 gen rr_pa_endomet = 0.9933 //BMI - RR per unit increase of BMI gen rr_bmi_breast = 1.0229 gen rr_bmi_prebreast = 0.9856 gen rr_bmi_crc = 1.030 gen rr_bmi_oesoph = 1.087 gen rr_bmi_kidney = 1.04 gen rr_bmi_endomet = 1.034 //1b. Average valaues from Health Survey for England or National Diet and Nutrition Survey //For ages under 40 use 20kg/m2 for BMI and 0 for others just as place holders for future adaptation. //No data for individuals under age 40 used in subsequent validation. gen bmi_mean_female_20 = 20 gen bmi_mean_female_30 = 20 gen bmi_mean_female_40 = 27.3 gen bmi_mean_female_50 = 27.9 gen bmi_mean_female_60 = 28.3 gen bmi_mean_female_70 = 27.7 gen bmi_mean_female_80 = 27.7 gen bmi_mean_male_20 = 20 gen bmi_mean_male_30 = 20 gen bmi_mean_male_40 = 28 gen bmi_mean_male_50 = 28.1 gen bmi_mean_male_60 = 28.1 gen bmi_mean_male_70 = 27.5 gen bmi_mean_male_80 = 27.5 gen alcohol_mean_female_20 = 0 gen alcohol_mean_female_30 = 0 gen alcohol_mean_female_40 = 9.4 gen alcohol_mean_female_50 = 10.9 gen alcohol_mean_female_60 = 7.6 gen alcohol_mean_female_70 = 4.7 gen alcohol_mean_female_80 = 4.7 gen alcohol_mean_male_20 = 0 gen alcohol_mean_male_30 = 0 gen alcohol_mean_male_40 = 25.9 gen alcohol_mean_male_50 = 21.5 gen alcohol_mean_male_60 = 18.9 gen alcohol_mean_male_70 = 11.0 gen alcohol_mean_male_80 = 110. gen redmeat_mean_female_20 = 0 gen redmeat_mean_female_30 = 0 gen redmeat_mean_female_40 = 40.4 gen redmeat_mean_female_50 = 47.7 gen redmeat_mean_female_60 = 45.9 gen redmeat_mean_female_70 = 38.5 gen redmeat_mean_female_80 = 38.5 gen redmeat_mean_male_20 = 0 gen redmeat_mean_male_30 = 0 gen redmeat_mean_male_40 = 67.8 gen redmeat_mean_male_50 = 58.1 gen redmeat_mean_male_60 = 63.2 gen redmeat_mean_male_70 = 48.7 gen redmeat_mean_male_80 = 48.7 gen procmeat_mean_female_20 = 0 gen procmeat_mean_female_30 = 0 gen procmeat_mean_female_40 = 14.2 gen procmeat_mean_female_50 = 13.2 gen procmeat_mean_female_60 = 15.6 gen procmeat_mean_female_70 = 14.0 gen procmeat_mean_female_80 = 14.0 gen procmeat_mean_male_20 = 0 gen procmeat_mean_male_30 = 0 gen procmeat_mean_male_40 = 20.9 gen procmeat_mean_male_50 = 22.4 gen procmeat_mean_male_60 = 23.5 gen procmeat_mean_male_70 = 15.7 gen procmeat_mean_male_80 = 15.7 gen pa_mean_female_20 = 0 gen pa_mean_female_30 = 0 gen pa_mean_female_40 = 7.5 gen pa_mean_female_50 = 6.4 gen pa_mean_female_60 = 6.1 gen pa_mean_female_70 = 2.7 gen pa_mean_female_80 = 2.7 gen pa_mean_male_20 = 0 gen pa_mean_male_30 = 0 gen pa_mean_male_40 = 13.7 gen pa_mean_male_50 = 13.2 gen pa_mean_male_60 = 12.6 gen pa_mean_male_70 = 7.4 gen pa_mean_male_80 = 7.4 gen smokstat_mean_female_20 = 0 gen smokstat_mean_female_30 = 0 gen smokstat_mean_female_40 = 0.305 gen smokstat_mean_female_50 = 0.233 gen smokstat_mean_female_60 = 0.155 gen smokstat_mean_female_70 = 0.117 gen smokstat_mean_female_80 = 0.117 gen exsmokstat_mean_female_20 = 0 gen exsmokstat_mean_female_30 = 0 gen exsmokstat_mean_female_40 = 0.166 gen exsmokstat_mean_female_50 = 0.245 gen exsmokstat_mean_female_60 = 0.297 gen exsmokstat_mean_female_70 = 0.311 gen exsmokstat_mean_female_80 = 0.311 gen smokstat_mean_male_20 = 0 gen smokstat_mean_male_30 = 0 gen smokstat_mean_male_40 = 0.31 gen smokstat_mean_male_50 = 0.259 gen smokstat_mean_male_60 = 0.144 gen smokstat_mean_male_70 = 0.125 gen smokstat_mean_male_80 = 0.125 gen exsmokstat_mean_male_20 = 0 gen exsmokstat_mean_male_30 = 0 gen exsmokstat_mean_male_40 = 0.245 gen exsmokstat_mean_male_50 = 0.329 gen exsmokstat_mean_male_60 = 0.505 gen exsmokstat_mean_male_70 = 0.560 gen exsmokstat_mean_male_80 = 0.560 gen fruit_mean_female_20 = 0 gen fruit_mean_female_30 = 0 gen fruit_mean_female_40 = 173.9 gen fruit_mean_female_50 = 210.4 gen fruit_mean_female_60 = 220.8 gen fruit_mean_female_70 = 205.8 gen fruit_mean_female_80 = 205.8 gen fruit_mean_male_20 = 0 gen fruit_mean_male_30 = 0 gen fruit_mean_male_40 = 168.3 gen fruit_mean_male_50 = 175 gen fruit_mean_male_60 = 196.9 gen fruit_mean_male_70 = 187.1 gen fruit_mean_male_80 = 187.1 gen veg_mean_female_20 = 0 gen veg_mean_female_30 = 0 gen veg_mean_female_40 = 124.7 gen veg_mean_female_50 = 126.5 gen veg_mean_female_60 = 122.3 gen veg_mean_female_70 = 111.2 gen veg_mean_female_80 = 111.2 gen veg_mean_male_20 = 0 gen veg_mean_male_30 = 0 gen veg_mean_male_40 = 121.3 gen veg_mean_male_50 = 117.2 gen veg_mean_male_60 = 115.8 gen veg_mean_male_70 = 113 gen veg_mean_male_80 = 113 //1c. Mean absolute risks for each cancer (from Current Probability calculations) gen female_ar_crc_20_mean = 0.029 if age>19.99999 & age<30 gen female_ar_crc_30_mean = 0.085 if age>29.99999 & age<40 gen female_ar_crc_40_mean = 0.17 if age>39.99999 & age<50 gen female_ar_crc_50_mean = 0.47 if age>49.99999 & age<60 gen female_ar_crc_60_mean = 0.96 if age>59.99999 & age<70 gen female_ar_crc_70_mean = 1.6 if age>69.99999 & age<80 gen female_ar_crc_80_mean = 2.17 if age>79.99999 gen male_ar_crc_20_mean = 0.023 if age>19.99999 & age<30 gen male_ar_crc_30_mean = 0.072 if age>29.99999 & age<40 gen male_ar_crc_40_mean = 0.17 if age>39.99999 & age<50 gen male_ar_crc_50_mean = 0.58 if age>49.99999 & age<60 gen male_ar_crc_60_mean = 1.51 if age>59.99999 & age<70 gen male_ar_crc_70_mean = 2.28 if age>69.99999 & age<80 gen male_ar_crc_80_mean = 2.47 if age>79.99999 gen female_ar_lung_20_mean = 0.005 if age>19.99999 & age<30 gen female_ar_lung_30_mean = 0.02 if age>29.99999 & age<40 gen female_ar_lung_40_mean = 0.11 if age>39.99999 & age<50 gen female_ar_lung_50_mean = 0.47 if age>49.99999 & age<60 gen female_ar_lung_60_mean = 1.38 if age>59.99999 & age<70 gen female_ar_lung_70_mean = 2.20 if age>69.99999 & age<80 gen female_ar_lung_80_mean = 2.01 if age>79.99999 gen male_ar_lung_20_mean = 0.005 if age>19.99999 & age<30 gen male_ar_lung_30_mean = 0.017 if age>29.99999 & age<40 gen male_ar_lung_40_mean = 0.10 if age>39.99999 & age<50 gen male_ar_lung_50_mean = 0.46 if age>49.99999 & age<60 gen male_ar_lung_60_mean = 1.57 if age>59.99999 & age<70 gen male_ar_lung_70_mean = 2.74 if age>69.99999 & age<80 gen male_ar_lung_80_mean = 2.68 if age>79.99999 gen female_ar_kidney_20_mean = 0.007 if age>19.99999 & age<30 gen female_ar_kidney_30_mean = 0.015 if age>29.99999 & age<40 gen female_ar_kidney_40_mean = 0.06 if age>39.99999 & age<50 gen female_ar_kidney_50_mean = 0.13 if age>49.99999 & age<60 gen female_ar_kidney_60_mean = 0.25 if age>59.99999 & age<70 gen female_ar_kidney_70_mean = 0.33 if age>69.99999 & age<80 gen female_ar_kidney_80_mean = 0.31 if age>79.99999 gen male_ar_kidney_20_mean = 0.005 if age>19.99999 & age<30 gen male_ar_kidney_30_mean = 0.027 if age>29.99999 & age<40 gen male_ar_kidney_40_mean = 0.12 if age>39.99999 & age<50 gen male_ar_kidney_50_mean = 0.27 if age>49.99999 & age<60 gen male_ar_kidney_60_mean = 0.49 if age>59.99999 & age<70 gen male_ar_kidney_70_mean = 0.55 if age>69.99999 & age<80 gen male_ar_kidney_80_mean = 0.50 if age>79.99999 gen female_ar_breast_20_mean = 0.06 if age>19.99999 & age<30 gen female_ar_breast_30_mean = 0.46 if age>29.99999 & age<40 gen female_ar_breast_40_mean = 1.72 if age>39.99999 & age<50 gen female_ar_breast_50_mean = 2.68 if age>49.99999 & age<60 gen female_ar_breast_60_mean = 3.48 if age>59.99999 & age<70 gen female_ar_breast_70_mean = 3.07 if age>69.99999 & age<80 gen female_ar_breast_80_mean = 3.08 if age>79.99999 gen male_ar_bladder_20_mean = 0.001 if age>19.99999 & age<30 gen male_ar_bladder_30_mean = 0.004 if age>29.99999 & age<40 gen male_ar_bladder_40_mean = 0.03 if age>39.99999 & age<50 gen male_ar_bladder_50_mean = 0.11 if age>49.99999 & age<60 gen male_ar_bladder_60_mean = 0.40 if age>59.99999 & age<70 gen male_ar_bladder_70_mean = 0.79 if age>69.99999 & age<80 gen male_ar_bladder_80_mean = 1.12 if age>79.99999 gen female_ar_endomet_20_mean = 0.004 if age>19.99999 & age<30 gen female_ar_endomet_30_mean = 0.018 if age>29.99999 & age<40 gen female_ar_endomet_40_mean = 0.10 if age>39.99999 & age<50 gen female_ar_endomet_50_mean = 0.40 if age>49.99999 & age<60 gen female_ar_endomet_60_mean = 0.70 if age>59.99999 & age<70 gen female_ar_endomet_70_mean = 0.71 if age>69.99999 & age<80 gen female_ar_endomet_80_mean = 0.45 if age>79.99999 gen male_ar_oesoph_20_mean = 0.001 if age>19.99999 & age<30 gen male_ar_oesoph_30_mean = 0.008 if age>29.99999 & age<40 gen male_ar_oesoph_40_mean = 0.037 if age>39.99999 & age<50 gen male_ar_oesoph_50_mean = 0.19 if age>49.99999 & age<60 gen male_ar_oesoph_60_mean = 0.46 if age>59.99999 & age<70 gen male_ar_oesoph_70_mean = 0.63 if age>69.99999 & age<80 gen male_ar_oesoph_80_mean = 0.57 if age>79.99999 //1d Combined cancer mean risks for each age group and overall to allow presentation of those in one of the new risk formats later gen female_ar_20_mean = 0.103 if age>19.99999 & age<30 gen female_ar_30_mean = 0.594 if age>29.99999 & age<40 gen female_ar_40_mean = 2.15 if age>39.99999 & age<50 gen female_ar_50_mean = 4.15 if age>49.99999 & age<60 gen female_ar_60_mean = 6.77 if age>59.99999 & age<70 gen female_ar_70_mean = 7.91 if age>69.99999 & age<80 gen female_ar_80_mean = 8.02 if age>79.99999 gen male_ar_20_mean = 0.034 if age>19.99999 & age<30 gen male_ar_30_mean = 0.127 if age>29.99999 & age<40 gen male_ar_40_mean = 0.458 if age>39.99999 & age<50 gen male_ar_50_mean = 1.61 if age>49.99999 & age<60 gen male_ar_60_mean = 4.43 if age>59.99999 & age<70 gen male_ar_70_mean = 7.0 if age>69.99999 & age<80 gen male_ar_80_mean = 7.32 if age>79.99999 egen female_ar_mean = rowtotal(female_ar_20_mean female_ar_30_mean female_ar_40_mean female_ar_50_mean female_ar_60_mean female_ar_70_mean female_ar_80_mean) if sex==0 egen male_ar_mean = rowtotal(male_ar_20_mean male_ar_30_mean male_ar_40_mean male_ar_50_mean male_ar_60_mean male_ar_70_mean male_ar_80_mean) if sex==1 //2. Calculate RISK RELATIVE TO IDEAL for individual cancers by multiplying RR for each risk factor //contributing to that cancer. The RR for each is calcuated as the RR per unit to the power //of the number of units and this is written to be relative to the "ideal person" - i.e. //someone who has a BMI of 25, drinks no alcohol, eats no red or processed meat, does 15 MET-hr //of physical activity per week, does not smoke and eats 5 portions of fruit and vegetables //a day. For BMI, physical activity and fruit and vegetable intake, the "unit" for each person //is therefore the difference between their current value and the "ideal" i.e. someone with a //BMI of 30 will have a RR of the RR for each unit of BMI * (30-25). //Female gen female_crc = 1 replace female_crc = (rr_bmi_crc^(bmi-25)) * (exp((rr_alcohol_crc*alcohol)-(0.00001*(alcohol^2)))) * (rr_redmeat_crc^redmeat) * (rr_procmeat_crc^procmeat) * (rr_pa_crc^(pa-15)) * (rr_smoking_crc*1) if smokstat==1 replace female_crc = (rr_bmi_crc^(bmi-25)) * (exp((rr_alcohol_crc*alcohol)-(0.00001*(alcohol^2)))) * (rr_redmeat_crc^redmeat) * (rr_procmeat_crc^procmeat) * (rr_pa_crc^(pa-15)) * (rr_exsmoking_crc*1) if smokstat==2 replace female_crc = (rr_bmi_crc^(bmi-25)) * (exp((rr_alcohol_crc*alcohol)-(0.00001*(alcohol^2)))) * (rr_redmeat_crc^redmeat) * (rr_procmeat_crc^procmeat) * (rr_pa_crc^(pa-15)) if smokstat==0 gen female_lung =1 replace female_lung = (rr_fruit_lung^(fruit-200)) * (rr_smoking_lung*1) if smokstat==1 replace female_lung = (rr_fruit_lung^(fruit-200)) * (rr_exsmoking_lung*1) if smokstat==2 replace female_lung = (rr_fruit_lung^(fruit-200)) if smokstat==0 gen female_kidney = 1 replace female_kidney = (rr_bmi_kidney^(bmi-25)) * (rr_smoking_kidney*1) if smokstat==1 replace female_kidney = (rr_bmi_kidney^(bmi-25)) * (rr_exsmoking_kidney*1) if smokstat==2 replace female_kidney = (rr_bmi_kidney^(bmi-25)) if smokstat==0 gen female_breast = 1 replace female_breast = (rr_bmi_breast^(bmi-25)) * (rr_alcohol_breast^alcohol) * (rr_pa_breast^(pa-15)) if age>49.99999999999 replace female_breast = (rr_bmi_prebreast^(bmi-25)) * (rr_alcohol_breast^alcohol) * (rr_pa_breast^(pa-15)) if age<50 gen female_endomet = 1 replace female_endomet = (rr_bmi_endomet^(bmi-25)) * (rr_pa_endomet^(pa-15)) //Male gen male_crc = 1 replace male_crc = (rr_bmi_crc^(bmi-25)) * (exp((rr_alcohol_crc*alcohol)-(0.00001*(alcohol^2)))) * (rr_redmeat_crc^redmeat) * (rr_procmeat_crc^procmeat) * (rr_pa_crc^(pa-15)) * (rr_smoking_crc*1) if smokstat==1 replace male_crc = (rr_bmi_crc^(bmi-25)) * (exp((rr_alcohol_crc*alcohol)-(0.00001*(alcohol^2)))) * (rr_redmeat_crc^redmeat) * (rr_procmeat_crc^procmeat) * (rr_pa_crc^(pa-15)) * (rr_exsmoking_crc*1) if smokstat==2 replace male_crc = (rr_bmi_crc^(bmi-25)) * (exp((rr_alcohol_crc*alcohol)-(0.00001*(alcohol^2)))) * (rr_redmeat_crc^redmeat) * (rr_procmeat_crc^procmeat) * (rr_pa_crc^(pa-15)) if smokstat==0 gen male_lung =1 replace male_lung = (rr_fruit_lung^(fruit-200)) * (rr_smoking_lung*1) if smokstat==1 replace male_lung = (rr_fruit_lung^(fruit-200)) * (rr_exsmoking_lung*1) if smokstat==2 replace male_lung = (rr_fruit_lung^(fruit-200)) if smokstat==0 gen male_bladder = 1 replace male_bladder = (rr_smoking_bladder*1) if smokstat==1 replace male_bladder = (rr_exsmoking_bladder*1) if smokstat==2 replace male_bladder = 1 if smokstat==0 gen male_kidney = 1 replace male_kidney = (rr_bmi_kidney^(bmi-25)) * (rr_smoking_kidney*1) if smokstat==1 replace male_kidney = (rr_bmi_kidney^(bmi-25)) * (rr_exsmoking_kidney*1) if smokstat==2 replace male_kidney = (rr_bmi_kidney^(bmi-25)) if smokstat==0 gen male_oesoph = 1 replace male_oesoph = (rr_bmi_oesoph^(bmi-25)) * (rr_alcohol_oesoph^alcohol) * (rr_fruit_oesoph^(fruit-200)) * (rr_veg_oesoph^(veg-200)) * (rr_smoking_oesoph*1) if smokstat==1 replace male_oesoph = (rr_bmi_oesoph^(bmi-25)) * (rr_alcohol_oesoph^alcohol) * (rr_fruit_oesoph^(fruit-200)) * (rr_veg_oesoph^(veg-200)) * (rr_exsmoking_oesoph*1) if smokstat==2 replace male_oesoph = (rr_bmi_oesoph^(bmi-25)) * (rr_alcohol_oesoph^alcohol) * (rr_fruit_oesoph^(fruit-200)) * (rr_veg_oesoph^(veg-200)) if smokstat==0 // Generate variables for each age which are then needed later gen female_crc_20 = female_crc if age>19.99999 & age<30 gen female_crc_30 = female_crc if age>29.99999 & age<40 gen female_crc_40 = female_crc if age>39.99999 & age<50 gen female_crc_50 = female_crc if age>49.99999 & age<60 gen female_crc_60 = female_crc if age>59.99999 & age<70 gen female_crc_70 = female_crc if age>69.99999 & age<80 gen female_crc_80 = female_crc if age>79.99999 gen male_crc_20 = male_crc if age>19.99999 & age<30 gen male_crc_30 = male_crc if age>29.99999 & age<40 gen male_crc_40 = male_crc if age>39.99999 & age<50 gen male_crc_50 = male_crc if age>49.99999 & age<60 gen male_crc_60 = male_crc if age>59.99999 & age<70 gen male_crc_70 = male_crc if age>69.99999 & age<80 gen male_crc_80 = male_crc if age>79.99999 gen female_lung_20 = female_lung if age>19.99999 & age<30 gen female_lung_30 = female_lung if age>29.99999 & age<40 gen female_lung_40 = female_lung if age>39.99999 & age<50 gen female_lung_50 = female_lung if age>49.99999 & age<60 gen female_lung_60 = female_lung if age>59.99999 & age<70 gen female_lung_70 = female_lung if age>69.99999 & age<80 gen female_lung_80 = female_lung if age>79.99999 gen male_lung_20 = male_lung if age>19.99999 & age<30 gen male_lung_30 = male_lung if age>29.99999 & age<40 gen male_lung_40 = male_lung if age>39.99999 & age<50 gen male_lung_50 = male_lung if age>49.99999 & age<60 gen male_lung_60 = male_lung if age>59.99999 & age<70 gen male_lung_70 = male_lung if age>69.99999 & age<80 gen male_lung_80 = male_lung if age>79.99999 gen female_kidney_20 = female_kidney if age>19.99999 & age<30 gen female_kidney_30 = female_kidney if age>29.99999 & age<40 gen female_kidney_40 = female_kidney if age>39.99999 & age<50 gen female_kidney_50 = female_kidney if age>49.99999 & age<60 gen female_kidney_60 = female_kidney if age>59.99999 & age<70 gen female_kidney_70 = female_kidney if age>69.99999 & age<80 gen female_kidney_80 = female_kidney if age>79.99999 gen male_kidney_20 = male_kidney if age>19.99999 & age<30 gen male_kidney_30 = male_kidney if age>29.99999 & age<40 gen male_kidney_40 = male_kidney if age>39.99999 & age<50 gen male_kidney_50 = male_kidney if age>49.99999 & age<60 gen male_kidney_60 = male_kidney if age>59.99999 & age<70 gen male_kidney_70 = male_kidney if age>69.99999 & age<80 gen male_kidney_80 = male_kidney if age>79.99999 gen female_breast_20 = female_breast if age>19.99999 & age<30 gen female_breast_30 = female_breast if age>29.99999 & age<40 gen female_breast_40 = female_breast if age>39.99999 & age<50 gen female_breast_50 = female_breast if age>49.99999 & age<60 gen female_breast_60 = female_breast if age>59.99999 & age<70 gen female_breast_70 = female_breast if age>69.99999 & age<80 gen female_breast_80 = female_breast if age>79.99999 gen male_bladder_20 = male_bladder if age>19.99999 & age<30 gen male_bladder_30 = male_bladder if age>29.99999 & age<40 gen male_bladder_40 = male_bladder if age>39.99999 & age<50 gen male_bladder_50 = male_bladder if age>49.99999 & age<60 gen male_bladder_60 = male_bladder if age>59.99999 & age<70 gen male_bladder_70 = male_bladder if age>69.99999 & age<80 gen male_bladder_80 = male_bladder if age>79.99999 gen female_endomet_20 = female_endomet if age>19.99999 & age<30 gen female_endomet_30 = female_endomet if age>29.99999 & age<40 gen female_endomet_40 = female_endomet if age>39.99999 & age<50 gen female_endomet_50 = female_endomet if age>49.99999 & age<60 gen female_endomet_60 = female_endomet if age>59.99999 & age<70 gen female_endomet_70 = female_endomet if age>69.99999 & age<80 gen female_endomet_80 = female_endomet if age>79.99999 gen male_oesoph_20 = male_oesoph if age>19.99999 & age<30 gen male_oesoph_30 = male_oesoph if age>29.99999 & age<40 gen male_oesoph_40 = male_oesoph if age>39.99999 & age<50 gen male_oesoph_50 = male_oesoph if age>49.99999 & age<60 gen male_oesoph_60 = male_oesoph if age>59.99999 & age<70 gen male_oesoph_70 = male_oesoph if age>69.99999 & age<80 gen male_oesoph_80 = male_oesoph if age>79.99999 //Weighted overall for the 5 cancers combined for each age group - this gives the risk for an individual relative to the ideal lifestyle //(weights come from Current Probability calculations) gen female_20 = (female_crc*0.28) + (female_lung*0.05) + (female_breast*0.56) + (female_kidney*0.07) + (female_endomet*0.04) if age>19.99999 & age<30 gen female_30 = (female_crc*0.14) + (female_lung*0.03) + (female_breast*0.77) + (female_kidney*0.02) + (female_endomet*0.03) if age>29.99999 & age<40 gen female_40 = (female_crc*0.08) + (female_lung*0.05) + (female_breast*0.80) + (female_kidney*0.03) + (female_endomet*0.04) if age>39.99999 & age<50 gen female_50 = (female_crc*0.11) + (female_lung*0.11) + (female_breast*0.64) + (female_kidney*0.03) + (female_endomet*0.10) if age>49.99999 & age<60 gen female_60 = (female_crc*0.14) + (female_lung*0.20) + (female_breast*0.51) + (female_kidney*0.04) + (female_endomet*0.10) if age>59.99999 & age<70 gen female_70 = (female_crc*0.20) + (female_lung*0.28) + (female_breast*0.39) + (female_kidney*0.04) + (female_endomet*0.09) if age>69.99999 & age<80 gen female_80 = (female_crc*0.27) + (female_lung*0.25) + (female_breast*0.38) + (female_kidney*0.04) + (female_endomet*0.06) if age>79.99999 egen female_rri = rowtotal(female_20 female_30 female_40 female_50 female_60 female_70 female_80) if sex==0 gen male_20 = (male_crc*0.68) + (male_lung*0.13) + (male_bladder*0.02) + (male_kidney*0.13) + (male_oesoph*0.04) if age>19.99999 & age<30 gen male_30 = (male_crc*0.56) + (male_lung*0.13) + (male_bladder*0.03) + (male_kidney*0.21) + (male_oesoph*0.06) if age>29.99999 & age<40 gen male_40 = (male_crc*0.38) + (male_lung*0.22) + (male_bladder*0.07) + (male_kidney*0.26) + (male_oesoph*0.08) if age>39.99999 & age<50 gen male_50 = (male_crc*0.36) + (male_lung*0.29) + (male_bladder*0.07) + (male_kidney*0.17) + (male_oesoph*0.12) if age>49.99999 & age<60 gen male_60 = (male_crc*0.34) + (male_lung*0.35) + (male_bladder*0.09) + (male_kidney*0.11) + (male_oesoph*0.10) if age>59.99999 & age<70 gen male_70 = (male_crc*0.33) + (male_lung*0.39) + (male_bladder*0.11) + (male_kidney*0.08) + (male_oesoph*0.09) if age>69.99999 & age<80 gen male_80 = (male_crc*0.34) + (male_lung*0.37) + (male_bladder*0.15) + (male_kidney*0.07) + (male_oesoph*0.08) if age>79.99999 egen male_rri = rowtotal(male_20 male_30 male_40 male_50 male_60 male_70 male_80) if sex==1 //3. Calculate RELATIVE RISK OF AVERAGE PERSON RELATIVE TO IDEAL lifestyle (repeating step 2 above with the mean values rather than individual values) //for each individual cancer //Female forvalues i = 20(10)80 { gen female_crc_mean_`i' = 1 } forvalues i =20(10)80 { replace female_crc_mean_`i' = (rr_bmi_crc^(bmi_mean_female_`i'-25)) * (exp((rr_alcohol_crc*alcohol_mean_female_`i')-(0.00001*(alcohol_mean_female_`i'^2)))) * (rr_redmeat_crc^redmeat_mean_female_`i') * (rr_procmeat_crc^procmeat_mean_female_`i') * (rr_pa_crc^(pa_mean_female_`i'-15)) * ((rr_smoking_crc*smokstat_mean_female_`i') + (rr_exsmoking_crc*exsmokstat_mean_female_`i') + (1*(1-smokstat_mean_female_`i'-exsmokstat_mean_female_`i'))) } forvalues i = 20(10)80 { gen female_lung_mean_`i' = 1 } forvalues i =20(10)80 { replace female_lung_mean_`i' = (rr_fruit_lung^(fruit_mean_female_`i'-200)) * ((rr_smoking_lung*smokstat_mean_female_`i') + (rr_exsmoking_lung*exsmokstat_mean_female_`i') + (1*(1-smokstat_mean_female_`i'-exsmokstat_mean_female_`i'))) } forvalues i = 20(10)80 { gen female_kidney_mean_`i' = 1 } forvalues i =20(10)80 { replace female_kidney_mean_`i' = (rr_bmi_kidney^(bmi_mean_female_`i'-25)) * ((rr_smoking_kidney*smokstat_mean_female_`i') + (rr_exsmoking_kidney*exsmokstat_mean_female_`i') + (1*(1-smokstat_mean_female_`i'-exsmokstat_mean_female_`i'))) } forvalues i = 20(10)80 { gen female_breast_mean_`i' = 1 } forvalues i =20(10)40 { replace female_breast_mean_`i' = (rr_bmi_prebreast^(bmi_mean_female_`i'-25)) * (rr_alcohol_breast^alcohol_mean_female_`i') * (rr_pa_breast^(pa_mean_female_`i'-15)) } forvalues i =50(10)80 { replace female_breast_mean_`i' = (rr_bmi_breast^(bmi_mean_female_`i'-25)) * (rr_alcohol_breast^alcohol_mean_female_`i') * (rr_pa_breast^(pa_mean_female_`i'-15)) } forvalues i = 20(10)80 { gen female_endomet_mean_`i' = 1 } forvalues i =20(10)80 { replace female_endomet_mean_`i' = (rr_bmi_endomet^(bmi_mean_female_`i'-25)) * (rr_pa_endomet^(pa_mean_female_`i'-15)) } //Male forvalues i = 20(10)80 { gen male_crc_mean_`i' = 1 } forvalues i = 20(10)80 { replace male_crc_mean_`i' = (rr_bmi_crc^(bmi_mean_male_`i'-25)) * (exp((rr_alcohol_crc*alcohol_mean_male_`i')-(0.00001*(alcohol_mean_male_`i'^2)))) * (rr_redmeat_crc^redmeat_mean_male_`i') * (rr_procmeat_crc^procmeat_mean_male_`i') * (rr_pa_crc^(pa_mean_male_`i'-15)) * ((rr_smoking_crc*smokstat_mean_male_`i') + (rr_exsmoking_crc*exsmokstat_mean_male_`i') + (1*(1-smokstat_mean_male_`i'-exsmokstat_mean_male_`i'))) } forvalues i = 20(10)80 { gen male_lung_mean_`i' = 1 } forvalues i =20(10)80 { replace male_lung_mean_`i' = (rr_fruit_lung^(fruit_mean_male_`i'-200)) * ((rr_smoking_lung*smokstat_mean_male_`i') + (rr_exsmoking_lung*exsmokstat_mean_male_`i') + (1*(1-smokstat_mean_male_`i'-exsmokstat_mean_male_`i'))) } forvalues i = 20(10)80 { gen male_kidney_mean_`i' = 1 } forvalues i =20(10)80 { replace male_kidney_mean_`i' = (rr_bmi_kidney^(bmi_mean_male_`i'-25)) * ((rr_smoking_kidney*smokstat_mean_male_`i') + (rr_exsmoking_kidney*exsmokstat_mean_male_`i') + (1*(1-smokstat_mean_male_`i'-exsmokstat_mean_male_`i'))) } forvalues i = 20(10)80 { gen male_bladder_mean_`i' = 1 } forvalues i =20(10)80 { replace male_bladder_mean_`i' = ((rr_smoking_bladder*smokstat_mean_male_`i') + (rr_exsmoking_bladder*exsmokstat_mean_male_`i') + (1*(1-smokstat_mean_male_`i'-exsmokstat_mean_male_`i'))) } forvalues i = 20(10)80 { gen male_oesoph_mean_`i' = 1 } forvalues i =20(10)80 { replace male_oesoph_mean_`i' = (rr_bmi_oesoph^(bmi_mean_male_`i'-25)) * (rr_alcohol_oesoph^alcohol_mean_male_`i') * (rr_fruit_oesoph^(fruit_mean_male_`i'-200)) * (rr_veg_oesoph^(veg_mean_male_`i'-200)) * ((rr_smoking_oesoph*smokstat_mean_male_`i') + (rr_exsmoking_oesoph*exsmokstat_mean_male_`i') + (1*(1-smokstat_mean_male_`i'-exsmokstat_mean_male_`i'))) } //4. Combine step 2 and step 3 to get relative risk for the average person relative to ideal for the cancers combined //to use to present risk relative to the average person gen female_mean_rr_crc = . replace female_mean_rr_crc = female_crc_mean_20 if age>19.99999 & age<30 replace female_mean_rr_crc = female_crc_mean_30 if age>29.99999 & age<40 replace female_mean_rr_crc = female_crc_mean_40 if age>39.99999 & age<50 replace female_mean_rr_crc = female_crc_mean_50 if age>49.99999 & age<60 replace female_mean_rr_crc = female_crc_mean_60 if age>59.99999 & age<70 replace female_mean_rr_crc = female_crc_mean_70 if age>69.99999 & age<80 replace female_mean_rr_crc = female_crc_mean_80 if age>79.99999 gen female_mean_rr_lung = . replace female_mean_rr_lung = female_lung_mean_20 if age>19.99999 & age<30 replace female_mean_rr_lung = female_lung_mean_30 if age>29.99999 & age<40 replace female_mean_rr_lung = female_lung_mean_40 if age>39.99999 & age<50 replace female_mean_rr_lung = female_lung_mean_50 if age>49.99999 & age<60 replace female_mean_rr_lung = female_lung_mean_60 if age>59.99999 & age<70 replace female_mean_rr_lung = female_lung_mean_70 if age>69.99999 & age<80 replace female_mean_rr_lung = female_lung_mean_80 if age>79.99999 gen female_mean_rr_kidney = . replace female_mean_rr_kidney = female_kidney_mean_20 if age>19.99999 & age<30 replace female_mean_rr_kidney = female_kidney_mean_30 if age>29.99999 & age<40 replace female_mean_rr_kidney = female_kidney_mean_40 if age>39.99999 & age<50 replace female_mean_rr_kidney = female_kidney_mean_50 if age>49.99999 & age<60 replace female_mean_rr_kidney = female_kidney_mean_60 if age>59.99999 & age<70 replace female_mean_rr_kidney = female_kidney_mean_70 if age>69.99999 & age<80 replace female_mean_rr_kidney = female_kidney_mean_80 if age>79.99999 gen female_mean_rr_breast = . replace female_mean_rr_breast = female_breast_mean_20 if age>19.99999 & age<30 replace female_mean_rr_breast = female_breast_mean_30 if age>29.99999 & age<40 replace female_mean_rr_breast = female_breast_mean_40 if age>39.99999 & age<50 replace female_mean_rr_breast = female_breast_mean_50 if age>49.99999 & age<60 replace female_mean_rr_breast = female_breast_mean_60 if age>59.99999 & age<70 replace female_mean_rr_breast = female_breast_mean_70 if age>69.99999 & age<80 replace female_mean_rr_breast = female_breast_mean_80 if age>79.99999 gen female_mean_rr_endomet = . replace female_mean_rr_endomet = female_endomet_mean_20 if age>19.99999 & age<30 replace female_mean_rr_endomet = female_endomet_mean_30 if age>29.99999 & age<40 replace female_mean_rr_endomet = female_endomet_mean_40 if age>39.99999 & age<50 replace female_mean_rr_endomet = female_endomet_mean_50 if age>49.99999 & age<60 replace female_mean_rr_endomet = female_endomet_mean_60 if age>59.99999 & age<70 replace female_mean_rr_endomet = female_endomet_mean_70 if age>69.99999 & age<80 replace female_mean_rr_endomet = female_endomet_mean_80 if age>79.99999 gen male_mean_rr_crc = . replace male_mean_rr_crc = male_crc_mean_20 if age>19.99999 & age<30 replace male_mean_rr_crc = male_crc_mean_30 if age>29.99999 & age<40 replace male_mean_rr_crc = male_crc_mean_40 if age>39.99999 & age<50 replace male_mean_rr_crc = male_crc_mean_50 if age>49.99999 & age<60 replace male_mean_rr_crc = male_crc_mean_60 if age>59.99999 & age<70 replace male_mean_rr_crc = male_crc_mean_70 if age>69.99999 & age<80 replace male_mean_rr_crc = male_crc_mean_80 if age>79.99999 gen male_mean_rr_lung = . replace male_mean_rr_lung = male_lung_mean_20 if age>19.99999 & age<30 replace male_mean_rr_lung = male_lung_mean_30 if age>29.99999 & age<40 replace male_mean_rr_lung = male_lung_mean_40 if age>39.99999 & age<50 replace male_mean_rr_lung = male_lung_mean_50 if age>49.99999 & age<60 replace male_mean_rr_lung = male_lung_mean_60 if age>59.99999 & age<70 replace male_mean_rr_lung = male_lung_mean_70 if age>69.99999 & age<80 replace male_mean_rr_lung = male_lung_mean_80 if age>79.99999 gen male_mean_rr_kidney = . replace male_mean_rr_kidney = male_kidney_mean_20 if age>19.99999 & age<30 replace male_mean_rr_kidney = male_kidney_mean_30 if age>29.99999 & age<40 replace male_mean_rr_kidney = male_kidney_mean_40 if age>39.99999 & age<50 replace male_mean_rr_kidney = male_kidney_mean_50 if age>49.99999 & age<60 replace male_mean_rr_kidney = male_kidney_mean_60 if age>59.99999 & age<70 replace male_mean_rr_kidney = male_kidney_mean_70 if age>69.99999 & age<80 replace male_mean_rr_kidney = male_kidney_mean_80 if age>79.99999 gen male_mean_rr_bladder = . replace male_mean_rr_bladder = male_bladder_mean_20 if age>19.99999 & age<30 replace male_mean_rr_bladder = male_bladder_mean_30 if age>29.99999 & age<40 replace male_mean_rr_bladder = male_bladder_mean_40 if age>39.99999 & age<50 replace male_mean_rr_bladder = male_bladder_mean_50 if age>49.99999 & age<60 replace male_mean_rr_bladder = male_bladder_mean_60 if age>59.99999 & age<70 replace male_mean_rr_bladder = male_bladder_mean_70 if age>69.99999 & age<80 replace male_mean_rr_bladder = male_bladder_mean_80 if age>79.99999 gen male_mean_rr_oesoph = . replace male_mean_rr_oesoph = male_oesoph_mean_20 if age>19.99999 & age<30 replace male_mean_rr_oesoph = male_oesoph_mean_30 if age>29.99999 & age<40 replace male_mean_rr_oesoph = male_oesoph_mean_40 if age>39.99999 & age<50 replace male_mean_rr_oesoph = male_oesoph_mean_50 if age>49.99999 & age<60 replace male_mean_rr_oesoph = male_oesoph_mean_60 if age>59.99999 & age<70 replace male_mean_rr_oesoph = male_oesoph_mean_70 if age>69.99999 & age<80 replace male_mean_rr_oesoph = male_oesoph_mean_80 if age>79.99999 capture drop female_mean_rr_20 female_mean_rr_30 female_mean_rr_40 female_mean_rr_50 female_mean_rr_60 female_mean_rr_70 female_mean_rr_80 gen female_mean_rr_20 = (female_crc_mean_20*0.28) + (female_lung_mean_20*0.05) + (female_breast_mean_20*0.56) + (female_kidney_mean_20*0.07) + (female_endomet_mean_20*0.04) if age>19.99999 & age<30 gen female_mean_rr_30 = (female_crc_mean_30*0.14) + (female_lung_mean_30*0.03) + (female_breast_mean_30*0.77) + (female_kidney_mean_30*0.02) + (female_endomet_mean_30*0.03) if age>29.99999 & age<40 gen female_mean_rr_40 = (female_crc_mean_40*0.08) + (female_lung_mean_40*0.05) + (female_breast_mean_40*0.80) + (female_kidney_mean_40*0.03) + (female_endomet_mean_40*0.04) if age>39.99999 & age<50 gen female_mean_rr_50 = (female_crc_mean_50*0.11) + (female_lung_mean_50*0.11) + (female_breast_mean_50*0.64) + (female_kidney_mean_50*0.03) + (female_endomet_mean_50*0.10) if age>49.99999 & age<60 gen female_mean_rr_60 = (female_crc_mean_60*0.14) + (female_lung_mean_60*0.20) + (female_breast_mean_60*0.51) + (female_kidney_mean_60*0.04) + (female_endomet_mean_60*0.10) if age>59.99999 & age<70 gen female_mean_rr_70 = (female_crc_mean_70*0.20) + (female_lung_mean_70*0.28) + (female_breast_mean_70*0.39) + (female_kidney_mean_70*0.04) + (female_endomet_mean_70*0.09) if age>69.99999 & age<80 gen female_mean_rr_80 = (female_crc_mean_80*0.27) + (female_lung_mean_80*0.25) + (female_breast_mean_80*0.38) + (female_kidney_mean_80*0.04) + (female_endomet_mean_80*0.06) if age>79.99999 capture drop female_mean_rr egen female_mean_rr = rowtotal(female_mean_rr_20 female_mean_rr_30 female_mean_rr_40 female_mean_rr_50 female_mean_rr_60 female_mean_rr_70 female_mean_rr_80) if sex==0 capture drop male_mean_rr_20 male_mean_rr_30 male_mean_rr_40 male_mean_rr_50 male_mean_rr_60 male_mean_rr_70 male_mean_rr_80 gen male_mean_rr_20 = (male_crc_mean_20*0.68) + (male_lung_mean_20*0.13) + (male_bladder_mean_20*0.02) + (male_kidney_mean_20*0.13) + (male_oesoph_mean_20*0.04) if age>19.99999 & age<30 gen male_mean_rr_30 = (male_crc_mean_30*0.56) + (male_lung_mean_30*0.13) + (male_bladder_mean_30*0.03) + (male_kidney_mean_30*0.21) + (male_oesoph_mean_30*0.06) if age>29.99999 & age<40 gen male_mean_rr_40 = (male_crc_mean_40*0.38) + (male_lung_mean_40*0.22) + (male_bladder_mean_40*0.07) + (male_kidney_mean_40*0.26) + (male_oesoph_mean_40*0.08) if age>39.99999 & age<50 gen male_mean_rr_50 = (male_crc_mean_50*0.36) + (male_lung_mean_50*0.29) + (male_bladder_mean_50*0.07) + (male_kidney_mean_50*0.17) + (male_oesoph_mean_50*0.12) if age>49.99999 & age<60 gen male_mean_rr_60 = (male_crc_mean_60*0.34) + (male_lung_mean_60*0.35) + (male_bladder_mean_60*0.09) + (male_kidney_mean_60*0.11) + (male_oesoph_mean_60*0.10) if age>59.99999 & age<70 gen male_mean_rr_70 = (male_crc_mean_70*0.33) + (male_lung_mean_70*0.39) + (male_bladder_mean_70*0.11) + (male_kidney_mean_70*0.08) + (male_oesoph_mean_70*0.09) if age>69.99999 & age<80 gen male_mean_rr_80 = (male_crc_mean_80*0.34) + (male_lung_mean_80*0.37) + (male_bladder_mean_80*0.15) + (male_kidney_mean_80*0.07) + (male_oesoph_mean_80*0.08) if age>79.99999 capture drop male_mean_rr egen male_mean_rr = rowtotal(male_mean_rr_20 male_mean_rr_30 male_mean_rr_40 male_mean_rr_50 male_mean_rr_60 male_mean_rr_70 male_mean_rr_80) if sex==1 //5. Generate RELATIVE RISKS RELATIVE TO THE AVERAGE PERSON for each cancer forvalues i = 20(10)80 { gen female_rr_crc_`i' = (female_crc_`i' / female_crc_mean_`i') } forvalues i = 20(10)80 { gen male_rr_crc_`i' = (male_crc_`i' / male_crc_mean_`i') } forvalues i = 20(10)80 { gen female_rr_lung_`i' = (female_lung_`i' / female_lung_mean_`i') } forvalues i = 20(10)80 { gen male_rr_lung_`i' = (male_lung_`i' / male_lung_mean_`i') } forvalues i = 20(10)80 { gen female_rr_kidney_`i' = (female_kidney_`i' / female_kidney_mean_`i') } forvalues i = 20(10)80 { gen male_rr_kidney_`i' = (male_kidney_`i' / male_kidney_mean_`i') } forvalues i = 20(10)80 { gen female_rr_breast_`i' = (female_breast_`i' / female_breast_mean_`i') } forvalues i = 20(10)80 { gen male_rr_bladder_`i' = (male_bladder_`i' / male_bladder_mean_`i') } forvalues i = 20(10)80 { gen female_rr_endomet_`i' = (female_endomet_`i' / female_endomet_mean_`i') } forvalues i = 20(10)80 { gen male_rr_oesoph_`i' = (male_oesoph_`i' / male_oesoph_mean_`i') } gen female_rr_crc = . replace female_rr_crc = female_rr_crc_20 if age>19.99999 & age<30 replace female_rr_crc = female_rr_crc_30 if age>29.99999 & age<40 replace female_rr_crc = female_rr_crc_40 if age>39.99999 & age<50 replace female_rr_crc = female_rr_crc_50 if age>49.99999 & age<60 replace female_rr_crc = female_rr_crc_60 if age>59.99999 & age<70 replace female_rr_crc = female_rr_crc_70 if age>69.99999 & age<80 replace female_rr_crc = female_rr_crc_80 if age>79.99999 gen female_rr_lung = . replace female_rr_lung = female_rr_lung_20 if age>19.99999 & age<30 replace female_rr_lung = female_rr_lung_30 if age>29.99999 & age<40 replace female_rr_lung = female_rr_lung_40 if age>39.99999 & age<50 replace female_rr_lung = female_rr_lung_50 if age>49.99999 & age<60 replace female_rr_lung = female_rr_lung_60 if age>59.99999 & age<70 replace female_rr_lung = female_rr_lung_70 if age>69.99999 & age<80 replace female_rr_lung = female_rr_lung_80 if age>79.99999 gen female_rr_kidney = . replace female_rr_kidney = female_rr_kidney_20 if age>19.99999 & age<30 replace female_rr_kidney = female_rr_kidney_30 if age>29.99999 & age<40 replace female_rr_kidney = female_rr_kidney_40 if age>39.99999 & age<50 replace female_rr_kidney = female_rr_kidney_50 if age>49.99999 & age<60 replace female_rr_kidney = female_rr_kidney_60 if age>59.99999 & age<70 replace female_rr_kidney = female_rr_kidney_70 if age>69.99999 & age<80 replace female_rr_kidney = female_rr_kidney_80 if age>79.99999 gen female_rr_breast = . replace female_rr_breast = female_rr_breast_20 if age>19.99999 & age<30 replace female_rr_breast = female_rr_breast_30 if age>29.99999 & age<40 replace female_rr_breast = female_rr_breast_40 if age>39.99999 & age<50 replace female_rr_breast = female_rr_breast_50 if age>49.99999 & age<60 replace female_rr_breast = female_rr_breast_60 if age>59.99999 & age<70 replace female_rr_breast = female_rr_breast_70 if age>69.99999 & age<80 replace female_rr_breast = female_rr_breast_80 if age>79.99999 gen female_rr_endomet = . replace female_rr_endomet = female_rr_endomet_20 if age>19.99999 & age<30 replace female_rr_endomet = female_rr_endomet_30 if age>29.99999 & age<40 replace female_rr_endomet = female_rr_endomet_40 if age>39.99999 & age<50 replace female_rr_endomet = female_rr_endomet_50 if age>49.99999 & age<60 replace female_rr_endomet = female_rr_endomet_60 if age>59.99999 & age<70 replace female_rr_endomet = female_rr_endomet_70 if age>69.99999 & age<80 replace female_rr_endomet = female_rr_endomet_80 if age>79.99999 gen male_rr_crc = . replace male_rr_crc = male_rr_crc_20 if age>19.99999 & age<30 replace male_rr_crc = male_rr_crc_30 if age>29.99999 & age<40 replace male_rr_crc = male_rr_crc_40 if age>39.99999 & age<50 replace male_rr_crc = male_rr_crc_50 if age>49.99999 & age<60 replace male_rr_crc = male_rr_crc_60 if age>59.99999 & age<70 replace male_rr_crc = male_rr_crc_70 if age>69.99999 & age<80 replace male_rr_crc = male_rr_crc_80 if age>79.99999 gen male_rr_lung = . replace male_rr_lung = male_rr_lung_20 if age>19.99999 & age<30 replace male_rr_lung = male_rr_lung_30 if age>29.99999 & age<40 replace male_rr_lung = male_rr_lung_40 if age>39.99999 & age<50 replace male_rr_lung = male_rr_lung_50 if age>49.99999 & age<60 replace male_rr_lung = male_rr_lung_60 if age>59.99999 & age<70 replace male_rr_lung = male_rr_lung_70 if age>69.99999 & age<80 replace male_rr_lung = male_rr_lung_80 if age>79.99999 gen male_rr_kidney = . replace male_rr_kidney = male_rr_kidney_20 if age>19.99999 & age<30 replace male_rr_kidney = male_rr_kidney_30 if age>29.99999 & age<40 replace male_rr_kidney = male_rr_kidney_40 if age>39.99999 & age<50 replace male_rr_kidney = male_rr_kidney_50 if age>49.99999 & age<60 replace male_rr_kidney = male_rr_kidney_60 if age>59.99999 & age<70 replace male_rr_kidney = male_rr_kidney_70 if age>69.99999 & age<80 replace male_rr_kidney = male_rr_kidney_80 if age>79.99999 gen male_rr_bladder = . replace male_rr_bladder = male_rr_bladder_20 if age>19.99999 & age<30 replace male_rr_bladder = male_rr_bladder_30 if age>29.99999 & age<40 replace male_rr_bladder = male_rr_bladder_40 if age>39.99999 & age<50 replace male_rr_bladder = male_rr_bladder_50 if age>49.99999 & age<60 replace male_rr_bladder = male_rr_bladder_60 if age>59.99999 & age<70 replace male_rr_bladder = male_rr_bladder_70 if age>69.99999 & age<80 replace male_rr_bladder = male_rr_bladder_80 if age>79.99999 gen male_rr_oesoph = . replace male_rr_oesoph = male_rr_oesoph_20 if age>19.99999 & age<30 replace male_rr_oesoph = male_rr_oesoph_30 if age>29.99999 & age<40 replace male_rr_oesoph = male_rr_oesoph_40 if age>39.99999 & age<50 replace male_rr_oesoph = male_rr_oesoph_50 if age>49.99999 & age<60 replace male_rr_oesoph = male_rr_oesoph_60 if age>59.99999 & age<70 replace male_rr_oesoph = male_rr_oesoph_70 if age>69.99999 & age<80 replace male_rr_oesoph = male_rr_oesoph_80 if age>79.99999 // Generate overall relative risk weighted for incidence of each cancer capture drop female_rr_20 female_rr_30 female_rr_40 female_rr_50 female_rr_60 female_rr_70 female_rr_80 gen female_rr_20 = (female_rr_crc_20*0.28) + (female_rr_lung_20*0.05) + (female_rr_breast_20*0.56) + (female_rr_kidney_20*0.07) + (female_rr_endomet_20*0.04) if age>19.99999 & age<30 gen female_rr_30 = (female_rr_crc_30*0.14) + (female_rr_lung_30*0.03) + (female_rr_breast_30*0.77) + (female_rr_kidney_30*0.02) + (female_rr_endomet_30*0.03) if age>29.99999 & age<40 gen female_rr_40 = (female_rr_crc_40*0.08) + (female_rr_lung_40*0.05) + (female_rr_breast_40*0.80) + (female_rr_kidney_40*0.03) + (female_rr_endomet_40*0.04) if age>39.99999 & age<50 gen female_rr_50 = (female_rr_crc_50*0.11) + (female_rr_lung_50*0.11) + (female_rr_breast_50*0.64) + (female_rr_kidney_50*0.03) + (female_rr_endomet_50*0.10) if age>49.99999 & age<60 gen female_rr_60 = (female_rr_crc_60*0.14) + (female_rr_lung_60*0.20) + (female_rr_breast_60*0.51) + (female_rr_kidney_60*0.04) + (female_rr_endomet_60*0.10) if age>59.99999 & age<70 gen female_rr_70 = (female_rr_crc_70*0.20) + (female_rr_lung_70*0.28) + (female_rr_breast_70*0.39) + (female_rr_kidney_70*0.04) + (female_rr_endomet_70*0.09) if age>69.99999 & age<80 gen female_rr_80 = (female_rr_crc_80*0.27) + (female_rr_lung_80*0.25) + (female_rr_breast_80*0.38) + (female_rr_kidney_80*0.04) + (female_rr_endomet_80*0.06) if age>79.99999 capture drop female_rr egen female_rr = rowtotal(female_rr_20 female_rr_30 female_rr_40 female_rr_50 female_rr_60 female_rr_70 female_rr_80) if sex==0 capture drop male_rr_20 male_rr_30 male_rr_40 male_rr_50 male_rr_60 male_rr_70 male_rr_80 gen male_rr_20 = (male_rr_crc_20*0.68) + (male_rr_lung_20*0.13) + (male_rr_bladder_20*0.02) + (male_rr_kidney_20*0.13) + (male_rr_oesoph_20*0.04) if age>19.99999 & age<30 gen male_rr_30 = (male_rr_crc_30*0.56) + (male_rr_lung_30*0.13) + (male_rr_bladder_30*0.03) + (male_rr_kidney_30*0.21) + (male_rr_oesoph_30*0.06) if age>29.99999 & age<40 gen male_rr_40 = (male_rr_crc_40*0.38) + (male_rr_lung_40*0.22) + (male_rr_bladder_40*0.07) + (male_rr_kidney_40*0.26) + (male_rr_oesoph_40*0.08) if age>39.99999 & age<50 gen male_rr_50 = (male_rr_crc_50*0.36) + (male_rr_lung_50*0.29) + (male_rr_bladder_50*0.07) + (male_rr_kidney_50*0.17) + (male_rr_oesoph_50*0.12) if age>49.99999 & age<60 gen male_rr_60 = (male_rr_crc_60*0.34) + (male_rr_lung_60*0.35) + (male_rr_bladder_60*0.09) + (male_rr_kidney_60*0.11) + (male_rr_oesoph_60*0.10) if age>59.99999 & age<70 gen male_rr_70 = (male_rr_crc_70*0.33) + (male_rr_lung_70*0.39) + (male_rr_bladder_70*0.11) + (male_rr_kidney_70*0.08) + (male_rr_oesoph_70*0.09) if age>69.99999 & age<80 gen male_rr_80 = (male_rr_crc_80*0.34) + (male_rr_lung_80*0.37) + (male_rr_bladder_80*0.15) + (male_rr_kidney_80*0.07) + (male_rr_oesoph_80*0.08) if age>79.99999 capture drop male_rr egen male_rr = rowtotal(male_rr_20 male_rr_30 male_rr_40 male_rr_50 male_rr_60 male_rr_70 male_rr_80) if sex==1 //6. Repeat step 5 to calculate IDEAL RELATIVE RISK RELATIVE TO MEAN RELATIVE RISK. //(This allow accurate calculation of the absolute risk for the a person with the ideal lifestyle) forvalues i = 20(10)80 { gen female_ideal_mean_crc_`i' = (1 / female_crc_mean_`i') } forvalues i = 20(10)80 { gen male_ideal_mean_crc_`i' = (1 / male_crc_mean_`i') } forvalues i = 20(10)80 { gen female_ideal_mean_lung_`i' = (1 / female_lung_mean_`i') } forvalues i = 20(10)80 { gen male_ideal_mean_lung_`i' = (1 / male_lung_mean_`i') } forvalues i = 20(10)80 { gen female_ideal_mean_kidney_`i' = (1 / female_kidney_mean_`i') } forvalues i = 20(10)80 { gen male_ideal_mean_kidney_`i' = (1 / male_kidney_mean_`i') } forvalues i = 20(10)80 { gen female_ideal_mean_breast_`i' = (1 / female_breast_mean_`i') } forvalues i = 20(10)80 { gen male_ideal_mean_bladder_`i' = (1 / male_bladder_mean_`i') } forvalues i = 20(10)80 { gen female_ideal_mean_endomet_`i' = (1 / female_endomet_mean_`i') } forvalues i = 20(10)80 { gen male_ideal_mean_oesoph_`i' = (1 / male_oesoph_mean_`i') } gen female_ideal_mean_crc = . replace female_ideal_mean_crc = female_ideal_mean_crc_20 if age>19.99999 & age<30 replace female_ideal_mean_crc = female_ideal_mean_crc_30 if age>29.99999 & age<40 replace female_ideal_mean_crc = female_ideal_mean_crc_40 if age>39.99999 & age<50 replace female_ideal_mean_crc = female_ideal_mean_crc_50 if age>49.99999 & age<60 replace female_ideal_mean_crc = female_ideal_mean_crc_60 if age>59.99999 & age<70 replace female_ideal_mean_crc = female_ideal_mean_crc_70 if age>69.99999 & age<80 replace female_ideal_mean_crc = female_ideal_mean_crc_80 if age>79.99999 gen female_ideal_mean_lung = . replace female_ideal_mean_lung = female_ideal_mean_lung_20 if age>19.99999 & age<30 replace female_ideal_mean_lung = female_ideal_mean_lung_30 if age>29.99999 & age<40 replace female_ideal_mean_lung = female_ideal_mean_lung_40 if age>39.99999 & age<50 replace female_ideal_mean_lung = female_ideal_mean_lung_50 if age>49.99999 & age<60 replace female_ideal_mean_lung = female_ideal_mean_lung_60 if age>59.99999 & age<70 replace female_ideal_mean_lung = female_ideal_mean_lung_70 if age>69.99999 & age<80 replace female_ideal_mean_lung = female_ideal_mean_lung_80 if age>79.99999 gen female_ideal_mean_kidney = . replace female_ideal_mean_kidney = female_ideal_mean_kidney_20 if age>19.99999 & age<30 replace female_ideal_mean_kidney = female_ideal_mean_kidney_30 if age>29.99999 & age<40 replace female_ideal_mean_kidney = female_ideal_mean_kidney_40 if age>39.99999 & age<50 replace female_ideal_mean_kidney = female_ideal_mean_kidney_50 if age>49.99999 & age<60 replace female_ideal_mean_kidney = female_ideal_mean_kidney_60 if age>59.99999 & age<70 replace female_ideal_mean_kidney = female_ideal_mean_kidney_70 if age>69.99999 & age<80 replace female_ideal_mean_kidney = female_ideal_mean_kidney_80 if age>79.99999 gen female_ideal_mean_breast = . replace female_ideal_mean_breast = female_ideal_mean_breast_20 if age>19.99999 & age<30 replace female_ideal_mean_breast = female_ideal_mean_breast_30 if age>29.99999 & age<40 replace female_ideal_mean_breast = female_ideal_mean_breast_40 if age>39.99999 & age<50 replace female_ideal_mean_breast = female_ideal_mean_breast_50 if age>49.99999 & age<60 replace female_ideal_mean_breast = female_ideal_mean_breast_60 if age>59.99999 & age<70 replace female_ideal_mean_breast = female_ideal_mean_breast_70 if age>69.99999 & age<80 replace female_ideal_mean_breast = female_ideal_mean_breast_80 if age>79.99999 gen female_ideal_mean_endomet = . replace female_ideal_mean_endomet = female_ideal_mean_endomet_20 if age>19.99999 & age<30 replace female_ideal_mean_endomet = female_ideal_mean_endomet_30 if age>29.99999 & age<40 replace female_ideal_mean_endomet = female_ideal_mean_endomet_40 if age>39.99999 & age<50 replace female_ideal_mean_endomet = female_ideal_mean_endomet_50 if age>49.99999 & age<60 replace female_ideal_mean_endomet = female_ideal_mean_endomet_60 if age>59.99999 & age<70 replace female_ideal_mean_endomet = female_ideal_mean_endomet_70 if age>69.99999 & age<80 replace female_ideal_mean_endomet = female_ideal_mean_endomet_80 if age>79.99999 gen male_ideal_mean_crc = . replace male_ideal_mean_crc = male_ideal_mean_crc_20 if age>19.99999 & age<30 replace male_ideal_mean_crc = male_ideal_mean_crc_30 if age>29.99999 & age<40 replace male_ideal_mean_crc = male_ideal_mean_crc_40 if age>39.99999 & age<50 replace male_ideal_mean_crc = male_ideal_mean_crc_50 if age>49.99999 & age<60 replace male_ideal_mean_crc = male_ideal_mean_crc_60 if age>59.99999 & age<70 replace male_ideal_mean_crc = male_ideal_mean_crc_70 if age>69.99999 & age<80 replace male_ideal_mean_crc = male_ideal_mean_crc_80 if age>79.99999 gen male_ideal_mean_lung = . replace male_ideal_mean_lung = male_ideal_mean_lung_20 if age>19.99999 & age<30 replace male_ideal_mean_lung = male_ideal_mean_lung_30 if age>29.99999 & age<40 replace male_ideal_mean_lung = male_ideal_mean_lung_40 if age>39.99999 & age<50 replace male_ideal_mean_lung = male_ideal_mean_lung_50 if age>49.99999 & age<60 replace male_ideal_mean_lung = male_ideal_mean_lung_60 if age>59.99999 & age<70 replace male_ideal_mean_lung = male_ideal_mean_lung_70 if age>69.99999 & age<80 replace male_ideal_mean_lung = male_ideal_mean_lung_80 if age>79.99999 gen male_ideal_kidney = . replace male_ideal_kidney = male_ideal_mean_kidney_20 if age>19.99999 & age<30 replace male_ideal_kidney = male_ideal_mean_kidney_30 if age>29.99999 & age<40 replace male_ideal_kidney = male_ideal_mean_kidney_40 if age>39.99999 & age<50 replace male_ideal_kidney = male_ideal_mean_kidney_50 if age>49.99999 & age<60 replace male_ideal_kidney = male_ideal_mean_kidney_60 if age>59.99999 & age<70 replace male_ideal_kidney = male_ideal_mean_kidney_70 if age>69.99999 & age<80 replace male_ideal_kidney = male_ideal_mean_kidney_80 if age>79.99999 gen male_ideal_mean_bladder = . replace male_ideal_mean_bladder = male_ideal_mean_bladder_20 if age>19.99999 & age<30 replace male_ideal_mean_bladder = male_ideal_mean_bladder_30 if age>29.99999 & age<40 replace male_ideal_mean_bladder = male_ideal_mean_bladder_40 if age>39.99999 & age<50 replace male_ideal_mean_bladder = male_ideal_mean_bladder_50 if age>49.99999 & age<60 replace male_ideal_mean_bladder = male_ideal_mean_bladder_60 if age>59.99999 & age<70 replace male_ideal_mean_bladder = male_ideal_mean_bladder_70 if age>69.99999 & age<80 replace male_ideal_mean_bladder = male_ideal_mean_bladder_80 if age>79.99999 gen male_ideal_mean_oesoph = . replace male_ideal_mean_oesoph = male_ideal_mean_oesoph_20 if age>19.99999 & age<30 replace male_ideal_mean_oesoph = male_ideal_mean_oesoph_30 if age>29.99999 & age<40 replace male_ideal_mean_oesoph = male_ideal_mean_oesoph_40 if age>39.99999 & age<50 replace male_ideal_mean_oesoph = male_ideal_mean_oesoph_50 if age>49.99999 & age<60 replace male_ideal_mean_oesoph = male_ideal_mean_oesoph_60 if age>59.99999 & age<70 replace male_ideal_mean_oesoph = male_ideal_mean_oesoph_70 if age>69.99999 & age<80 replace male_ideal_mean_oesoph = male_ideal_mean_oesoph_80 if age>79.99999 // Generate overall ideal relative risk weighted for incidence of each cancer gen female_ideal_mean_rr_20 = (female_ideal_mean_crc_20*0.28) + (female_ideal_mean_lung_20*0.05) + (female_ideal_mean_breast_20*0.56) + (female_ideal_mean_kidney_20*0.07) + (female_ideal_mean_endomet_20*0.04) if age>19.99999 & age<30 gen female_ideal_mean_rr_30 = (female_ideal_mean_crc_30*0.14) + (female_ideal_mean_lung_30*0.03) + (female_ideal_mean_breast_30*0.77) + (female_ideal_mean_kidney_30*0.02) + (female_ideal_mean_endomet_30*0.03) if age>29.99999 & age<40 gen female_ideal_mean_rr_40 = (female_ideal_mean_crc_40*0.08) + (female_ideal_mean_lung_40*0.05) + (female_ideal_mean_breast_40*0.80) + (female_ideal_mean_kidney_40*0.03) + (female_ideal_mean_endomet_40*0.04) if age>39.99999 & age<50 gen female_ideal_mean_rr_50 = (female_ideal_mean_crc_50*0.11) + (female_ideal_mean_lung_50*0.11) + (female_ideal_mean_breast_50*0.64) + (female_ideal_mean_kidney_50*0.03) + (female_ideal_mean_endomet_50*0.10) if age>49.99999 & age<60 gen female_ideal_mean_rr_60 = (female_ideal_mean_crc_60*0.14) + (female_ideal_mean_lung_60*0.20) + (female_ideal_mean_breast_60*0.51) + (female_ideal_mean_kidney_60*0.04) + (female_ideal_mean_endomet_60*0.10) if age>59.99999 & age<70 gen female_ideal_mean_rr_70 = (female_ideal_mean_crc_70*0.20) + (female_ideal_mean_lung_70*0.28) + (female_ideal_mean_breast_70*0.39) + (female_ideal_mean_kidney_70*0.04) + (female_ideal_mean_endomet_70*0.09) if age>69.99999 & age<80 gen female_ideal_mean_rr_80 = (female_ideal_mean_crc_80*0.27) + (female_ideal_mean_lung_80*0.25) + (female_ideal_mean_breast_80*0.38) + (female_ideal_mean_kidney_80*0.04) + (female_ideal_mean_endomet_80*0.06) if age>79.99999 egen female_ideal_mean_rr = rowtotal(female_ideal_mean_rr_20 female_ideal_mean_rr_30 female_ideal_mean_rr_40 female_ideal_mean_rr_50 female_ideal_mean_rr_60 female_ideal_mean_rr_70 female_ideal_mean_rr_80) if sex==0 gen male_ideal_mean_rr_20 = (male_ideal_mean_crc_20*0.68) + (male_ideal_mean_lung_20*0.13) + (male_ideal_mean_bladder_20*0.02) + (male_ideal_mean_kidney_20*0.13) + (male_ideal_mean_oesoph_20*0.04) if age>19.99999 & age<30 gen male_ideal_mean_rr_30 = (male_ideal_mean_crc_30*0.56) + (male_ideal_mean_lung_30*0.13) + (male_ideal_mean_bladder_30*0.03) + (male_ideal_mean_kidney_30*0.21) + (male_ideal_mean_oesoph_30*0.06) if age>29.99999 & age<40 gen male_ideal_mean_rr_40 = (male_ideal_mean_crc_40*0.38) + (male_ideal_mean_lung_40*0.22) + (male_ideal_mean_bladder_40*0.07) + (male_ideal_mean_kidney_40*0.26) + (male_ideal_mean_oesoph_40*0.08) if age>39.99999 & age<50 gen male_ideal_mean_rr_50 = (male_ideal_mean_crc_50*0.36) + (male_ideal_mean_lung_50*0.29) + (male_ideal_mean_bladder_50*0.07) + (male_ideal_mean_kidney_50*0.17) + (male_ideal_mean_oesoph_50*0.12) if age>49.99999 & age<60 gen male_ideal_mean_rr_60 = (male_ideal_mean_crc_60*0.34) + (male_ideal_mean_lung_60*0.35) + (male_ideal_mean_bladder_60*0.09) + (male_ideal_mean_kidney_60*0.11) + (male_ideal_mean_oesoph_60*0.10) if age>59.99999 & age<70 gen male_ideal_mean_rr_70 = (male_ideal_mean_crc_70*0.33) + (male_ideal_mean_lung_70*0.39) + (male_ideal_mean_bladder_70*0.11) + (male_ideal_mean_kidney_70*0.08) + (male_ideal_mean_oesoph_70*0.09) if age>69.99999 & age<80 gen male_ideal_mean_rr_80 = (male_ideal_mean_crc_80*0.34) + (male_ideal_mean_lung_80*0.37) + (male_ideal_mean_bladder_80*0.15) + (male_ideal_mean_kidney_80*0.07) + (male_ideal_mean_oesoph_80*0.08) if age>79.99999 egen male_ideal_mean_rr = rowtotal(male_ideal_mean_rr_20 male_ideal_mean_rr_30 male_ideal_mean_rr_40 male_ideal_mean_rr_50 male_ideal_mean_rr_60 male_ideal_mean_rr_70 male_ideal_mean_rr_80) if sex==1 //7. Calculate absolute risks for each cancer //This calculates the absolute risk for each age group for each cancer. They can then be summed together to get an overall estimated //absolute risk of developing one of the 5 cancers. forvalues i = 20(10)80 { gen female_ar_crc_`i' = (female_crc_`i' / female_crc_mean_`i') * female_ar_crc_`i'_mean } forvalues i = 20(10)80 { gen male_ar_crc_`i' = (male_crc_`i' / male_crc_mean_`i') * male_ar_crc_`i'_mean } forvalues i = 20(10)80 { gen female_ar_lung_`i' = (female_lung_`i' / female_lung_mean_`i') * female_ar_lung_`i'_mean } forvalues i = 20(10)80 { gen male_ar_lung_`i' = (male_lung_`i' / male_lung_mean_`i') * male_ar_lung_`i'_mean } forvalues i = 20(10)80 { gen female_ar_kidney_`i' = (female_kidney_`i' / female_kidney_mean_`i') * female_ar_kidney_`i'_mean } forvalues i = 20(10)80 { gen male_ar_kidney_`i' = (male_kidney_`i' / male_kidney_mean_`i') * male_ar_kidney_`i'_mean } forvalues i = 20(10)80 { gen female_ar_breast_`i' = (female_breast_`i' / female_breast_mean_`i') * female_ar_breast_`i'_mean } forvalues i = 20(10)80 { gen male_ar_bladder_`i' = (male_bladder_`i' / male_bladder_mean_`i') * male_ar_bladder_`i'_mean } forvalues i = 20(10)80 { gen female_ar_endomet_`i' = (female_endomet_`i' / female_endomet_mean_`i') * female_ar_endomet_`i'_mean } forvalues i = 20(10)80 { gen male_ar_oesoph_`i' = (male_oesoph_`i' / male_oesoph_mean_`i') * male_ar_oesoph_`i'_mean } gen female_ar_crc = . replace female_ar_crc = female_ar_crc_20 if age>19.99999 & age<30 replace female_ar_crc = female_ar_crc_30 if age>29.99999 & age<40 replace female_ar_crc = female_ar_crc_40 if age>39.99999 & age<50 replace female_ar_crc = female_ar_crc_50 if age>49.99999 & age<60 replace female_ar_crc = female_ar_crc_60 if age>59.99999 & age<70 replace female_ar_crc = female_ar_crc_70 if age>69.99999 & age<80 replace female_ar_crc = female_ar_crc_80 if age>79.99999 gen female_ar_lung = . replace female_ar_lung = female_ar_lung_20 if age>19.99999 & age<30 replace female_ar_lung = female_ar_lung_30 if age>29.99999 & age<40 replace female_ar_lung = female_ar_lung_40 if age>39.99999 & age<50 replace female_ar_lung = female_ar_lung_50 if age>49.99999 & age<60 replace female_ar_lung = female_ar_lung_60 if age>59.99999 & age<70 replace female_ar_lung = female_ar_lung_70 if age>69.99999 & age<80 replace female_ar_lung = female_ar_lung_80 if age>79.99999 gen female_ar_kidney = . replace female_ar_kidney = female_ar_kidney_20 if age>19.99999 & age<30 replace female_ar_kidney = female_ar_kidney_30 if age>29.99999 & age<40 replace female_ar_kidney = female_ar_kidney_40 if age>39.99999 & age<50 replace female_ar_kidney = female_ar_kidney_50 if age>49.99999 & age<60 replace female_ar_kidney = female_ar_kidney_60 if age>59.99999 & age<70 replace female_ar_kidney = female_ar_kidney_70 if age>69.99999 & age<80 replace female_ar_kidney = female_ar_kidney_80 if age>79.99999 gen female_ar_breast = . replace female_ar_breast = female_ar_breast_20 if age>19.99999 & age<30 replace female_ar_breast = female_ar_breast_30 if age>29.99999 & age<40 replace female_ar_breast = female_ar_breast_40 if age>39.99999 & age<50 replace female_ar_breast = female_ar_breast_50 if age>49.99999 & age<60 replace female_ar_breast = female_ar_breast_60 if age>59.99999 & age<70 replace female_ar_breast = female_ar_breast_70 if age>69.99999 & age<80 replace female_ar_breast = female_ar_breast_80 if age>79.99999 gen female_ar_endomet = . replace female_ar_endomet = female_ar_endomet_20 if age>19.99999 & age<30 replace female_ar_endomet = female_ar_endomet_30 if age>29.99999 & age<40 replace female_ar_endomet = female_ar_endomet_40 if age>39.99999 & age<50 replace female_ar_endomet = female_ar_endomet_50 if age>49.99999 & age<60 replace female_ar_endomet = female_ar_endomet_60 if age>59.99999 & age<70 replace female_ar_endomet = female_ar_endomet_70 if age>69.99999 & age<80 replace female_ar_endomet = female_ar_endomet_80 if age>79.99999 gen male_ar_crc = . replace male_ar_crc = male_ar_crc_20 if age>19.99999 & age<30 replace male_ar_crc = male_ar_crc_30 if age>29.99999 & age<40 replace male_ar_crc = male_ar_crc_40 if age>39.99999 & age<50 replace male_ar_crc = male_ar_crc_50 if age>49.99999 & age<60 replace male_ar_crc = male_ar_crc_60 if age>59.99999 & age<70 replace male_ar_crc = male_ar_crc_70 if age>69.99999 & age<80 replace male_ar_crc = male_ar_crc_80 if age>79.99999 gen male_ar_lung = . replace male_ar_lung = male_ar_lung_20 if age>19.99999 & age<30 replace male_ar_lung = male_ar_lung_30 if age>29.99999 & age<40 replace male_ar_lung = male_ar_lung_40 if age>39.99999 & age<50 replace male_ar_lung = male_ar_lung_50 if age>49.99999 & age<60 replace male_ar_lung = male_ar_lung_60 if age>59.99999 & age<70 replace male_ar_lung = male_ar_lung_70 if age>69.99999 & age<80 replace male_ar_lung = male_ar_lung_80 if age>79.99999 gen male_ar_kidney = . replace male_ar_kidney = male_ar_kidney_20 if age>19.99999 & age<30 replace male_ar_kidney = male_ar_kidney_30 if age>29.99999 & age<40 replace male_ar_kidney = male_ar_kidney_40 if age>39.99999 & age<50 replace male_ar_kidney = male_ar_kidney_50 if age>49.99999 & age<60 replace male_ar_kidney = male_ar_kidney_60 if age>59.99999 & age<70 replace male_ar_kidney = male_ar_kidney_70 if age>69.99999 & age<80 replace male_ar_kidney = male_ar_kidney_80 if age>79.99999 gen male_ar_bladder = . replace male_ar_bladder = male_ar_bladder_20 if age>19.99999 & age<30 replace male_ar_bladder = male_ar_bladder_30 if age>29.99999 & age<40 replace male_ar_bladder = male_ar_bladder_40 if age>39.99999 & age<50 replace male_ar_bladder = male_ar_bladder_50 if age>49.99999 & age<60 replace male_ar_bladder = male_ar_bladder_60 if age>59.99999 & age<70 replace male_ar_bladder = male_ar_bladder_70 if age>69.99999 & age<80 replace male_ar_bladder = male_ar_bladder_80 if age>79.99999 gen male_ar_oesoph = . replace male_ar_oesoph = male_ar_oesoph_20 if age>19.99999 & age<30 replace male_ar_oesoph = male_ar_oesoph_30 if age>29.99999 & age<40 replace male_ar_oesoph = male_ar_oesoph_40 if age>39.99999 & age<50 replace male_ar_oesoph = male_ar_oesoph_50 if age>49.99999 & age<60 replace male_ar_oesoph = male_ar_oesoph_60 if age>59.99999 & age<70 replace male_ar_oesoph = male_ar_oesoph_70 if age>69.99999 & age<80 replace male_ar_oesoph = male_ar_oesoph_80 if age>79.99999 //Calculate overall absolute risk by summing over the cancers forvalues i = 20(10)80 { gen female_ar_`i' = female_ar_crc_`i' + female_ar_lung_`i' + female_ar_kidney_`i' + female_ar_breast_`i' + female_ar_endomet_`i' } forvalues i = 20(10)80 { gen male_ar_`i' = male_ar_crc_`i' + male_ar_lung_`i' + male_ar_kidney_`i' + male_ar_bladder_`i' + male_ar_oesoph_`i' } gen female_ar = . replace female_ar = female_ar_20 if age>19.99999 & age<30 & sex==0 replace female_ar = female_ar_30 if age>29.99999 & age<40 & sex==0 replace female_ar = female_ar_40 if age>39.99999 & age<50 & sex==0 replace female_ar = female_ar_50 if age>49.99999 & age<60 & sex==0 replace female_ar = female_ar_60 if age>59.99999 & age<70 & sex==0 replace female_ar = female_ar_70 if age>69.99999 & age<80 & sex==0 replace female_ar = female_ar_80 if age>79.99999 & sex==0 gen male_ar = . replace male_ar = male_ar_20 if age>19.99999 & age<30 & sex==1 replace male_ar = male_ar_30 if age>29.99999 & age<40 & sex==1 replace male_ar = male_ar_40 if age>39.99999 & age<50 & sex==1 replace male_ar = male_ar_50 if age>49.99999 & age<60 & sex==1 replace male_ar = male_ar_60 if age>59.99999 & age<70 & sex==1 replace male_ar = male_ar_70 if age>69.99999 & age<80 & sex==1 replace male_ar = male_ar_80 if age>79.99999 & sex==1 //8. Calculate absolute risk for IDEAL PERSON //(Essentially repeats step 7 above but using the ideal relative risks instead of the individual's relative risk) forvalues i = 20(10)80 { gen female_ideal_ar_crc_`i' = (1 / female_crc_mean_`i') * female_ar_crc_`i'_mean } forvalues i = 20(10)80 { gen male_ideal_ar_crc_`i' = (1 / male_crc_mean_`i') * male_ar_crc_`i'_mean } forvalues i = 20(10)80 { gen female_ideal_ar_lung_`i' = (1 / female_lung_mean_`i') * female_ar_lung_`i'_mean } forvalues i = 20(10)80 { gen male_ideal_ar_lung_`i' = (1 / male_lung_mean_`i') * male_ar_lung_`i'_mean } forvalues i = 20(10)80 { gen female_ideal_ar_kidney_`i' = (1 / female_kidney_mean_`i') * female_ar_kidney_`i'_mean } forvalues i = 20(10)80 { gen male_ideal_ar_kidney_`i' = (1 / male_kidney_mean_`i') * male_ar_kidney_`i'_mean } forvalues i = 20(10)80 { gen female_ideal_ar_breast_`i' = (1 / female_breast_mean_`i') * female_ar_breast_`i'_mean } forvalues i = 20(10)80 { gen male_ideal_ar_bladder_`i' = (1 / male_bladder_mean_`i') * male_ar_bladder_`i'_mean } forvalues i = 20(10)80 { gen female_ideal_ar_endomet_`i' = (1 / female_endomet_mean_`i') * female_ar_endomet_`i'_mean } forvalues i = 20(10)80 { gen male_ideal_ar_oesoph_`i' = (1 / male_oesoph_mean_`i') * male_ar_oesoph_`i'_mean } gen female_ideal_ar_crc = . replace female_ideal_ar_crc = female_ideal_ar_crc_20 if age>19.99999 & age<30 replace female_ideal_ar_crc = female_ideal_ar_crc_30 if age>29.99999 & age<40 replace female_ideal_ar_crc = female_ideal_ar_crc_40 if age>39.99999 & age<50 replace female_ideal_ar_crc = female_ideal_ar_crc_50 if age>49.99999 & age<60 replace female_ideal_ar_crc = female_ideal_ar_crc_60 if age>59.99999 & age<70 replace female_ideal_ar_crc = female_ideal_ar_crc_70 if age>69.99999 & age<80 replace female_ideal_ar_crc = female_ideal_ar_crc_80 if age>79.99999 gen female_ideal_ar_lung = . replace female_ideal_ar_lung = female_ideal_ar_lung_20 if age>19.99999 & age<30 replace female_ideal_ar_lung = female_ideal_ar_lung_30 if age>29.99999 & age<40 replace female_ideal_ar_lung = female_ideal_ar_lung_40 if age>39.99999 & age<50 replace female_ideal_ar_lung = female_ideal_ar_lung_50 if age>49.99999 & age<60 replace female_ideal_ar_lung = female_ideal_ar_lung_60 if age>59.99999 & age<70 replace female_ideal_ar_lung = female_ideal_ar_lung_70 if age>69.99999 & age<80 replace female_ideal_ar_lung = female_ideal_ar_lung_80 if age>79.99999 gen female_ideal_ar_kidney = . replace female_ideal_ar_kidney = female_ideal_ar_kidney_20 if age>19.99999 & age<30 replace female_ideal_ar_kidney = female_ideal_ar_kidney_30 if age>29.99999 & age<40 replace female_ideal_ar_kidney = female_ideal_ar_kidney_40 if age>39.99999 & age<50 replace female_ideal_ar_kidney = female_ideal_ar_kidney_50 if age>49.99999 & age<60 replace female_ideal_ar_kidney = female_ideal_ar_kidney_60 if age>59.99999 & age<70 replace female_ideal_ar_kidney = female_ideal_ar_kidney_70 if age>69.99999 & age<80 replace female_ideal_ar_kidney = female_ideal_ar_kidney_80 if age>79.99999 gen female_ideal_ar_breast = . replace female_ideal_ar_breast = female_ideal_ar_breast_20 if age>19.99999 & age<30 replace female_ideal_ar_breast = female_ideal_ar_breast_30 if age>29.99999 & age<40 replace female_ideal_ar_breast = female_ideal_ar_breast_40 if age>39.99999 & age<50 replace female_ideal_ar_breast = female_ideal_ar_breast_50 if age>49.99999 & age<60 replace female_ideal_ar_breast = female_ideal_ar_breast_60 if age>59.99999 & age<70 replace female_ideal_ar_breast = female_ideal_ar_breast_70 if age>69.99999 & age<80 replace female_ideal_ar_breast = female_ideal_ar_breast_80 if age>79.99999 gen female_ideal_ar_endomet = . replace female_ideal_ar_endomet = female_ideal_ar_endomet_20 if age>19.99999 & age<30 replace female_ideal_ar_endomet = female_ideal_ar_endomet_30 if age>29.99999 & age<40 replace female_ideal_ar_endomet = female_ideal_ar_endomet_40 if age>39.99999 & age<50 replace female_ideal_ar_endomet = female_ideal_ar_endomet_50 if age>49.99999 & age<60 replace female_ideal_ar_endomet = female_ideal_ar_endomet_60 if age>59.99999 & age<70 replace female_ideal_ar_endomet = female_ideal_ar_endomet_70 if age>69.99999 & age<80 replace female_ideal_ar_endomet = female_ideal_ar_endomet_80 if age>79.99999 gen male_ideal_ar_crc = . replace male_ideal_ar_crc = male_ideal_ar_crc_20 if age>19.99999 & age<30 replace male_ideal_ar_crc = male_ideal_ar_crc_30 if age>29.99999 & age<40 replace male_ideal_ar_crc = male_ideal_ar_crc_40 if age>39.99999 & age<50 replace male_ideal_ar_crc = male_ideal_ar_crc_50 if age>49.99999 & age<60 replace male_ideal_ar_crc = male_ideal_ar_crc_60 if age>59.99999 & age<70 replace male_ideal_ar_crc = male_ideal_ar_crc_70 if age>69.99999 & age<80 replace male_ideal_ar_crc = male_ideal_ar_crc_80 if age>79.99999 gen male_ideal_ar_lung = . replace male_ideal_ar_lung = male_ideal_ar_lung_20 if age>19.99999 & age<30 replace male_ideal_ar_lung = male_ideal_ar_lung_30 if age>29.99999 & age<40 replace male_ideal_ar_lung = male_ideal_ar_lung_40 if age>39.99999 & age<50 replace male_ideal_ar_lung = male_ideal_ar_lung_50 if age>49.99999 & age<60 replace male_ideal_ar_lung = male_ideal_ar_lung_60 if age>59.99999 & age<70 replace male_ideal_ar_lung = male_ideal_ar_lung_70 if age>69.99999 & age<80 replace male_ideal_ar_lung = male_ideal_ar_lung_80 if age>79.99999 gen male_ideal_ar_kidney = . replace male_ideal_ar_kidney = male_ideal_ar_kidney_20 if age>19.99999 & age<30 replace male_ideal_ar_kidney = male_ideal_ar_kidney_30 if age>29.99999 & age<40 replace male_ideal_ar_kidney = male_ideal_ar_kidney_40 if age>39.99999 & age<50 replace male_ideal_ar_kidney = male_ideal_ar_kidney_50 if age>49.99999 & age<60 replace male_ideal_ar_kidney = male_ideal_ar_kidney_60 if age>59.99999 & age<70 replace male_ideal_ar_kidney = male_ideal_ar_kidney_70 if age>69.99999 & age<80 replace male_ideal_ar_kidney = male_ideal_ar_kidney_80 if age>79.99999 gen male_ideal_ar_bladder = . replace male_ideal_ar_bladder = male_ideal_ar_bladder_20 if age>19.99999 & age<30 replace male_ideal_ar_bladder = male_ideal_ar_bladder_30 if age>29.99999 & age<40 replace male_ideal_ar_bladder = male_ideal_ar_bladder_40 if age>39.99999 & age<50 replace male_ideal_ar_bladder = male_ideal_ar_bladder_50 if age>49.99999 & age<60 replace male_ideal_ar_bladder = male_ideal_ar_bladder_60 if age>59.99999 & age<70 replace male_ideal_ar_bladder = male_ideal_ar_bladder_70 if age>69.99999 & age<80 replace male_ideal_ar_bladder = male_ideal_ar_bladder_80 if age>79.99999 gen male_ideal_ar_oesoph = . replace male_ideal_ar_oesoph = male_ideal_ar_oesoph_20 if age>19.99999 & age<30 replace male_ideal_ar_oesoph = male_ideal_ar_oesoph_30 if age>29.99999 & age<40 replace male_ideal_ar_oesoph = male_ideal_ar_oesoph_40 if age>39.99999 & age<50 replace male_ideal_ar_oesoph = male_ideal_ar_oesoph_50 if age>49.99999 & age<60 replace male_ideal_ar_oesoph = male_ideal_ar_oesoph_60 if age>59.99999 & age<70 replace male_ideal_ar_oesoph = male_ideal_ar_oesoph_70 if age>69.99999 & age<80 replace male_ideal_ar_oesoph = male_ideal_ar_oesoph_80 if age>79.99999 //Calculate overall ideal absolute risk by summing over the cancers forvalues i = 20(10)80 { gen female_ideal_ar_`i' = female_ideal_ar_crc_`i' + female_ideal_ar_lung_`i' + female_ideal_ar_kidney_`i' + female_ideal_ar_breast_`i' + female_ideal_ar_endomet_`i' } forvalues i = 20(10)80 { gen male_ideal_ar_`i' = male_ideal_ar_crc_`i' + male_ideal_ar_lung_`i' + male_ideal_ar_kidney_`i' + male_ideal_ar_bladder_`i' + male_ideal_ar_oesoph_`i' } gen female_ideal_ar = . replace female_ideal_ar = female_ideal_ar_20 if age>19.99999 & age<30 replace female_ideal_ar = female_ideal_ar_30 if age>29.99999 & age<40 replace female_ideal_ar = female_ideal_ar_40 if age>39.99999 & age<50 replace female_ideal_ar = female_ideal_ar_50 if age>49.99999 & age<60 replace female_ideal_ar = female_ideal_ar_60 if age>59.99999 & age<70 replace female_ideal_ar = female_ideal_ar_70 if age>69.99999 & age<80 replace female_ideal_ar = female_ideal_ar_80 if age>79.99999 gen male_ideal_ar = . replace male_ideal_ar = male_ideal_ar_20 if age>19.99999 & age<30 replace male_ideal_ar = male_ideal_ar_30 if age>29.99999 & age<40 replace male_ideal_ar = male_ideal_ar_40 if age>39.99999 & age<50 replace male_ideal_ar = male_ideal_ar_50 if age>49.99999 & age<60 replace male_ideal_ar = male_ideal_ar_60 if age>59.99999 & age<70 replace male_ideal_ar = male_ideal_ar_70 if age>69.99999 & age<80 replace male_ideal_ar = male_ideal_ar_80 if age>79.99999 //Female capture drop female_ideal_ar egen female_ideal_ar = rowtotal(female_ideal_ar_20 female_ideal_ar_30 female_ideal_ar_40 female_ideal_ar_50 female_ideal_ar_60 female_ideal_ar_70 female_ideal_ar_80) if sex==0 //Male capture drop male_ideal_ar egen male_ideal_ar = rowtotal(male_ideal_ar_20 male_ideal_ar_30 male_ideal_ar_40 male_ideal_ar_50 male_ideal_ar_60 male_ideal_ar_70 male_ideal_ar_80) if sex==1 //9. Generate all values egen rri = rowtotal(female_rri male_rri) // Relative risk of individual compared to a person of the same age and sex with ideal lifestyle egen rr = rowtotal(female_rr male_rr) // Relative risk of individual compared to average person of same age and sex egen mean_rr = rowtotal(female_mean_rr male_mean_rr) // Relative risk of average person of same age and sex relative to ideal lifestyle (not used in presentation) egen ideal_mean_rr = rowtotal(female_ideal_mean_rr male_ideal_mean_rr) //Relative risk of person with ideal lifestyle compared to average person gen ideal_rr = 1 // Relative risk of person of same age and sex with ideal lifestyle egen ar = rowtotal(female_ar male_ar) // Absolute risk of individual egen mean_ar = rowtotal(female_ar_mean male_ar_mean) // Absolute risk of average person of same age and sex (not used in current versions of presentation) egen ideal_ar = rowtotal(female_ideal_ar male_ideal_ar) // Absolute risk of person same age and sex with ideal lifestyle