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  • ItemOpen Access
    Random effects models for complex designs-plaid
    Jarrett, RG; Farewell, Vernon; Herzberg, AM; Farewell, Vernon [0000-0001-6704-5295]
  • ItemOpen AccessPublished version Peer-reviewed
    An optimised multi-arm multi-stage clinical trial design for unknown variance.
    (Elsevier BV, 2018-04) Grayling, Michael J; Wason, James MS; Mander, Adrian P; Grayling, Michael [0000-0002-0680-6668]; Wason, James [0000-0002-4691-126X]; Mander, Adrian [0000-0002-0742-9040]
    Multi-arm multi-stage trial designs can bring notable gains in efficiency to the drug development process. However, for normally distributed endpoints, the determination of a design typically depends on the assumption that the patient variance in response is known. In practice, this will not usually be the case. To allow for unknown variance, previous research explored the performance of t-test statistics, coupled with a quantile substitution procedure for modifying the stopping boundaries, at controlling the familywise error-rate to the nominal level. Here, we discuss an alternative method based on Monte Carlo simulation that allows the group size and stopping boundaries of a multi-arm multi-stage t-test to be optimised, according to some nominated optimality criteria. We consider several examples, provide R code for general implementation, and show that our designs confer a familywise error-rate and power close to the desired level. Consequently, this methodology will provide utility in future multi-arm multi-stage trials.
  • ItemOpen AccessPublished version Peer-reviewed
    Increased uptake and new therapies are needed to avert rising hepatitis C-related end stage liver disease in England: modelling the predicted impact of treatment under different scenarios.
    (Elsevier BV, 2014-09) Harris, Ross J; Thomas, Brenda; Griffiths, Jade; Costella, Annastella; Chapman, Ruth; Ramsay, Mary; De Angelis, Daniela; Harris, Helen E; De Angelis, Daniela [0000-0001-6619-6112]
    BACKGROUND & AIMS: Hepatitis C (HCV) related disease in England is predicted to rise, and it is unclear whether treatment at current levels will be able to avert this. The aim of this study was to estimate the number of people with chronic HCV infection in England that are treated and assess the impact and costs of increasing treatment uptake. METHODS: Numbers treated were estimated using national data sources for pegylated interferon supplied, dispensed, or purchased from 2006 to 2011. A back-calculation approach was used to project disease burden over the next 30 years and determine outcomes under various scenarios of treatment uptake. RESULTS: 5000 patients were estimated to have been treated in 2011 and 28,000 in total from 2006 to 2011; approximately 3.1% and 17% respectively of estimated chronic infections. Without treatment, incident cases of decompensated cirrhosis and hepatocellular carcinoma were predicted to increase until 2035 and reach 2290 cases per year. Treatment at current levels should reduce incidence by 600 cases per year, with a peak around 2030. Large increases in treatment are needed to halt the rise; and with more effective treatment the best case scenario predicts incidence of around 500 cases in 2030, although treatment uptake must still be increased considerably to achieve this. CONCLUSIONS: If the infected population is left untreated, the number of patients with severe HCV-related disease will continue to increase and represent a substantial future burden on healthcare resources. This can be mitigated by increasing treatment uptake, which will have the greatest impact if implemented quickly.
  • ItemOpen AccessAccepted version Peer-reviewed
    Calibration of complex models through Bayesian evidence synthesis: a demonstration and tutorial.
    (SAGE Publications, 2015-02) Jackson, Christopher H; Jit, Mark; Sharples, Linda D; De Angelis, Daniela; Jackson, Christopher [0000-0002-6656-8913]; De Angelis, Daniela [0000-0001-6619-6112]
    Decision-analytic models must often be informed using data that are only indirectly related to the main model parameters. The authors outline how to implement a Bayesian synthesis of diverse sources of evidence to calibrate the parameters of a complex model. A graphical model is built to represent how observed data are generated from statistical models with unknown parameters and how those parameters are related to quantities of interest for decision making. This forms the basis of an algorithm to estimate a posterior probability distribution, which represents the updated state of evidence for all unknowns given all data and prior beliefs. This process calibrates the quantities of interest against data and, at the same time, propagates all parameter uncertainties to the results used for decision making. To illustrate these methods, the authors demonstrate how a previously developed Markov model for the progression of human papillomavirus (HPV-16) infection was rebuilt in a Bayesian framework. Transition probabilities between states of disease severity are inferred indirectly from cross-sectional observations of prevalence of HPV-16 and HPV-16-related disease by age, cervical cancer incidence, and other published information. Previously, a discrete collection of plausible scenarios was identified but with no further indication of which of these are more plausible. Instead, the authors derive a Bayesian posterior distribution, in which scenarios are implicitly weighted according to how well they are supported by the data. In particular, we emphasize the appropriate choice of prior distributions and checking and comparison of fitted models.
  • ItemOpen AccessPublished version Peer-reviewed
    Estimating HIV Incidence, Time to Diagnosis, and the Undiagnosed HIV Epidemic Using Routine Surveillance Data.
    (Ovid Technologies (Wolters Kluwer Health), 2015-09) van Sighem, Ard; Nakagawa, Fumiyo; De Angelis, Daniela; Quinten, Chantal; Bezemer, Daniela; de Coul, Eline Op; Egger, Matthias; de Wolf, Frank; Fraser, Christophe; Phillips, Andrew; De Angelis, Daniela [0000-0001-6619-6112]
    BACKGROUND: Estimates of the size of the undiagnosed HIV-infected population are important to understand the HIV epidemic and to plan interventions, including "test-and-treat" strategies. METHODS: We developed a multi-state back-calculation model to estimate HIV incidence, time between infection and diagnosis, and the undiagnosed population by CD4 count strata, using surveillance data on new HIV and AIDS diagnoses. The HIV incidence curve was modelled using cubic splines. The model was tested on simulated data and applied to surveillance data on men who have sex with men in The Netherlands. RESULTS: The number of HIV infections could be estimated accurately using simulated data, with most values within the 95% confidence intervals of model predictions. When applying the model to Dutch surveillance data, 15,400 (95% confidence interval [CI] = 15,000, 16,000) men who have sex with men were estimated to have been infected between 1980 and 2011. HIV incidence showed a bimodal distribution, with peaks around 1985 and 2005 and a decline in recent years. Mean time to diagnosis was 6.1 (95% CI = 5.8, 6.4) years between 1984 and 1995 and decreased to 2.6 (2.3, 3.0) years in 2011. By the end of 2011, 11,500 (11,000, 12,000) men who have sex with men in The Netherlands were estimated to be living with HIV, of whom 1,750 (1,450, 2,200) were still undiagnosed. Of the undiagnosed men who have sex with men, 29% (22, 37) were infected for less than 1 year, and 16% (13, 20) for more than 5 years. CONCLUSIONS: This multi-state back-calculation model will be useful to estimate HIV incidence, time to diagnosis, and the undiagnosed HIV epidemic based on routine surveillance data.
  • ItemOpen AccessPublished version Peer-reviewed
    Recapture or precapture? Fallibility of standard capture-recapture methods in the presence of referrals between sources.
    (Oxford University Press (OUP), 2014-06-01) Jones, Hayley E; Hickman, Matthew; Welton, Nicky J; De Angelis, Daniela; Harris, Ross J; Ades, AE; De Angelis, Daniela [0000-0001-6619-6112]
    Capture-recapture methods, largely developed in ecology, are now commonly used in epidemiology to adjust for incomplete registries and to estimate the size of difficult-to-reach populations such as problem drug users. Overlapping lists of individuals in the target population, taken from administrative data sources, are considered analogous to overlapping "captures" of animals. Log-linear models, incorporating interaction terms to account for dependencies between sources, are used to predict the number of unobserved individuals and, hence, the total population size. A standard assumption to ensure parameter identifiability is that the highest-order interaction term is 0. We demonstrate that, when individuals are referred directly between sources, this assumption will often be violated, and the standard modeling approach may lead to seriously biased estimates. We refer to such individuals as having been "precaptured," rather than truly recaptured. Although sometimes an alternative identifiable log-linear model could accommodate the referral structure, this will not always be the case. Further, multiple plausible models may fit the data equally well but provide widely varying estimates of the population size. We demonstrate an alternative modeling approach, based on an interpretable parameterization and driven by careful consideration of the relationships between the sources, and we make recommendations for capture-recapture in practice.
  • ItemOpen AccessAccepted version Peer-reviewed
    Hepatitis C virus treatment as prevention in people who inject drugs: testing the evidence.
    (Ovid Technologies (Wolters Kluwer Health), 2015-12) Hickman, Matthew; De Angelis, Daniela; Vickerman, Peter; Hutchinson, Sharon; Martin, Natasha Kaleta; De Angelis, Daniela [0000-0001-6619-6112]
    PURPOSE OF REVIEW: The majority of hepatitis C virus (HCV) infections in the United Kingdom and many developing countries were acquired through injecting. New clinical guidance suggests that HCV treatment should be offered to people with a transmission risk - such as people who inject drugs (PWID) - irrespective of severity of liver disease. We consider the strength of the evidence base and potential problems in evaluating HCV treatment as prevention among PWID. RECENT FINDINGS: There is good theoretical evidence from dynamic models that HCV treatment for PWID could reduce HCV chronic prevalence and incidence among PWID. Economic evaluations from high-income settings have suggested HCV treatment for PWID is cost-effective, and that in many settings HCV treatment of PWID could be more cost-effective than treating those at an equivalent stage with no ongoing transmission risk. Epidemiological studies of older interferon treatments have suggested that PWID can achieve similar treatment outcomes to other patient groups treated for chronic HCV. Impact and cost-effectiveness of HCV treatment is driven by the potential 'prevention benefit' of treating PWID. Model projections suggest that more future infections, end stage liver disease, and HCV-related deaths will be averted than lost through reinfection of PWID treated successfully for HCV. However, there is to date no empirical evidence from trials or observational studies that test the model projections and 'prevention benefit' hypothesis. In part this is because of uncertainty in the evidence base but also there is unlikely to have been a change in HCV prevalence due to HCV treatment because PWID HCV treatment rates historically in most sites have been low, and any scale-up and switch to the new direct acting antiviral has not yet occurred. There are a number of key uncertainties in the data available on PWID that need to be improved and addressed to evaluate treatment as prevention. These include estimates of the prevalence of PWID, measurements of HCV chronic prevalence and incidence among PWID, and how to interpret reinfection rates as potential outcome measures. SUMMARY: Eliminating HCV through scaling up treatment is a theoretical possibility. But empirical data are required to demonstrate that HCV treatment can reduce HCV transmission, which will require an improved evidence base and analytic framework for measuring PWID and HCV prevalence.
  • ItemOpen AccessPublished version Peer-reviewed
    New treatments for hepatitis C virus (HCV): scope for preventing liver disease and HCV transmission in England.
    (Wiley, 2016-08) Harris, RJ; Martin, NK; Rand, E; Mandal, S; Mutimer, D; Vickerman, P; Ramsay, ME; De Angelis, D; Hickman, M; Harris, HE; De Angelis, Daniela [0000-0001-6619-6112]
    New direct-acting antivirals have the potential to transform the hepatitis C (HCV) treatment landscape, with rates of sustained viral response in excess of 90%. As these new agents are expensive, an important question is whether to focus on minimizing the consequences of severe liver disease, or reducing transmission via 'treatment as prevention'. A back-calculation model was used to estimate the impact of treatment of mild, moderate and compensated cirrhosis on incident cases of HCV-related end-stage liver disease/hepatocellular carcinoma (ESLD/HCC). In addition, a dynamic model was used to determine the impact on incidence and prevalence of chronic infection in people who inject drugs (PWID), the main risk group in England. Treating 3500 cirrhotics per year was predicted to reduce ESLD/HCC incidence from 1100 (95% CrI 970-1240) cases per year in 2015 to 630 (95% CrI 530-770) in 2020, around half that currently expected, although treating moderate-stage disease will also be needed to sustain this reduction. Treating mild-stage PWID was required to make a substantial impact on transmission: with 2500 treated per year, chronic prevalence/annual incidence in PWID was reduced from 34%/4.8% in 2015 to 11%/1.4% in 2030. There was little overlap between the two goals: treating mild stage had virtually no impact on ESLD/HCC within 15 years, but the long timescale of liver disease means relatively few PWID reach cirrhosis before cessation of injecting. Strategies focussing on treating advanced disease have the potential for dramatic reductions in severe morbidity, but virtually no preventative impact.
  • ItemOpen AccessPublished version Peer-reviewed
    Stochastic interest model driven by compound Poisson process and Brownian motion with applications in life contingencies
    Li, Shilong; Xia, Zhao; Chuancun, Yin; Huang, Z; Huang, Robin [0000-0003-3416-8939]
    In this paper, we introduce a class of stochastic interest model driven by a compound Poisson process and a Brownian motion, in which the jumping times of force of interest obeys compound Poisson process and the continuous tiny fluctuations are described by Brownian motion, and the adjustment in each jump of interest force is assumed to be random. Based on the proposed interest model, we discuss the expected discounted function, the validity of the model and actuarial present values of life annuities and life insurances under different parameters and distribution settings. Ournumerical results show actuarial values could be sensitive to the parameters and distribution settings,which shows the importance of introducing this kind interest model.
  • ItemOpen AccessPublished version Peer-reviewed
    Transancestral fine-mapping of four type 2 diabetes susceptibility loci highlights potential causal regulatory mechanisms.
    (Oxford University Press (OUP), 2016-05-15) Horikoshi, Momoko; Pasquali, Lorenzo; Wiltshire, Steven; Huyghe, Jeroen R; Mahajan, Anubha; Asimit, Jennifer L; Ferreira, Teresa; Locke, Adam E; Robertson, Neil R; Wang, Xu; Sim, Xueling; Fujita, Hayato; Hara, Kazuo; Young, Robin; Zhang, Weihua; Choi, Sungkyoung; Chen, Han; Kaur, Ismeet; Takeuchi, Fumihiko; Fontanillas, Pierre; Thuillier, Dorothée; Yengo, Loic; Below, Jennifer E; Tam, Claudia HT; Wu, Ying; Abecasis, Gonçalo; Altshuler, David; Bell, Graeme I; Blangero, John; Burtt, Noél P; Duggirala, Ravindranath; Florez, Jose C; Hanis, Craig L; Seielstad, Mark; Atzmon, Gil; Chan, Juliana CN; Ma, Ronald CW; Froguel, Philippe; Wilson, James G; Bharadwaj, Dwaipayan; Dupuis, Josee; Meigs, James B; Cho, Yoon Shin; Park, Taesung; Kooner, Jaspal S; Chambers, John C; Saleheen, Danish; Kadowaki, Takashi; Tai, E Shyong; Mohlke, Karen L; Cox, Nancy J; Ferrer, Jorge; Zeggini, Eleftheria; Kato, Norihiro; Teo, Yik Ying; Boehnke, Michael; McCarthy, Mark I; Morris, Andrew P; T2D-GENES Consortium; Asimit, Jennifer [0000-0002-4857-2249]
    To gain insight into potential regulatory mechanisms through which the effects of variants at four established type 2 diabetes (T2D) susceptibility loci (CDKAL1, CDKN2A-B, IGF2BP2 and KCNQ1) are mediated, we undertook transancestral fine-mapping in 22 086 cases and 42 539 controls of East Asian, European, South Asian, African American and Mexican American descent. Through high-density imputation and conditional analyses, we identified seven distinct association signals at these four loci, each with allelic effects on T2D susceptibility that were homogenous across ancestry groups. By leveraging differences in the structure of linkage disequilibrium between diverse populations, and increased sample size, we localised the variants most likely to drive each distinct association signal. We demonstrated that integration of these genetic fine-mapping data with genomic annotation can highlight potential causal regulatory elements in T2D-relevant tissues. These analyses provide insight into the mechanisms through which T2D association signals are mediated, and suggest future routes to understanding the biology of specific disease susceptibility loci.
  • ItemOpen AccessPublished version Peer-reviewed
    Trans-ethnic study design approaches for fine-mapping.
    (Springer Science and Business Media LLC, 2016-08) Asimit, Jennifer L; Hatzikotoulas, Konstantinos; McCarthy, Mark; Morris, Andrew P; Zeggini, Eleftheria; Zeggini, Eleftheria [0000-0003-4238-659X]
    Studies that traverse ancestrally diverse populations may increase power to detect novel loci and improve fine-mapping resolution of causal variants by leveraging linkage disequilibrium differences between ethnic groups. The inclusion of African ancestry samples may yield further improvements because of low linkage disequilibrium and high genetic heterogeneity. We investigate the fine-mapping resolution of trans-ethnic fixed-effects meta-analysis for five type II diabetes loci, under various settings of ancestral composition (European, East Asian, African), allelic heterogeneity, and causal variant minor allele frequency. In particular, three settings of ancestral composition were compared: (1) single ancestry (European), (2) moderate ancestral diversity (European and East Asian), and (3) high ancestral diversity (European, East Asian, and African). Our simulations suggest that the European/Asian and European ancestry-only meta-analyses consistently attain similar fine-mapping resolution. The inclusion of African ancestry samples in the meta-analysis leads to a marked improvement in fine-mapping resolution.
  • ItemOpen AccessPublished version Peer-reviewed
    Biased sampling activity: an investigation to promote discussion.
    (Wiley, 2019) White, Simon R; Bonnett, Laura J; White, Simon [0000-0001-8642-7037]
    The statistical concept of sampling is often given little direct attention, typically reduced to the mantra "take a random sample". This low resource and adaptable activity demonstrates sampling and explores issues that arise due to biased sampling.
  • ItemOpen AccessPublished version Peer-reviewed
    Inferring causal molecular networks: empirical assessment through a community-based effort.
    (Springer Science and Business Media LLC, 2016-04) Hill, Steven M; Heiser, Laura M; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K; Carlin, Daniel E; Zhang, Yang; Sokolov, Artem; Paull, Evan O; Wong, Chris K; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V; Favorov, Alexander V; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W; Long, Byron L; Noren, David P; Bisberg, Alexander J; HPN-DREAM Consortium; Mills, Gordon B; Gray, Joe W; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A; Fertig, Elana J; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M; Spellman, Paul T; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach; Graim, Kiley [0000-0002-4569-8444]; Danilova, Ludmila V [0000-0003-2813-3094]; Taylor, Dane [0000-0003-1851-3309]; Kellen, Michael [0000-0002-4096-7078]; Friend, Stephen [0000-0002-0830-7600]; Song, Mingzhou [0000-0002-6883-6547]; Saez-Rodriguez, Julio [0000-0002-8552-8976]
    It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.
  • ItemOpen AccessPublished version Peer-reviewed
    Multiple Imputation of Missing Composite Outcomes in Longitudinal Data.
    (Springer Science and Business Media LLC, 2016) O'Keeffe, Aidan G; Farewell, Daniel M; Tom, Brian DM; Farewell, Vernon T; Tom, Brian [0000-0002-3335-9322]; Farewell, Vernon [0000-0001-6704-5295]
    In longitudinal randomised trials and observational studies within a medical context, a composite outcome-which is a function of several individual patient-specific outcomes-may be felt to best represent the outcome of interest. As in other contexts, missing data on patient outcome, due to patient drop-out or for other reasons, may pose a problem. Multiple imputation is a widely used method for handling missing data, but its use for composite outcomes has been seldom discussed. Whilst standard multiple imputation methodology can be used directly for the composite outcome, the distribution of a composite outcome may be of a complicated form and perhaps not amenable to statistical modelling. We compare direct multiple imputation of a composite outcome with separate imputation of the components of a composite outcome. We consider two imputation approaches. One approach involves modelling each component of a composite outcome using standard likelihood-based models. The other approach is to use linear increments methods. A linear increments approach can provide an appealing alternative as assumptions concerning both the missingness structure within the data and the imputation models are different from the standard likelihood-based approach. We compare both approaches using simulation studies and data from a randomised trial on early rheumatoid arthritis patients. Results suggest that both approaches are comparable and that for each, separate imputation offers some improvement on the direct imputation of a composite outcome.
  • ItemOpen AccessPublished version Peer-reviewed
    The versatility of multi-state models for the analysis of longitudinal data with unobservable features.
    (Springer Science and Business Media LLC, 2014-01) Farewell, Vernon T; Tom, Brian DM; Farewell, Vernon [0000-0001-6704-5295]; Tom, Brian [0000-0002-3335-9322]
    Multi-state models provide a convenient statistical framework for a wide variety of medical applications characterized by multiple events and longitudinal data. We illustrate this through four examples. The potential value of the incorporation of unobserved or partially observed states is highlighted. In addition, joint modelling of multiple processes is illustrated with application to potentially informative loss to follow-up, mis-measured or missclassified data and causal inference.
  • ItemOpen AccessPublished version Peer-reviewed
    Mixture distributions in multi-state modelling: some considerations in a study of psoriatic arthritis.
    (Wiley, 2013-02-20) O'Keeffe, Aidan G; Tom, Brian DM; Farewell, Vernon T; Tom, Brian [0000-0002-3335-9322]; Farewell, Vernon [0000-0001-6704-5295]
    In many studies, interest lies in determining whether members of the study population will undergo a particular event of interest. Such scenarios are often termed 'mover-stayer' scenarios, and interest lies in modelling two sub-populations of 'movers' (those who have a propensity to undergo the event of interest) and 'stayers' (those who do not). In general, mover-stayer scenarios within data sets are accounted for through the use of mixture distributions, and in this paper, we investigate the use of various random effects distributions for this purpose. Using data from the University of Toronto psoriatic arthritis clinic, we present a multi-state model to describe the progression of clinical damage in hand joints of patients with psoriatic arthritis. We consider the use of mover-stayer gamma, inverse Gaussian and compound Poisson distributions to account for both the correlation amongst joint locations and the possible mover-stayer situation with regard to clinical hand joint damage. We compare the fits obtained from these models and discuss the extent to which a mover-stayer scenario exists in these data. Furthermore, we fit a mover-stayer model that allows a dependence of the probability of a patient being a stayer on a patient-level explanatory variable.
  • ItemOpen AccessPublished version Peer-reviewed
    A randomized placebo-controlled trial of methotrexate in psoriatic arthritis.
    (Oxford University Press (OUP), 2012-08) Kingsley, Gabrielle H; Kowalczyk, Anna; Taylor, Helen; Ibrahim, Fowzia; Packham, Jonathan C; McHugh, Neil J; Mulherin, Diarmuid M; Kitas, George D; Chakravarty, Kuntal; Tom, Brian DM; O'Keeffe, Aidan G; Maddison, Peter J; Scott, David L; Tom, Brian [0000-0002-3335-9322]
    OBJECTIVE: MTX is widely used to treat synovitis in PsA without supporting trial evidence. The aim of our study was to test the value of MTX in the first large randomized placebo-controlled trial (RCT) in PsA. METHODS: A 6-month double-blind RCT compared MTX (15 mg/week) with placebo in active PsA. The primary outcome was PsA response criteria (PsARC). Other outcomes included ACR20, DAS-28 and their individual components. Missing data were imputed using multiple imputation methods. Treatments were compared using logistic regression analysis (adjusted for age, sex, disease duration and, where appropriate, individual baseline scores). RESULTS: Four hundred and sixty-two patients were screened and 221 recruited. One hundred and nine patients received MTX and 112 received placebo. Forty-four patients were lost to follow-up (21 MTX, 23 placebo). Twenty-six patients discontinued treatment (14 MTX, 12 placebo). Comparing MTX with placebo in all randomized patients at 6 months showed no significant effect on PsARC [odds ratio (OR) 1.77, 95% CI 0.97, 3.23], ACR20 (OR 2.00, 95% CI 0.65, 6.22) or DAS-28 (OR 1.70, 95% CI 0.90, 3.17). There were also no significant treatment effects on tender and swollen joint counts, ESR, CRP, HAQ and pain. The only benefits of MTX were reductions in patient and assessor global scores and skin scores at 6 months (P = 0.03, P < 0.001 and P = 0.02, respectively). There were no unexpected adverse events. CONCLUSIONS: This trial of active PsA found no evidence for MTX improving synovitis and consequently raises questions about its classification as a disease-modifying drug in PsA. Trial registration. Current Controlled Trials, www.controlled-trials.com, ISRCTN:54376151.
  • ItemOpen AccessPublished version Peer-reviewed
    A case-study in the clinical epidemiology of psoriatic arthritis: multistate models and causal arguments.
    (Oxford University Press (OUP), 2011-11) O'Keeffe, Aidan G; Tom, Brian DM; Farewell, Vernon T; Tom, Brian [0000-0002-3335-9322]; Farewell, Vernon [0000-0001-6704-5295]
    In psoriatic arthritis, permanent joint damage characterizes disease progression and represents a major debilitating aspect of the disease. Understanding the process of joint damage will assist in the treatment and disease management of patients. Multistate models provide a means to examine patterns of disease, such as symmetric joint damage. Additionally, the link between damage and the dynamic course of disease activity (represented by joint swelling and stress pain) at both the individual joint level and otherwise can be represented within a correlated multistate model framework. Correlation is reflected through the use of random effects for progressive models and robust variance estimation for non-progressive models. Such analyses, undertaken with data from a large psoriatic arthritis cohort, are discussed and the extent to which they permit causal reasoning is considered. For this, emphasis is given to the use of the Bradford Hill criteria for causation in observational studies and the concept of local (in)dependence to capture the dynamic nature of the relationships.
  • ItemOpen AccessAccepted version Peer-reviewed
    Cross-Sectional HIV Incidence Surveillance: A Benchmarking of Approaches for Estimating the 'Mean Duration of Recent Infection'.
    (Walter de Gruyter GmbH, 2017-03) Kassanjee, Reshma; De Angelis, Daniela; Farah, Marian; Hanson, Debra; Labuschagne, Jan Phillipus Lourens; Laeyendecker, Oliver; Le Vu, Stéphane; Tom, Brian; Wang, Rui; Welte, Alex; De Angelis, Daniela [0000-0001-6619-6112]; Tom, Brian [0000-0002-3335-9322]
    The application of biomarkers for 'recent' infection in cross-sectional HIV incidence surveillance requires the estimation of critical biomarker characteristics. Various approaches have been employed for using longitudinal data to estimate the Mean Duration of Recent Infection (MDRI) - the average time in the 'recent' state. In this systematic benchmarking of MDRI estimation approaches, a simulation platform was used to measure accuracy and precision of over twenty approaches, in thirty scenarios capturing various study designs, subject behaviors and test dynamics that may be encountered in practice. Results highlight that assuming a single continuous sojourn in the 'recent' state can produce substantial bias. Simple interpolation provides useful MDRI estimates provided subjects are tested at regular intervals. Regression performs the best - while 'random effects' describe the subject-clustering in the data, regression models without random effects proved easy to implement, stable, and of similar accuracy in scenarios considered; robustness to parametric assumptions was improved by regressing 'recent'/'non-recent' classifications rather than continuous biomarker readings. All approaches were vulnerable to incorrect assumptions about subjects' (unobserved) infection times. Results provided show the relationships between MDRI estimation performance and the number of subjects, inter-visit intervals, missed visits, loss to follow-up, and aspects of biomarker signal and noise.
  • ItemOpen AccessPublished version Peer-reviewed
    Does intensive management improve remission rates in patients with intermediate rheumatoid arthritis? (the TITRATE trial): study protocol for a randomised controlled trial.
    (Springer Science and Business Media LLC, 2017-12-08) Martin, Naomi H; Ibrahim, Fowzia; Tom, Brian; Galloway, James; Wailoo, Allan; Tosh, Jonathan; Lempp, Heidi; Prothero, Louise; Georgopoulou, Sofia; Sturt, Jackie; Scott, David L; TITRATE Programme Investigators; Tom, Brian [0000-0002-3335-9322]
    BACKGROUND: Uncontrolled active rheumatoid arthritis can lead to increasing disability and reduced quality of life over time. 'Treating to target' has been shown to be effective in active established disease and also in early disease. However, there is a lack of nationally agreed treatment protocols for patients with established rheumatoid arthritis who have intermediate disease activity. This trial is designed to investigate whether intensive management of disease leads to a greater number of remissions at 12 months. Levels of disability and quality of life, and acceptability and cost-effectiveness of the intervention will also be examined. METHODS: The trial is a 12-month, pragmatic, randomised, open-label, two-arm, parallel-group, multicentre trial undertaken at specialist rheumatology centres across England. Three hundred and ninety-eight patients with established rheumatoid arthritis will be recruited. They will currently have intermediate disease activity (disease activity score for 28 joints assessed using an erythrocyte sedimentation rate of 3.2 to 5.1 with at least three active joints) and will be taking at least one disease-modifying anti-rheumatic drug. Participants will be randomly selected to receive intensive management or standard care. Intensive management will involve monthly clinical reviews with a specialist health practitioner, where drug treatment will be optimised and an individualised treatment support programme delivered based on several principles of motivational interviewing to address identified problem areas, such as pain, fatigue and adherence. Standard care will follow standard local pathways and will be in line with current English guidelines from the National Institute for Health and Clinical Excellence. Patients will be assessed initially and at 6 and 12 months through self-completed questionnaires and clinical evaluation. DISCUSSION: The trial will establish whether the known benefits of intensive treatment strategies in active rheumatoid arthritis are also seen in patients with established rheumatoid arthritis who have moderately active disease. It will evaluate both the clinical and cost-effectiveness of intensive treatment. TRIAL REGISTRATION: Current Controlled Trials, ID: ISRCTN70160382 . Registered on 16 January 2014.