A latent variable model for improving inference in trials assessing the effect of dose on toxicity and composite efficacy endpoints.
Statistical methods in medical research
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Wason, J., & Seaman, S. (2020). A latent variable model for improving inference in trials assessing the effect of dose on toxicity and composite efficacy endpoints.. Statistical methods in medical research, 29 (1), 230-242. https://doi.org/10.1177/0962280219831038
It is often of interest to explore how dose affects the toxicity and efficacy properties of a novel treatment. In oncology, efficacy is often assessed through response, which is defined by a patient having no new tumour lesions and their tumour size shrinking by 30%. Usually response and toxicity are analysed as binary outcomes in early phase trials. Methods have been proposed to improve the efficiency of analysing response by utilising the continuous tumour size information instead of dichotomising it. However these methods do not allow for toxicity or for different doses. Motivated by a phase II trial testing multiple doses of a treatment against placebo, we propose a latent variable model that can estimate the probability of response and no toxicity (or other related outcomes) for different doses. We assess the confidence interval coverage and efficiency properties of the method, compared to methods that do not use the continuous tumour size, in a simulation study and the real study. The coverage is close to nominal when model assumptions are met, although can be below nominal when the model is misspecified. Compared to methods that treat response as binary, the method has confidence intervals with 30-50% narrower widths. The method adds considerable efficiency but care must be taken that the model assumptions are reasonable.
Cancer Research UK (18113)
External DOI: https://doi.org/10.1177/0962280219831038
This record's URL: https://www.repository.cam.ac.uk/handle/1810/289320