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A latent variable model for improving inference in trials assessing the effect of dose on toxicity and composite efficacy endpoints.

ABSTRACT: 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.

SUBMITTER: Wason JM

PROVIDER: S-EPMC6986906 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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A latent variable model for improving inference in trials assessing the effect of dose on toxicity and composite efficacy endpoints.

Wason James Ms JM Seaman Shaun R SR

Statistical methods in medical research 20190225 1

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 dichotomi ...[more]

PMID: 30799777

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Project description:BackgroundComposite endpoints are recommended in rare diseases to increase power and/or to sufficiently capture complexity. Often, they are in the form of responder indices which contain a mixture of continuous and binary components. Analyses of these outcomes typically treat them as binary, thus only using the dichotomisations of continuous components. The augmented binary method offers a more efficient alternative and is therefore especially useful for rare diseases. Previous work has indicated the method may have poorer statistical properties when the sample size is small. Here we investigate small sample properties and implement small sample corrections.MethodsWe re-sample from a previous trial with sample sizes varying from 30 to 80. We apply the standard binary and augmented binary methods and determine the power, type I error rate, coverage and average confidence interval width for each of the estimators. We implement Firth's adjustment for the binary component models and a small sample variance correction for the generalized estimating equations, applying the small sample adjusted methods to each sub-sample as before for comparison.ResultsFor the log-odds treatment effect the power of the augmented binary method is 20-55% compared to 12-20% for the standard binary method. Both methods have approximately nominal type I error rates. The difference in response probabilities exhibit similar power but both unadjusted methods demonstrate type I error rates of 6-8%. The small sample corrected methods have approximately nominal type I error rates. On both scales, the reduction in average confidence interval width when using the adjusted augmented binary method is 17-18%. This is equivalent to requiring a 32% smaller sample size to achieve the same statistical power.ConclusionsThe augmented binary method with small sample corrections provides a substantial improvement for rare disease trials using composite endpoints. We recommend the use of the method for the primary analysis in relevant rare disease trials. We emphasise that the method should be used alongside other efforts in improving the quality of evidence generated from rare disease trials rather than replace them.

Project description:PurposeThere have been concerns that short-term clinical trials for evaluating new treatments in glaucoma would require prohibitively large sample sizes when using visual field endpoints, given that glaucoma is often a slowly progressive disease. This study sought to determine the required sample size for such trials using event-based analyses, and whether it can be reduced using trend-based analyses.DesignLongitudinal, observational study.Participants321 eyes of 240 glaucoma participants followed under routine clinical care using 242 visual field for an average of 10 years.MethodsSample size requirements were derived using computer simulations that reconstructed "real-world" visual fields by combining estimates of point-wise variability according to different threshold levels and rates of change obtained from the clinical glaucoma cohort. A clinical trial lasting 2 years with testing every 3 months was simulated, assuming that the new treatment halted visual field change in various percentages of participants (or "responders"). Treatment efficacy was evaluated by: (a) Difference in incidence of point-wise event-based progression (similar to the commercially available Guided Progression Analysis), and (b) Difference in rate of visual field mean deviation (MD) change between groups using linear mixed models (LMMs).Main outcome measuresSample size to detect a statistically significance difference between groups.ResultsBetween-group trend-based analyses using LMMs reduced sample size requirements by 85-90% across the range of new treatment effects when compared to the conventional point-wise event-based analysis. To detect the effect of a new treatment that halted progression in 30% of the participants under routine clinical care (equal to a 30% reduction in average rate of MD change) with 90% power, for example, 1924 participants would be required per group using event-based analysis, but only 277 participants per group if LMMs were used.ConclusionsThe feasibility of future glaucoma clinical trials can be substantially improved by evaluating differences in the rate of visual field change between groups.

Dataset Information

A latent variable model for improving inference in trials assessing the effect of dose on toxicity and composite efficacy endpoints.

Publications

A latent variable model for improving inference in trials assessing the effect of dose on toxicity and composite efficacy endpoints.

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