Many clinical trials restrict randomisation using stratified blocks or minimisation to balance prognostic factors across treatment groups. It is widely acknowledged in the statistical literature that the subsequent analysis should reflect the design of the study, and any stratification or minimisation variables should be adjusted for in the analysis. However, a review of recent general medical literature showed only 14 of 41 eligible studies reported adjusting their primary analysis for stratification or minimisation variables. We show that balancing treatment groups using stratification leads to correlation between the treatment groups. If this correlation is ignored and an unadjusted analysis is performed, standard errors for the treatment effect will be biased upwards, resulting in 95% confidence intervals that are too wide, type I error rates that are too low and a reduction in power. Conversely, an adjusted analysis will give valid inference. We explore the extent of this issue using simulation for continuous, binary and time-to-event outcomes where treatment is allocated using stratified block randomisation or minimisation.

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Improper analysis of trials randomised using stratified blocks or minimisation.

There has been a split in the statistics community about the need for taking covariates into account in the design phase of a clinical trial. There are many advocates of using stratification and covariate-adaptive randomization to promote balance on certain known covariates. However, balance does not always promote efficiency or ensure more patients are assigned to the better treatment. We describe these procedures, including model-based procedures, for incorporating covariates into the design of clinical trials, and give examples where balance, efficiency and ethical considerations may be in conflict. We advocate a new class of procedures, covariate-adjusted response-adaptive (CARA) randomization procedures that attempt to optimize both efficiency and ethical considerations, while maintaining randomization. We review all these procedures, present a few new simulation studies, and conclude with our philosophy.

https://projecteuclid.org/download/pdfview_1/euclid.ss/1233153066

Handling Covariates in the Design of Clinical Trials.

Response-adaptive designs have been extensively studied and used in clinical trials. However, there is a lack of a comprehensive study of response-adaptive designs that include covariates, despite their importance in clinical trials. Because the allocation scheme and the estimation of parameters are affected by both the responses and the covariates, covariate-adjusted response-adaptive (CARA) designs are very complex to formulate. In this paper, we overcome the technical hurdles and lay out a framework for general CARA designs for the allocation of subjects to K (≥ 2) treatments. The asymptotic properties are studied under certain widely satisfied conditions. The proposed CARA designs can be applied to generalized linear models. Two important special cases, the linear model and the logistic regression model, are considered in detail.

/pdf/asymptotic-properties-of-covariate-adjusted-response-2gee7rj48c.pdf

Asymptotic properties of covariate-adjusted response-adaptive designs

A question that arises from the literature on therapy is whether second-level treatment is effective for patients with recurrent binge eating who fail first-level treatment. It has been shown that subjects who do not stop binge eating after an initial structured cognitive-behavioural treatment (CBT) programme benefit from additional CBT (A-CBT) sessions; however, it has been suggested that these resistant patients would benefit even more from cue exposure therapy (CET) targeting features associated with poor response (e.g. urge to binge in response to a cue and anxiety experienced in the presence of binge-related cues). We assessed the effectiveness of virtual reality-CET as a second-level treatment strategy for 64 patients with bulimia nervosa and binge eating disorder who had been treated with limited results after using a structured CBT programme, in comparison with A-CBT. The significant differences observed between the two groups at post-treatment in dimensional (behavioural and attitudinal features, anxiety, food craving) and categorical (abstinence rates) outcomes highlighted the superiority of virtual reality-CET over A-CBT. Copyright © 2017 John Wiley & Sons, Ltd and Eating Disorders Association.

A Randomised Controlled Comparison of Second-Level Treatment Approaches for Treatment-Resistant Adults with Bulimia Nervosa and Binge Eating Disorder: Assessing the Benefits of Virtual Reality Cue Exposure Therapy

A flexible, generalized method of treatment allocation is proposed. The method uses a set of controlling parameters that enables the generic algorithm to produce a family of possible outcomes ranging from simple randomization to deterministic allocation. The method controls balance at stratum level, stratification level and overall without detriment to the predictability of the method. The paper lists the desirable characteristics of allocation methods and shows that the proposed method fulfils the majority and is easy to use in the clinical context, once the coding has been established. An explanation of the method for 2, 3 and 4 treatment group allocations is given. Simulations demonstrate the flexibility of the method. Copyright © 2011 John Wiley & Sons, Ltd.

Herman Chernoff

Papers

Forcing a Sequential Experiment to be Balanced