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Simultaneous inference on treatment effects in survival studies with factorial designs.

ABSTRACT: A clinical trial with a 2×2 factorial design involves randomization of subjects to treatment A or A? and, within each group, further randomization to treatment B or B?. Under this design, one can assess the effects of treatments A and B on a clinical endpoint using all patients. One may additionally compare treatment A, treatment B, or combination therapy AB to A?B?. With multiple comparisons, however, it may be desirable to control the overall type I error, especially for regulatory purposes. Because the subjects overlap in the comparisons, the test statistics are generally correlated. By accounting for the correlations, one can achieve higher statistical power compared to the conventional Bonferroni correction. Herein, we derive the correlation between any two (stratified or unstratified) log-rank statistics for a 2×2 factorial design with a survival time endpoint, such that the overall type I error for multiple treatment comparisons can be properly controlled. In addition, we allow for adjustment of prognostic factors in the treatment comparisons and conduct simultaneous inference on the effect sizes. We use simulation studies to show that the proposed methods perform well in realistic situations. We then provide an application to a recently completed randomized controlled clinical trial on alcohol dependence. Finally, we discuss extensions of our approach to other factorial designs and multiple endpoints.

SUBMITTER: Lin DY

PROVIDER: S-EPMC5026867 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Simultaneous inference on treatment effects in survival studies with factorial designs.

Lin Dan-Yu DY Gong Jianjian J Gallo Paul P Bunn Paul H PH Couper David D

Biometrics 20160317 4

A clinical trial with a 2×2 factorial design involves randomization of subjects to treatment A or A‾ and, within each group, further randomization to treatment B or B‾. Under this design, one can assess the effects of treatments A and B on a clinical endpoint using all patients. One may additionally compare treatment A, treatment B, or combination therapy AB to A‾B‾. With multiple comparisons, however, it may be desirable to control the overall type I error, especially for regulatory purposes. B ...[more]

PMID: 26991149

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Project description:Clinical studies aimed at identifying effective treatments to reduce the risk of disease or death often require long term follow-up of participants in order to observe a sufficient number of events to precisely estimate the treatment effect. In such studies, observing the outcome of interest during follow-up may be difficult and high rates of censoring may be observed which often leads to reduced power when applying straightforward statistical methods developed for time-to-event data. Alternative methods have been proposed to take advantage of auxiliary information that may potentially improve efficiency when estimating marginal survival and improve power when testing for a treatment effect. Recently, Parast et al. (J Am Stat Assoc 109(505):384-394, 2014) proposed a landmark estimation procedure for the estimation of survival and treatment effects in a randomized clinical trial setting and demonstrated that significant gains in efficiency and power could be obtained by incorporating intermediate event information as well as baseline covariates. However, the procedure requires the assumption that the potential outcomes for each individual under treatment and control are independent of treatment group assignment which is unlikely to hold in an observational study setting. In this paper we develop the landmark estimation procedure for use in an observational setting. In particular, we incorporate inverse probability of treatment weights (IPTW) in the landmark estimation procedure to account for selection bias on observed baseline (pretreatment) covariates. We demonstrate that consistent estimates of survival and treatment effects can be obtained by using IPTW and that there is improved efficiency by using auxiliary intermediate event and baseline information. We compare our proposed estimates to those obtained using the Kaplan-Meier estimator, the original landmark estimation procedure, and the IPTW Kaplan-Meier estimator. We illustrate our resulting reduction in bias and gains in efficiency through a simulation study and apply our procedure to an AIDS dataset to examine the effect of previous antiretroviral therapy on survival.

Project description:BackgroundA 2×2 factorial design evaluates two interventions (A versus control and B versus control) by randomising to control, A-only, B-only or both A and B together. Extended factorial designs are also possible (e.g. 3×3 or 2×2×2). Factorial designs often require fewer resources and participants than alternative randomised controlled trials, but they are not widely used. We identified several issues that investigators considering this design need to address, before they use it in a late-phase setting.MethodsWe surveyed journal articles published in 2000-2022 relating to designing factorial randomised controlled trials. We identified issues to consider based on these and our personal experiences.ResultsWe identified clinical, practical, statistical and external issues that make factorial randomised controlled trials more desirable. Clinical issues are (1) interventions can be easily co-administered; (2) risk of safety issues from co-administration above individual risks of the separate interventions is low; (3) safety or efficacy data are wanted on the combination intervention; (4) potential for interaction (e.g. effect of A differing when B administered) is low; (5) it is important to compare interventions with other interventions balanced, rather than allowing randomised interventions to affect the choice of other interventions; (6) eligibility criteria for different interventions are similar. Practical issues are (7) recruitment is not harmed by testing many interventions; (8) each intervention and associated toxicities is unlikely to reduce either adherence to the other intervention or overall follow-up; (9) blinding is easy to implement or not required. Statistical issues are (10) a suitable scale of analysis can be identified; (11) adjustment for multiplicity is not required; (12) early stopping for efficacy or lack of benefit can be done effectively. External issues are (13) adequate funding is available and (14) the trial is not intended for licensing purposes. An overarching issue (15) is that factorial design should give a lower sample size requirement than alternative designs. Across designs with varying non-adherence, retention, intervention effects and interaction effects, 2×2 factorial designs require lower sample size than a three-arm alternative when one intervention effect is reduced by no more than 24%-48% in the presence of the other intervention compared with in the absence of the other intervention.ConclusionsFactorial designs are not widely used and should be considered more often using our issues to consider. Low potential for at most small to modest interaction is key, for example, where the interventions have different mechanisms of action or target different aspects of the disease being studied.

Dataset Information

Simultaneous inference on treatment effects in survival studies with factorial designs.

Publications

Simultaneous inference on treatment effects in survival studies with factorial designs.

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