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Increasing the efficiency of pragmatic trials using innovative designs and analyses

University of British Columbia

Pragmatic trials are randomized controlled trials (RCTs) conducted in usual health-care settings to evaluate the effectiveness of treatments. These trials often require more complex methodologies to address real-world issues and ensure validity and efficiency. Inspired by three real-world examples, in this dissertation, we developed novel design and analysis methodologies to enhance the efficiency of pragmatic trials. First, we showed that in a stepped-wedge design with unequal cluster sizes, the post-randomization attained power may differ substantially from the pre-randomization expected power. Allocations with a large treatment-vs-time period correlation yield lower attained power. The risk of obtaining an allocation with inadequate attained power increases with lower ICCs, higher CV of the cluster sizes, and smaller numbers of clusters. Trialists can reduce the risk by restricting the randomization algorithm to exclude allocations with low attained power. We then extended the methodology to other cluster-randomized designs and multiple types of outcomes and then implemented them in an R package to enable trial designers to apply these methods to their trials. Second, we developed a prototype online elicitation app to assist experts in eliciting informative joint prior distributions to reduce the sample size in Bayesian clinical trials. The app implemented three different approaches, two novel and one pre-existing, to eliciting the joint prior distribution. Usability testers reported satisfaction with the user interface but suggested that additional explanation of the meaning of elicitation parameters would be helpful. Last, we showed that in a trial comparing three or more treatment durations with a time-to-event outcome, re-casting the primary hypotheses based on a pragmatic perspective and analyzing using appropriate time-varying Cox proportional hazards models leads to results that are more interpretable and precise than what is obtained using the conventional pair-wise comparison of arms. Simulation results showed that with the same number of patients, the new approach significantly increased statistical power, typically by more than 10%. In addition, we developed a novel sample size reallocation algorithm to balance the powers of the multiple primary hypothesis tests.

Text

2021-04-14

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/77763

Population and Public Health

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/