Thursday, Aug 7: 8:30 AM - 10:20 AM
4209
Contributed Papers
Music City Center
Room: CC-211
Main Sponsor
Biopharmaceutical Section
Presentations
Clinical trials for complex diseases often use a single primary endpoint, which may overlook the multifaceted nature of complex diseases. As per currently used conventional approaches, if multiple endpoints are assessed in a trial, stringent multiplicity adjustments are required which may inflate sample sizes, trial duration, and costs. A possible solution is to use composite scores. We propose new composite scoring methods with normalized and binary components and compare them with traditional univariate, multivariate, and equally weighted composite scoring approaches. We examine different weighting schemes based on component variability and correlations, identifying scenarios where certain composite scores perform better. Using simulations based on the Assessment of Weekly Administration of Dulaglutide in Diabetes (AWARD) studies, we provide empirical evidence on the best use cases for composite endpoints using type 2 diabetes clinical trials as an example. Our findings offer guidelines for choosing composite score methods that provide power gains and reduce sample size, facilitating better decision-making in trials with multiple outcomes.
Keywords
composite score
power
sample size
clinical trials
multiplicity
Co-Author
Shesh N. Rai, Biostats, Health Inform & Data Sci | College of Medicine
First Author
Rachana Lele, Biostatistician II, Syneos Health
Presenting Author
Rachana Lele, Biostatistician II, Syneos Health
Although clinical trials in many therapeutic areas evaluate a single binary endpoint as the primary endpoint, clinical trials in certain therapeutic areas require the use of two co-primary binary endpoints to evaluate treatment benefit multi-dimensionally. When designing clinical trials with two co-primary binary endpoints, considering the correlation between the two endpoints can increase the power of the trial and can consequently reduce the required sample size, leading to improved trial efficiency. For this study, we derive formulae for calculating the exact power and sample size in clinical trials with two co-primary binary endpoints. The proposed formulae are useful for any statistical test for binary endpoints. Numerical investigation under various scenarios showed that our proposed formulae can incorporate consideration of the correlation between two co-primary binary endpoints in the sample size calculation, thereby allowing the required sample size to be reduced. We also demonstrate that the exact power for the required sample size calculated using our proposed formula is approximately equal to a target power.
Keywords
binary endpoint
co-primary endpoints
correlation
exact
power
sample size
Factorial trials continue to grow in popularity as a method to test multiple combinations of intervention components simultaneously in a single randomized trial, but there is still a lack of clarity around how to determine a sufficient sample size. Part of this confusion stems from the fact that study teams conduct factorial trials for different reasons. In this talk, we will consider three research questions that could motivate a factorial trial: 1) identifying intervention components that have a statistically significant effect on the outcome; 2) identifying statistically significant interactions between intervention components; 3) determining which combination of components is most likely to have an optimal effect on the outcome. For each of these potential research questions, we discuss how to approach a sample size justification/power analysis. We show that studies that are powered to address research question 1 are sufficient to reach decisions within 10% of the optimal combination in answer to research question 3, but that addressing research question 2 can require considerably larger sample sizes. We introduce an R shiny package that assists with these calculations.
Keywords
factorial trial
power analysis
Futility monitoring is essential in clinical trial design to allow early termination for treatment inefficacy. Due to time varying patterns in relative risk it is relevant to consider time trends. Current futility analysis methods are not designed to identify time trends. We propose weighted upstrapping as a solution. Weighted upstraping involves assigning a time dependent weight to all observations and repeatedly sampling from the interim data to simulate thousands of fully enrolled trials. A p-value is calculated for each upstrapped dataset and the proportion of upstrapped trials meeting a significance criterion is compared to a decision threshold to determine futility. We implemented a simulation study with varying sample sizes and relative risk trends, for both null and alternative cases. We applied upstrapped futility designs as well as traditional group sequential designs for comparison. Weighted upstrapping more accurately identified futility for non-constant relative risk trends. Weighted upstrap designs were 7.1% more likely than group sequential designs to stop in the non-constant relative risk null setting and 2.6% less likely to stop in the equivalent alternative case.
Keywords
Clinical trials
Interim futility monitoring
Weighted upstrap
Time trends
Nonparametric
Alpha-spending
In immunology clinical trials, the primary endpoint to evaluate the drug efficacy comparing to placebo is usually the binary endpoint. Odds ratio, rate difference and rate ratio are three most popular measures to analyze binary endpoint. Which measure to choose for binary endpoint is not only a clinical question but also a statistical question. As odds ratio has good statistical properties, CMH test or logistic regression have been widely used to derive adjusted odds ratio and the corresponding p-value in the immunology clinical trials for binary endpoints. In this work, population-level summary and covariate adjustment for unconditional treatment effect of binary endpoint have been discussed based on recent FDA's guidance, and a comprehensive review through different clinical trials has been conducted and the methods used for binary endpoints in published trials have been summarized. The performance of commonly used statistical methods for binary endpoint has been evaluated with simulations. The recommendations for analysis methods of binary endpoints will be given in the end.
Keywords
Immunology clinical trial
binary endpoints
CMH analysis
odds ratio
rate difference
For a balanced, cross-sectional parallel cluster randomized trial with count outcomes, we examined several methods to determine the number of clusters (N) necessary for a given power of the hypothesis test on the intervention effect. We applied the methods by either estimating parameter inputs using analytic derivations or leveraging empirical data. We compared methods across key parameters from a two-level Poisson generalized linear mixed model and developed a novel technique to evaluate the impact of parameter uncertainty. Using the analytic approach at 80% power, we conducted a simulation-based sensitivity analysis to estimate actual power. For the empirical approach assuming we had available control cluster data, we generated sampling distributions for N then conducted a sensitivity analysis. Power was most sensitive to the anticipated intervention effect. Except for a few cases, methods were equally sufficient. Given similar power between the two approaches, the empirical approach is sufficient, but the analytic approach is recommended as control cluster data are unneeded, sampling variability is not a concern, and implementation is simpler via straightforward formulae.
Keywords
count outcome
cluster randomized trial
parameter uncertainty
sample size
sampling variability