A generalized weighted combination test of treatment effect for clinical trials with a sequential parallel comparison design and binary endpoint.
Hui QuanXiaofei ChenJunxiang LuoXun ChenPublished in: Statistics in medicine (2022)
To address the issue of a large placebo effect in certain therapeutic areas, rather than the application of the traditional gold standard parallel group placebo-controlled design, different versions of the sequential parallel comparison design have been advocated. In general, the design consists of two consecutive stages and three treatment groups. Stage 1 placebo nonresponders potentially form a prespecified patient subgroup for formal between-treatment comparison at the final analysis. In this research, a version of the design is considered for a binary endpoint. To fully utilize all available data, a generalized weighted combination test is proposed in case placebo has a relatively small effect for some of the study endpoints. The weighted combination of the test based on stage 1 data and the test based on stage 2 data of stage 1 placebo nonresponders suggested in the literature uses only a part of the study data and is a special case of this generalized weighted combination test. A multiple imputation approach is outlined for handling missing not at random data. Simulation is conducted to evaluate the performances of the methods and a data example is employed to illustrate the applications of the methods.
Keyphrases
- electronic health record
- big data
- clinical trial
- magnetic resonance
- double blind
- phase iii
- placebo controlled
- systematic review
- contrast enhanced
- data analysis
- randomized controlled trial
- magnetic resonance imaging
- radiation therapy
- deep learning
- machine learning
- artificial intelligence
- study protocol
- psychometric properties