G-computation for increasing performances of clinical trials with individual randomization and binary response
In a clinical trial, the random allocation aims to balance prognostic factors between arms, preventing true confounders. However, residual differences due to chance may introduce near-confounders. Adjusting on prognostic factors is therefore recommended, especially because the related increase of the power. In this paper, we hypothesized that G-computation associated with machine learning could be a suitable method for randomized clinical trials even with small sample sizes. It allows for flexible estimation of the outcome model, even when the covariates' relationships with outcomes are complex. Through simulations, penalized regressions (Lasso, Elasticnet) and algorithm-based methods (neural network, support vector machine, super learner) were compared. Penalized regressions reduced variance but may introduce a slight increase in bias. The associated reductions in sample size ranged from 17\% to 54\%. In contrast, algorithm-based methods, while effective for larger and more complex data structures, underestimated the standard deviation, especially with small sample sizes. In conclusion, G-computation with penalized models, particularly Elasticnet with splines when appropriate, represents a relevant approach for increasing the power of RCTs and accounting for potential near-confounders.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Evaluation of machine-learning models to measure individualized treatment effects from randomized clinical trial data with time-to-event outcomes
In randomized clinical trials, regression models can be used to explore the relationships between patients' variables (e.g., clinical, pathological or lifestyle variables, and also biomarker or genomics data) and the mag…
Factors Affecting the Performance of Automated Speaker Verification in Alzheimer's Disease Clinical Trials
Detecting duplicate patient participation in clinical trials is a major challenge because repeated patients can undermine the credibility and accuracy of the trial's findings and result in significant health and financia…
FairnessSpeaker VerificationIn silico clinical trials in drug development: a systematic review
In the context of clinical research, computational models have received increasing attention over the past decades. In this systematic review, we aimed to provide an overview of the role of so-called in silico clinical t…
ArticlesOptimal personalised treatment computation through in silico clinical trials on patient digital twins
In Silico Clinical Trials (ISTC), i.e., clinical experimental campaigns carried out by means of computer simulations, hold the promise to decrease time and cost for the safety and efficacy assessment of pharmacological t…
Artificial Intelligence for In Silico Clinical Trials: A Review
A clinical trial is an essential step in drug development, which is often costly and time-consuming. In silico trials are clinical trials conducted digitally through simulation and modeling as an alternative to tradition…