ArticleJournal of the Royal Statistical Society. Series A, (Statistics in Society)2022
Optimising precision and power by machine learning in randomised trials with ordinal and time-to-event outcomes with an application to COVID-19.
Article in Journal of the Royal Statistical Society. Series A, (Statistics in Society), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Machine learning to optimize precision in the analysis of randomized trials: A journey in pre-specified, yet data-adaptive learning.Clinical trials (London, England) · 2026Article
- Modern Causal Inference Approaches to Improve Power for Subgroup Analysis in Randomized Controlled Trials.Statistics in medicine · 2026Article
- Machine learning assisted adjustment boosts efficiency of exact inference in randomized controlled trials.Scientific reports · 2025Article
- Negative Control Outcome Adjustment in Early-Phase Randomized Trials: Estimating Vaccine Effects on Immune Responses in HIV Exposed Uninfected Infants.Statistics in medicine · 2025Article
- A comparison of covariate adjustment approaches under model misspecification in individually randomized trials.Trials · 2023Article
- Optimising precision and power by machine learning in randomised trials with ordinal and time-to-event outcomes with an application to COVID-19.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2022Article
- Artificial intelligence-based strategies to identify patient populations and advance analysis in age-related macular degeneration clinical trials.Experimental eye research · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid finding of effective therapeutics requires efficient use of available resources in clinical trials. Covariate adjustment can yield statistical estimates with improved precision, resulting in a reduction in the number of participants required to draw futility or efficacy conclusions. We focus on time-to-event and ordinal outcomes. When more than a few baseline covariates are available, a key question for covariate adjustment in randomised studies is how to fit a model relating the outcome and the baseline covariates to maximise precision. We present a novel theoretical result establishing conditions for asymptotic normality of a variety of covariate-adjusted estimators that rely on machine learning (e.g.,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.