ArticleJournal of the American Statistical Association2019
Robust Alternatives to ANCOVA for Estimating the Treatment Effect via a Randomized Comparative Study.
Article in Journal of the American Statistical Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Heterogeneity in design and analysis of ICU delirium randomized trials: a systematic review.Trials · 2021Pooled it
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- Covariate adjustment in randomized clinical trials: From general theory to practical insights.Clinical trials (London, England) · 2026Review
- Outcome-based education and student learning in probability and statistics: the mediating roles of engagement and self-efficacy in a new liberal arts context.Frontiers in psychology · 2026Article
- Metabolic Characteristics of Obese Adolescents with Different Degrees of Weight Loss After Identical Exercise Training Intervention.Metabolites · 2025Article
- On Sample Size Determination for Augmented Tests Based on Restricted Mean Survival Time in Randomized Clinical Trials.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Bayesian adaptive design for covariate-adaptive historical control information borrowing.Statistics in medicine · 2023Article
- Inference in response-adaptive clinical trials when the enrolled population varies over time.Biometrics · 2023Article
- Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment, for binary, ordinal, and time-to-event outcomes.Biometrics · 2021Article
- Moving beyond conventional stratified analysis to assess the treatment effect in a comparative oncology study.Journal for immunotherapy of cancer · 2021Article
- Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Binary, Ordinal, and Time-to-Event Outcomes.medRxiv : the preprint server for health sciences · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 3 countries.
Funding
Abstract
In comparing two treatments via a randomized clinical trial, the analysis of covariance (ANCOVA) technique is often utilized to estimate an overall treatment effect. The ANCOVA is generally perceived as a more efficient procedure than its simple two sample estimation counterpart. Unfortunately, when the ANCOVA model is nonlinear, the resulting estimator is generally not consistent. Recently, various nonparametric alternatives to the ANCOVA, such as the augmentation methods, have been proposed to estimate the treatment effect by adjusting the covariates. However, the properties of these alternatives have not been studied in the presence of treatment allocation imbalance. In this article, we take a different approach to explore how to improve the precision of the naive two-sample estimate even when the observed distributions of baseline covariates between two groups are dissimilar. Specifically, we derive a bias-adjusted estimation procedure constructed from a conditional inference principle via relevant ancillary statistics from the observed covariates. This estimator is shown to be asymptotically equivalent to an augmentation estimator under the unconditional setting. We utilize the data from a clinical trial for evaluating a combination treatment of cardiovascular diseases to illustrate our findings.
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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.