BenkeserDDíazILuedtkeA, et al. Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment, for binary, ordinal, and time-to-event outcomes. Biometrics2021; 77: 1467–1481.
MartensEPPestmanWRKlungelOH.Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study (p n/a) by Peter C. Austin, Paul Grootendorst, Sharon-Lise T. Normand, Geoffrey M. Anderson, statistics in medicine, published online: 16 June 2006. Stat Med2007; 26(16): 3208–3212.
4.
FreedmanDA. Randomization does not justify logistic regression. Stat Sci2008; 23: 237–249.
5.
RosenblumMVan der LaanMJ. Simple, efficient estimators of treatment effects in randomized trials using generalized linear models to leverage baseline variables. Int J Biostat2010; 6(1): 13.
6.
US Food and Drug Administration (FDA). Adjusting for covariates in randomized clinical trials for drugs and biological products: guidance for industry (Note: an earlier version of this guidance was published in 2021), 2023, https://www.fda.gov/media/148910/download (accessed 4 February 2024).
7.
TackneyMSMorrisTWhiteI, et al. A comparison of covariate adjustment approaches under model misspecification in individually randomized trials. Trials2023; 24(1): 14.
8.
HernánMRobinsJM. Causal inference: what if. Boca Raton, FL: Chapman & Hall/CRC; Taylor & Francis, 2020.
9.
DanielRZhangJFarewellD. Making apples from oranges: comparing noncollapsible effect estimators and their standard errors after adjustment for different covariate sets. Biom J2021; 63(3): 528–557.
10.
DahabrehIJRobertsonSESteingrimssonJA, et al. Extending inferences from a randomized trial to a new target population. Stat Med2020; 39(14): 1999–2014.
11.
HuitfeldtASwansonSAStensrudMJ, et al. Effect heterogeneity and variable selection for standardizing causal effects to a target population. Eur J Epidemiol2019; 34(12): 1119–1129.
TsiatisAADavidianMZhangM, et al. Covariate adjustment for two-sample treatment comparisons in randomized clinical trials: a principled yet flexible approach. Stat Med2008; 27(23): 4658–4677.
14.
LinW. Agnostic notes on regression adjustments to experimental data: Reexamining Freedman’s critique. Ann Appl Stat2013; 7(1): 295–318.
15.
LeebHPötscherBM. Model selection and inference: facts and fiction. Economet Theor2005; 21(1): 21–59.
16.
WilliamsNRosenblumMDíazI. Optimising precision and power by machine learning in randomised trials with ordinal and time-to-event outcomes with an application to COVID-19. J Roy Stat Soc A Sta2022; 185(4): 2156–2178.
17.
Van LanckerKDíazIVansteelandtS. Automated, efficient and model-free inference for randomized clinical trials via data-driven covariate adjustment. 2024, arXiv preprint arXiv:2404.11150.
18.
GreenlandS. Interpretation and choice of effect measures in epidemiologic analyses. Am J Epidemiol1987; 125(5): 761–768.
19.
XiaoMChuHColeSR, et al. Odds ratios are far from ‘portable’– a call to use realistic models for effect variation in meta-analysis. J Clin Epidemiol2022; 142: 294–304.
20.
VansteelandtSDukesO. Assumption-lean inference for generalised linear model parameters. J Roy Stat Soc B2022; 84(3): 657–685.
21.
RobinsJMRotnitzkyA. Comment on the Bickel and Kwon article, ‘inference for semiparametric models: some questions and an answer’. Stat Sin2001; 11(4): 920–936.
22.
RosenblumMVan Der LaanMJ. Using regression models to analyze randomized trials: asymptotically valid hypothesis tests despite incorrectly specified models. Biometrics2009; 65(3): 937–945.
23.
RosenblumMSteingrimssonJA. Matching the efficiency gains of the logistic regression estimator while avoiding its interpretability problems, in randomized trials. Working papers, Department of Biostatistics, Johns Hopkins University, 2016, http://biostats.bepress.com/jhubiostat/paper281
