eprintid: 10146925
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/14/69/25
datestamp: 2022-04-21 12:52:37
lastmod: 2022-04-21 12:52:37
status_changed: 2022-04-21 12:52:37
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Morris, Tim P
creators_name: Walker, A Sarah
creators_name: Williamson, Elizabeth J
creators_name: White, Ian R
title: Planning a method for covariate adjustment in individually randomised trials: a practical guide
ispublished: pub
divisions: UCL
divisions: J38
divisions: D65
divisions: B02
keywords: Covariate adjustment; Estimands; Standardisation; Inverse probability of treatment weighting; Randomised controlled trials; Clinical trials; Missing data
note: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
abstract: Background: It has long been advised to account for baseline covariates in the analysis of confirmatory randomised trials, with the main statistical justifications being that this increases power and, when a randomisation scheme balanced covariates, permits a valid estimate of experimental error. There are various methods available to account for covariates but it is not clear how to choose among them. // Methods: Taking the perspective of writing a statistical analysis plan, we consider how to choose between the three most promising broad approaches: direct adjustment, standardisation and inverse-probability-of-treatment weighting. // Results: The three approaches are similar in being asymptotically efficient, in losing efficiency with mis-specified covariate functions and in handling designed balance. If a marginal estimand is targeted (for example, a risk difference or survival difference), then direct adjustment should be avoided because it involves fitting non-standard models that are subject to convergence issues. Convergence is most likely with IPTW. Robust standard errors used by IPTW are anti-conservative at small sample sizes. All approaches can use similar methods to handle missing covariate data. With missing outcome data, each method has its own way to estimate a treatment effect in the all-randomised population. We illustrate some issues in a reanalysis of GetTested, a randomised trial designed to assess the effectiveness of an electonic sexually transmitted infection testing and results service. // Conclusions: No single approach is always best: the choice will depend on the trial context. We encourage trialists to consider all three methods more routinely.
date: 2022-04-18
date_type: published
publisher: Springer Science and Business Media LLC
official_url: https://doi.org/10.1186/s13063-022-06097-z
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1877386
doi: 10.1186/s13063-022-06097-z
lyricists_name: Morris, Timothy
lyricists_id: TNMOR17
actors_name: Morris, Timothy
actors_id: TNMOR17
actors_role: owner
full_text_status: public
publication: Trials
volume: 23
article_number: 328
issn: 1745-6215
citation:        Morris, Tim P;    Walker, A Sarah;    Williamson, Elizabeth J;    White, Ian R;      (2022)    Planning a method for covariate adjustment in individually randomised trials: a practical guide.                   Trials , 23     , Article 328.  10.1186/s13063-022-06097-z <https://doi.org/10.1186/s13063-022-06097-z>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10146925/1/2022%20-%20Morris%20-%20planning%20a%20method%20for%20covariate%20adjustment%20-%20trials.pdf