ArticleBMC medical research methodology2016
A comparison of methods to adjust for continuous covariates in the analysis of randomised trials.
Article in BMC medical research methodology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Categorisation of continuous covariates for stratified randomisation: How should we adjust?Statistics in medicine · 2024Article
- Covariate adjustment in randomized clinical trials with missing covariate and outcome data.Statistics in medicine · 2023Article
- Nonlinear effects and effect modification at the participant-level in IPD meta-analysis part 2: methodological guidance is available.Journal of clinical epidemiology · 2023Article
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Authors and funding
4 authors.
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Abstract
backgroundAlthough covariate adjustment in the analysis of randomised trials can be beneficial, adjustment for continuous covariates is complicated by the fact that the association between covariate and outcome must be specified. Misspecification of this association can lead to reduced power, and potentially incorrect conclusions regarding treatment efficacy.
methodsWe compared several methods of adjustment to determine which is best when the association between covariate and outcome is unknown. We assessed (a) dichotomisation or categorisation; (b) assuming a linear association with outcome; (c) using fractional polynomials with one (FP1) or two (FP2) polynomial terms; and (d) using restricted cubic splines with 3 or 5 knots. We evaluated each method using simulation and through a re-analysis of trial datasets.
resultsMethods which kept covariates as continuous typically had higher power than methods which used categorisation. Dichotomisation, categorisation, and assuming a linear association all led to large reductions in power when the true association was non-linear. FP2 models and restricted cubic splines with 3 or 5 knots performed best overall.
conclusionsFor the analysis of randomised trials we recommend (1) adjusting for continuous covariates even if their association with outcome is unknown; (2) keeping covariates as continuous; and (3) using fractional polynomials with two polynomial terms or restricted cubic splines with 3 to 5 knots when a linear association is in doubt.
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