  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.
