It is well known that adjusting for one or more baseline covariates can increase statistical power in randomized controlled trials. One reason that adjusted analyses are not used more widely may be because researchers may be concerned that results may be biased if the baseline covariate(s)’ effects are not modelled correctly in the regression model for outcome. For example, a continuous baseline covariate would by default be entered linearly in a regression model, but in truth it’s effect on outcome may be non-linear. In this post we’ll review an important result which shows that for continuous outcomes modelled with linear regression, this does not matter in terms of bias – we obtain unbiased estimates of treatment effect even if we mis-specify a baseline covariate’s effect on outcome.
Jonathan Bartlett
Clustering in randomized controlled trials
Randomized clinical trials often involve some sort of clustering. The most obvious is in a cluster randomized trial, where clusters form the unit of randomization. It is well known that in this case the clustering must be allowed for in the analysis. But even in the common setting where individuals are randomized, clustering may be present. Perhaps the most common situation is where a trial involves a number of hospitals or centres, and individuals are recruited into the trial when they attend their local centre. Another example is where the intervention is administered to each individual by some professional (e.g. surgeon, therapist), such that outcomes from individuals treated by the same professional may be more similar to each other. In both of these situations, an obvious question is whether we need to allow for the clustering in the analysis?
Fixed versus random-effects meta-analysis – efficiency and confidence interval coverage
Meta-analysis is a critical tool for synthesizing existing evidence. It is commonly used within medical and clinical settings to evaluate the existing evidence regarding the effect of a treatment or exposure on an outcome of interest. The essential idea is that the estimates of the effect of interest from previous study are pooled together. A choice which has to be made when conducting a meta-analysis is between fixed-effects and random-effects. In this post we’ll look at some of the consequences of this choice, when in truth the studies are measuring different effects.