Final FDA Guidance "Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biological Products" - what we learned?

In May 2023, FDA published the final guidance for industry "Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biological Products". This final version was based on the draft guidance with the same title that was released 4 years ago in April 2019 (see a previous post "FDA and EMA Guidance on Adjusting for Covariates in Randomized Clinical Trials".

FDA created guidance snapshot below:

The final guidance provided the general guidelines on several issues related to the covariates or baseline covariates. 

Both unadjusted analysis and analysis adjusted for baseline covariates are acceptable. However, if analysis is adjusted for baseline covariates, the details about the covariates need to be pre-specified in the statistical analysis plan before the study unblinding. Our experience is that the details about which baseline covariates to be included and whether the baseline covariates are continuous or categorized need to be pre-specified. 

Usually, the analysis adjusted for baseline covariates leads to efficiency gain and is more powerful than the unadjusted analysis. 

It is acceptable to calculate the sample size based on adjusted analysis, but perform the final analysis based on analysis adjusted for baseline covariates. In practice, the sample size calculation is commonly based on adjusted analysis regardless of the final analysis. For example, for a study to compare two group means, the sample size may be calculated based on t-test approach, but the analysis may be based on analysis of covariates where the adjustment for baseline covariates are used. 

For studies with stratified randomization where the randomization is stratified by one or more baseline covariates (categorical), the stratification factors are usually included in the analysis model even though the treatment assignments are generally balanced within each stratum. 

For studies with stratified randomization, it is not uncommon that incorrect stratification may occur where the treatment assignment is picked from the incorrect stratum. When this occurs, there will be two sets of the randomization stratification information (two different strata variables): strata as randomized versus actual strata. It is acceptable to use either strata variable as randomized (intention-to-treat principle) or actual strata variable (correct strata information for all patients). When mis-stratification occurs, there should be any attempt to go back to the randomization systems (such as IRT, IVR, IWR) to correct the stratification allocation. Once randomized, it is randomized. While the incorrect stratification is used for randomization, the correct stratification can be recorded on the case report form or EDC (electronic data capture). See a previous post "Handling Randomization Errors in Clinical Trials with Stratified Randomization".

For studies with continuous outcome measures, the endpoint is usually the change from baseline to a specific visit. Baseline covariate is used in the change from baseline calculation. In the analysis adjusted for baseline covariate, the baseline covariate can still be included in the model even though it gives an impression that the baseline measure is used twice. 

The guidance contains additional guidelines on linear models and non-linear models. For example, for linear models, the issue related to treatment group by covariate interactions is discussed: 

For non-linear models, binary outcome (logistic regression), ordinal outcome (generalized linear model), count outcome (Poisson regression), or time-to-event outcome (Cox regression) are analyzed. The estimators like odds ratio and hazard ratio are called are non-collapsible effect measures. Non-collapsibility implies that the effect parameter is not the same for different sets of covariates that are conditioned on, even if these covariates are independent of the exposure. Even when all subgroup treatment effects are identical, this subgroup specific conditional treatment effect can differ from the unconditional treatment effect. 

References: raps.org: FDA finalizes guidance on adjusting for covariates in randomized trials