Making Adjustments
FDA provides revised guidance on covariate treatment in clinical trials
The US Food and Drug Administration (FDA) has provided a revised draft guidance to clarify the ways in which drug developers should adjust for covariates in certain clinical trials, reported Karri Oakes in Regulatory Focus. The revision offers “more detailed recommendations for the use of linear models for covariate adjustment and also includes recommendations for covariate adjustment using nonlinear models,” according to FDA’s Federal Register report of the newly revised draft.
The document is an update to the April 2019 draft. It does not make changes to the other recommendations for adjusting for covariates in randomized clinical trials in drug and biologics development programs. In its introduction to the guidance, FDA wrote, “The main focus of the guidance is on the use of prognostic baseline factors to improve precision for estimating treatment effects rather than the use of predictive biomarkers to identify groups more likely to benefit from treatment. The agency also clarified the fact that the use of covariates to control for confounders for trials that are not randomized and covariate adjustment in analysis of longitudinal repeated measures are both outside of the scope of the draft guidance.
The International Council for Harmonization (ICH) issued a 1998 E9 guidance on statistical principles for clinical trials, but the FDA’s newly revised draft guidance gives additional explanations for the questions of covariate adjustment using both linear and nonlinear models. The newly issued FDA guidance offers specific recommendations on statistical techniques that can be used “to increase precision” without corrupting the study’s analysis of the treatment effect in question.
Additionally, the FDA draft recommends adjusting for baseline covariates when efficacy endpoints are analyzed. While an unadjusted analysis is still acceptable for the primary analysis, adjustment for baseline covariates will generally reduce the variability of estimation of treatment effects and lead to narrower confidence intervals and more powerful hypothesis testing, according to FDA.
The guidance addresses both linear and nonlinear models. For some of the complex statistical techniques that a nonlinear model may require, FDA consultation about a specific approach is best accomplished early in the game, according to the guidance. There would be a greater risk of incorrect estimation of the treatment effect “if the model is misspecified and treatment effects substantially differ across subgroups,” FDA explained.
For nonlinear regression models, FDA provides a stepwise approach for one “statistically reliable” method of covariate adjustment. Also included in the draft guidance is a list of references.
According to the FDA website, “Guidance documents represent the FDA's current thinking on a topic. They do not create or confer any rights for or on any person and do not operate to bind FDA or the public. You can use an alternative approach if the approach satisfies the requirements of the applicable statutes and regulations. Guidance documents describe FDA’s interpretation of our policy on a regulatory issue (21 CFR 10.115(b)). These documents usually discuss more specific products or issues that relate to the design, production, labeling, promotion, manufacturing, and testing of regulated products. Guidance documents may also relate to the processing, content, and evaluation or approval of submissions as well as to inspection and enforcement policies.”