Impact of Baseline Adjustment Strategies on Treatment Effect Estimates in Randomized Controlled Trials: A Meta-Epidemiological and Simulation Study

Randomized controlled trials (RCTs) are studies in which people are randomly assigned to different treatments. Randomization is meant to make the treatment groups similar at the start of the study. However, in the real world, groups are not always perfectly balanced. This can happen when the number of participants is small, when randomization does not work as expected, or when some people drop out of the study. When groups differ at the start, researchers use “baseline adjustment,” which means using statistical methods to account for those differences. Although this approach is widely recommended, researchers still vary in which baseline factors they adjust for and which methods they use. These choices can lead to different results and uncertainty about which approach is best.

Type 2 diabetes is a chronic condition in which the body does not make enough insulin or does not use it properly, leading to high blood sugar. Without adequate treatment, it can cause serious complications such as heart disease, kidney damage, and vision loss. Because millions of people worldwide live with type 2 diabetes, it is important that RCTs in this field are analyzed using the most reliable and consistent methods.

The project will use individual participant data (IPD) from a previously conducted clinical trial to study how different baseline adjustment methods affect the analysis of the effectiveness of the treatment. Having access to IPD will allow a closer look closely at the relationships between baseline measures (such as blood sugar levels or body weight) and later outcomes. The results will be used to build computer simulations that mimic real clinical trials. These simulations will be used to test how different baseline adjustment methods perform in many possible trial situations, including scenarios that cannot be evaluated using published results alone.

First, the project will examine which baseline characteristics in the trial (for example, blood sugar level, body mass index, kidney function, and use of other medications) are most strongly linked with later outcomes. Understanding how important these factors are—and how balanced or unbalanced they were between treatment groups—will help show how they might influence trial results.

Next, the trial will be re-analyzed using several baseline adjustment approaches. These include simple comparisons between groups, change-from-baseline methods, and models that account for baseline values when estimating treatment effects. A comparison will be done of how each method affects the size of the treatment effect and the certainty of the results. Computer simulations based on real patterns observed in the IPD will then be created. These simulations will allow testing of how different adjustment methods perform under a wide range of realistic trial conditions.

Finally, all finding will be combined to provide clear recommendations for how future RCTs should choose baseline variables and adjustment methods. This work will help make future diabetes research more reliable and may also improve the quality of trials in other chronic diseases.