DOI: 10.25934/pr00008957
title: Two-sample testing for clinical trials data with missing values

description: Two-sample testing is a crucial statistical tool used in clinical trials to compare the effects of different treatments or interventions. It assesses the statistical difference between two groups, typically a control group and an experimental group, providing robust evidence of treatment efficiacy or differences in outcomes. These statistical tests help ensure the validity of clinical trials results, allowing medical researchers to draw conclusions with confidence. Ultimately, this contributes to medical advancements, enhancing our understanding of disease treatment and patient care, and supporting evidence-based healthcare decisions. Without two-sample testing, proving the efficacy of new therapies would be considerably more challenging. Two examples of popular statistical two-sample tests are Student's t-test and the Wilcoxon-Mann-Whitney test.

Missing data in clinical trials can occur due to various reasons such as patient dropping out, not taking the medication as they have been directed, or not coming back for follow-up appointments. It presents a significant challenge in statistical analyses because it can introduce bias, reduce statistical power (i.e. the sensitivity of the statistical test to detecting an effect when there actually is one), and undermine the validity of the results. Missing data can distort the true effect of a treatment or intervention, which can possibly result in misleading conclusions. If not handled appropriately, it can compromise the reliability of the trial findings, affecting subsequent clinical decision making and potential advancements in healthcare. Therefore, it is critical to develop strategies to prevent, monitor and manage missing data in clinical trials. Two of the most popular methods for handling missing data are to either ignore the missing data or to impute (guess) the missing values; both of these strategies carry risks.

The requested study data investigates the efficacy of a drug that could possibly treat diabetes, and contains several separate treatment groups. In each treatment group, each subject is given the same treatment, either a placebo (no drug) or the experimental drug at a certain dosage level. For each treatment group, one sample consists of baseline (pre-treatment) measurements, and the second sample consists of post-treatment measurements. However, in each treatment group, there are some post-treatment measurements missing. In our research, we shall analyse the requested study data using a newly-developed statistical two-sample test. This two-sample test is robust to missing data and does not rely on imputation, giving a statistical result with a guaranteed false positive rate (i.e. the proportion of results categorized as positive which are not actually positive is controlled to below a pre-specified threshold). The first goal of our research is to confirm the statistical conclusions reached in other statistical analyses of this clinical trial which relied on imputing (guessing) the missing values. The second goal of our research will be to use this example to demonstrate the effectiveness of our new statistical test for analysing clinical trials data which contains missing values. This will provide a powerful new statistical tool for clinical trials researchers that will enable more robust conclusions fostering the development of more effective and safer treatments.