Over the past 7 decades, the randomized clinical trial (RCT) has become the gold standard for evaluation of new drugs, biologics, devices, procedures, and behavioral interventions.  In a half-day short course, two critically important topics will be discussed that have broad implications in the design and conduct of clinical trials: the Design of Non-Inferiority Trials and the Prevention of Missing Data.

Design of Non-Inferiority Trials:  Suppose a standard therapy has been established to provide a clinically important reduction in risk of irreversible morbidity or mortality. In that setting, the safety and efficacy of an experimental intervention often would be assessed in a clinical trial providing a comparison with that standard therapy rather than with a placebo arm. Such a trial often is designed to assess whether the efficacy of the experimental intervention is not unacceptably worse than that of standard therapy, and is called a non-inferiority trial. Formally, the non-inferiority trial would be designed to rule out a non-inferiority margin, defined as the minimum threshold for what would constitute an unacceptable loss of efficacy.  In this session, we will present scientific insights into many factors that should be addressed in the design and conduct of non-inferiority trials to enhance their integrity and reliability. Among essential considerations are recognizing the importance of the Constancy Assumption, addressing uncertainty about its validity, and properly formulating the non-inferiority margin.  We also will provide insights from recent experiences that illustrate the successful design and conduct of non-inferiority trials, and then provide recommendations and conclusions regarding best practices in their use.

Addressing Missingness:  Missing data on outcome measures in clinical trials meaningfully reduce the integrity and interpretability of results, adversely affecting science and thus the medical community pursing an evidence-based practice of medicine. The effect is substantial when a nontrivial fraction of patients have missing data on primary and secondary efficacy and safety end points in the trial and when mechanisms for missingness are related to or informative about the outcome measures. For too long, there has been overreliance on methods to adjust for missing data in clinical trials. Worse yet, these methods often have been naively inadequate, such as making adjustments in sample size that address the variability but not the bias induced by missingness or using simplistic approaches, such as last-observation-carried-forward, complete-case, or worst-case analyses. Although rational imputation methods may be useful to treat missingness after it has occurred, such methods depend on untestable assumptions. The preferred and often only satisfactory approach to addressing missing data is to prevent it. Procedures should be in place to maximize the likelihood that outcome data will be obtained at scheduled times of evaluation for all surviving patients who have not withdrawn consent. To meaningfully reduce missing data, it is important to recognize and address many factors that commonly lead to higher levels of missingness.  These issues will be discussed in detail in this presentation. There is wisdom in the recognition that "an ounce of prevention is worth a pound of cure."

This module will review the rationale and role for Non-inferiority Trial Designs and Methods for Addressing Missing Data, describe current best practices, and share examples of the successful implementation of these methods.