THOUGHT LEADERSHIP

The win ratio, introduced by S. Pocock in 2012, is an alternative and practical approach for analyzing composite endpoints. It was originally designed to address challenges faced in cardiovascular (CV) trials, but over the years the win ratio has been utilized in multiple therapeutic areas.

The accurate analysis and reporting of data is necessary to the evaluation of new treatments for human diseases. Regulatory authorities must weigh the risks with the benefits of treatments in their approval decisions. Often, regulators will ask sponsors to provide information about the analyses, such as the datasets and data selection criteria used to generate the results. The Clinical Data Interchange Standards Consortium (CDISC) has described their newest initiative to standardize analysis results in the form of tables, listings, and figures (TLF) and reporting of data across the industry.

All team members at PROMETRIKA take responsibility for remaining well versed in the changes in regulations and trends that impact their work. As a biostatistician at PROMETRIKA, I recently attended the American Statistical Association annual Biophamaceutical Section Regulatory-Industry Statistics conference where we discussed the addendum to the E9 (R1) Statistical Principles for Clinical Trials titled “Estimands and Sensitivity Analysis in Clinical Trials.”  The addendum introduces the Estimands Framework and strategies for selecting an estimand. PROMETRIKA invites you to explore this addendum and learn about the estimand strategies put forth in this update. PROMETRIKA can assist you with these and other clinical trial needs.

This is the last in a series of three posts on missing data imputation.  In the first post, we reviewed the underlying assumptions and limitations of single imputation methods, in particular, last observation carried forward (LOCF).  In the second post, we considered more sophisticated data imputation methods for longitudinal data, Mixed Models for Repeated Measures (MMRM) and Multiple Imputation (MI).  An important assumption for both MMRM and MI is that the data are ‘missing at random’ (MAR), if not ‘missing completely at random’ (MCAR).  But what if this assumption is not true for all the missing data?

In a previous post on missing data imputation, we reviewed the underlying assumptions and limitations of last observation carried forward (LOCF). This method was widely used in the past because of its straightforward application, ease of understanding, and often incorrect assumption that it is a conservative approach. Over the years, more sophisticated data imputation methods for longitudinal data have been developed that have advantages over the single imputation methods, such as LOCF, baseline observation carried forward (BOCF) or worst observation carried forward (WOCF).  We will briefly review two of these methods, mixed models for repeated measures (MMRM) and multiple imputation (MI).