DOI: 10.25934/pr00007401
title: Randomized Controlled Trial Data Availability and Re-Analysis Project

description: Making individual patient level data (IPLD) publicly available is increasingly required of authors who wish to publish in major medical journals. Availability of this data is crucial for other researchers to 1) verify and ensure the accuracy of published findings and 2) to repurpose the data for new analyses not performed by the original investigators. This repurposing of data allows us to maximize the potential learning and benefits from a study, in relation to the risk and the time commitment present for participants in any clinical trial.

The first portion of our study seeks to determine the availability of IPLD from randomized controlled trials (RCTs) published in the last 365 days in 7 major medical journals, in which RCTs are likely to appear. The results from this study will be presented as a cross-sectional analysis that outlines 1) the extent of data availability and 2) the characteristics of RCTs for which data is available and unavailable. We intend to repeat this analysis periodically (every 3-5 years) to look for any trends in IPLD availability. This will hopefully provide us and the community at large, an overview of how available IPLD is as well as the quality of shared IPLD, as no such analysis has yet been performed. We also intend to use data shared with us, from select studies, for two further projects.

The first repurposed analysis involves estimating the proportion of participants that respond to a therapy.  It has been suggested that most therapeutics only work in a subset of people - that is to say, the average effect of a therapy combines a certain amount of participants who respond to the drug better than the average and a proportion who respond worse than average, similar to if they only received a placebo. To verify this effect, we will be using a new approach to non-linear regression that models data under the assumption that only some participants are responders. This analysis, applied to continuous outcomes such as blood pressure readings, will produce a best fit estimate for the proportion of participants who respond to a therapy and the degree of that response.

The second repurposed analysis will explore whether differences exist between trial participants who are considered more medically complex and those considered less medically complex, when it comes to the outcomes measured in a particular study and the adverse events tracked by that study. We will divide participants into more and less medically complex groups based on the median of the entire participant pool's complexity scores. This second analysis is based on the assumption that risks and benefits for a therapy are likely to differ based on the medical complexity of a patient.

We intend for these repurposed analyses, once published, to form the basis for further re-analyses of other studies, in order to maximize learning and benefit from those trials. As well, we hope for these methods to be included in future trials and their analyses, as another method to maximize the benefit for patients who may respond/not respond to a particular therapy, as well as those who may be more medically complex.