Unstructured Primary Outcome in Randomized Controlled Trials

TL;DR

This paper introduces kernel methods to analyze unstructured primary outcomes in RCTs, enabling assessment and sample size calculation for complex data types like images, sequences, and audio.

Contribution

It proposes a novel kernel-based framework for handling unstructured outcomes in RCTs, extending traditional biostatistics methods to complex data types.

Findings

Kernel methods outperform generalized mixed effect models in simulations.

The approach effectively analyzes unstructured data like images and sequences.

Real data application demonstrates practical utility.

Abstract

The primary outcome of Randomized clinical Trials (RCTs) are typically dichotomous, continuous, multivariate continuous, or time-to-event. However, what if this outcome is unstructured, e.g., a list of variables of mixed types, longitudinal sequences, images, audio recordings, etc. When the outcome is unstructured it is unclear how to assess RCT success and how to compute sample size. We show that kernel methods offer natural extensions to traditional biostatistics methods. We demonstrate our approach with the measurements of computer usage in a cohort of aging participants, some of which will become cognitively impaired. Simulations as well as a real data experiment show the superiority of the proposed approach compared to the standard in this situation: generalized mixed effect models.