Overview of modern approaches for identifying and evaluating heterogeneous treatment effects from clinical data

Abstract

There has been much interest in the evaluation of heterogeneous treatment effects (HTE) and multiple statistical methods have emerged under the heading of personalized/precision medicine combining ideas from hypothesis testing, causal inference, and machine learning over the past 10-15 years. We discuss new ideas and approaches for evaluating HTE in randomized clinical trials and observational studies using the features introduced earlier by Lipkovich, Dmitrienko, and D’Agostino that distinguish principled methods from simplistic approaches to data-driven subgroup identification and estimating individual treatment effects and use a case study to illustrate these approaches. We identified and provided a high-level overview of several classes of modern statistical approaches for personalized/precision medicine, elucidated the underlying principles and challenges, and compared findings for a case study across different methods. Different approaches to evaluating HTEs may produce (and actually produced) highly disparate results when applied to a specific data set. Evaluating HTE with machine learning methods presents special challenges since most of machine learning algorithms are optimized for prediction rather than for estimating causal effects. An additional challenge is in that the output of machine learning methods is typically a “black box” that needs to be transformed into interpretable personalized solutions in order to gain acceptance and usability.

Baseline functional connectivity predicts who will benefit from neuromodulation: evidence from primary progressive aphasia

“…In our prediction analysis, we modeled the additional, individual-specific tDCS effect using the so called modified outcome method, which adheres to established guidelines that account for heterogeneity in treatment effects [35][36][37][38][39]. For each participant, the individual-specific additional tDCS effect was defined as the difference between the potential change in the outcome if the participant had been assigned to active tDCS vs. sham tDCS, Yi T = 1 -Yi T = 0 .…”

Integrating randomized and observational studies to estimate optimal dynamic treatment regimes

“…Q-learning is a regression-based method which maximizes the outcome at each sequential stage working backwards to arrive at the optimal treatment rule at each stage (Murphy, 2005 ). At each stage in the Q-learning estimation, we utilize the framework of the doubly robust augmented IPSW (APISW) estimator, which was originally used for estimating the average treatment effect or heterogeneous treatment effect in a single stage study (Lunceford and Davidian, 2004 ; Lipkovich et al., 2023 ). The estimator is doubly robust in that it will give unbiased results as long as either the the outcome model or the probabilities of receiving a given treatment are accurate.…”