A Precision Medicine Approach to Develop and Internally Validate Optimal Exercise and Weight Loss Treatments for Overweight and Obese Adults with Knee Osteoarthritis

by   Xiaotong Jiang, et al.

We proposed a precision medicine approach to determine the optimal treatment regime for participants in an exercise (E), dietary weight loss (D), and D+E trial for knee osteoarthritis (KOA) that would have maximized their expected outcomes. Using data from 343 participants of the Intensive Diet and Exercise for Arthritis (IDEA) trial, we applied 24 machine-learning models to develop individualized treatment rules on seven outcomes: SF-36 physical component score, weight loss, WOMAC pain/function/stiffness scores, compressive force, and IL-6. The optimal model was selected based on jackknife value function estimates that indicate improvement in the outcome(s) if future participants follow the estimated decision rule compared against the optimal single, fixed treatment model. Multiple outcome random forest was the optimal model for the WOMAC outcomes. For the other outcomes, list-based models were optimal. For example, the estimated optimal decision rule for weight loss assigns the D+E intervention to participants with baseline weight not exceeding 109.35 kg and waist circumference above 90.25 cm, and assigns D to all other participants except those with history of a heart attack. If applied to future participants, the optimal rule for weight loss is estimated to increase average weight loss to 11.2 kg at 18 months, contrasted with 9.8 kg if all received D+E (p = 0.01). The precision medicine models supported the overall findings from IDEA that the D+E intervention was optimal for most participants, but there was evidence that a subgroup of participants would likely benefit more from diet alone for two outcomes.


Estimation and Optimization of Composite Outcomes

There is tremendous interest in precision medicine as a means to improve...

Estimating optimal individualized treatment rules with multistate processes

Multistate process data are common in studies of chronic diseases such a...

Estimation of Individualized Decision Rules Based on an Optimized Covariate-Dependent Equivalent of Random Outcomes

Recent exploration of optimal individualized decision rules (IDRs) for p...

A Model of a Randomized Experiment with an Application to the PROWESS Clinical Trial

I develop a model of a randomized experiment with a binary intervention ...

Dynamic treatment regime characterization via value function surrogate with an application to partial compliance

Precision medicine is a promising framework for generating evidence to i...

Transformation-Invariant Learning of Optimal Individualized Decision Rules with Time-to-Event Outcomes

In many important applications of precision medicine, the outcome of int...

Please sign up or login with your details

Forgot password? Click here to reset