Using Pilot Data to Size Observational Studies for the Estimation of Dynamic Treatment Regimes

02/18/2022
by   Eric J. Rose, et al.
0

There has been significant attention given to developing data-driven methods for tailoring patient care based on individual patient characteristics. Dynamic treatment regimes formalize this through a sequence of decision rules that map patient information to a suggested treatment. The data for estimating and evaluating treatment regimes are ideally gathered through the use of Sequential Multiple Assignment Randomized Trials (SMARTs) though longitudinal observational studies are commonly used due to the potentially prohibitive costs of conducting a SMART. These studies are typically sized for simple comparisons of fixed treatment sequences or, in the case of observational studies, a priori sample size calculations are often not performed. We develop sample size procedures for the estimation of dynamic treatment regimes from observational studies. Our approach uses pilot data to ensure a study will have sufficient power for comparing the value of the optimal regime, i.e. the expected outcome if all patients in the population were treated by following the optimal regime, with a known comparison mean. Our approach also ensures the value of the estimated optimal treatment regime is within an a priori set range of the value of the true optimal regime with a high probability. We examine the performance of the proposed procedure with a simulation study and use it to size a study for reducing depressive symptoms using data from electronic health records.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/18/2022

Monte Carlo Sensitivity Analysis for Unmeasured Confounding in Dynamic Treatment Regimes

Data-driven methods for personalizing treatment assignment have garnered...
research
06/16/2019

Sample Size Calculations for SMARTs

Sequential Multiple Assignment Randomized Trials (SMARTs) are considered...
research
02/19/2012

Q- and A-Learning Methods for Estimating Optimal Dynamic Treatment Regimes

In clinical practice, physicians make a series of treatment decisions ov...
research
07/07/2021

Identifying optimally cost-effective dynamic treatment regimes with a Q-learning approach

Health policy decisions regarding patient treatment strategies require c...
research
09/01/2021

On Estimation and Cross-validation of Dynamic Treatment Regimes with Competing Risks

The optimal moment to start renal replacement therapy in a patient with ...
research
07/06/2016

An optimal learning method for developing personalized treatment regimes

A treatment regime is a function that maps individual patient informatio...
research
04/06/2020

Comment: Entropy Learning for Dynamic Treatment Regimes

I congratulate Profs. Binyan Jiang, Rui Song, Jialiang Li, and Donglin Z...

Please sign up or login with your details

Forgot password? Click here to reset