Statistical Methods for Comparative Effectiveness Research of Medical Devices

Abstract

A recent focus in health care policy is on comparative effectiveness of treatments--from drugs to behavioral interventions to medical devices. Medical devices bring a unique set of challenges for comparative effectiveness research. In this dissertation, I develop statistical methods for comparative effectiveness estimation and illustrate the methodology in the context of three different medical devices. In chapter 2, I review approaches for causal inference in the context of observational cohort studies, utilizing a potential outcomes framework demonstrated using data for patients undergoing revascularization surgery with radial versus femoral artery access. Propensity score methods; G-computation; augmented inverse probability of treatment weighting; and targeted maximum likelihood estimation are implemented and their causal and statistical assumptions evaluated. In chapter 3, I undertake a theoretical and simulation-based assessment of differential follow-up information per treatment arm on inference in meta-analysis where applied researchers commonly assume similar follow-up duration across treatment groups. When applied to the implantation of cardiovascular resynchronization therapies to examine comparative survival, only 3 of 8 studies report arm-specific follow-up. I derive the bias of the rate ratio for an individual study using the number of deaths and total patients per arm and show that the bias can be large, even for modest violations of the assumption that follow-up is the same in the two arms. Furthermore, when pooling multiple studies with Bayesian methods for random effects meta-analysis, the direction and magnitude of the bias is unpredictable. In chapter 4, I examine the statistical power for designing a study of devices when it is difficult to blind patients and providers, everyone wants the device, and clustering by hospitals where the devices are implanted needs to be taken into account. In these situations, a stepped wedge design (SWD) cluster randomized design may be used to rigorously assess the roll-out of novel devices. I determine the exact asymptotic theoretical power using Romberg integration over cluster random effects to calculate power in a two-treatment, binary outcome SWD. Over a range of design parameters, the exact method is from 9% to 2.4 times more efficient than designs based on the existing method.