Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Summary

When randomized controlled trials are not feasible, a comprehensive health care data source like the Military Health System Data Repository provides an attractive alternative for retrospective analyses. Incorporating mortality data from the national death index and balancing differences between groups using propensity weighting helps reduce biases inherent in retrospective designs.

Abstract

When randomized controlled trials are not feasible, retrospective studies using big data provide an efficient and cost-effective alternative, though they are at risk for treatment selection bias. Treatment selection bias occurs in a non-randomized study when treatment selection is based on pre-treatment characteristics that are also associated with the outcome. These pre-treatment characteristics, or confounders, can influence evaluation of a treatment's effect on the outcome. Propensity scores minimize this bias by balancing the known confounders between treatment groups. There are a few approaches to performing propensity score analyses, including stratifying by the propensity score, propensity matching, and inverse probability of treatment weighting (IPTW). Described here is the use of IPTW to balance baseline comorbidities in a cohort of patients within the US Military Health System Data Repository (MDR). The MDR is a relatively optimal data source, as it provides a contained cohort in which nearly complete information on inpatient and outpatient services is available for eligible beneficiaries. Outlined below is the use of the MDR supplemented with information from the national death index to provide robust mortality data. Also provided are suggestions for using administrative data. Finally, the protocol shares an SAS code for using IPTW to balance known confounders and plot the cumulative incidence function for the outcome of interest.

Introduction

Randomized, placebo-controlled trials are the strongest study design to quantify efficacy of treatment, but they are not always feasible due to cost and time requirements or a lack of equipoise between treatment groups¹. In these instances, a retrospective cohort design using large-scale administrative data ("big data") often provides an efficient and cost-effective alternative, though the lack of randomization introduces treatment selection bias². Treatment selection bias occurs in non-randomized studies when the treatment decision is dependent on pre-treatment characteristics that are associated with the outcom....

Protocol

The following protocol follows the guidelines of our institutional human ethics committees.

1. Defining the cohort

1. Determine and clearly define the inclusion and exclusion criteria of the planned cohort using either 1) a registry or 2) data points that can be extracted from the MDR such as administrative codes for diagnoses or procedures (i.e., all patients with more than two outpatient diagnoses or one inpatient diagnosis of atrial fibrillation).
   1. If using a registry, include two or more patient identifiers for accurate matching with the Military Health System Data Repository such as medical record number (listed in ....

Results

Upon completion of IPTW, tables or plots of the absolute standardized differences can be generated using the stddiff macro code or the asdplot macro code, respectively. Figure 1 shows an example of appropriate balancing in a large cohort of 10,000 participants using the asdplot macro. After application of the propensity score, the absolute standardized differences were reduced significantly. The cutoff used for the absolute standardized difference is somewhat.......

Discussion

Retrospective analyses using large administrative datasets provide an efficient and cost-effective alternative when randomized controlled trials are not feasible. The appropriate data set will depend on the population and variables of interest, but the MDR is an attractive option that does not have the age restrictions seen with Medicare data. With any data set, it is important to be intimately familiar with its layout and data dictionary. Care should be taken along the way to ensure that complete data are captured, and .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CD Burner (for NDI Request)
Computer
Putty.exe	Putty.org
SAS 9.4	SAS Institute Cary, NC
WinSCP or other FTP software	https://winscp.net/eng/index.php