Margret V Bjarnadottir1, David Czerwinski2, Eberechukwu Onukwugha3. 1. Department of Decision, Operations, and Information Technologies, Robert H. Smith School of Business, University of Maryland College Park, College Park, MD, USA. 2. Department of Marketing and Decision Sciences, College of Business, San José State University, San José, CA, USA. 3. Department of Pharmaceutical Health Services Research, University of Maryland School of Pharmacy, 220 Arch Street, Baltimore, MD, 21201, USA. eonukwug@rx.umaryland.edu.
Abstract
OBJECTIVES: When preparing administrative medical and pharmacy claims data for analysis, decisions about data clean up and analytical approach need to be made. However, information about the effects of various modelling decisions on adherence measures such as the medication possession ratio (MPR) is limited. We address this gap with this study. METHODS: We utilized cross-sectional administrative claims data for commercially insured members filling at least two prescriptions for drugs within five classes of hypertension medication between 2008 and 2010. We divided nine modelling decisions into three categories: data scrubbing, study design, and MPR definition/calculations. We defined the base-case settings with commonly used values, varied each modelling decision singly and in combination, and measured the effects on the MPR. RESULTS: Claims data for 358,418 individuals were available for analysis. Two modelling decisions were found to be highly influential, each yielding a difference of over 25 percentage points from the base case: the decision of whether to use interval- or prescription-based study periods, and the decision of how to handle overlapping prescription claims. The effect of other decisions was smaller, with a difference of 1-9 percentage points from the base case. CONCLUSIONS: Some of the decisions considered had a large impact on the MPR. Therefore, it is important for researchers to standardize approaches for study period length and overlapping prescription claims. We also conclude that transparent reporting of modelling decisions will facilitate the interpretation of results and comparisons across studies.
OBJECTIVES: When preparing administrative medical and pharmacy claims data for analysis, decisions about data clean up and analytical approach need to be made. However, information about the effects of various modelling decisions on adherence measures such as the medication possession ratio (MPR) is limited. We address this gap with this study. METHODS: We utilized cross-sectional administrative claims data for commercially insured members filling at least two prescriptions for drugs within five classes of hypertension medication between 2008 and 2010. We divided nine modelling decisions into three categories: data scrubbing, study design, and MPR definition/calculations. We defined the base-case settings with commonly used values, varied each modelling decision singly and in combination, and measured the effects on the MPR. RESULTS: Claims data for 358,418 individuals were available for analysis. Two modelling decisions were found to be highly influential, each yielding a difference of over 25 percentage points from the base case: the decision of whether to use interval- or prescription-based study periods, and the decision of how to handle overlapping prescription claims. The effect of other decisions was smaller, with a difference of 1-9 percentage points from the base case. CONCLUSIONS: Some of the decisions considered had a large impact on the MPR. Therefore, it is important for researchers to standardize approaches for study period length and overlapping prescription claims. We also conclude that transparent reporting of modelling decisions will facilitate the interpretation of results and comparisons across studies.
Authors: Sarah Clifford; Magaly Perez-Nieves; Anne M Skalicky; Matthew Reaney; Karin S Coyne Journal: Curr Med Res Opin Date: 2014-02-11 Impact factor: 2.580
Authors: Reed F Beall; Alexander A Leung; Amity E Quinn; Charleen Salmon; Tayler D Scory; Lauren C Bresee; Paul E Ronksley Journal: J Clin Hypertens (Greenwich) Date: 2022-09-20 Impact factor: 2.885