Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Veridical Causal Inference for Comparative Effectiveness Research Using Medical Claims

Version 1 : Received: 22 June 2020 / Approved: 24 June 2020 / Online: 24 June 2020 (09:51:17 CEST)

A peer-reviewed article of this Preprint also exists.

Ross, R.D., Shi, X., Caram, M.E.V. et al. Veridical causal inference using propensity score methods for comparative effectiveness research with medical claims. Health Serv Outcomes Res Method (2020). https://doi.org/10.1007/s10742-020-00222-8 Ross, R.D., Shi, X., Caram, M.E.V. et al. Veridical causal inference using propensity score methods for comparative effectiveness research with medical claims. Health Serv Outcomes Res Method (2020). https://doi.org/10.1007/s10742-020-00222-8

Journal reference: Health Services and Outcomes Research Methodology 2020
DOI: 10.1007/s10742-020-00222-8

Abstract

Medical insurance claims are becoming increasingly common data sources to answer a variety of questions in biomedical research. Although comprehensive in terms of longitudinal characterization of disease development and progression for a potentially large number of patients, population-based studies using these datasets require thoughtful modification to sample selection and analytic strategies, relative to other types of studies. Along with complex selection bias and missing data issues, claims-based studies are purely observational, which limits effective understanding and characterization of the treatment differences between groups being compared. All these issues contribute to a crisis in reproducibility and replication of comparative findings. This paper offers some practical guidance to the full analytical process, demonstrates methods for estimating causal treatment effects on several types of outcomes common to such studies, such as binary, count, time to event and longitudinally varying repeated measures outcomes, and aims to increase transparency and reproducibility. We provide an online version of the paper with readily implementable code for the entire analysis pipeline to serve as a guided tutorial for practitioners. The online version can be accessed at https://rydaro.github.io/. The analytic pipeline is illustrated using a sub-cohort of patients with advanced prostate cancer from the large Clinformatics TM Data Mart Database (OptumInsight, Eden Prairie, Minnesota), consisting of 73 million distinct private payer insurees from 2001-2016.

Supplementary and Associated Material

https://rydaro.github.io/: Tutorial Webpage

Subject Areas

insurance claims; reproducibility; propensity score; veridical data science; sensitivity analysis

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