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Methodologic Issues when Estimating Risks in Pharmacoepidemiology

  • Pharmacoepidemiology (T Stürmer, Section Editor)
  • Published:
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Abstract

Purpose of Review

Risk is an important parameter to describe the occurrence of health outcomes over time. However, many outcomes of interest in healthcare settings, such as disease incidence, treatment initiation, and cause-specific mortality, may be precluded from occurring by other events, often referred to as competing events. Here, we review straightforward approaches to estimate risk in the presence of competing events.

Recent Findings

We illustrate the application of these methods using timely examples in pharmacoepidemiologic research and compare results to those obtained using analytic simplifications commonly used to handle competing events.

Summary

These examples demonstrate how the analytic methods used to account for competing events affect the interpretation of results from pharmacoepidemiologic studies.

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Acknowledgments

This work was supported in part by NIH U01 HL121812, U01 DA036935, R01 AI100654, and 5R25CA116339-07.

The project presented in example #1 was supported by the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health through Grant Award Number 1UL1TR001111. The database infrastructure for example #1 was funded by the CER Strategic Initiative of UNC’s Clinical Translational Science Award (1 ULI RR025747) and the UNC School of Medicine. The authors thank Dr. Jennifer Lund, the PI for this project, for use of the data. The authors also acknowledge the efforts of the National Cancer Institute’s Applied Research Program, the Centers for Medicare and Medicaid Services’ Office of Research, Development, and Information, the Information Management Services, Inc., and the Surveillance, Epidemiology, and End Results (SEER) Program tumor registries in the creation of the SEER-Medicare database.

The database infrastructure used for example #2 was funded by the Pharmacoepidemiology Gillings Innovation Lab (PEGIL) for the Population-Based Evaluation of Drug Benefits and Harms in Older US Adults (GIL200811.0010), the Center for Pharmacoepidemiology, Department of Epidemiology, UNC Gillings School of Global Public Health, the CER Strategic Initiative of UNC’s Clinical Translational Science Award (UL1TR001111), the Cecil G. Sheps Center for Health Services Research, UNC, and the UNC School of Medicine.)

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Authors and Affiliations

  1. Department of Epidemiology, University of North Carolina at Chapel Hill, 135 Dauer Dr. 2101 McGavran Greenberg Hall CB #7435, Chapel Hill, NC, 27510, USA

    Jessie K. Edwards, Laura L. Hester & Mugdha Gokhale

  2. Epidemiology, Real World Evidence, GlaxoSmithKline, Collegeville, PA, USA

    Mugdha Gokhale

  3. Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA

    Catherine R. Lesko

Authors
  1. Jessie K. Edwards
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  2. Laura L. Hester
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  3. Mugdha Gokhale
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  4. Catherine R. Lesko
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Corresponding author

Correspondence to Jessie K. Edwards.

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Conflict of Interest

Mugdha Gokhale is an employee of GlaxoSmithKline.

Laure Hester, Catherine Lesko, and Jessie Edwards declare that they have no conflicts of interest.

Human and Animal Rights and Informed Consent

All studies referenced in this work by Edwards, Lesko, Gokhale, and Hester involving human subjects were performed after approval by the appropriate institutional review board. When required, written informed consent was obtained from all participants.

Additional information

This article is part of the Topical Collection on Pharmacoepidemiology

Appendices

Appendix 1: SAS code to estimate risks of cancer-related mortality in example #1

This SAS code assumes an input dataset with at least 2 variables: (1) a time variable t indicating the time from the origin to death or censoring and (2) an event indicator j with 3 levels (0 = censoring, 1 = cancer death, 2 = other death). This code assumes no tied event times. If ties are present, we recommend adding a very small amount of random noise to the event times so that all event times are unique. The output of this code is a dataset with estimates of cancer-related mortality risk and noncancer-related mortality risk for each event time.

figure a

Appendix 2: Subdistribution hazard ratios for example 1

Table 2 Cancer-related cause-specific and subdistribution hazard ratios associated with prevalent diabetes, congestive heart failure (CHF), or chronic obstructive pulmonary disease (COPD) at Non-Hodgkins Lymphoma (NHL) diagnosis, among N = 5192 patients over age 65 in the linked SEER-Medicare database diagnosed 2007–2009
Full size table

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Edwards, J.K., Hester, L.L., Gokhale, M. et al. Methodologic Issues when Estimating Risks in Pharmacoepidemiology. Curr Epidemiol Rep 3, 285–296 (2016). https://doi.org/10.1007/s40471-016-0089-1

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