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Evaluation of methods to estimate missing days’ supply within pharmacy data of the Clinical Practice Research Datalink (CPRD) and The Health Improvement Network (THIN)

  • Pharmacoepidemiology and Prescription
  • Published:
European Journal of Clinical Pharmacology Aims and scope Submit manuscript

Abstract

Purpose

The extent to which days’ supply data are missing in pharmacoepidemiologic databases and effective methods for estimation is unknown. We determined the percentage of missing days’ supply on prescription and patient levels for oral anti-diabetic drugs (OADs) and evaluated three methods for estimating days’ supply within the Clinical Practice Research Datalink (CPRD) and The Health Improvement Network (THIN).

Methods

We estimated the percentage of OAD prescriptions and patients with missing days’ supply in each database from 2009 to 2013. Within a random sample of prescriptions with known days’ supply, we measured the accuracy of three methods to estimate missing days’ supply by imputing the following: (1) 28 days’ supply, (2) mode number of tablets/day by drug strength and number of tablets/prescription, and (3) number of tablets/day via a machine learning algorithm. We determined incidence rates (IRs) of acute myocardial infarction (AMI) using each method to evaluate the impact on ascertainment of exposure time and outcomes.

Results

Days’ supply was missing for 24 % of OAD prescriptions in CPRD and 33 % in THIN (affecting 48 and 57 % of patients, respectively). Methods 2 and 3 were very accurate in estimating days’ supply for OADs prescribed at a consistent number of tablets/day. Method 3 was more accurate for OADs prescribed at varying number of tablets/day. IRs of AMI were similar across methods for most OADs.

Conclusions

Missing days’ supply is a substantial problem in both databases. Method 2 is easy and very accurate for most OADs and results in IRs comparable to those from method 3.

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Acknowledgments

This study was sponsored by AstraZeneca.

Authors’ contributions

All authors contributed to the research and development of new methods. AMG and HB provided expertise on Clinical Practice Research Datalink and The Health Improvement Network, respectively. KJL and CWN were responsible for data analysis. All authors interpreted the results of analyses. KJL drafted the manuscript and all authors contributed with critical revision of the manuscript. All authors read and approved the final manuscript.

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Authors and Affiliations

  1. Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Kirsten J. Lum, Craig W. Newcomb, Jason A. Roy, Dena M. Carbonari, M. Elle Saine & Vincent Lo Re III

  2. Center for Pharmacoepidemiology Research and Training, Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Kirsten J. Lum, Jason A. Roy, Dena M. Carbonari, M. Elle Saine & Vincent Lo Re III

  3. Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Serena Cardillo & Vincent Lo Re III

  4. IMS Health, London, UK

    Harshvinder Bhullar

  5. Clinical Practice Research Datalink, London, UK

    Arlene M. Gallagher

Authors
  1. Kirsten J. Lum
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  2. Craig W. Newcomb
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  3. Jason A. Roy
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  4. Dena M. Carbonari
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  5. M. Elle Saine
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  6. Serena Cardillo
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  7. Harshvinder Bhullar
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  8. Arlene M. Gallagher
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  9. Vincent Lo Re III
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Corresponding author

Correspondence to Vincent Lo Re III.

Ethics declarations

This study was approved by the UK Independent Scientific Advisory Committees for CPRD (Protocol 10_149RMn) and THIN (Protocol 11-039V) and the Institutional Review Board of the University of Pennsylvania. Informed consent of participants was not required.

Conflict of interest

This study was funded by AstraZeneca and KJL, CWN, JAR, DMC, MES, SC, and VLR received funding from AstraZeneca through their employers. AMG and HB are representatives from Clinical Practice Research Datalink and The Health Improvement Network, respectively. Authors report no other relevant potential conflicts of interest related to this manuscript.

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Lum, K.J., Newcomb, C.W., Roy, J.A. et al. Evaluation of methods to estimate missing days’ supply within pharmacy data of the Clinical Practice Research Datalink (CPRD) and The Health Improvement Network (THIN). Eur J Clin Pharmacol 73, 115–123 (2017). https://doi.org/10.1007/s00228-016-2148-4

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  • DOI: https://doi.org/10.1007/s00228-016-2148-4

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