Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0

Shirley V. Wang; Sebastian Schneeweiss; Marc L. Berger; Jeffrey Brown; Frank de Vries; Ian Douglas; Joshua J. Gagne; Rosa Gini; Olaf Klungel; C. Daniel Mullins; Michael D. Nguyen; Jeremy A. Rassen; Liam Smeeth; Miriam Sturkenboom; joint ISPE-ISPOR Special Task

doi:10.1002/pds.4295

Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0

Shirley V. Wang^*, Sebastian Schneeweiss, Marc L. Berger, Jeffrey Brown, Frank de Vries, Ian Douglas, Joshua J. Gagne, Rosa Gini, Olaf Klungel, C. Daniel Mullins, Michael D. Nguyen, Jeremy A. Rassen, Liam Smeeth, Miriam Sturkenboom, joint ISPE-ISPOR Special Task

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

PurposeDefining a study population and creating an analytic dataset from longitudinal healthcare databases involves many decisions. Our objective was to catalogue scientific decisions underpinning study execution that should be reported to facilitate replication and enable assessment of validity of studies conducted in large healthcare databases.

MethodsWe reviewed key investigator decisions required to operate a sample of macros and software tools designed to create and analyze analytic cohorts from longitudinal streams of healthcare data. A panel of academic, regulatory, and industry experts in healthcare database analytics discussed and added to this list.

ConclusionEvidence generated from large healthcare encounter and reimbursement databases is increasingly being sought by decision-makers. Varied terminology is used around the world for the same concepts. Agreeing on terminology and which parameters from a large catalogue are the most essential to report for replicable research would improve transparency and facilitate assessment of validity. At a minimum, reporting for a database study should provide clarity regarding operational definitions for key temporal anchors and their relation to each other when creating the analytic dataset, accompanied by an attrition table and a design diagram. A substantial improvement in reproducibility, rigor and confidence in real world evidence generated from healthcare databases could be achieved with greater transparency about operational study parameters used to create analytic datasets from longitudinal healthcare databases.

Original language	English
Pages (from-to)	1018-1032
Number of pages	15
Journal	Pharmacoepidemiology and Drug Safety
Volume	26
Issue number	9
DOIs	https://doi.org/10.1002/pds.4295
Publication status	Published - Sept 2017

Keywords

Transparency
reproducibility
replication
healthcare databases
pharmacoepidemiology
methods
longitudinal data
SAFETY SURVEILLANCE
CLINICAL-TRIALS
PHARMACOEPIDEMIOLOGY
DRUG
GUIDELINES
PCORNET
DESIGN
COHORT
RISK

Access to Document

10.1002/pds.4295Licence: CC BY

Cite this

Wang, S. V., Schneeweiss, S., Berger, M. L., Brown, J., de Vries, F., Douglas, I., Gagne, J. J., Gini, R., Klungel, O., Mullins, C. D., Nguyen, M. D., Rassen, J. A., Smeeth, L., Sturkenboom, M., & joint ISPE-ISPOR Special Task (2017). Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0. Pharmacoepidemiology and Drug Safety, 26(9), 1018-1032. https://doi.org/10.1002/pds.4295

@article{3f95f6cfcfa64bdc93708e9fdcea50b1,

title = "Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0",

abstract = "PurposeDefining a study population and creating an analytic dataset from longitudinal healthcare databases involves many decisions. Our objective was to catalogue scientific decisions underpinning study execution that should be reported to facilitate replication and enable assessment of validity of studies conducted in large healthcare databases.MethodsWe reviewed key investigator decisions required to operate a sample of macros and software tools designed to create and analyze analytic cohorts from longitudinal streams of healthcare data. A panel of academic, regulatory, and industry experts in healthcare database analytics discussed and added to this list.ConclusionEvidence generated from large healthcare encounter and reimbursement databases is increasingly being sought by decision-makers. Varied terminology is used around the world for the same concepts. Agreeing on terminology and which parameters from a large catalogue are the most essential to report for replicable research would improve transparency and facilitate assessment of validity. At a minimum, reporting for a database study should provide clarity regarding operational definitions for key temporal anchors and their relation to each other when creating the analytic dataset, accompanied by an attrition table and a design diagram. A substantial improvement in reproducibility, rigor and confidence in real world evidence generated from healthcare databases could be achieved with greater transparency about operational study parameters used to create analytic datasets from longitudinal healthcare databases.",

keywords = "Transparency, reproducibility, replication, healthcare databases, pharmacoepidemiology, methods, longitudinal data, SAFETY SURVEILLANCE, CLINICAL-TRIALS, PHARMACOEPIDEMIOLOGY, DRUG, GUIDELINES, PCORNET, DESIGN, COHORT, RISK",

author = "Wang, {Shirley V.} and Sebastian Schneeweiss and Berger, {Marc L.} and Jeffrey Brown and {de Vries}, Frank and Ian Douglas and Gagne, {Joshua J.} and Rosa Gini and Olaf Klungel and Mullins, {C. Daniel} and Nguyen, {Michael D.} and Rassen, {Jeremy A.} and Liam Smeeth and Miriam Sturkenboom and {joint ISPE-ISPOR Special Task}",

year = "2017",

month = sep,

doi = "10.1002/pds.4295",

language = "English",

volume = "26",

pages = "1018--1032",

journal = "Pharmacoepidemiology and Drug Safety",

issn = "1053-8569",

publisher = "Wiley",

number = "9",

}

Wang, SV, Schneeweiss, S, Berger, ML, Brown, J, de Vries, F, Douglas, I, Gagne, JJ, Gini, R, Klungel, O, Mullins, CD, Nguyen, MD, Rassen, JA, Smeeth, L, Sturkenboom, M & joint ISPE-ISPOR Special Task 2017, 'Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0', Pharmacoepidemiology and Drug Safety, vol. 26, no. 9, pp. 1018-1032. https://doi.org/10.1002/pds.4295

TY - JOUR

T1 - Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0

AU - Wang, Shirley V.

AU - Schneeweiss, Sebastian

AU - Berger, Marc L.

AU - Brown, Jeffrey

AU - de Vries, Frank

AU - Douglas, Ian

AU - Gagne, Joshua J.

AU - Gini, Rosa

AU - Klungel, Olaf

AU - Mullins, C. Daniel

AU - Nguyen, Michael D.

AU - Rassen, Jeremy A.

AU - Smeeth, Liam

AU - Sturkenboom, Miriam

AU - joint ISPE-ISPOR Special Task

PY - 2017/9

Y1 - 2017/9

N2 - PurposeDefining a study population and creating an analytic dataset from longitudinal healthcare databases involves many decisions. Our objective was to catalogue scientific decisions underpinning study execution that should be reported to facilitate replication and enable assessment of validity of studies conducted in large healthcare databases.MethodsWe reviewed key investigator decisions required to operate a sample of macros and software tools designed to create and analyze analytic cohorts from longitudinal streams of healthcare data. A panel of academic, regulatory, and industry experts in healthcare database analytics discussed and added to this list.ConclusionEvidence generated from large healthcare encounter and reimbursement databases is increasingly being sought by decision-makers. Varied terminology is used around the world for the same concepts. Agreeing on terminology and which parameters from a large catalogue are the most essential to report for replicable research would improve transparency and facilitate assessment of validity. At a minimum, reporting for a database study should provide clarity regarding operational definitions for key temporal anchors and their relation to each other when creating the analytic dataset, accompanied by an attrition table and a design diagram. A substantial improvement in reproducibility, rigor and confidence in real world evidence generated from healthcare databases could be achieved with greater transparency about operational study parameters used to create analytic datasets from longitudinal healthcare databases.

AB - PurposeDefining a study population and creating an analytic dataset from longitudinal healthcare databases involves many decisions. Our objective was to catalogue scientific decisions underpinning study execution that should be reported to facilitate replication and enable assessment of validity of studies conducted in large healthcare databases.MethodsWe reviewed key investigator decisions required to operate a sample of macros and software tools designed to create and analyze analytic cohorts from longitudinal streams of healthcare data. A panel of academic, regulatory, and industry experts in healthcare database analytics discussed and added to this list.ConclusionEvidence generated from large healthcare encounter and reimbursement databases is increasingly being sought by decision-makers. Varied terminology is used around the world for the same concepts. Agreeing on terminology and which parameters from a large catalogue are the most essential to report for replicable research would improve transparency and facilitate assessment of validity. At a minimum, reporting for a database study should provide clarity regarding operational definitions for key temporal anchors and their relation to each other when creating the analytic dataset, accompanied by an attrition table and a design diagram. A substantial improvement in reproducibility, rigor and confidence in real world evidence generated from healthcare databases could be achieved with greater transparency about operational study parameters used to create analytic datasets from longitudinal healthcare databases.

KW - Transparency

KW - reproducibility

KW - replication

KW - healthcare databases

KW - pharmacoepidemiology

KW - methods

KW - longitudinal data

KW - SAFETY SURVEILLANCE

KW - CLINICAL-TRIALS

KW - PHARMACOEPIDEMIOLOGY

KW - DRUG

KW - GUIDELINES

KW - PCORNET

KW - DESIGN

KW - COHORT

KW - RISK

U2 - 10.1002/pds.4295

DO - 10.1002/pds.4295

M3 - Article

C2 - 28913963

SN - 1053-8569

VL - 26

SP - 1018

EP - 1032

JO - Pharmacoepidemiology and Drug Safety

JF - Pharmacoepidemiology and Drug Safety

IS - 9

ER -