Methods for time-varying exposure related problems in pharmacoepidemiology: An overview
Research output: Contribution to journal › Review › Research › peer-review
Standard
Methods for time-varying exposure related problems in pharmacoepidemiology : An overview. / Pazzagli, Laura; Linder, Marie; Zhang, Mingliang; Vago, Emese; Stang, Paul; Myers, David; Andersen, Morten; Bahmanyar, Shahram.
In: Pharmacoepidemiology and Drug Safety, Vol. 27, No. 2, 2018, p. 148-160.Research output: Contribution to journal › Review › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Methods for time-varying exposure related problems in pharmacoepidemiology
T2 - An overview
AU - Pazzagli, Laura
AU - Linder, Marie
AU - Zhang, Mingliang
AU - Vago, Emese
AU - Stang, Paul
AU - Myers, David
AU - Andersen, Morten
AU - Bahmanyar, Shahram
N1 - © 2017 The Authors. Pharmacoepidemiology & Drug Safety Published by John Wiley & Sons Ltd.
PY - 2018
Y1 - 2018
N2 - PURPOSE: Lack of control for time-varying exposures can lead to substantial bias in estimates of treatment effects. The aim of this study is to provide an overview and guidance on some of the available methodologies used to address problems related to time-varying exposure and confounding in pharmacoepidemiology and other observational studies. The methods are explored from a conceptual rather than an analytical perspective.METHODS: The methods described in this study have been identified exploring the literature concerning to the time-varying exposure concept and basing the search on four fundamental pharmacoepidemiological problems, construction of treatment episodes, time-varying confounders, cumulative exposure and latency, and treatment switching.RESULTS: A correct treatment episodes construction is fundamental to avoid bias in treatment effect estimates. Several methods exist to address time-varying covariates, but the complexity of the most advanced approaches-eg, marginal structural models or structural nested failure time models-and the lack of user-friendly statistical packages have prevented broader adoption of these methods. Consequently, simpler methods are most commonly used, including, for example, methods without any adjustment strategy and models with time-varying covariates. The magnitude of exposure needs to be considered and properly modelled.CONCLUSIONS: Further research on the application and implementation of the most complex methods is needed. Because different methods can lead to substantial differences in the treatment effect estimates, the application of several methods and comparison of the results is recommended. Treatment episodes estimation and exposure quantification are key parts in the estimation of treatment effects or associations of interest.
AB - PURPOSE: Lack of control for time-varying exposures can lead to substantial bias in estimates of treatment effects. The aim of this study is to provide an overview and guidance on some of the available methodologies used to address problems related to time-varying exposure and confounding in pharmacoepidemiology and other observational studies. The methods are explored from a conceptual rather than an analytical perspective.METHODS: The methods described in this study have been identified exploring the literature concerning to the time-varying exposure concept and basing the search on four fundamental pharmacoepidemiological problems, construction of treatment episodes, time-varying confounders, cumulative exposure and latency, and treatment switching.RESULTS: A correct treatment episodes construction is fundamental to avoid bias in treatment effect estimates. Several methods exist to address time-varying covariates, but the complexity of the most advanced approaches-eg, marginal structural models or structural nested failure time models-and the lack of user-friendly statistical packages have prevented broader adoption of these methods. Consequently, simpler methods are most commonly used, including, for example, methods without any adjustment strategy and models with time-varying covariates. The magnitude of exposure needs to be considered and properly modelled.CONCLUSIONS: Further research on the application and implementation of the most complex methods is needed. Because different methods can lead to substantial differences in the treatment effect estimates, the application of several methods and comparison of the results is recommended. Treatment episodes estimation and exposure quantification are key parts in the estimation of treatment effects or associations of interest.
KW - Journal Article
KW - Review
U2 - 10.1002/pds.4372
DO - 10.1002/pds.4372
M3 - Review
C2 - 29285840
VL - 27
SP - 148
EP - 160
JO - Pharmacoepidemiology and Drug Safety
JF - Pharmacoepidemiology and Drug Safety
SN - 1053-8569
IS - 2
ER -
ID: 187553828