Comparison of Statistical Approaches for Dealing with Immortal Time Bias in Drug Effectiveness Studies

Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study Group

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

In time-to-event analyses of observational studies of drug effectiveness, incorrect handling of the period between cohort entry and first treatment exposure during follow-up may result in immortal time bias. This bias can be eliminated by acknowledging a change in treatment exposure status with time-dependent analyses, such as fitting a time-dependent Cox model. The prescription time-distribution matching (PTDM) method has been proposed as a simpler approach for controlling immortal time bias. Using simulation studies and theoretical quantification of bias, we compared the performance of the PTDM approach with that of the time-dependent Cox model in the presence of immortal time. Both assessments revealed that the PTDM approach did not adequately address immortal time bias. Based on our simulation results, another recently proposed observational data analysis technique, the sequential Cox approach, was found to be more useful than the PTDM approach (Cox: bias = -0.002, mean squared error = 0.025; PTDM: bias = -1.411, mean squared error = 2.011). We applied these approaches to investigate the association of β-interferon treatment with delaying disability progression in a multiple sclerosis cohort in British Columbia, Canada (Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study, 1995-2008).

Original languageEnglish (US)
Pages (from-to)325-335
Number of pages11
JournalAmerican Journal of Epidemiology
Volume184
Issue number4
DOIs
StatePublished - Aug 15 2016

Fingerprint

Pharmaceutical Preparations
Prescriptions
Proportional Hazards Models
Multiple Sclerosis
British Columbia
Interferon-beta
Interferons
Canada
Observational Studies
Theoretical Models

Keywords

  • bias (epidemiology)
  • confounding factors (epidemiology)
  • epidemiologic methods
  • immortal time bias
  • longitudinal studies
  • models
  • survival analysis

ASJC Scopus subject areas

  • Epidemiology

Cite this

Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study Group (2016). Comparison of Statistical Approaches for Dealing with Immortal Time Bias in Drug Effectiveness Studies. American Journal of Epidemiology, 184(4), 325-335. https://doi.org/10.1093/aje/kwv445

Comparison of Statistical Approaches for Dealing with Immortal Time Bias in Drug Effectiveness Studies. / Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study Group.

In: American Journal of Epidemiology, Vol. 184, No. 4, 15.08.2016, p. 325-335.

Research output: Contribution to journalArticle

Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study Group 2016, 'Comparison of Statistical Approaches for Dealing with Immortal Time Bias in Drug Effectiveness Studies', American Journal of Epidemiology, vol. 184, no. 4, pp. 325-335. https://doi.org/10.1093/aje/kwv445
Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study Group. Comparison of Statistical Approaches for Dealing with Immortal Time Bias in Drug Effectiveness Studies. American Journal of Epidemiology. 2016 Aug 15;184(4):325-335. https://doi.org/10.1093/aje/kwv445
Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study Group. / Comparison of Statistical Approaches for Dealing with Immortal Time Bias in Drug Effectiveness Studies. In: American Journal of Epidemiology. 2016 ; Vol. 184, No. 4. pp. 325-335.
@article{f61a0b5409be4729b35efe262c210c55,
title = "Comparison of Statistical Approaches for Dealing with Immortal Time Bias in Drug Effectiveness Studies",
abstract = "In time-to-event analyses of observational studies of drug effectiveness, incorrect handling of the period between cohort entry and first treatment exposure during follow-up may result in immortal time bias. This bias can be eliminated by acknowledging a change in treatment exposure status with time-dependent analyses, such as fitting a time-dependent Cox model. The prescription time-distribution matching (PTDM) method has been proposed as a simpler approach for controlling immortal time bias. Using simulation studies and theoretical quantification of bias, we compared the performance of the PTDM approach with that of the time-dependent Cox model in the presence of immortal time. Both assessments revealed that the PTDM approach did not adequately address immortal time bias. Based on our simulation results, another recently proposed observational data analysis technique, the sequential Cox approach, was found to be more useful than the PTDM approach (Cox: bias = -0.002, mean squared error = 0.025; PTDM: bias = -1.411, mean squared error = 2.011). We applied these approaches to investigate the association of β-interferon treatment with delaying disability progression in a multiple sclerosis cohort in British Columbia, Canada (Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study, 1995-2008).",
keywords = "bias (epidemiology), confounding factors (epidemiology), epidemiologic methods, immortal time bias, longitudinal studies, models, survival analysis",
author = "{Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study Group} and Karim, {Mohammad Ehsanul} and Paul Gustafson and John Petkau and Helen Tremlett and Afsaneh Shirani and Yinshan Zhao and Charity Evans and Elaine Kingwell and {Van Der Kop}, {Mia L.} and Joel Oger",
year = "2016",
month = "8",
day = "15",
doi = "10.1093/aje/kwv445",
language = "English (US)",
volume = "184",
pages = "325--335",
journal = "American Journal of Epidemiology",
issn = "0002-9262",
publisher = "Oxford University Press",
number = "4",

}

TY - JOUR

T1 - Comparison of Statistical Approaches for Dealing with Immortal Time Bias in Drug Effectiveness Studies

AU - Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study Group

AU - Karim, Mohammad Ehsanul

AU - Gustafson, Paul

AU - Petkau, John

AU - Tremlett, Helen

AU - Shirani, Afsaneh

AU - Zhao, Yinshan

AU - Evans, Charity

AU - Kingwell, Elaine

AU - Van Der Kop, Mia L.

AU - Oger, Joel

PY - 2016/8/15

Y1 - 2016/8/15

N2 - In time-to-event analyses of observational studies of drug effectiveness, incorrect handling of the period between cohort entry and first treatment exposure during follow-up may result in immortal time bias. This bias can be eliminated by acknowledging a change in treatment exposure status with time-dependent analyses, such as fitting a time-dependent Cox model. The prescription time-distribution matching (PTDM) method has been proposed as a simpler approach for controlling immortal time bias. Using simulation studies and theoretical quantification of bias, we compared the performance of the PTDM approach with that of the time-dependent Cox model in the presence of immortal time. Both assessments revealed that the PTDM approach did not adequately address immortal time bias. Based on our simulation results, another recently proposed observational data analysis technique, the sequential Cox approach, was found to be more useful than the PTDM approach (Cox: bias = -0.002, mean squared error = 0.025; PTDM: bias = -1.411, mean squared error = 2.011). We applied these approaches to investigate the association of β-interferon treatment with delaying disability progression in a multiple sclerosis cohort in British Columbia, Canada (Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study, 1995-2008).

AB - In time-to-event analyses of observational studies of drug effectiveness, incorrect handling of the period between cohort entry and first treatment exposure during follow-up may result in immortal time bias. This bias can be eliminated by acknowledging a change in treatment exposure status with time-dependent analyses, such as fitting a time-dependent Cox model. The prescription time-distribution matching (PTDM) method has been proposed as a simpler approach for controlling immortal time bias. Using simulation studies and theoretical quantification of bias, we compared the performance of the PTDM approach with that of the time-dependent Cox model in the presence of immortal time. Both assessments revealed that the PTDM approach did not adequately address immortal time bias. Based on our simulation results, another recently proposed observational data analysis technique, the sequential Cox approach, was found to be more useful than the PTDM approach (Cox: bias = -0.002, mean squared error = 0.025; PTDM: bias = -1.411, mean squared error = 2.011). We applied these approaches to investigate the association of β-interferon treatment with delaying disability progression in a multiple sclerosis cohort in British Columbia, Canada (Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study, 1995-2008).

KW - bias (epidemiology)

KW - confounding factors (epidemiology)

KW - epidemiologic methods

KW - immortal time bias

KW - longitudinal studies

KW - models

KW - survival analysis

UR - http://www.scopus.com/inward/record.url?scp=84983341712&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84983341712&partnerID=8YFLogxK

U2 - 10.1093/aje/kwv445

DO - 10.1093/aje/kwv445

M3 - Article

C2 - 27455963

AN - SCOPUS:84983341712

VL - 184

SP - 325

EP - 335

JO - American Journal of Epidemiology

JF - American Journal of Epidemiology

SN - 0002-9262

IS - 4

ER -