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 journalArticlepeer-review

25 Scopus citations

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

Keywords

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

ASJC Scopus subject areas

  • Epidemiology

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