Comparison of statistical approaches dealing with time-dependent confounding in drug effectiveness studies

The BeAMS study group

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

In longitudinal studies, if the time-dependent covariates are affected by the past treatment, time-dependent confounding may be present. For a time-to-event response, marginal structural Cox models are frequently used to deal with such confounding. To avoid some of the problems of fitting marginal structural Cox model, the sequential Cox approach has been suggested as an alternative. Although the estimation mechanisms are different, both approaches claim to estimate the causal effect of treatment by appropriately adjusting for time-dependent confounding. We carry out simulation studies to assess the suitability of the sequential Cox approach for analyzing time-to-event data in the presence of a time-dependent covariate that may or may not be a time-dependent confounder. Results from these simulations revealed that the sequential Cox approach is not as effective as marginal structural Cox model in addressing the time-dependent confounding. The sequential Cox approach was also found to be inadequate in the presence of a time-dependent covariate. We propose a modified version of the sequential Cox approach that correctly estimates the treatment effect in both of the above scenarios. All approaches are applied to investigate the impact of beta-interferon treatment in delaying disability progression in the British Columbia Multiple Sclerosis cohort (1995–2008).

Original languageEnglish (US)
Pages (from-to)1709-1722
Number of pages14
JournalStatistical Methods in Medical Research
Volume27
Issue number6
DOIs
StatePublished - Jun 1 2018

Keywords

  • Bias (epidemiology)
  • causality
  • confounding factors (epidemiology)
  • epidemiologic methods
  • inverse probability weighting
  • longitudinal studies
  • models
  • survival analysis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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