Cited by 425Advanced Causal Inference TechniquesStatistical Methods in EpidemiologyStatistical Methods in Clinical Trials

Survival Bias Associated with Time-to-Treatment Initiation in Drug Effectiveness Evaluation: A Comparison of Methods

Zhou, Zheng, Rahme, Elham, Abrahamowicz, Michal, Pilote, Louise·2005·American Journal of Epidemiology

Abstract

The authors compared five methods of studying survival bias associated with time-to-treatment initiation in a drug effectiveness study using medical administrative databases (1996-2002) from Quebec, Canada. The first two methods illustrated how survival bias could be introduced. Three additional methods were considered to control for this bias. Methods were compared in the context of evaluating statins for secondary prevention in elderly patients post-acute myocardial infarction who initiated statins within 90 days after discharge and those who did not. Method 1 that classified patients into users and nonusers at discharge resulted in an overestimation of the benefit (38% relative risk reduction at 1 year). In method 2, following users from the time of the first prescription and nonusers from a randomly selected time between 0 and 90 days attenuated the effect toward the null (10% relative risk reduction). Method 3 controlled for survival bias by following patients from the end of the 90-day time window; however, it suffered a major loss of statistical efficiency and precision. Method 4 matched prescription time distribution between users and nonusers at cohort entry. Method 5 used a time-dependent variable for treatment initiation. Methods 4 and 5 better controlled for survival bias and yielded similar results, suggesting a 20% risk reduction of recurrent myocardial infarction or death events.

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Crossref · 10.1093/aje/kwi307

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  • A recent simulation study by Nelson et al. showed that first-generation methylation ageing clocks do worse than random chance at identifying causal ageing loci due to cohort selection effects in cross-sectional ageing cohorts.

    “A recent simulation study by Nelson et al.168 bolsters the rationale for training these composite models, showing that first-generation methylation ageing clocks do worse than random chance at identifying causal ageing loci due to cohort selection effects that occur in cross-sectional ageing cohorts169,170.”

    From Measuring biological age using omics data (2022) · cites this paper as [170]

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Authors
Zhou, Z., Rahme, E., Abrahamowicz, M., & Pilote, L.
Affiliations
McGill University (CA)
Corresponding author
Zhou, Zheng
Venue
American Journal of Epidemiology
Published
2005-09-28
Volume
162
Issue
10
Pages
1016-1023
ISSN
  • 0002-9262 (ISSN-L)
  • 1476-6256
OpenAlex
W2161515661
Cited by
425
References
26
Citations by year
2026: 27 · 2025: 36 · 2024: 34 · 2023: 31 · 2022: 43 · 2021: 36 · 2020: 24 · 2019: 22 · 2018: 22 · 2017: 27 · 2016: 18 · 2015: 12 · 2014: 17 · 2013: 10 · 2012: 7
FWCI
8.65
Type
journal article
Language
en
Open access
closed
Locations
  • American Journal of Epidemiology (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords
survivorship bias, time to treatment, statins, secondary prevention, acute myocardial infarction, elderly patients, reinfarction, time-varying covariates
MeSH terms
Aged, Aged, 80 and over, Drug Administration Schedule, Female, Follow-Up Studies, Humans, Male, Myocardial Infarction, Outcome and Process Assessment, Health Care, Drug Prescriptions, Quebec, Retrospective Studies, Cohort Studies, Bias, Survival Analysis, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Secondary Prevention

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