ArticleBMC medical research methodology2024
Non-collapsibility and built-in selection bias of period-specific and conventional hazard ratio in randomized controlled trials.
Article in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Work-focused cognitive behavioural therapy for common mental disorders: a systematic review and meta-analysis.BMC psychology · 2026Pooled it
- A New Parametric Accelerated Failure Time Model for Semi-Competing Risks Data.Statistics in medicine · 2026Article
- A new method for dealing with collider bias in the PWP model for recurrent events in randomized controlled trials.BMC medical research methodology · 2025Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe hazard ratio of the Cox proportional hazards model is widely used in randomized controlled trials to assess treatment effects. However, two properties of the hazard ratio including the non-collapsibility and built-in selection bias need to be further investigated.
methodsWe conduct simulations to differentiate the non-collapsibility effect and built-in selection bias from the difference between the marginal and the conditional hazard ratio. Meanwhile, we explore the performance of the Cox model with inverse probability of treatment weighting for covariate adjustment when estimating the marginal hazard ratio. The built-in selection bias is further assessed in the period-specific hazard ratio.
resultsThe conditional hazard ratio is a biased estimate of the marginal effect due to the non-collapsibility property. In contrast, the hazard ratio estimated from the inverse probability of treatment weighting Cox model provides an unbiased estimate of the true marginal hazard ratio. The built-in selection bias only manifests in the period-specific hazard ratios even when the proportional hazards assumption is satisfied. The Cox model with inverse probability of treatment weighting can be used to account for confounding bias and provide an unbiased effect under the randomized controlled trials setting when the parameter of interest is the marginal effect.
conclusionsWe propose that the period-specific hazard ratios should always be avoided due to the profound effects of built-in selection bias.
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