Non-collapsibility and built-in selection bias of period-specific and conventional hazard ratio in randomized controlled trials
Abstract Background The 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. Methods We...
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2024-11-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | https://doi.org/10.1186/s12874-024-02402-3 |
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| author | Helen Bian Menglan Pang Guanbo Wang Zihang Lu |
| author_facet | Helen Bian Menglan Pang Guanbo Wang Zihang Lu |
| author_sort | Helen Bian |
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| description | Abstract Background The 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. Methods We 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. Results The 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. Conclusions We propose that the period-specific hazard ratios should always be avoided due to the profound effects of built-in selection bias. |
| format | Article |
| id | doaj-art-f7e6de9e36a44ea0af8b9b8da9718614 |
| institution | Kabale University |
| issn | 1471-2288 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
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| series | BMC Medical Research Methodology |
| spelling | doaj-art-f7e6de9e36a44ea0af8b9b8da97186142024-12-01T12:32:15ZengBMCBMC Medical Research Methodology1471-22882024-11-0124111610.1186/s12874-024-02402-3Non-collapsibility and built-in selection bias of period-specific and conventional hazard ratio in randomized controlled trialsHelen Bian0Menglan PangGuanbo Wang1Zihang Lu2Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 2001, McGill College CAUSALab, Harvard T.H. Chan School of Public HealthDepartment of Public Health Sciences & Department of Mathematics and Statistics, Queen’s UniversityAbstract Background The 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. Methods We 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. Results The 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. Conclusions We propose that the period-specific hazard ratios should always be avoided due to the profound effects of built-in selection bias.https://doi.org/10.1186/s12874-024-02402-3Non-collapsibilityBuilt-in selection biasCox proportional hazards modelInverse probability of treatment weightingRandomized controlled trials |
| spellingShingle | Helen Bian Menglan Pang Guanbo Wang Zihang Lu Non-collapsibility and built-in selection bias of period-specific and conventional hazard ratio in randomized controlled trials BMC Medical Research Methodology Non-collapsibility Built-in selection bias Cox proportional hazards model Inverse probability of treatment weighting Randomized controlled trials |
| title | Non-collapsibility and built-in selection bias of period-specific and conventional hazard ratio in randomized controlled trials |
| title_full | Non-collapsibility and built-in selection bias of period-specific and conventional hazard ratio in randomized controlled trials |
| title_fullStr | Non-collapsibility and built-in selection bias of period-specific and conventional hazard ratio in randomized controlled trials |
| title_full_unstemmed | Non-collapsibility and built-in selection bias of period-specific and conventional hazard ratio in randomized controlled trials |
| title_short | Non-collapsibility and built-in selection bias of period-specific and conventional hazard ratio in randomized controlled trials |
| title_sort | non collapsibility and built in selection bias of period specific and conventional hazard ratio in randomized controlled trials |
| topic | Non-collapsibility Built-in selection bias Cox proportional hazards model Inverse probability of treatment weighting Randomized controlled trials |
| url | https://doi.org/10.1186/s12874-024-02402-3 |
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