The competing risk between in-hospital mortality and recovery: A pitfall in COVID-19 survival analysis research
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Abstract
Background
A plethora of studies on COVID-19 investigating mortality and recovery have used the Cox Proportional Hazards (Cox PH) model without taking into account the presence of competing risks. We investigate, through extensive simulations, the bias in estimating the hazard ratio (HR) and the absolute risk reduction (ARR) of death when competing risks are ignored, and suggest an alternative method.
Methods
We simulated a fictive clinical trial on COVID-19 mimicking studies investigating interventions such as Hydroxychloroquine, Remdesivir, or convalescent plasma. The outcome is time from randomization until death. Six scenarios for the effect of treatment on death and recovery were considered. The HR and the 28-day ARR of death were estimated using the Cox PH and the Fine and Gray (FG) models. Estimates were then compared with the true values, and the magnitude of misestimation was quantified.
Results
The Cox PH model misestimated the true HR and the 28-day ARR of death in the majority of scenarios. The magnitude of misestimation increased when recovery was faster and/or chance of recovery was higher. In some scenarios, this model has shown harmful treatment effect when it was beneficial. Estimates obtained from FG model were all consistent and showed no misestimation or changes in direction.
Conclusion
There is a substantial risk of misleading results in COVID-19 research if recovery and death due to COVID-19 are not considered as competing risk events. We strongly recommend the use of a competing risk approach to re-analyze relevant published data that have used the Cox PH model.
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SciScore for 10.1101/2020.07.11.20151472: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank…
SciScore for 10.1101/2020.07.11.20151472: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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