Accounting for competing risks in randomized controlled trials: a review and recommendations for improvement

Austin, Peter C.; Fine, Jason P.

doi:10.1002/sim.7215

Cited by 101 publications

(89 citation statements)

References 23 publications

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“…We recently published a review of how competing risks were addressed in reports of randomized controlled trials (RCTs) published in 4 leading general medical journals . In this previous review, we estimated that 77.5% of RCTs with a time‐to‐event outcome were potentially susceptible to competing risks.…”

Section: Discussionmentioning

confidence: 99%

Practical recommendations for reportingFine‐Gray model analyses for competing risk data

Austin

Fine

2017

Statistics in Medicine

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740

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In survival analysis, a competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. Outcomes in medical research are frequently subject to competing risks. In survival analysis, there are 2 key questions that can be addressed using competing risk regression models: first, which covariates affect the rate at which events occur, and second, which covariates affect the probability of an event occurring over time. The cause‐specific hazard model estimates the effect of covariates on the rate at which events occur in subjects who are currently event‐free. Subdistribution hazard ratios obtained from the Fine‐Gray model describe the relative effect of covariates on the subdistribution hazard function. Hence, the covariates in this model can also be interpreted as having an effect on the cumulative incidence function or on the probability of events occurring over time. We conducted a review of the use and interpretation of the Fine‐Gray subdistribution hazard model in articles published in the medical literature in 2015. We found that many authors provided an unclear or incorrect interpretation of the regression coefficients associated with this model. An incorrect and inconsistent interpretation of regression coefficients may lead to confusion when comparing results across different studies. Furthermore, an incorrect interpretation of estimated regression coefficients can result in an incorrect understanding about the magnitude of the association between exposure and the incidence of the outcome. The objective of this article is to clarify how these regression coefficients should be reported and to propose suggestions for interpreting these coefficients.

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Section: Discussionmentioning

confidence: 99%

Practical recommendations for reportingFine‐Gray model analyses for competing risk data

Austin

Fine

2017

Statistics in Medicine

Self Cite

740

582

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“…24 In longitudinal studies, epidemiologists frequently investigate competing events in both non-causal 21,22,25,26 and causal settings. 27 Here, we present a general theory comparing the sHR and csHR and how these estimators can be different, which is consistent with, but not restricted to, different estimand choices for causal inference. We propose and describe nonparametric estimates of the ratio of sHR to csHR, corresponding 95% confidence intervals, and present an applied non-causal example that updates a description of racial differences in time to competing renal replacement therapy events in a pediatric population with chronic kidney disease (CKD).…”

Section: Introductionmentioning

confidence: 70%

<p>Nonparametric Assessment of Differences Between Competing Risk Hazard Ratios: Application to Racial Differences in Pediatric Chronic Kidney Disease Progression</p>

Ng¹,

Antiporta²,

Matheson³

et al. 2020

CLEP

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Associations between an exposure and multiple competing events are typically described by cause-specific hazard ratios (csHR) or subdistribution hazard ratios (sHR). However, diagnostic tools to assess differences between them have not been described. Under the proportionality assumption for both, it can be shown mathematically that the sHR and csHR must be equal, so reporting different time-constant sHR and csHR implies non-proportionality for at least one. We propose a simple, intuitive approach using the ratio of sHR/csHR to nonparametrically compare these metrics. In general, for the non-null case, there must be at least one event type for which the sHR and csHR differ, and the proposed diagnostic will be useful to identify these cases. Furthermore, once standard methods are used to estimate the csHR, multiplying it with our nonparametric estimate for the sHR/csHR ratio will yield estimates of sHR which fulfill intrinsic linkages of the subhazards that separate analysis may violate. In addition, for non-null cases, at least one must be time dependent (i.e., non-proportional), and thus our tool serves as an indirect test of the proportionality assumption. We applied this proposed diagnostic tool to data from a cohort of children with congenital kidney disease to describe racial differences in the time to first dialysis or first transplant and extend methods to include adjustment for socioeconomic factors.

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“…We recently published two reviews addressing issues related to the handling of competing risks in the medical literature. The first review examined how competing risks were addressed in reports of randomized controlled trials (RCTs) published in four leading general medical journals . We estimated that 77.5% of RCTs with a time‐to‐event outcome were potentially susceptible to competing risks.…”

Section: Discussionmentioning

confidence: 99%

A review of the use of time‐varying covariates in the Fine‐Gray subdistribution hazard competing risk regression model

Austin

Latouche

Fine

2019

Statistics in Medicine

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In survival analysis, time‐varying covariates are covariates whose value can change during follow‐up. Outcomes in medical research are frequently subject to competing risks (events precluding the occurrence of the primary outcome). We review the types of time‐varying covariates and highlight the effect of their inclusion in the subdistribution hazard model. External time‐dependent covariates are external to the subject, can effect the failure process, but are not otherwise involved in the failure mechanism. Internal time‐varying covariates are measured on the subject, can effect the failure process directly, and may also be impacted by the failure mechanism. In the absence of competing risks, a consequence of including internal time‐dependent covariates in the Cox model is that one cannot estimate the survival function or the effect of covariates on the survival function. In the presence of competing risks, the inclusion of internal time‐varying covariates in a subdistribution hazard model results in the loss of the ability to estimate the cumulative incidence function (CIF) or the effect of covariates on the CIF. Furthermore, the definition of the risk set for the subdistribution hazard function can make defining internal time‐varying covariates difficult or impossible. We conducted a review of the use of time‐varying covariates in subdistribution hazard models in articles published in the medical literature in 2015 and in the first 5 months of 2019. Seven percent of articles published included a time‐varying covariate. Several inappropriately described a time‐varying covariate as having an association with the risk of the outcome.

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Accounting for competing risks in randomized controlled trials: a review and recommendations for improvement

Cited by 101 publications

References 23 publications

Practical recommendations for reportingFine‐Gray model analyses for competing risk data

Practical recommendations for reportingFine‐Gray model analyses for competing risk data

<p>Nonparametric Assessment of Differences Between Competing Risk Hazard Ratios: Application to Racial Differences in Pediatric Chronic Kidney Disease Progression</p>

A review of the use of time‐varying covariates in the Fine‐Gray subdistribution hazard competing risk regression model

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