### Abstract

Objectives To examine the effect of the number of events per variable (EPV) on the accuracy of estimated regression coefficients, standard errors, empirical coverage rates of estimated confidence intervals, and empirical estimates of statistical power when using the Fine–Gray subdistribution hazard regression model to assess the effect of covariates on the incidence of events that occur over time in the presence of competing risks. Study Design and Setting Monte Carlo simulations were used. We considered two different definitions of the number of EPV. One included events of any type that occurred (both primary events and competing events), whereas the other included only the number of primary events that occurred. Results The definition of EPV that included only the number of primary events was preferable to the alternative definition, as the number of competing events had minimal impact on estimation. In general, 40–50 EPV were necessary to ensure accurate estimation of regression coefficients and associated quantities. However, if all of the covariates are continuous or are binary with moderate prevalence, then 10 EPV are sufficient to ensure accurate estimation. Conclusion Analysts must base the number of EPV on the number of primary events that occurred.

Language | English (US) |
---|---|

Pages | 75-84 |

Number of pages | 10 |

Journal | Journal of Clinical Epidemiology |

Volume | 83 |

DOIs | |

State | Published - Mar 1 2017 |

### Fingerprint

### Keywords

- Competing risks
- Events per variable
- Fine–Gray regression model
- Sample size
- Subdistribution hazard model
- Survival analysis

### ASJC Scopus subject areas

- Epidemiology

### Cite this

*Journal of Clinical Epidemiology*,

*83*, 75-84. DOI: 10.1016/j.jclinepi.2016.11.017

**The number of primary events per variable affects estimation of the subdistribution hazard competing risks model.** / Austin, Peter C.; Allignol, Arthur; Fine, Jason P.

Research output: Contribution to journal › Article

*Journal of Clinical Epidemiology*, vol. 83, pp. 75-84. DOI: 10.1016/j.jclinepi.2016.11.017

}

TY - JOUR

T1 - The number of primary events per variable affects estimation of the subdistribution hazard competing risks model

AU - Austin,Peter C.

AU - Allignol,Arthur

AU - Fine,Jason P.

PY - 2017/3/1

Y1 - 2017/3/1

N2 - Objectives To examine the effect of the number of events per variable (EPV) on the accuracy of estimated regression coefficients, standard errors, empirical coverage rates of estimated confidence intervals, and empirical estimates of statistical power when using the Fine–Gray subdistribution hazard regression model to assess the effect of covariates on the incidence of events that occur over time in the presence of competing risks. Study Design and Setting Monte Carlo simulations were used. We considered two different definitions of the number of EPV. One included events of any type that occurred (both primary events and competing events), whereas the other included only the number of primary events that occurred. Results The definition of EPV that included only the number of primary events was preferable to the alternative definition, as the number of competing events had minimal impact on estimation. In general, 40–50 EPV were necessary to ensure accurate estimation of regression coefficients and associated quantities. However, if all of the covariates are continuous or are binary with moderate prevalence, then 10 EPV are sufficient to ensure accurate estimation. Conclusion Analysts must base the number of EPV on the number of primary events that occurred.

AB - Objectives To examine the effect of the number of events per variable (EPV) on the accuracy of estimated regression coefficients, standard errors, empirical coverage rates of estimated confidence intervals, and empirical estimates of statistical power when using the Fine–Gray subdistribution hazard regression model to assess the effect of covariates on the incidence of events that occur over time in the presence of competing risks. Study Design and Setting Monte Carlo simulations were used. We considered two different definitions of the number of EPV. One included events of any type that occurred (both primary events and competing events), whereas the other included only the number of primary events that occurred. Results The definition of EPV that included only the number of primary events was preferable to the alternative definition, as the number of competing events had minimal impact on estimation. In general, 40–50 EPV were necessary to ensure accurate estimation of regression coefficients and associated quantities. However, if all of the covariates are continuous or are binary with moderate prevalence, then 10 EPV are sufficient to ensure accurate estimation. Conclusion Analysts must base the number of EPV on the number of primary events that occurred.

KW - Competing risks

KW - Events per variable

KW - Fine–Gray regression model

KW - Sample size

KW - Subdistribution hazard model

KW - Survival analysis

UR - http://www.scopus.com/inward/record.url?scp=85011556536&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85011556536&partnerID=8YFLogxK

U2 - 10.1016/j.jclinepi.2016.11.017

DO - 10.1016/j.jclinepi.2016.11.017

M3 - Article

VL - 83

SP - 75

EP - 84

JO - Journal of Clinical Epidemiology

T2 - Journal of Clinical Epidemiology

JF - Journal of Clinical Epidemiology

SN - 0895-4356

ER -