### Abstract

To compare the survival functions based on right-truncated data, Lagakos et al. proposed a weighted logrank test based on a reverse time scale. This is in contrast to Bilker and Wang, who suggested a semi-parametric version of the Mann-Whitney test by assuming that the distribution of truncation times is known or can be estimated parametrically. The approach of Lagakos et al. is simple and elegant, but the weight function in their method depends on the underlying cumulative hazard functions even under proportional hazards models. On the other hand, a semi-parametric test may have better efficiency, but it may be sensitive to misspecification of the distribution of truncation times. Therefore, this paper proposes a non-parametric test statistic based on the integrated weighted difference between two estimated survival functions in forward time. The comparative results from a simulation study are presented and the implementation of these methods to a real data set is demonstrated.

Original language | English |
---|---|

Pages (from-to) | 812-827 |

Number of pages | 16 |

Journal | Statistics in Medicine |

Volume | 26 |

Issue number | 4 |

DOIs | |

Publication status | Published - 2007 Feb 20 |

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### All Science Journal Classification (ASJC) codes

- Epidemiology
- Statistics and Probability

### Cite this

*Statistics in Medicine*,

*26*(4), 812-827. https://doi.org/10.1002/sim.2556

}

*Statistics in Medicine*, vol. 26, no. 4, pp. 812-827. https://doi.org/10.1002/sim.2556

**Testing the equality of two survival functions with right truncated data.** / Chi, Yun-Chan; Tsai, Wei Yann; Chiang, Chia Ling.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Testing the equality of two survival functions with right truncated data

AU - Chi, Yun-Chan

AU - Tsai, Wei Yann

AU - Chiang, Chia Ling

PY - 2007/2/20

Y1 - 2007/2/20

N2 - To compare the survival functions based on right-truncated data, Lagakos et al. proposed a weighted logrank test based on a reverse time scale. This is in contrast to Bilker and Wang, who suggested a semi-parametric version of the Mann-Whitney test by assuming that the distribution of truncation times is known or can be estimated parametrically. The approach of Lagakos et al. is simple and elegant, but the weight function in their method depends on the underlying cumulative hazard functions even under proportional hazards models. On the other hand, a semi-parametric test may have better efficiency, but it may be sensitive to misspecification of the distribution of truncation times. Therefore, this paper proposes a non-parametric test statistic based on the integrated weighted difference between two estimated survival functions in forward time. The comparative results from a simulation study are presented and the implementation of these methods to a real data set is demonstrated.

AB - To compare the survival functions based on right-truncated data, Lagakos et al. proposed a weighted logrank test based on a reverse time scale. This is in contrast to Bilker and Wang, who suggested a semi-parametric version of the Mann-Whitney test by assuming that the distribution of truncation times is known or can be estimated parametrically. The approach of Lagakos et al. is simple and elegant, but the weight function in their method depends on the underlying cumulative hazard functions even under proportional hazards models. On the other hand, a semi-parametric test may have better efficiency, but it may be sensitive to misspecification of the distribution of truncation times. Therefore, this paper proposes a non-parametric test statistic based on the integrated weighted difference between two estimated survival functions in forward time. The comparative results from a simulation study are presented and the implementation of these methods to a real data set is demonstrated.

UR - http://www.scopus.com/inward/record.url?scp=33846828540&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33846828540&partnerID=8YFLogxK

U2 - 10.1002/sim.2556

DO - 10.1002/sim.2556

M3 - Article

C2 - 16708350

AN - SCOPUS:33846828540

VL - 26

SP - 812

EP - 827

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 4

ER -