### Abstract

When designing a cancer clinical trial, it is usual to assume an exponential distribution for a time-to-event outcome such as overall survival (OS). OS is often expressed as the sum of progression-free survival (PFS) and survival post-progression (SPP), each of which is assumed to be exponentially distributed. Then, OS does not follow an exponential distribution any more but a gamma or hypo-exponential distribution. In this study, we derived a sample size calculation formula for comparing OS between two treatment arms using the log-rank test for OS following a gamma or hypo-exponential distribution. We conducted a simulation study to evaluate the sample size and power calculation based on the gamma or hypo-exponential distribution. We found that we could reduce the sample sizes considerably compared to when assuming an exponential distribution for OS.

Original language | English |
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Pages (from-to) | 90-91 |

Number of pages | 2 |

Journal | Contemporary Clinical Trials Communications |

Volume | 12 |

DOIs | |

Publication status | Published - 2018 Dec |

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### All Science Journal Classification (ASJC) codes

- Pharmacology

### Cite this

*Contemporary Clinical Trials Communications*,

*12*, 90-91. https://doi.org/10.1016/j.conctc.2018.09.007

}

*Contemporary Clinical Trials Communications*, vol. 12, pp. 90-91. https://doi.org/10.1016/j.conctc.2018.09.007

**Reconsideration of sample size and power calculation for overall survival in cancer clinical trials.** / Jung, Inkyung; Ko, Hee Jung; Rha, Sun Young; Nam, Chung Mo.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Reconsideration of sample size and power calculation for overall survival in cancer clinical trials

AU - Jung, Inkyung

AU - Ko, Hee Jung

AU - Rha, Sun Young

AU - Nam, Chung Mo

PY - 2018/12

Y1 - 2018/12

N2 - When designing a cancer clinical trial, it is usual to assume an exponential distribution for a time-to-event outcome such as overall survival (OS). OS is often expressed as the sum of progression-free survival (PFS) and survival post-progression (SPP), each of which is assumed to be exponentially distributed. Then, OS does not follow an exponential distribution any more but a gamma or hypo-exponential distribution. In this study, we derived a sample size calculation formula for comparing OS between two treatment arms using the log-rank test for OS following a gamma or hypo-exponential distribution. We conducted a simulation study to evaluate the sample size and power calculation based on the gamma or hypo-exponential distribution. We found that we could reduce the sample sizes considerably compared to when assuming an exponential distribution for OS.

AB - When designing a cancer clinical trial, it is usual to assume an exponential distribution for a time-to-event outcome such as overall survival (OS). OS is often expressed as the sum of progression-free survival (PFS) and survival post-progression (SPP), each of which is assumed to be exponentially distributed. Then, OS does not follow an exponential distribution any more but a gamma or hypo-exponential distribution. In this study, we derived a sample size calculation formula for comparing OS between two treatment arms using the log-rank test for OS following a gamma or hypo-exponential distribution. We conducted a simulation study to evaluate the sample size and power calculation based on the gamma or hypo-exponential distribution. We found that we could reduce the sample sizes considerably compared to when assuming an exponential distribution for OS.

UR - http://www.scopus.com/inward/record.url?scp=85054253723&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85054253723&partnerID=8YFLogxK

U2 - 10.1016/j.conctc.2018.09.007

DO - 10.1016/j.conctc.2018.09.007

M3 - Article

AN - SCOPUS:85054253723

VL - 12

SP - 90

EP - 91

JO - Contemporary Clinical Trials Communications

JF - Contemporary Clinical Trials Communications

SN - 2451-8654

ER -