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|a 10.1080/02664763.2022.2145272
|2 doi
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|a eng
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| 100 |
1 |
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|a Zhong, Zihang
|e verfasserin
|4 aut
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| 245 |
1 |
4 |
|a The heterogeneity effect of surveillance intervals on progression free survival
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a Date Revised 29.02.2024
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © 2022 Informa UK Limited, trading as Taylor & Francis Group.
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|a Progression-free survival (PFS) is an increasingly important surrogate endpoint in cancer clinical trials. However, the true time of progression is typically unknown if the evaluation of progression status is only scheduled at given surveillance intervals. In addition, comparison between treatment arms under different surveillance schema is not uncommon. Our aim is to explore whether the heterogeneity of the surveillance intervals may interfere with the validity of the conclusion of efficacy based on PFS, and the extent to which the variation would bias the results. We conduct comprehensive simulation studies to explore the aforementioned goals in a two-arm randomized control trial. We introduce three steps to simulate survival data with predefined surveillance intervals under different censoring rate considerations. We report the estimated hazard ratios and examine false positive rate, power and bias under different surveillance intervals, given different baseline median PFS, hazard ratio and censoring rate settings. Results show that larger heterogeneous lengths of surveillance intervals lead to higher false positive rate and overestimate the power, and the effect of the heterogeneous surveillance intervals may depend upon both the life expectancy of the tumor prognoses and the censoring proportion of the survival data. We also demonstrate such heterogeneity effect of surveillance intervals on PFS in a phase III metastatic colorectal cancer trial. In our opinions, adherence to consistent surveillance intervals should be favored in designing the comparative trials. Otherwise, it needs to be appropriately taken into account when analyzing data
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|a Journal Article
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|a Progression-free survival
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| 650 |
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4 |
|a cancer clinical trial
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| 650 |
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4 |
|a false positive rate
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| 650 |
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4 |
|a power
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| 650 |
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4 |
|a surveillance interval
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| 700 |
1 |
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|a Yang, Min
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ni, Senmiao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cai, Lixin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wu, Jingwei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Bai, Jianling
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yu, Hao
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Journal of applied statistics
|d 1991
|g 51(2024), 4 vom: 01., Seite 646-663
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|x 0266-4763
|7 nnas
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| 773 |
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|g volume:51
|g year:2024
|g number:4
|g day:01
|g pages:646-663
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|u http://dx.doi.org/10.1080/02664763.2022.2145272
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