Two-sample survival probability curves: A graphical approach for the analysis of time to event data in clinical trials

Sandra Castro-Pearson; Chap T. Le; Xianghua Luo

doi:10.1016/j.cct.2022.106707

Two-sample survival probability curves: A graphical approach for the analysis of time to event data in clinical trials

Sandra Castro-Pearson, Chap T. Le, Xianghua Luo

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Abstract

With the aim to improve the communication of trial results, we introduce a novel graphical approach that complements the analysis of time to event outcomes in two-arm randomized trials. We define the so-called two-sample survival probability curve and propose a nonparametric estimator of the curve based on a random walk using Kaplan-Meier survival estimates for the two arms. We then use the estimated curve to visualize treatment effect as well as potential effect modification of factors of interest. We also propose to estimate two-sample survival probability curves within the framework of the Cox model to graphically assess model fit. The proposed two-sample survival probability plot puts trials in a standardized [0, 1] × [0, 1] space, allowing for a simple visualization of the main effect, effect modification, and the adequacy of a model fit.

Original language	English (US)
Article number	106707
Journal	Contemporary Clinical Trials
Volume	115
DOIs	https://doi.org/10.1016/j.cct.2022.106707
State	Published - Apr 2022

Bibliographical note

Funding Information:
This research is partially supported by the National Cancer Institute grants P01CA065493 , P01CA111412 , and P30CA077598 and the National Center for Advancing Translational Sciences of the National Institutes of Health Award UL1TR002494 (to Le and Luo), as well as the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number T32HL129956 . We would like to thank Drs. Jim Hodges, Anne Eaton, and Jing Ning for their encouragement and insightful discussion about this work.

Funding Information:
We thank the Blood and Marrow Transplant Clinical Trials Network for access to the data from the peripheral-blood stem-cells versus bone marrow transplantation from unrelated donors trial [ 24 ]. Data requests related to this trial can be submitted to www.bmtctn.net . The Blood and Marrow Transplant Clinical Trials Network is supported in part by grant U10HL069294 from the National Heart, Lung, and Blood Institute and the National Cancer Institute. The data from the 6-mercaptopurine (6-MP) versus placebo trial and the radiation and 5-Fluorouracil (RoRx+5-Fu) versus control trial are public and can be found in [ 4 , 25 ] respectively.

Publisher Copyright:
© 2022

Keywords

Failure times
Hazard ratios
Kaplan-Meier
ROC
Survival analysis
Time to event

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10.1016/j.cct.2022.106707

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title = "Two-sample survival probability curves: A graphical approach for the analysis of time to event data in clinical trials",

abstract = "With the aim to improve the communication of trial results, we introduce a novel graphical approach that complements the analysis of time to event outcomes in two-arm randomized trials. We define the so-called two-sample survival probability curve and propose a nonparametric estimator of the curve based on a random walk using Kaplan-Meier survival estimates for the two arms. We then use the estimated curve to visualize treatment effect as well as potential effect modification of factors of interest. We also propose to estimate two-sample survival probability curves within the framework of the Cox model to graphically assess model fit. The proposed two-sample survival probability plot puts trials in a standardized [0, 1] × [0, 1] space, allowing for a simple visualization of the main effect, effect modification, and the adequacy of a model fit.",

keywords = "Failure times, Hazard ratios, Kaplan-Meier, ROC, Survival analysis, Time to event",

author = "Sandra Castro-Pearson and Le, {Chap T.} and Xianghua Luo",

note = "Funding Information: This research is partially supported by the National Cancer Institute grants P01CA065493 , P01CA111412 , and P30CA077598 and the National Center for Advancing Translational Sciences of the National Institutes of Health Award UL1TR002494 (to Le and Luo), as well as the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number T32HL129956 . We would like to thank Drs. Jim Hodges, Anne Eaton, and Jing Ning for their encouragement and insightful discussion about this work. Funding Information: We thank the Blood and Marrow Transplant Clinical Trials Network for access to the data from the peripheral-blood stem-cells versus bone marrow transplantation from unrelated donors trial [ 24 ]. Data requests related to this trial can be submitted to www.bmtctn.net . The Blood and Marrow Transplant Clinical Trials Network is supported in part by grant U10HL069294 from the National Heart, Lung, and Blood Institute and the National Cancer Institute. The data from the 6-mercaptopurine (6-MP) versus placebo trial and the radiation and 5-Fluorouracil (RoRx+5-Fu) versus control trial are public and can be found in [ 4 , 25 ] respectively. Publisher Copyright: {\textcopyright} 2022",

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doi = "10.1016/j.cct.2022.106707",

language = "English (US)",

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N2 - With the aim to improve the communication of trial results, we introduce a novel graphical approach that complements the analysis of time to event outcomes in two-arm randomized trials. We define the so-called two-sample survival probability curve and propose a nonparametric estimator of the curve based on a random walk using Kaplan-Meier survival estimates for the two arms. We then use the estimated curve to visualize treatment effect as well as potential effect modification of factors of interest. We also propose to estimate two-sample survival probability curves within the framework of the Cox model to graphically assess model fit. The proposed two-sample survival probability plot puts trials in a standardized [0, 1] × [0, 1] space, allowing for a simple visualization of the main effect, effect modification, and the adequacy of a model fit.

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Two-sample survival probability curves: A graphical approach for the analysis of time to event data in clinical trials

Abstract

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