Choice of conditional models in bivariate survival

Henderson, Robin; Prince, Helen

doi:10.1002/(sici)1097-0258(20000229)19:4<563::aid-sim356>3.0.co;2-k

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…Conditional survival represented by—CS(A|B)—is the probability that an individual will survive B years, given the condition that the patient has survived A years. 14 Conditional survival is represented by:…”

Section: Methodsmentioning

confidence: 99%

“…Conditional survival represented by-CS(A|B)-is the probability that an individual will survive B years, given the condition that the patient has survived A years. 14 Conditional survival is represented by: The content is the responsibility of the authors alone and does not necessarily reflect the view or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.…”

Section: Conditional Survivalmentioning

confidence: 99%

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Conditional Survival in Lung Transplantation: An Organ Procurement and Transplantation Network Database Analysis

et al. 2023

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Lung transplantation survival estimates are traditionally reported as fixed 1-, 5-, and 10-year mortality rates. Alternatively, this study aims to demonstrate how conditional survival models can provide useful prognostic information tailored to the time a recipient has already survived from the date of transplantation. Recipient data was obtained from the Organ Procurement and Transplantation Network database. Data from 24,820 adult recipients over age 18 who received a lung transplant between 2002 and 2017 were included in the study. Five-year observed conditional survival estimates were calculated by recipient age, sex, race, transplant indication, transplant type (i.e., single or double), and renal function at the time of transplantation. Significant variability exists in conditional survival following lung transplantation. Each specific recipient characteristic significantly impacted conditional survival during at least one time point in the first 5 years. Younger age and double lung transplantation were the two most positive predictors of improved conditional survival consistently throughout the 5-year study period. Conditional survival in lung transplantation recipients changes over time and across recipient characteristics. Hazards of mortality are not fixed and need to be dynamically evaluated as a function of time. Conditional survival calculations can provide more accurate prognostic predictions than unconditional survival estimates.

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Section: Methodsmentioning

confidence: 99%

Section: Conditional Survivalmentioning

confidence: 99%

Conditional Survival in Lung Transplantation: An Organ Procurement and Transplantation Network Database Analysis

et al. 2023

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“…Henderson and Prince [6] discuss the form that bivariate survival functions can take for different correlations between log(t 1 ) and log(t 2 ). The bivariate log-normal model can be written in terms of the parameters 1 , 2 , 1 , 2 and , where 1 and 2 are the expected values of log(T 1 ) and log(T 2 ) respectively, 1 , 2 are the corresponding marginal standard deviations and is their correlation.…”

Section: Bivariate Log-normal Models For Randomized Trialsmentioning

confidence: 99%

“…However, if we turn to parametric survival analysis, the bivariate log-normal distribution is an obvious choice when t 1 and t 2 are expected to be negatively correlated. Evidence suggests that this distribution models negative correlations more satisfactorily than other parametric bivariate distributions [6]. The likelihood approach we outline here could be implemented with other distributions.…”

Section: Introductionmentioning

confidence: 99%