Factorial survival analysis for treatment effects under dependent censoring

Emura, Takeshi; Ditzhaus, Marc; Dobler, Dennis; Murotani, Kenta

doi:10.1177/09622802231215805

Cited by 3 publications

(2 citation statements)

References 75 publications

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“…For survival data, the follow-up period is often limitted. When a subject survives longer than the follow-up period, one may treat the survival time of the subject as equal to the follow-up period ( [5], [39], [40]). In this section, we assume that every subject has a common follow-up time τ.…”

Section: Computing P With Follow-up Timementioning

confidence: 99%

Computation of The Mann-Whitney Effect under Parametric Survival Copula Models

Nakazono,

Lin,

Liao

et al. 2024

Preprint

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The Mann-Whitney effect is a measure for comparing survival distributions between two groups. The Mann-Whitney effect is interpreted as the probability that a randomly selected subject in a group survives longer than a randomly selected subject in the other group. Under the independence assumption of two groups, the Mann-Whitney effect can be expressed as the traditional integral formula of survival functions. However, when the survival times in two groups are not independent each other, the traditional expression of the Mann-Whitney effect has to be modified. In this article, we propose a copula-based approach to compute the Mann-Witney effect with parametric survival models under dependence of two groups, which may arise in the potential outcome framework. In addition, we develop a Shiny web app that can implement the proposed method via simple commands (https://nkosuke.shinyapps.io/shiny_survival/). Through a simulation study, we show the correctness of the proposed calculator. We apply the proposed methods to two real datasets.

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Section: Computing P With Follow-up Timementioning

confidence: 99%

Computation of The Mann-Whitney Effect under Parametric Survival Copula Models

Nakazono,

Lin,

Liao

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…For survival data, the follow-up period is often limited. When a subject survives longer than the follow-up period, one may treat the survival time of the subject as equal to the follow-up period [6,43,44]. This means that we define the Mann-Whitney effect for min(T 1 , τ) and min(T 2 , τ).…”

Section: Computing P With Follow-up Timementioning

confidence: 99%

Computation of the Mann–Whitney Effect under Parametric Survival Copula Models

Nakazono,

Lin,

Liao

et al. 2024

Mathematics

Self Cite

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The Mann–Whitney effect is a measure for comparing survival distributions between two groups. The Mann–Whitney effect is interpreted as the probability that a randomly selected subject in a group survives longer than a randomly selected subject in the other group. Under the independence assumption of two groups, the Mann–Whitney effect can be expressed as the traditional integral formula of survival functions. However, when the survival times in two groups are not independent of each other, the traditional formula of the Mann–Whitney effect has to be modified. In this article, we propose a copula-based approach to compute the Mann–Whitney effect with parametric survival models under dependence of two groups, which may arise in the potential outcome framework. In addition, we develop a Shiny web app that can implement the proposed method via simple commands . Through a simulation study, we show the correctness of the proposed calculator. We apply the proposed methods to two real datasets.

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A nonparametric relative treatment effect for direct comparisons of censored paired survival outcomes

Dobler,

Möllenhoff

2024

Statistics in Medicine

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A frequently addressed issue in clinical trials is the comparison of censored paired survival outcomes, for example, when individuals were matched based on their characteristics prior to the analysis. In this regard, a proper incorporation of the dependence structure of the paired censored outcomes is required and, up to now, appropriate methods are only rarely available in the literature. Moreover, existing methods are not motivated by the strive for insights by means of an easy‐to‐interpret parameter. Hence, we seek to develop a new estimand‐driven method to compare the effectiveness of two treatments in the context of right‐censored survival data with matched pairs. With the help of competing risks techniques, the so‐called relative treatment effect is estimated. This estimand describes the probability that individuals under Treatment 1 have a longer lifetime than comparable individuals under Treatment 2. We derive hypothesis tests and confidence intervals based on a studentized version of the estimator, where resampling‐based inference is established by means of a randomization method. In a simulation study, we demonstrate for numerous sample sizes and different amounts of censoring that the developed test exhibits a good power. Finally, we apply the methodology to a well‐known benchmark data set from a trial with patients suffering from diabetic retinopathy.

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Factorial survival analysis for treatment effects under dependent censoring

Cited by 3 publications

References 75 publications

Computation of The Mann-Whitney Effect under Parametric Survival Copula Models

Computation of The Mann-Whitney Effect under Parametric Survival Copula Models

Computation of the Mann–Whitney Effect under Parametric Survival Copula Models

A nonparametric relative treatment effect for direct comparisons of censored paired survival outcomes

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