Copula link-based additive models for bivariate time-to-event outcomes with general censoring scheme

Petti, Danilo; Eletti, Alessia; Marra, Giampiero; Radice, Rosalba

doi:10.1016/j.csda.2022.107550

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…A bivariate copula is a bivariate distribution function for two uniformly distributed variables on [0, 1] [24,25]. In many applications of copulas on survival analysis, bivariate survival functions are modeled via survival copulas [20,24,26,27]. Below, we introduce a survival copula model for a bivariate survival function…”

Section: Survival Copula Models For Dependent Survival Timementioning

confidence: 99%

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

Nakazono,

Lin,

Liao

et al. 2024

Mathematics

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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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Section: Survival Copula Models For Dependent Survival Timementioning

confidence: 99%

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

Nakazono,

Lin,

Liao

et al. 2024

Mathematics

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“…Several works use copula to model bivariate interval-censored data. 23–25 However, these earlier works could not deal with interval-censored semi-competing risks data. Ma et al 26 develop a copula model for dependent current status data where the non-terminal event is only examined once at either the terminal event time or the study stopping time.…”

Section: Introductionmentioning

confidence: 99%

Semiparametric copula method for semi-competing risks data subject to interval censoring and left truncation: Application to disability in elderly

Sun

Zheng-yan

et al. 2023

Stat Methods Med Res

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We aim to evaluate the marginal effects of covariates on time-to-disability in the elderly under the semi-competing risks framework, as death dependently censors disability, not vice versa. It becomes particularly challenging when time-to-disability is subject to interval censoring due to intermittent assessments. A left truncation issue arises when the age time scale is applied. We develop a flexible two-parameter copula-based semiparametric transformation model for semi-competing risks data subject to interval censoring and left truncation. The two-parameter copula quantifies both upper and lower tail dependence between two margins. The semiparametric transformation models incorporate proportional hazards and proportional odds models in both margins. We propose a two-step sieve maximum likelihood estimation procedure and study the sieve estimators’ asymptotic properties. Simulations show that the proposed method corrects biases in the marginal method. We demonstrate the proposed method in a large-scale Chinese Longitudinal Healthy Longevity Study and provide new insights into preventing disability in the elderly. The proposed method could be applied to the general semi-competing risks data with intermittently assessed disease status.

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“…As a result, the association parameter for the copula model is not easy to estimate, and this parameter estimation is a vital step when using copulas in survival analysis. Another application of the copula to the survival model was proposed by Petti et al [24] to link various time-to-event outcomes that are measured on the same individuals.…”

Section: Introductionmentioning

confidence: 99%

Survival and Duration Analysis of MSMEs in Chiang Mai, Thailand: Evidence from the Post-COVID-19 Recovery

et al. 2023

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This study attempts to reveal the consequences of coronavirus disease 2019 (COVID-19) on micro, small, and medium enterprises (MSMEs) in Chiang Mai, Thailand. A total of 786 MSMEs were surveyed during May and August 2022, corresponding to the period when the recovery of businesses and livelihoods from the ongoing COVID-19 crisis became more perceptible. The perceptions of COVID-19’s impact on MSMEs and their survivability are explored and investigated. To achieve this goal, a copula-based sample selection survival model is introduced. This idea of the model is extended from the concept of the Cox proportional hazards model and copula-based sample selection model, enabling us to construct simultaneous equations—namely, the probability-of-failure equation (selection equation) and the duration-of-survival equation (time-to-event or outcome equation). Several copula functions with different dependence patterns are considered to join the failure equation and the duration-of-survival equation. By comparing the Akaike and Bayesian information criteria values of the candidate copulas, we find that Farlie–Gumbel–Morgenstern (FGM) copula performs the best-fit joint function in our analysis. Empirically, the results from this best-fit model reveal that the survival probability of MSMEs in the next year is around 80%. However, some MSMEs may not survive more than three months after the interview. Finally, our results also reveal that the tourism MSMEs have a lower chance of survival than the commercial and manufacturing MSMEs. Notably, the business size and the support schemes from the government—such as the debt restructuring process, the tax payment deadline extension, and the reduced social security contributions—exhibited a role in lengthening the survival duration of the non-surviving MSMEs.

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Copula link-based additive models for bivariate time-to-event outcomes with general censoring scheme

Cited by 5 publications

References 33 publications

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

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

Semiparametric copula method for semi-competing risks data subject to interval censoring and left truncation: Application to disability in elderly

Survival and Duration Analysis of MSMEs in Chiang Mai, Thailand: Evidence from the Post-COVID-19 Recovery

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