Analysis of Censored Survival Data with Intermittently Observed Time-Dependent Binary Covariates

Faucett, Cheryl L.; Schenker, Nathaniel; Elashoff, Robert M.

doi:10.1080/01621459.1998.10473692

Cited by 38 publications

(29 citation statements)

References 21 publications

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“…, n i denote the observed event times for subject i. The development of the MCMC procedure follows similar techniques as in Faucett et al (1998).…”

Section: Markov Chain Monte Carlomentioning

confidence: 99%

“…If the Bernoulli random variable is 1, we set the associated Z i (t) value to missing. The follow-up time C i is generated from a mixture of a degenerate random variable at 20 and an uniform random variable on [10,20] so that 50% of the subjects are expected to be lost to follow-up before the end of the study.…”

Section: Simulationsmentioning

confidence: 99%

“…Various authors have proposed models for characterizing the transition patterns from one state to the other via logistic regression (Zeger and Qaqish 1988;Cox and Snell 1989;Albert 2000). In the context of joint modeling, Faucett et al (1998) proposed a discretetime Markov model for the binary covariate process in the survival data setting, where the covariates are assumed constant in each partitioned interval and the conditional transition probability is modeled by a logistic model. By jointly modeling the covariate process and the event time process, the missing covariate value can be interpolated by borrowing information from the entire covariate process.…”

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confidence: 99%

“…By jointly modeling the covariate process and the event time process, the missing covariate value can be interpolated by borrowing information from the entire covariate process. In this paper, we follow Faucett et al (1998) to assume a discrete-Markov model for the binary covariate process, and investigate the joint modeling approach in the recurrent event setting.…”

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confidence: 99%

“…Using the Indian pharyngitis study as an example, ACCF carries forward the GAS status from both weekly event visits and monthly carriage visits, while CCCF carries forward the GAS status from monthly carriage visits only. Research has shown that the ACCF approach or CCCF approach leads to biased inferential results of the covariate effects in the survival setting (Prentice 1982;Faucett et al 1998). In this manuscript, we investigate the bias induced by the ACCF method or the CCCF method in recurrent event data analysis, and compare it to the proposed joint modeling approach.…”

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confidence: 99%

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Joint modeling of recurrent event processes and intermittently observed time-varying binary covariate processes

2015

Lifetime Data Anal

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When conducting recurrent event data analysis, it is common to assume that the covariate processes are observed throughout the follow-up period. In most applications, however, the values of time-varying covariates are only observed periodically rather than continuously. A popular ad-hoc approach is to carry forward the last observed covariate value until it is measured again. This simple approach, however, usually leads to biased estimation. To tackle this problem, we propose to model the covariate effect on the risk of the recurrent events through jointly modeling the recurrent event process and the longitudinal measures. Despite its popularity, estimation of the joint model with binary longitudinal measurements remains a challenge, because the standard linear mixed effects model approach is not appropriate for binary measures. In this paper, we postulate a Markov model for the binary covariate process and a random-effect proportional intensity model for the recurrent event process. We use a Markov chain Monte Carlo algorithm to estimate all the unknown parameters. The performance of the proposed estimator is evaluated via simulations. The methodology is applied to an observational study designed to evaluate the effect of Group A streptococcus (GAS) on pharyngitis among school children in India.

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“…, n i denote the observed event times for subject i. The development of the MCMC procedure follows similar techniques as in Faucett et al (1998).…”

Section: Markov Chain Monte Carlomentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Joint modeling of recurrent event processes and intermittently observed time-varying binary covariate processes

2015

Lifetime Data Anal

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Joint Modeling of Longitudinal and Event Time Data

Henderson

2005

Encyclopedia of Biostatistics

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The relationship between the development over time of a quantitative response variable and a parallel process recording events in time can be investigated through a joint model for the two outcome types. Interest in this topic arose from two separate backgrounds: the possibility and consequences of informative dropout in longitudinal trials, and measurement error problems for survival studies with time‐varying covariates. Modeling strategies include combining a marginal distribution for one response type with a conditional distribution for the other, or adopting a correlated random effects model with otherwise conditional independence. Naive methods can lead to significant bias, yet more sophisticated modeling approaches are often sensitive to assumptions, some of which cannot be checked from observable data. When simple summaries such as treatment effect estimators are of interest, sensitivity analyses can bring some reassurance as to whether important conclusions are maintained as assumptions are allowed to change. Overall, the topic is relatively new and research is continuing in many directions.

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Joint modelling of longitudinal and event time data

Henderson

2014

Wiley StatsRef: Statistics Reference Online

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Analysis of Censored Survival Data with Intermittently Observed Time-Dependent Binary Covariates

Cited by 38 publications

References 21 publications

Joint modeling of recurrent event processes and intermittently observed time-varying binary covariate processes

Joint modeling of recurrent event processes and intermittently observed time-varying binary covariate processes

Joint Modeling of Longitudinal and Event Time Data

Joint modelling of longitudinal and event time data

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