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- Frailty (2)
- Recurrent events (2)
- Alternating renewal process (1)
- Bivariate survival data; Copula model; Interval sampling; Semi-stationarity. (1)
- Bivariate survival distributions; Copula; Interval sampling; Nonparametric and semiparametric; Stationarity and semi-stationarity. (1)
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- Bootstrp (1)
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- Brain tumors; Causal inference; Censoring by death; Estimating equations (1)
- Cause-specific; Kendall's tau; Multivariate; Paired; Survival; U-statistic (1)
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- Dependent censoring; Frailty; Poisson process; Rate function; Recurrent events (1)
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Articles 1 - 10 of 10
Full-Text Articles in Survival Analysis
Cox Regression Models With Functional Covariates For Survival Data, Jonathan E. Gellar, Elizabeth Colantuoni, Dale M. Needham, Ciprian M. Crainiceanu
Cox Regression Models With Functional Covariates For Survival Data, Jonathan E. Gellar, Elizabeth Colantuoni, Dale M. Needham, Ciprian M. Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally-spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge …
Assessing Association For Bivariate Survival Data With Interval Sampling: A Copula Model Approach With Application To Aids Study, Hong Zhu, Mei-Cheng Wang
Assessing Association For Bivariate Survival Data With Interval Sampling: A Copula Model Approach With Application To Aids Study, Hong Zhu, Mei-Cheng Wang
Johns Hopkins University, Dept. of Biostatistics Working Papers
In disease surveillance systems or registries, bivariate survival data are typically collected under interval sampling. It refers to a situation when entry into a registry is at the time of the first failure event (e.g., HIV infection) within a calendar time interval, the time of the initiating event (e.g., birth) is retrospectively identified for all the cases in the registry, and subsequently the second failure event (e.g., death) is observed during the follow-up. Sampling bias is induced due to the selection process that the data are collected conditioning on the first failure event occurs within a time interval. Consequently, the …
Analyzing Bivariate Survival Data With Interval Sampling And Application To Cancer Epidemiology, Hong Zhu, Mei-Cheng Wang
Analyzing Bivariate Survival Data With Interval Sampling And Application To Cancer Epidemiology, Hong Zhu, Mei-Cheng Wang
Johns Hopkins University, Dept. of Biostatistics Working Papers
In medical follow-up studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as the outcomes to identify the progression of a disease. In cancer studies interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme where the first failure event (cancer onset) is identified within a calendar time interval, the time of the initiating event (birth) can be retrospectively confirmed, and the occurrence of the second event (death) is observed sub ject to right censoring. To …
Semiparametric Bivariate Quantile-Quantile Regression For Analyzing Semi-Competing Risks Data, Daniel O. Scharfstein, James M. Robins, Mark Van Der Laan
Semiparametric Bivariate Quantile-Quantile Regression For Analyzing Semi-Competing Risks Data, Daniel O. Scharfstein, James M. Robins, Mark Van Der Laan
Johns Hopkins University, Dept. of Biostatistics Working Papers
In this paper, we consider estimation of the effect of a randomized treatment on time to disease progression and death, possibly adjusting for high-dimensional baseline prognostic factors. We assume that patients may or may not have a specific type of disease progression prior to death and those who have this endpoint are followed for their survival information. Progression and survival may also be censored due to loss to follow-up or study termination. We posit a semi-parametric bivariate quantile-quantile regression failure time model and show how to construct estimators of the regression parameters. The causal interpretation of the parameters depends on …
Recurrent Event Models In The Presence Of A Terminal Event: Comparison, Inference And Data Analysis, Xianghua Luo, Mei-Cheng Wang
Recurrent Event Models In The Presence Of A Terminal Event: Comparison, Inference And Data Analysis, Xianghua Luo, Mei-Cheng Wang
Johns Hopkins University, Dept. of Biostatistics Working Papers
This article focuses on statistical implications of proportional rate models for recurrent event data in the presence of a terminal event. In such circumstances, various definitions of the recurrent rate function have been adopted in the proportional rate models. Although these rate functions have quite different interpretations, recognition of the differences has been lacking theoretically and practically. We compare three types of rate functions from both conceptual and quantitative perspectives; conclude that the inappropriate choice of a rate function may lead to misleading scientific conclusions. Simulations are conducted for comparisons of the focused models. Analysis of data from an AIDS …
Nonparametric Estimation Of Bivariate Failure Time Associations In The Presence Of A Competing Risk, Karen Bandeen-Roche, Jing Ning
Nonparametric Estimation Of Bivariate Failure Time Associations In The Presence Of A Competing Risk, Karen Bandeen-Roche, Jing Ning
Johns Hopkins University, Dept. of Biostatistics Working Papers
There has been much research on the study of associations among paired failure times. Most has either assumed time invariance of association or been based on complex measures or estimators. Little has accommodated failures arising amid competing risks. This paper targets the conditional cause specific hazard ratio, a recent modification of the conditional hazard ratio to accommodate competing risks data. Estimation is accomplished by an intuitive, nonparametric method that localizes Kendall’s tau. Time variance is accommodated through a partitioning of space into “bins” between which the strength of association may differ. Inferential procedures are researched, small sample performance evaluated, and …
Analyzing Panel Count Data With Informative Observation Times, Chiung-Yu Huang, Mei-Cheng Wang, Ying Zhang
Analyzing Panel Count Data With Informative Observation Times, Chiung-Yu Huang, Mei-Cheng Wang, Ying Zhang
Johns Hopkins University, Dept. of Biostatistics Working Papers
In this paper, we study panel count data with informative observation times. We assume nonparametric and semiparametric proportional rate models for the underlying recurrent event process, where the form of the baseline rate function is left unspecified and a subject-specific frailty variable inflates or deflates the rate function multiplicatively. The proposed models allow the recurrent event processes and observation times to be correlated through their connections with the unobserved frailty; moreover, the distributions of both the frailty variable and observation times are considered as nuisance parameters. The baseline rate function and the regression parameters are estimated by maximizing a conditional …
Kernel Estimation Of Rate Function For Recurrent Event Data, Chin-Tsang Chiang, Mei-Cheng Wang, Chiung-Yu Huang
Kernel Estimation Of Rate Function For Recurrent Event Data, Chin-Tsang Chiang, Mei-Cheng Wang, Chiung-Yu Huang
Johns Hopkins University, Dept. of Biostatistics Working Papers
Recurrent event data are largely characterized by the rate function but smoothing techniques for estimating the rate function have never been rigorously developed or studied in statistical literature. This paper considers the moment and least squares methods for estimating the rate function from recurrent event data. With an independent censoring assumption on the recurrent event process, we study statistical properties of the proposed estimators and propose bootstrap procedures for the bandwidth selection and for the approximation of confidence intervals in the estimation of the occurrence rate function. It is identified that the moment method without resmoothing via a smaller bandwidth …
Joint Modeling And Estimation For Recurrent Event Processes And Failure Time Data, Chiung-Yu Huang, Mei-Cheng Wang
Joint Modeling And Estimation For Recurrent Event Processes And Failure Time Data, Chiung-Yu Huang, Mei-Cheng Wang
Johns Hopkins University, Dept. of Biostatistics Working Papers
Recurrent event data are commonly encountered in longitudinal follow-up studies related to biomedical science, econometrics, reliability, and demography. In many studies, recurrent events serve as important measurements for evaluating disease progression, health deterioration, or insurance risk. When analyzing recurrent event data, an independent censoring condition is typically required for the construction of statistical methods. Nevertheless, in some situations, the terminating time for observing recurrent events could be correlated with the recurrent event process and, as a result, the assumption of independent censoring is violated. In this paper, we consider joint modeling of a recurrent event process and a failure time …
Nonparametric Estimation Of The Bivariate Recurrence Time Distribution, Chiung-Yu Huang, Mei-Cheng Wang
Nonparametric Estimation Of The Bivariate Recurrence Time Distribution, Chiung-Yu Huang, Mei-Cheng Wang
Johns Hopkins University, Dept. of Biostatistics Working Papers
This paper considers statistical models in which two different types of events, such as the diagnosis of a disease and the remission of the disease, occur alternately over time and are observed subject to right censoring. We propose nonparametric estimators for the joint distribution of bivariate recurrence times and the marginal distribution of the first recurrence time. In general, the marginal distribution of the second recurrence time cannot be estimated due to an identifiability problem, but a conditional distribution of the second recurrence time can be estimated non-parametrically. In literature, statistical methods have been developed to estimate the joint distribution …