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The Statistical Analysis of Multivariate Failure Time Data A Marginal Modeling Approach Chapman HallCRC Monographs on Statistics and Applied Probability

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The Statistical Analysis Of Multivariate Failure Time Data ~ The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of regression information.

The statistical analysis of multivariate failure time data ~ The statistical analysis of multivariate failure time data: A marginal modeling approach. Edited by Ross L.Prentice and ShanshanZhao (2019). New York, NY: Chapman and Hall/CRC Press. 240 pages.

The Statistical Analysis of Multivariate Failure Time Data ~ The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of regression information. For example, in a context of randomized trial or cohort studies, the results go .

Statistical analysis of multivariate failure time data ~ Multivariate failure time data arise when data consist of clusters in which the failure times may be dependent. A popular approach to such data is the marginal proportional hazards model with .

ANALYSIS OF MULTIVARIATE FAILURE TIME DATA USING MARGINAL ~ analysis of multivariate failure time data in the presence of censoring. We propose a method of estimation via the linear combinations of martingale residuals. The estimation and inference procedures are easy to implement numerically. The es-timation is generally more accurate than the existing pseudo-likelihood approach:

A marginal regression model for multivariate failure time ~ A marginal regression approach for correlated censored survival data has become a widely used statistical method. Examples of this approach in survival analysis include from the early work by Wei et al. (J Am Stat Assoc 84:1065–1073, 1989) to more recent work by Spiekerman and Lin (J Am Stat Assoc 93:1164–1175, 1998). This approach is particularly useful if a covariate’s population .

Marginal analysis of multivariate failure time data with a ~ Marginal analysis of multivariate failure time data with a surviving fraction based on semiparametric transformation cure models . applied a Bayesian approach to analyze the same dataset based on a mixture model consisting of a . L. WeissfeldRegression analysis of multivariate incomplete failure time data by modeling marginal distributions .

Regression Analysis of Multivariate Incomplete Failure ~ (1989). Regression Analysis of Multivariate Incomplete Failure Time Data by Modeling Marginal Distributions. Journal of the American Statistical Association: Vol. 84, No. 408, pp. 1065-1073.

Chapman & Hall/CRC Monographs on Statistics and Applied ~ The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach 1st Edition. Ross L. Prentice, Shanshan Zhao May 16, 2019. The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times.

Rank Regression Analysis of Multivariate Failure Time Data ~ failure time on covariates. Rank estimation of the accelerated failure time model has been studied by Prentice (1978), Tsiatis (1990), Wei et al. (1990) and Lai & Ying (1991) among others for univariate failure time data, and by Lin & Wei (1992), Lee et al. (1993) and Lin et al. (1998) for multivariate failure time data.

Cox regression analysis of multivariate failure time data ~ Chris Metcalfe, Simon G. Thompson, Wei, Lin and Weissfeld's marginal analysis of multivariate failure time data, Statistical Methods in Medical Research, 10.1177/0962280206071926, 16, 2, (103-122), (2016).

ANALYSIS OF MULTIVARIATE FAILURE TIME DATA USING MARGINAL ~ Multivariate failure time data are common in biomedical studies, engineering, and financial economics. A key feature of this type of data is that the failure times may be related to each other. To analyze the dependence of the failure times on certain covariates, Wei, Lin and Weissfeld (1989) proposed to use a marginal proportional hazards (MPH .

The Statistical Analysis of Multivariate Failure Time Data ~ The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach (Chapman & Hall/CRC Monographs on Statistics and Applied Probability) [Prentice, Ross L., Zhao, Shanshan] on . *FREE* shipping on qualifying offers. The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach (Chapman & Hall/CRC Monographs on Statistics and Applied .

Marginal Regression Models for Multivariate Failure Time Data ~ (1998). Marginal Regression Models for Multivariate Failure Time Data. Journal of the American Statistical Association: Vol. 93, No. 443, pp. 1164-1175.

Regression Analysis of Multivariate Incomplete Failure ~ Regression Analysis of Multivariate Incomplete Failure Time Data by Modeling Marginal Distributions L. J. WEI, D. Y. LIN, and L. WEISSFELD* Many survival studies record the times to two or more distinct failures on each subject. The failures may be events of different natures or may be repetitions of the same kind of event.

STATISTICAL ANALYSIS OF MULTIVARIATE INTERVAL-CENSORED ~ Additional problems arise in the analysis of multivariate failure times. They include estimating the correlation between failure times. A typical example of correlated or multivariate failure time data is given by a twin study and Duﬁy et al. (1990) described such a study comparing monozygotic and dizygotic twins with respect to the strength

Marginal Regression Models for Multivariate Failure Time Data ~ Abstract In this article we propose a general Cox-type regression model to formulate the marginal distributions of multivariate failure time data. This model has a nested structure in that it allows different baseline hazard functions among distinct failure types and imposes a common baseline hazard function on the failure times of the same type.

Stata / FAQ: Analysis of multiple failure-time survival data ~ This FAQ first appeared as an article in STB-49, ssa13, under the heading Analysis of multiple failure-time data with Stata.Minor revisions have subsequently been made. In this article, when a subject experiences one of the events, it still remains at risk for events of different types.

Joint Modeling of Longitudinal and Time-to-Event Data ~ Joint Modeling of Longitudinal and Time-to-Event Data (Chapman & Hall/CRC Monographs on Statistics and Applied Probability) - Kindle edition by Elashoff, Robert, li, Gang, Li, Ning. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Joint Modeling of Longitudinal and Time-to-Event Data (Chapman .

Multivariate statistics - Wikipedia ~ Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable. The application of multivariate statistics is multivariate analysis.. Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other.