MULCOX2: a general computer program for the Cox regression analysis of multivariate failure time data

Multivariate failure time data is commonly encountered in biomedicine, because each study subject may experience multiple events or because there exists clustering of subjects such that failure times within the same cluster are correlated. MULCOX2 implements a general statistical methodology for ana...

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Bibliographic Details
Published inComputer methods and programs in biomedicine Vol. 40; no. 4; pp. 279 - 293
Main Author Lin, D.Y.
Format Journal Article
LanguageEnglish
Published Shannon Elsevier Ireland Ltd 01.08.1993
Elsevier Science
Subjects
Age Factors
Biological and medical sciences
Censoring
Cluster Analysis
Clustering
Computerized, statistical medical data processing and models in biomedicine
Correlated survival times
Female
FORTRAN
Granulomatous Disease, Chronic - therapy
Humans
Interferon-gamma - therapeutic use
Male
Matched pair
Mathematical Computing
Medical computing and teaching
Medical sciences
Multiple endpoints
Multivariate Analysis
Proportional hazards
Proportional Hazards Models
Recurrence
Schizophrenia - genetics
Software
Survival Analysis
Recurrence
Survival analysis
Censoring
Multiple endpoints
Matched pair
FORTRAN
Proportional hazards
Clustering
Correlated survival times
Biomedical data processing
Computer program
Regression analysis
Multivariate analysis
Failure rate
Survival
Online AccessGet full text
ISSN0169-2607
1872-7565
DOI10.1016/0169-2607(93)90013-B

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Summary:Multivariate failure time data is commonly encountered in biomedicine, because each study subject may experience multiple events or because there exists clustering of subjects such that failure times within the same cluster are correlated. MULCOX2 implements a general statistical methodology for analyzing such data. This approach formulates the marginal distributions of multivariate failure times by Cox proportional hazards models without specifying the nature of dependence among related failure times. The baseline hazard functions for the marginal models may be identical or different. A variety of statistical inference can be made regarding the effects of (possibly time-dependent) covariates on the failure rates. Although designed primarily for the marginal approach, MULCOX2 is general enough to implement several alternative methods. The program runs on any computer with a FORTRAN compiler. The running time is minimal. Two illustrative examples are provided.
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ISSN:0169-2607
1872-7565
DOI:10.1016/0169-2607(93)90013-B