Biomaterial Database

Increasing efficiency from censored survival data by using random effects to model longitudinal covariates. 1998

J W Hogan, and N M Laird

Center for Statistical Sciences, Brown University, Providence, RI 02912, USA. jhogan@stat.brown.edu

When estimating a survival time distribution, the loss of information due to right censoring results in a loss of efficiency in the estimator. In many circumstances, however, repeated measurements on a longitudinal process which is associated with survival time are made throughout the observation time, and these measurements may be used to recover information lost to censoring. For example, patients in an AIDS clinical trial may be measured at regular intervals on CD4 count and viral load. We describe a model for the joint distribution of a survival time and a repeated measures process. The joint distribution is specified by linking the survival time to subject-specific random effects characterizing the repeated measures, and is similar in form to the pattern mixture model for multivariate data with nonignorable nonresponse. We also describe an estimator of survival derived from this model. We apply the methods to a long-term AIDS clinical trial, and study properties of the survival estimator. Monte Carlo simulation is used to estimate gains in efficiency when the survival time is related to the location and scale of the random effects distribution. Under relatively light censoring (20%), the methods yield a modest gain in efficiency for estimating three-year survival in the AIDS clinical trial. Our simulation study, which mimics characteristics of the clinical trial, indicates that much larger gains in efficiency can be realized under heavier censoring or with studies designed for long term follow up on survival.

UI	MeSH Term	Description	Entries
D008137	Longitudinal Studies	Studies in which variables relating to an individual or group of individuals are assessed over a period of time.	Bogalusa Heart Study,California Teachers Study,Framingham Heart Study,Jackson Heart Study,Longitudinal Survey,Tuskegee Syphilis Study,Bogalusa Heart Studies,California Teachers Studies,Framingham Heart Studies,Heart Studies, Bogalusa,Heart Studies, Framingham,Heart Studies, Jackson,Heart Study, Bogalusa,Heart Study, Framingham,Heart Study, Jackson,Jackson Heart Studies,Longitudinal Study,Longitudinal Surveys,Studies, Bogalusa Heart,Studies, California Teachers,Studies, Jackson Heart,Studies, Longitudinal,Study, Bogalusa Heart,Study, California Teachers,Study, Longitudinal,Survey, Longitudinal,Surveys, Longitudinal,Syphilis Studies, Tuskegee,Syphilis Study, Tuskegee,Teachers Studies, California,Teachers Study, California,Tuskegee Syphilis Studies
D002986	Clinical Trials as Topic	Works about pre-planned studies of the safety, efficacy, or optimum dosage schedule (if appropriate) of one or more diagnostic, therapeutic, or prophylactic drugs, devices, or techniques selected according to predetermined criteria of eligibility and observed for predefined evidence of favorable and unfavorable effects. This concept includes clinical trials conducted both in the U.S. and in other countries.	Clinical Trial as Topic
D003627	Data Interpretation, Statistical	Application of statistical procedures to analyze specific observed or assumed facts from a particular study.	Data Analysis, Statistical,Data Interpretations, Statistical,Interpretation, Statistical Data,Statistical Data Analysis,Statistical Data Interpretation,Analyses, Statistical Data,Analysis, Statistical Data,Data Analyses, Statistical,Interpretations, Statistical Data,Statistical Data Analyses,Statistical Data Interpretations
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000163	Acquired Immunodeficiency Syndrome	An acquired defect of cellular immunity associated with infection by the human immunodeficiency virus (HIV), a CD4-positive T-lymphocyte count under 200 cells/microliter or less than 14% of total lymphocytes, and increased susceptibility to opportunistic infections and malignant neoplasms. Clinical manifestations also include emaciation (wasting) and dementia. These elements reflect criteria for AIDS as defined by the CDC in 1993.	AIDS,Immunodeficiency Syndrome, Acquired,Immunologic Deficiency Syndrome, Acquired,Acquired Immune Deficiency Syndrome,Acquired Immuno-Deficiency Syndrome,Acquired Immuno Deficiency Syndrome,Acquired Immuno-Deficiency Syndromes,Acquired Immunodeficiency Syndromes,Immuno-Deficiency Syndrome, Acquired,Immuno-Deficiency Syndromes, Acquired,Immunodeficiency Syndromes, Acquired,Syndrome, Acquired Immuno-Deficiency,Syndrome, Acquired Immunodeficiency,Syndromes, Acquired Immuno-Deficiency,Syndromes, Acquired Immunodeficiency
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D000704	Analysis of Variance	A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable.	ANOVA,Analysis, Variance,Variance Analysis,Analyses, Variance,Variance Analyses
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D016013	Likelihood Functions	Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.	Likelihood Ratio Test,Maximum Likelihood Estimates,Estimate, Maximum Likelihood,Estimates, Maximum Likelihood,Function, Likelihood,Functions, Likelihood,Likelihood Function,Maximum Likelihood Estimate,Test, Likelihood Ratio
D016019	Survival Analysis	A class of statistical procedures for estimating the survival function (function of time, starting with a population 100% well at a given time and providing the percentage of the population still well at later times). The survival analysis is then used for making inferences about the effects of treatments, prognostic factors, exposures, and other covariates on the function.	Analysis, Survival,Analyses, Survival,Survival Analyses