Biomaterial Database

Models for empirical Bayes estimators of longitudinal CD4 counts. 1996

M P LaValley, and V DeGruttola

Department of Epidemiology and Biostatistics, Boston University 02118, USA.

In this paper we consider the choice of model used in estimation of trajectories of CD4 T-cell counts by empirical Bayes estimators. Tsiatis et al. have demonstrated that empirical Bayes estimates of CD4 values correct for the bias resulting from measurement error when using CD4 as a covariate in a Cox model to predict clinical events. Here, empirical Bayes estimates from a random effects model are compared to estimates from the more general stochastic regression model presented in Taylor et al. Empirical Bayes estimators based on the two models are judged according to their ability to provide parameter estimates in a Cox model predicting clinical outcomes. Data from ACTG 118 are used as an illustration.

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
D012044	Regression Analysis	Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.	Regression Diagnostics,Statistical Regression,Analysis, Regression,Analyses, Regression,Diagnostics, Regression,Regression Analyses,Regression, Statistical,Regressions, Statistical,Statistical Regressions
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
D001499	Bayes Theorem	A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.	Bayesian Analysis,Bayesian Estimation,Bayesian Forecast,Bayesian Method,Bayesian Prediction,Analysis, Bayesian,Bayesian Approach,Approach, Bayesian,Approachs, Bayesian,Bayesian Approachs,Estimation, Bayesian,Forecast, Bayesian,Method, Bayesian,Prediction, Bayesian,Theorem, Bayes
D013269	Stochastic Processes	Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables.	Process, Stochastic,Stochastic Process,Processes, Stochastic
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
D016014	Linear Models	Statistical models in which the value of a parameter for a given value of a factor is assumed to be equal to a + bx, where a and b are constants. The models predict a linear regression.	Linear Regression,Log-Linear Models,Models, Linear,Linear Model,Linear Regressions,Log Linear Models,Log-Linear Model,Model, Linear,Model, Log-Linear,Models, Log-Linear,Regression, Linear,Regressions, Linear
D016016	Proportional Hazards Models	Statistical models used in survival analysis that assert that the effect of the study factors on the hazard rate in the study population is multiplicative and does not change over time.	Cox Model,Cox Proportional Hazards Model,Hazard Model,Hazards Model,Hazards Models,Models, Proportional Hazards,Proportional Hazard Model,Proportional Hazards Model,Cox Models,Cox Proportional Hazards Models,Hazard Models,Proportional Hazard Models,Hazard Model, Proportional,Hazard Models, Proportional,Hazards Model, Proportional,Hazards Models, Proportional,Model, Cox,Model, Hazard,Model, Hazards,Model, Proportional Hazard,Model, Proportional Hazards,Models, Cox,Models, Hazard,Models, Hazards,Models, Proportional Hazard
D016049	Didanosine	A dideoxynucleoside compound in which the 3'-hydroxy group on the sugar moiety has been replaced by a hydrogen. This modification prevents the formation of phosphodiester linkages which are needed for the completion of nucleic acid chains. Didanosine is a potent inhibitor of HIV replication, acting as a chain-terminator of viral DNA by binding to reverse transcriptase; ddI is then metabolized to dideoxyadenosine triphosphate, its putative active metabolite.	2',3'-Dideoxyinosine,Dideoxyinosine,ddI (Antiviral),NSC-612049,Videx,2',3' Dideoxyinosine,NSC 612049,NSC612049