Biomaterial Database

Regression analysis of grouped survival data with incomplete covariates: nonignorable missing-data and censoring mechanisms. 1994

S G Baker

Biometry Branch, National Cancer Institute, Bethesda, Maryland 20892-7354.

Previous methods for the analysis of survival data with incomplete covariates have assumed that the missing-data and censoring mechanisms are ignorable. We relax both assumptions for the analysis of grouped survival data.

UI	MeSH Term	Description	Entries
D008433	Mathematics	The deductive study of shape, quantity, and dependence. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)	Mathematic
D011336	Probability	The study of chance processes or the relative frequency characterizing a chance process.	Probabilities
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
D004812	Epidemiologic Methods	Research techniques that focus on study designs and data gathering methods in human and animal populations.	Epidemiologic Method,Epidemiological Methods,Methods, Epidemiologic,Epidemiological Method,Method, Epidemiologic,Method, Epidemiological,Methods, Epidemiological
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
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
D015996	Survival Rate	The proportion of survivors in a group, e.g., of patients, studied and followed over a period, or the proportion of persons in a specified group alive at the beginning of a time interval who survive to the end of the interval. It is often studied using life table methods.	Cumulative Survival Rate,Mean Survival Time,Cumulative Survival Rates,Mean Survival Times,Rate, Cumulative Survival,Rate, Survival,Rates, Cumulative Survival,Rates, Survival,Survival Rate, Cumulative,Survival Rates,Survival Rates, Cumulative,Survival Time, Mean,Survival Times, Mean,Time, Mean Survival,Times, Mean Survival
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