Statistical modeling of lifetime data, or survival analysis, is studied in many fields, including medicine, information technology and economics. This type of data gives the time to a certain event, such as death in studies of cancer treatment, or time until a computer program crashes. Researchers are often interested in how covariates affect the time to event and wish to determine ways of incorporating such covariates into statistical models. Covariates are explanatory variables that are suspected to affect the lifetime of interest. Lifetime data are typically subject to censoring and this fact needs to be taken into account when choosing the statistical model.
D.R. Cox (1972) proposed a statistical model that can be used to explore the relationship between survival and various covariates and takes censoring into account. This is called the Cox proportional hazards (PH) model. In particular, the model will be presented and estimation procedures for parameters and functions of interest will be developed. Statistical properties of the resulting estimators will be derived and used in developing inference procedures.
|Advisor:||Agustin, Zenia, Agustin, Marcus|
|Commitee:||Chew, Song Foh, Pailden, Junvie|
|School:||Southern Illinois University at Edwardsville|
|Department:||Mathematics and Statistics|
|School Location:||United States -- Illinois|
|Source:||MAI 54/02M(E), Masters Abstracts International|
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