This unique reference/text provides an understanding of nonparametric and quasi­ parametric (regression) methods to analyze survivorship data in clinical studies­ emphasizing the interpretation and reasoning behind these methods.

Written for clinicians, as well as biostatisticians, Survivorship Analysis for Clinical Studies justifies each new methodology presented and clarifies its relationship to preceding material. It describes and explains established methods for summarizing the results of the majority of single-clinic survivorship studies, comparing two or more survival processes, and examining the effects of covariates on survival.

Including a diskette containing programs for computing confidence bands for survival curves, this book serves as a timely reference for biostatisticians, clinicians engaged in clinical trials, pharmacologists involved in new drug testing, epidemiologists, and biomedical engineers, and as a superb text for upper-level undergraduate and graduate students, as well as participants in professional seminars on survivorship data analysis in clinical settings.

This unique reference/text provides an understanding of nonparametric and quasi­ parametric (regression) methods to analyze survivorship data in clinical studies­ emphasizing the interpretation and reasoning behind these methods.

Written for clinicians, as well as biostatisticians, Survivorship Analysis for Clinical Studies justifies each new methodology presented and clarifies its relationship to preceding material. It describes and explains established methods for summarizing the results of the majority of single-clinic survivorship studies, comparing two or more survival processes, and examining the effects of covariates on survival.

Including a diskette containing programs for computing confidence bands for survival curves, this book serves as a timely reference for biostatisticians, clinicians engaged in clinical trials, pharmacologists involved in new drug testing, epidemiologists, and biomedical engineers, and as a superb text for upper-level undergraduate and graduate students, as well as participants in professional seminars on survivorship data analysis in clinical settings.

This unique reference/text provides an understanding of nonparametric and quasi­ parametric (regression) methods to analyze survivorship data in clinical studies­ emphasizing the interpretation and reasoning behind these methods.

Written for clinicians, as well as biostatisticians, Survivorship Analysis for Clinical Studies justifies each new methodology presented and clarifies its relationship to preceding material. It describes and explains established methods for summarizing the results of the majority of single-clinic survivorship studies, comparing two or more survival processes, and examining the effects of covariates on survival.

Including a diskette containing programs for computing confidence bands for survival curves, this book serves as a timely reference for biostatisticians, clinicians engaged in clinical trials, pharmacologists involved in new drug testing, epidemiologists, and biomedical engineers, and as a superb text for upper-level undergraduate and graduate students, as well as participants in professional seminars on survivorship data analysis in clinical settings.

This unique reference/text provides an understanding of nonparametric and quasi­ parametric (regression) methods to analyze survivorship data in clinical studies­ emphasizing the interpretation and reasoning behind these methods.

Written for clinicians, as well as biostatisticians, Survivorship Analysis for Clinical Studies justifies each new methodology presented and clarifies its relationship to preceding material. It describes and explains established methods for summarizing the results of the majority of single-clinic survivorship studies, comparing two or more survival processes, and examining the effects of covariates on survival.

Including a diskette containing programs for computing confidence bands for survival curves, this book serves as a timely reference for biostatisticians, clinicians engaged in clinical trials, pharmacologists involved in new drug testing, epidemiologists, and biomedical engineers, and as a superb text for upper-level undergraduate and graduate students, as well as participants in professional seminars on survivorship data analysis in clinical settings.