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· 분류 : 외국도서 > 과학/수학/생태 > 수학 > 확률과 통계 > 일반
· ISBN : 9781597181747
· 쪽수 : 428쪽
· 출판일 : 2016-05-10
목차
The problem of survival analysis
Parametric modeling
Semiparametric modeling
Nonparametric analysis
Linking the three approaches
Describing the distribution of failure times
The survivor and hazard functions
The quantile function
Interpreting the cumulative hazard and hazard rate
Means and medians
Hazard models
Parametric models
Semiparametric models
Analysis time (time at risk)
Censoring and truncation
Censoring
Truncation
Recording survival data
The desired format
Other formats
Example: Wide-form snapshot data
Using stset
A short lesson on dates
Purposes of the stset command
Syntax of the stset command
After stset
Look at stset’s output
List some of your data
Use stdescribe
Use stvary
Perhaps use stfill
Example: Hip-fracture data
Nonparametric analysis
Inadequacies of standard univariate methods
The Kaplan?Meier estimator
The Nelson?Aalen estimator
Estimating the hazard function
Estimating mean and median survival times
Tests of hypothesis
The Cox proportional hazards model
Using stcox
Likelihood calculations
Stratified analysis
Cox models with shared frailty
Cox models with survey data
Cox model with missing data?multiple imputation
Model building using stcox
Indicator variables
Categorical variables
Continuous variables
Interactions
Time-varying variables
Modeling group effects: fixed-effects, random-effects, stratification, and clustering
The Cox model: Diagnostics
Testing the proportional-hazards assumption
Residuals and diagnostic measures Reye’s syndrome data
Parametric models
Motivation
Classes of parametric models
A survey of parametric regression models in Stata
The exponential model
Weibull regression
Gompertz regression (PH metric)
Lognormal regression (AFT metric)
Loglogistic regression (AFT metric)
Generalized gamma regression (AFT metric)
Choosing among parametric models
Postestimation commands for parametric models
Use of predict after streg
Using stcurve
Predictive margins and marginal effects
Generalizing the parametric regression model
Frailty models
Power and sample-size determination for survival analysis
Estimating sample size
Accounting for withdrawal and accrual of subjects
Estimating power and effect size
Tabulating or graphing results
Competing risks
Cause-specific hazards
Cumulative incidence functions
Nonparametric analysis
Semiparametric analysis
Parametric analysis
