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The PHREG procedure performs regression analysis of survival data based on the Cox proportional hazards model and its extensions.
The PHREG Procedure......Page 4
Overview: PHREG Procedure......Page 6
Getting Started: PHREG Procedure......Page 9
Classical Method of Maximum Likelihood......Page 10
Bayesian Analysis......Page 14
Syntax: PHREG Procedure......Page 18
PROC PHREG Statement......Page 19
ASSESS Statement......Page 23
BASELINE Statement......Page 24
BAYES Statement......Page 28
BY Statement......Page 39
CLASS Statement......Page 40
CONTRAST Statement......Page 42
FREQ Statement......Page 44
HAZARDRATIO Statement......Page 45
MODEL Statement......Page 47
OUTPUT Statement......Page 56
Programming Statements......Page 58
TEST Statement......Page 60
WEIGHT Statement......Page 61
Failure Time Distribution......Page 62
CLASS Variable Parameterization......Page 63
Clarification of the Time and CLASS Variables Usage......Page 65
Partial Likelihood Function for the Cox Model......Page 70
Counting Process Style of Input......Page 71
Left Truncation of Failure Times......Page 72
Hazard Ratios......Page 73
Specifics for Classical Analysis......Page 76
Specifics for Bayesian Analysis......Page 103
Input and Output Data Sets......Page 113
Displayed Output......Page 115
ODS Table Names......Page 123
ODS Graphics......Page 126
Example 64.1: Stepwise Regression......Page 127
Example 64.2: Best Subset Selection......Page 135
Example 64.3: Modeling with Categorical Predictors......Page 137
Example 64.4: Firth's Correction for Monotone Likelihood......Page 145
Example 64.5: Conditional Logistic Regression for m:n Matching......Page 147
Example 64.6: Model Using Time-Dependent Explanatory Variables......Page 151
Example 64.7: Time-Dependent Repeated Measurements of a Covariate......Page 158
Example 64.8: Survivor Function Estimates for Specific Covariate Values......Page 166
Example 64.9: Analysis of Residuals......Page 168
Example 64.10: Analysis of Recurrent Events Data......Page 170
Example 64.11: Analysis of Clustered Data......Page 180
Example 64.12: Model Assessment Using Cumulative Sums of Martingale Residuals......Page 183
Example 64.13: Bayesian Analysis of the Cox Model......Page 195
Example 64.14: Bayesian Analysis of Piecewise Exponential Model......Page 206
References......Page 210
Subject Index......Page 214
Syntax Index......Page 220
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The LOGISTIC procedure fits linear logistic regression models for discrete response data by the method of maximum likelihood. It can also perform conditional logistic regression for binary response data and exact conditional logistic regression for binary and nominal response data.
The GENMOD procedure fits generalized linear models.
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The REG procedure is a general-purpose procedure for linear regression.
The MIXED procedure fits a variety of mixed linear models to data and enables you to use these fitted models to make statistical inferences about the data.
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