The PHREG Procedure
- Overview
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Getting Started
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Syntax
PROC PHREG Statement ASSESS Statement BASELINE Statement BAYES Statement BY Statement CLASS Statement CONTRAST Statement EFFECT Statement ESTIMATE Statement FREQ Statement HAZARDRATIO Statement ID Statement LSMEANS Statement LSMESTIMATE Statement MODEL Statement OUTPUT Statement Programming Statements RANDOM Statement STRATA Statement SLICE Statement STORE Statement TEST Statement WEIGHT Statement
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Details
Failure Time Distribution Time and CLASS Variables Usage Partial Likelihood Function for the Cox Model Counting Process Style of Input Left-Truncation of Failure Times The Multiplicative Hazards Model The Frailty Model Hazard Ratios Specifics for Classical Analysis Specifics for Bayesian Analysis Computational Resources Input and Output Data Sets Displayed Output ODS Table Names ODS Graphics
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Examples
Stepwise Regression Best Subset Selection Modeling with Categorical Predictors Firth’s Correction for Monotone Likelihood Conditional Logistic Regression for m:n Matching Model Using Time-Dependent Explanatory Variables Time-Dependent Repeated Measurements of a Covariate Survivor Function Estimates for Specific Covariate Values Analysis of Residuals Analysis of Recurrent Events Data Analysis of Clustered Data Model Assessment Using Cumulative Sums of Martingale Residuals Bayesian Analysis of the Cox Model Bayesian Analysis of Piecewise Exponential Model
- References
| ASSESS Statement |
- ASSESS <VAR=(list)> <PH> </options> ;
The ASSESS statement performs the graphical and numerical methods of Lin, Wei, and Ying (1993) for checking the adequacy of the Cox regression model. The methods are derived from cumulative sums of martingale residuals over follow-up times or covariate values. You can assess the functional form of a covariate or you can check the proportional hazards assumption for each covariate in the Cox model. PROC PHREG uses ODS Graphics for the graphical displays. You must specify at least one of the following options to create an analysis.
- VAR=(variable-list)
specifies the list of explanatory variables for which their functional forms are assessed. For each variable on the list, the observed cumulative martingale residuals are plotted against the values of the explanatory variable along with 20 (or n if NPATHS=n is specified) simulated residual patterns.
- PROPORTIONALHAZARDS
- PH
requests the checking of the proportional hazards assumption. For each explanatory variable in the model, the observed score process component is plotted against the follow-up time along with 20 (or n if NPATHS=n is specified) simulated patterns.
The following options can be specified after a slash (/):
- NPATHS=n
specifies the number of simulated residual patterns to be displayed in a cumulative martingale residual plot or a score process plot. The default is n=20.
- CRPANEL
requests that a plot with four panels, each containing the observed cumulative martingale residuals and two simulated residual patterns, be created.
- RESAMPLE <=n>
requests that the Kolmogorov-type supremum test be computed on 1,000 simulated patterns or on n simulated patterns if n is specified.
- SEED=n
specifies an integer seed for the random number generator used in creating simulated realizations for plots and for the Kolmogorov-type supremum tests. Specifying a seed enables you to reproduce identical graphs and
-values for the model assessments from the same PHREG specification. If the SEED= option is not specified, or if you specify a nonpositive seed, a random seed is derived from the time of day.