The PHREG Procedure

ODS Table Names

PROC PHREG assigns a name to each table it creates. You can use these names to reference the table when using the Output Delivery System (ODS) to select tables and create output data sets. These names are listed separately in Table 67.13 for the maximum likelihood analysis and in Table 67.14 for the Bayesian analysis. For more information about ODS, see Chapter 20: Using the Output Delivery System.

Each of the EFFECT, ESTIMATE, LSMEANS, LSMESTIMATE, and SLICE statements creates ODS tables, which are not listed in Table 67.13 and Table 67.14. For information about these tables, see the corresponding sections of Chapter 19: Shared Concepts and Topics.

Table 67.13: ODS Tables for a Maximum Likelihood Analysis Produced by PROC PHREG

ODS Table Name	Description	Statement / Option
BestSubsets	Best subset selection	MODEL / SELECTION=SCORE
CensoredSummary	Summary of event and censored observations	Default
ClassLevelFreq	Frequency distribution of CLASS variables	CLASS, PROC / SIMPLE
ClassLevelInfo	CLASS variable levels and design variables	CLASS
ClassLevelInfoR	Class levels for random effects	RANDOM
ContrastCoeff	L matrix for contrasts	CONTRAST / E
ContrastEstimate	Individual contrast estimates	CONTRAST / ESTIMATE=
ContrastTest	Wald test for contrasts	CONTRAST
ConvergenceStatus	Convergence status	Default
CorrB	Estimated correlation matrix of parameter estimators	MODEL / CORRB
CovB	Estimated covariance matrix of parameter estimators	MODEL / COVB
CovParms	Variance estimates of the random effects	RANDOM
EffectsToEnter	Analysis of effects for entry	MODEL / SELECTION=F\|S
EffectsToRemove	Analysis of effects for removal	MODEL / SELECTION=B\|S
FitStatistics	Model fit statistics	Default
FunctionalFormSupTest	Supremum test for functional form	ASSESS / VAR=
GlobalScore	Global chi-square test	MODEL / NOFIT
GlobalTests	Tests of the global null hypothesis	Default
HazardRatios	Hazard ratios and confidence limits	HAZARDRATIO
IterHistory	Iteration history	MODEL /ITPRINT
LastGradient	Last evaluation of gradient	MODEL / ITPRINT
ModelBuildingSummary	Summary of model building	MODEL / SELECTION=B\|F\|S
ModelInfo	Model information	Default
NObs	Number of observations	Default
ParameterEstimates	Maximum likelihood estimates of model parameters	Default
ProportionalHazardsSupTest	Supremum test for proportional hazards assumption	ASSESS / PH
ResidualChiSq	Residual chi-square	MODEL / SELECTION=F\|B
ReferenceSet	Reference set of covariates for plotting	PROC / PLOTS=
RiskSetInfo	Risk set information	PROC / ATRISK
SimpleStatistics	Summary statistics of input continuous explanatory variables	PROC / SIMPLE
SolutionR	Solutions for random effects	RANDOM / SOLUTION
TestAverage	Average effect for test	TEST / AVERAGE
TestCoeff	Coefficients for linear hypotheses	TEST / E
TestPrint1	L[cov(b)]L’ and Lb-c	TEST / PRINT
TestPrint2	Ginv(L[cov(b)]L’) and Ginv(L[cov(b)]L’)(Lb-c)	TEST / PRINT
TestStmts	Linear hypotheses testing results	TEST
Type1	Type 1 likelihood ratio tests	MODEL / TYPE1
Type3	Type 3 chi-square tests	MODEL / TYPE3 \| CLASS

Table 67.14: ODS Table for a Bayesian Analysis Produced by PROC PHREG

ODS Table Name	Description	Statement / Option
AutoCorr	Autocorrelations of the posterior samples	BAYES
CensoredSummary	Numbers of the event and censored observations	PROC
ClassLevelFreq	Frequency distribution of CLASS variables	CLASS, PROC / SIMPLE
ClassLevelInfo	CLASS variable levels and design variables	CLASS
CoeffPrior	Prior distribution of the regression coefficients	BAYES
Corr	Posterior correlation matrix	BAYES / SUMMARY=CORR
Cov	Posterior covariance Matrix	BAYES / SUMMARY=COV
ESS	Effective sample sizes	BAYES / DIAGNOSTICS=ESS
FitStatistics	Fit statistics	BAYES
Gelman	Gelman-Rubin convergence diagnostics	BAYES / DIAGNOSTICS=GELMAN
Geweke	Geweke convergence diagnostics	BAYES
HazardPrior	Prior distribution of the baseline hazards	BAYES / PIECEWISE
HazardRatios	Posterior summary statistics for hazard ratios	HAZARDRATIO
Heidelberger	Heidelberger-Welch convergence diagnostics	BAYES / DIAGNOSTICS=HEIDELBERGER
InitialValues	Initial values of the Markov chains	BAYES
ModelInfo	Model information	Default
NObs	Number of observations	Default
MCError	Monte Carlo standard errors	BAYES / DIAGNOSTICS=MCERROR
ParameterEstimates	Maximum likelihood estimates of model parameters	Default
ParmInfo	Names of regression coefficients	CLASS,BAYES
Partition	Partition of constant baseline hazard intervals	BAYES / PIECEWISE
PostIntervals	Equal-tail and high probability density intervals of the posterior samples	BAYES
PosteriorSample	Posterior samples	BAYES / (for ODS output data set only)
PostSummaries	Summary statistics of the posterior samples	BAYES
Raftery	Raftery-Lewis convergence diagnostics	BAYES / DIAGNOSTICS=RAFTERY
ReferenceSet	Reference set of covariates for plotting	PROC / PLOTS=
SimpleStatistics	Summary statistics of input continuous explanatory variables	PROC / SIMPLE