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 66.9 for the maximum likelihood analysis and in Table 66.10 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 66.9 and Table 66.10. For information about these tables, see the corresponding sections of Chapter 19, Shared Concepts and Topics.

Table 66.9 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

NumberAtRisk

Risk sets information

MODEL / ATRISK

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=

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 66.10 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