The PHREG Procedure
- Overview
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Getting Started
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SyntaxPROC PHREG StatementASSESS StatementBASELINE StatementBAYES StatementBY StatementCLASS StatementCONTRAST StatementEFFECT StatementESTIMATE StatementFREQ StatementHAZARDRATIO StatementID StatementLSMEANS StatementLSMESTIMATE StatementMODEL StatementOUTPUT StatementProgramming StatementsRANDOM StatementSTRATA StatementSLICE StatementSTORE StatementTEST StatementWEIGHT Statement
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DetailsFailure Time DistributionTime and CLASS Variables UsagePartial Likelihood Function for the Cox ModelCounting Process Style of InputLeft-Truncation of Failure TimesThe Multiplicative Hazards ModelProportional Rates/Means Models for Recurrent EventsThe Frailty ModelProportional Subdistribution Hazards Model for Competing-Risks DataHazard RatiosNewton-Raphson MethodFirth’s Modification for Maximum Likelihood EstimationRobust Sandwich Variance EstimateTesting the Global Null HypothesisType 3 Tests and Joint TestsConfidence Limits for a Hazard RatioUsing the TEST Statement to Test Linear HypothesesAnalysis of Multivariate Failure Time DataModel Fit StatisticsSchemper-Henderson Predictive MeasureResidualsDiagnostics Based on Weighted ResidualsInfluence of Observations on Overall Fit of the ModelSurvivor Function EstimatorsCaution about Using Survival Data with Left TruncationEffect Selection MethodsAssessment of the Proportional Hazards ModelThe Penalized Partial Likelihood Approach for Fitting Frailty ModelsSpecifics for Bayesian AnalysisComputational ResourcesInput and Output Data SetsDisplayed OutputODS Table NamesODS Graphics
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ExamplesStepwise RegressionBest Subset SelectionModeling with Categorical PredictorsFirth’s Correction for Monotone LikelihoodConditional Logistic Regression for m:n MatchingModel Using Time-Dependent Explanatory VariablesTime-Dependent Repeated Measurements of a CovariateSurvival CurvesAnalysis of ResidualsAnalysis of Recurrent Events DataAnalysis of Clustered DataModel Assessment Using Cumulative Sums of Martingale ResidualsBayesian Analysis of the Cox ModelBayesian Analysis of Piecewise Exponential ModelAnalysis of Competing-Risks Data
- References
The EFFECT statement enables you to construct special collections of columns for design matrices. These collections are referred to as constructed effects to distinguish them from the usual model effects that are formed from continuous or classification variables, as discussed in the section GLM Parameterization of Classification Variables and Effects in Chapter 19: Shared Concepts and Topics.
You can specify the following effect-types:
- COLLECTION
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is a collection effect that defines one or more variables as a single effect with multiple degrees of freedom. The variables in a collection are considered as a unit for estimation and inference.
- LAG
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is a classification effect in which the level that is used for a given period corresponds to the level in the preceding period.
- MULTIMEMBER | MM
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is a multimember classification effect whose levels are determined by one or more variables that appear in a CLASS statement.
- POLYNOMIAL | POLY
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is a multivariate polynomial effect in the specified numeric variables.
- SPLINE
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is a regression spline effect whose columns are univariate spline expansions of one or more variables. A spline expansion replaces the original variable with an expanded or larger set of new variables.
Table 73.5 summarizes the options available in the EFFECT statement.
Table 73.5: EFFECT Statement Options
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Option |
Description |
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Collection Effects Options |
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Displays the constituents of the collection effect |
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Lag Effects Options |
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Names a variable that controls to which lag design an observation is assigned |
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Displays the lag design of the lag effect |
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Specifies the number of periods in the lag |
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Names the variable that defines the period |
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Names the variable or variables that define the group within which each period is defined |
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Multimember Effects Options |
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Specifies that observations with all missing levels for the multimember variables should have zero values in the corresponding design matrix columns |
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Specifies the weight variable for the contributions of each of the classification effects |
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Polynomial Effects Options |
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Specifies the degree of the polynomial |
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Specifies the maximum degree of any variable in a term of the polynomial |
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Specifies centering and scaling suboptions for the variables that define the polynomial |
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Spline Effects Options |
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Specifies the type of basis (B-spline basis or truncated power function basis) for the spline effect |
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Specifies the degree of the spline effect |
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Specifies how to construct the knots for the spline effect |
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For more information about the syntax of these effect-types and how columns of constructed effects are computed, see the section EFFECT Statement in Chapter 19: Shared Concepts and Topics.