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TPHREG Procedure


TPHREG Procedure

The experimental TPHREG procedure adds the CLASS statement to the PHREG procedure. The CLASS statement enables you to specify categorical variables (also known as CLASS variables) as explanatory variables . Explanatory effects for the model, including covariates, main effects, interactions, and nested effects, can be specified in the same way as in the GLM procedure. The CLASS statement supports less-than -full-rank parameterization as well as various full-rank parameterizations such as reference coding and effect coding. Other CLASS statement features that are found in PROC LOGISTIC, such as specifying specific categories as reference levels, are also available.

The TPHREG procedure also enables you to specify CONTRAST statements for testing customized hypotheses concerning the regression parameters. Each CONTRAST statement also provides estimation of individual rows of contrasts, which is particularly useful in comparing the hazards between the categories of a CLASS explanatory variable.



TPSPLINE Procedure

9.1  

The COEF option in the OUTPUT statement enables you to output coefficients of the fitted function.



TRANSREG Procedure

The TRANSREG procedure has new transformation options for centering and standardizing variables , CENTER and Z, before the transformations. The new EXKNOTS= option specifies exterior knots for SPLINE and MSPLINE transformations and BSPLINE expansions.

The new algorithm option INDIVIDUAL with METHOD=MORALS fits each model for each dependent variable individually and independently of the other dependent variables.

With hypothesis tests, the TRANSREG procedure now produces a table with the number of observations, and, when there are CLASS variables, a class level information table.



References

Agresti, A. (1996), An Introduction to Categorical Data Analysis , New York: John Wiley & Sons, Inc.

Binder, D.A. (1983), 'On the Variances of Asymptotically Normal Estimators from Complex Surveys,' International Statistical Review , 51, 279-292.

Binder, D.A. (1992), 'Fitting Cox's Proportional Hazards Models from Survey Data,' Biometrika , 79, 139-47.

Cameron, A.C. and Trivedi, P.K. (1998), 'Regression Analysis of Count Data,' Cambridge: Cambridge University Press.

Cohen, R. (2002), 'SAS Meets Big Iron: High Performance Computing in SAS Analytical Procedures,' Proceedings of the Twenty-seventh Annual SAS Users Group International Conference .

Gail, M.H., Lubin, J.H., and Rubinstein, L.V. (1981), 'Likelihood Calculations for Matched Case-Control Studies and Survival Studies with Tied Survival Times,' Biometrika , 78, 703-7.

Hall, W.J. and Wellner, J.A. (1980), 'Confidence Bands for a Survival Curve for Censored Data,' Biometrika 69 , 133-143.

Hirji, K.F., Mehta, C.R., and Patel, N.R. (1987), 'Computing Distributions for Exact Logistic Regression,' Journal of the American Statistical Association , 82, 1110-1117.

Hirji, K.F., Tsiatis, A.A., and Mehta, C.R. (1989), 'Median Unbiased Estimation for Binary Data,' American Statistician , 43, 7-11.

Huber, P.J. (1973), 'Robust Regression: Asymptotics, Conjectures and Monte Carlo,' Annals of Statistics , 1, 799-821.

Mehta, C.R., Patel, N., and Senchaudhuri, P. (1992), 'Exact Stratified Linear Rank Tests for Ordered Categorical and Binary Data,' Journal of Computational and Graphical Statistics , 1, 21-40.

Nair, V.N. (1984), 'Confidence Bands for Survival Functions with Censored Data: A Comparative Study,' Technometrics , 14, 265-275.

Tarone, R. (1985), 'On Heterogeneity Tests Based on Efficient Scores,' Biometrika , 72, 91-95.