Quick Reference Sheet for PS_FRAIL Macro - by Youyi Shu - **************************************************************************** Note: The official documentation of the macro is the following paper: Shu, Y. and Klein, J.P. (1999). A SAS Macro for the Positive Stable Frailty Model. American Statistical Association Proceedings of the Statistical Computing Section, p.47-52. **************************************************************************** I. Data set in SAS Data Step (A) Data set must have the following ordering of its variables: Group, Time, Censoring Indicator, Covariates. (B) The censoring indicator needs to be specified as follows: 0-censored; 1-event. II. Macro Loading PS_FRAIL macro is loaded in the program via the "%INCLUDE 'filename'" statement where 'filename' is the name of the PS_FRAIL macro file. III. Macro Invocation PS_FRAIL macro is invoked in the form of %PS_FRAIL(dsn <,option 1 <...,option n>>) where dsn is the data set name and it must be given, the symbol of angle brackets identifies optional arguments. There are at most 13 options available and they can be given in any order after the data set name. Each option is specified as a keyword followed by an equal sign and a value (not case-sensitive). The options are: (1) GREPORT=Y: Prints the summary report of group sizes and the number of events per group. (2) ITPRINT=Y: Prints the iteration history and the main process of the analysis. (3) ITSUMM=Y: Prints the summary table of the iteration history. (4) ALPHA=value: Confidence level option. To construct 100(1-alpha)% confidence limits for the relative risks, the value is set to alpha. This option has no effect unless the RL=Y option (see below) is specified. The default value is 0.05, which results in the calculation of a 95% confidence limits. (5) RL=Y: Prints, for each explanatory variable, the 100(1-alpha)% confi- dence limits for the relative risk. Here, alpha is determined by the ALPHA= value option. (6) COV=Y: Prints the estimated variance-covariance matrix of THETAHAT (the estimate of the dependence parameter) and BETAHAT (the estimates of the regression coefficients). (7) OUTEST=value: Output data set option. If the value is specified as a legal SAS data set name, then that data set will contain THETAHAT and BETAHAT, the estimated variance-covariance matrix of THETAHAT and BETAHAT, and the last computed value of the log full likelihood. (8) BASELINE=value: Output data set option. If the value is specified as a legal SAS data set name, then that data set will contain ordered event times (_TIME_), baseline hazard rates (_BHR_) , standard errors of the baseline hazard rates (_SEBHR_), and the estimate of the dependence parameter (_THETA_). (9) OUTPUT=value: Output data set option. If the value is specified as a legal SAS data set name, then that data set will contain the grouping variable, survival time, censoring status, covariates, estimated linear predictor (_XBETA_), cumulative baseline hazard rate (_CBHR_), and the estimate of the dependence parameter (_THETA_). (10) CONVBETA=value: Option to set the convergence criterion based on the estimates of the regression coefficients. The iteration sequence stops when the maximum change among the estimates of the regression coefficients is less than the specified value for this option. Default value: 0.001. (11) LAMBDA=value: Option to set the blending constant used with Marquardt's method to find the roots of the score equations in a modified Cox profile likelihood analysis. Note that small value corresponds to Newton-Raphson method while large value corresponds to the steepest ascent method. Default value: 0.25. (12) GRIDSIZE=value: Option to set the step size in the grid search method. The macro starts from one and steps down by value at each step to bracket the maximum likelihood estimate of the dependence parameter. Default value: 0.05. (13) GOLDLENG=value: Option to set the stopping criterion for the golden section search algorithm. After initially bracketing a maximum through a grid search method (see GRIDSIZE=value option above), the macro then starts the golden section search to search for the maximum likelihood estimate of the dependence parameter. The search routine is continued until the length of the bracketing interval is less than the specified value for this option. Default value: 0.001.