Quick Reference for the Gamma Frailty Macro

I  Data set in SAS Data Step
	A.  Data set must:
		1.  Have the following ordering of it's variable: 
     		  Time, Censoring indicator, Group number, Covariates
		2.  Only contain covariates wanted in the model
		
	B.  The censoring indicator needs to be specified as follows:  
     		 0 - censored; 1 - event
	
II Proc IML
		
		
	A.  User now needs to be in IML, which is done by using the statement "Proc IML 
	symsize=200" in the program.  The symsize=200 is needed since there are a large 
	number of variables used in the macro.
	
	B.  Gamfrail macro is included in the program via the "%include 'filename' "
	statement where 'filename' is the name of the gamma frailty macro file.
	
	C. Parameters are defined
		1.  Data set parameter:  name of userÕs data set
		2.  Variable list parameter:  lists the names of the covariates, in the order 
		they are read into the data set
		3.  Initial value of the covariates parameter:  The initial value of the 
		covariates in the independence model, should all be zeros unless the data 
		has been previously run through a Cox regression program.
		4.  Parameter defining convergence criteria for Marquardt's compromise:  
		This is a small number that the program will use to check for convergence.  
		When the estimates of the covariate parameters are within the convergence 
		criteria of the covariate parameters from the previous MarquardtÕs 
		compromise step then the covariate parameters are considered to be 
		converged.
		5.  Parameter setting the first value of the frailty parameters:  this number 
		should be small enough (i.e. .001) so that it is very unlikely that the q that 
		maximizes the profile log likelihood is between it and zero.  This parameter 
		is the first q that the program computes the profile log likelihood for after it 
		computes the independence model.
		6.  Parameter setting the increment of the change in the frailty parameter:  
		This is the size of the increments on q until there exists a triplet that 
		surrounds the value of q that maximizes the profile log likelihood.
		7.  Option parameter:  defines which options of the six possible options the 
		user wants by placing a "y" for yes or a "n" for no in the position in the 
		position of the desired option.
		Options:
			(1) Print out the section of the variance-covariance matrix the only 
			deals with the frailty and covariate parameters.
			(2) Create an output data set that contains the estimates of the frailty 
			and covariate parameters as well as the matrix from option(1).
			(3) Print out the estimates of the baseline hazard rates and 
			corresponding times.
			(4) Output a data set that contains the baseline hazard rates and 
			corresponding times.
			(5) Output full variance-covariance matrix and estimates of frailty 
			and covariate parameter estimates as well as the baseline hazard 
			rates.
			(6) Print out the current value of the frailty parameter, the profile log 
			likelihood L3, the number of times that the Wi was estimated  until 
			convergence and the number of Marquardt Compromise iterations 
			for the last estimate of Wi, at each different value of  the frailty 
			parameter until the maximum is surrounded.
		8.  Parameter naming the first output data set:  containing the output data set 
		requested from option (2) above.
		9.  Parameter naming the second output data set:   containing the output data 
		set requested from option (4) above.
		10.  Parameter naming the third output data set:  containing the output data 
		set requested from option (5) above.
		D.  Macro invocation using the following statement:
		%gamfrail(data set name, variable list parameter, initial value of covariates 
		parameter, convergence criteria parameter, parameter setting initial value of 
		q, parameter setting increment of change for q, options parameter, 
		parameter naming the first output data set, parameter naming the second 
		output data set, parameter naming the third output data set;

	E.  Contents of first output data set
		1.  Contains frailty and covariate parameter estimates as well as the 
		variance-covariance matrix with only the entries that pertain to the frailty and 
		covariate parameters.
			a.  First column contains the estimates
				(1)  First row is the frailty parameter
				(2)  Next p rows are the covariate parameter estimates
			b.  Next p+1 columns contain the variance-covariance matrix
				(1)  First row contains the covariances with respect to the 
				frailty parameter.
				(2) Second row contains the covariances with respect to 
				covariate 1.
						. 
						. 
						.
				(p+1) p+1st row contains the covariances with respect to 
				covariate p.
	
	F.  Contents of the second output data set
		1.  Contains estimates of the baseline hazard rates as well as the 
		corresponding times
			a.  First column is time
			b.  Second column contains the respective baseline hazard rates

	G.  Contents of the third output data set.
		1.  Contains the estimates of frailty and covariate parameters, baseline 
		hazard rates and the full variance-covariance matrix.
			a.  First column contains the estimates.
				(1)  First row is the estimate of the frailty parameter
				(2)  Next p rows are estimates of covariate parameters
				(3)  Last D rows are estimates of baseline hazard rates.
			b.  The rest of the columns are the full variance-covariance matrix
				(1)  First row contains the covariances with respect to q
				(2)  Next p rows contain covariances with respect to the 
				respective covariate parameter
					(A)  The first of these rows is with respect to 
					covariate 1
					(B)  The second of these rows is with respect to 
					covariate 2
						. 
						.
						.
					(p) The pth of these rows is with respect to covariate p
			(3)  Last D rows contain the covariances with respect to the 
			baseline hazard rate.
				(A)  The first of these rows is with respect to the 
				largest event time
				(B)  The second of these rows is with respect to the 
				second largest event time
					.
					.
					.
				(D) The last of these rows is with respect to the 
				smallest event time