The PHREG Procedure 
In fitting the Cox regression model by maximizing the partial likelihood, the estimate of an explanatory variable X will be infinite if the value of X at each uncensored failure time is the largest of all the values of X in the risk set at that time (Tsiatis; 1981; Bryson and Johnson; 1981). You can exploit this information to artificially create a data set that has the condition of monotone likelihood for the Cox regression. The following DATA step modifies the Myeloma data in Example 64.1 to create a new explanatory variable, Contrived, which has the value 1 if the observed time is less than or equal to 65 and has the value 0 otherwise. The phenomenon of monotone likelihood will be demonstrated in the new data set Myeloma2.
data Myeloma2; set Myeloma; Contrived= (Time <= 65); run;
For illustration purposes, consider a Cox model with three explanatory variables, one of which is the variable Contrived. The following statements invoke PROC PHREG to perform the Cox regression. The IPRINT option is specified in the MODEL statement to print the iteration history of the optimization.
proc phreg data=Myeloma2; model Time*Vstatus(0)=LOGbun HGB Contrived / itprint; run;
The symptom of monotonity is demonstrated in Output 64.4.1. The log likelihood converges to the value of –136.56 while the coefficient for Contrived diverges. Although the NewtonRaphson optimization process did not fail, it is obvious that convergence is questionable. A close examination of the standard errors in the "Analyis of Maximum Likelihood Estimates" table reveals a very large value for the coefficient of Contrived. This is very typical of a diverged estimate.
Maximum Likelihood Iteration History  

Iter  Ridge  Log Likelihood  LogBUN  HGB  Contrived 
0  0  154.8579914384  0.0000000000  0.000000000  0.000000000 
1  0  140.6934052686  1.9948819671  0.084318519  1.466331269 
2  0  137.7841629416  1.6794678962  0.109067888  2.778361123 
3  0  136.9711897754  1.7140611684  0.111564202  3.938095086 
4  0  136.7078932606  1.7181735043  0.112273248  5.003053568 
5  0  136.6164264879  1.7187547532  0.112369756  6.027435769 
6  0  136.5835200895  1.7188294108  0.112382079  7.036444978 
7  0  136.5715152788  1.7188392687  0.112383700  8.039763533 
8  0  136.5671126045  1.7188405904  0.112383917  9.040984886 
9  0  136.5654947987  1.7188407687  0.112383947  10.041434266 
10  0  136.5648998913  1.7188407928  0.112383950  11.041599592 
11  0  136.5646810709  1.7188407960  0.112383951  12.041660414 
12  0  136.5646005760  1.7188407965  0.112383951  13.041682789 
13  0  136.5645709642  1.7188407965  0.112383951  14.041691020 
14  0  136.5645600707  1.7188407965  0.112383951  15.041694049 
15  0  136.5645560632  1.7188407965  0.112383951  16.041695162 
16  0  136.5645545889  1.7188407965  0.112383951  17.041695572 
Next, the Firth correction was applied as shown in the following statements. Also, the profilelikelihood confidence limits for the hazard ratios are requested by using the RISKLIMITS=PL option.
proc phreg data=Myeloma2; model Time*Vstatus(0)=LogBUN HGB Contrived / firth risklimits=pl itprint; run;
PROC PHREG uses the penalized likelihood maximum to obtain a finite estimate for the coefficient of Contrived (Output 64.4.2). The much preferred profilelikelihood confidence limits, as shown in (Heinze and Schemper; 2001), are also displayed.
Maximum Likelihood Iteration History  

Iter  Ridge  Log Likelihood  LogBUN  HGB  Contrived 
0  0  150.7361197494  0.0000000000  0.000000000  0.0000000000 
1  0  136.9933949142  2.0262484120  0.086519138  1.4338859318 
2  0  134.5796594364  1.6770836974  0.109172604  2.6221444778 
3  0  134.1572923217  1.7163408994  0.111166227  3.4458043289 
4  0  134.1229607193  1.7209210332  0.112007726  3.7923555412 
5  0  134.1228364805  1.7219588214  0.112178328  3.8174197804 
6  0  134.1228355256  1.7220081673  0.112187764  3.8151642206 
Analysis of Maximum Likelihood Estimates  

Parameter  DF  Parameter Estimate 
Standard Error 
ChiSquare  Pr > ChiSq  Hazard Ratio 
95% Hazard Ratio Profile Likelihood Confidence Limits 

LogBUN  1  1.72201  0.58379  8.7008  0.0032  5.596  1.761  17.231 
HGB  1  0.11219  0.06059  3.4279  0.0641  0.894  0.794  1.007 
Contrived  1  3.81516  1.55812  5.9955  0.0143  45.384  5.406  6005.404 
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