Proc phreg hazardratio statement

[cgsm233]

Hello,

I am investigating the effect of a -0.25 unit decrease of my continuous variable has on survival. My model has the continuous variable (PRALBUM) and other dichotomous variables; no interaction terms.

Since the HAZARDRATIO units option can only handle positive increments, I set units = 0.25. Next, I exponentiated the parameter estimate (0.97862) after multiplying by -1.0 to obtain the HR. To obtain the 95% CI, I multiplied the standard error (0.21116) by 1.0 and 1.96, then added/subtracted that value (0.41387) to/from the parameter estimate, and exponentiated those values. Final, result: HR = 2.66 (1.76, 4.02).

I used the HAZARDRATIO statement to calculate the HR (95% CI) for my dichotomous variables.

My question is: since I exponentiated the negative product of the parameter estimate of the continuous variable, do I need to do anything to the dichotamous variables to reflect the fact that I did that? Or, am I able to report the HR provided?

I feel like I need to do something to those other estimates as they are only reflecting a 0.25 unit increase in the continuous variable, not the -0.25 unit decrease.

Below is my code and Parameter Estimates:

*Adjusted;
proc phreg data=c.nsqip;
class 
ALBUM_NEW (PARAM = REF REF = "Normal")
PRSGOT_new (PARAM = REF REF = "<=40")
race_new (PARAM = REF REF = "White")
BMI_new (PARAM = REF REF = "Normal")
OPTIME_num (PARAM = REF REF = "0")
ASACLAS (PARAM = REF REF ="1-2")
PRHCT_new (PARAM = REF REF = ">=30")
PRPLATE_new (PARAM = REF REF = "<400K")
PRCREAT_new (PARAM = REF REF = "<=1.2")
WNDCLAS (PARAM = REF REF = "1-Clean")
cpt_group (PARAM = REF REF = "MIS")
DYSPNEA_num (PARAM = REF REF= "0");
model survival_time*failure(0) = 
PRALBUM OTHBLEED_num cardiac_event_num Pulmonary_event_num infectious_event_num/ties=efron rl = pl details;
hazardratio PRALBUM/units=0.25 cl = pl;
hazardratio OTHBLEED_num/diff=pairwise cl = pl;
hazardratio cardiac_event_num/diff=pairwise cl = pl;
hazardratio Pulmonary_event_num/diff = pairwise cl = pl;
hazardratio infectious_event_num/diff=pairwise cl = pl;
run;

*Calculate HR and CI;
data albumin;
x = -1.0*(-0.97862);
y=1.96*1*0.21116;
HR = exp(x);
LB = exp(x-y);
UB = exp(x+y);
run;
proc print data= albumin;
run;

Thank you

[svh]

Have you considered just using the CONTRAST statement to obtain the hazard ratio for this custom difference (which is a particular decrease in the value of the covariate)? I think this would be simpler than trying to calculate what the HR would be for a 1-point decrease in the covariate. Take this example that uses data from SAS's website. You can specify a negative number for your custom test for a continuous covariate in the CONTRAST statement and the result will be the hazard ratio for that change in the covariate. The CONTRAST statement is handy for occasions like these. https://documentation.sas.com/doc/en/statug/15.2/statug_phreg_syntax09.htm

data Myeloma;
   input Time VStatus LogBUN HGB Platelet Age LogWBC Frac
         LogPBM Protein SCalc;
   label Time='Survival Time'
         VStatus='0=Alive 1=Dead';
   datalines;
 1.25  1  2.2175   9.4  1  67  3.6628  1  1.9542  12  10
 1.25  1  1.9395  12.0  1  38  3.9868  1  1.9542  20  18
 2.00  1  1.5185   9.8  1  81  3.8751  1  2.0000   2  15
 2.00  1  1.7482  11.3  0  75  3.8062  1  1.2553   0  12
 2.00  1  1.3010   5.1  0  57  3.7243  1  2.0000   3   9
 3.00  1  1.5441   6.7  1  46  4.4757  0  1.9345  12  10
 5.00  1  2.2355  10.1  1  50  4.9542  1  1.6628   4   9
 5.00  1  1.6812   6.5  1  74  3.7324  0  1.7324   5   9
 6.00  1  1.3617   9.0  1  77  3.5441  0  1.4624   1   8
 6.00  1  2.1139  10.2  0  70  3.5441  1  1.3617   1   8
 6.00  1  1.1139   9.7  1  60  3.5185  1  1.3979   0  10
 6.00  1  1.4150  10.4  1  67  3.9294  1  1.6902   0   8
 7.00  1  1.9777   9.5  1  48  3.3617  1  1.5682   5  10
 7.00  1  1.0414   5.1  0  61  3.7324  1  2.0000   1  10
 7.00  1  1.1761  11.4  1  53  3.7243  1  1.5185   1  13
 9.00  1  1.7243   8.2  1  55  3.7993  1  1.7404   0  12
11.00  1  1.1139  14.0  1  61  3.8808  1  1.2788   0  10
11.00  1  1.2304  12.0  1  43  3.7709  1  1.1761   1   9
11.00  1  1.3010  13.2  1  65  3.7993  1  1.8195   1  10
11.00  1  1.5682   7.5  1  70  3.8865  0  1.6721   0  12
11.00  1  1.0792   9.6  1  51  3.5051  1  1.9031   0   9
13.00  1  0.7782   5.5  0  60  3.5798  1  1.3979   2  10
14.00  1  1.3979  14.6  1  66  3.7243  1  1.2553   2  10
15.00  1  1.6021  10.6  1  70  3.6902  1  1.4314   0  11
16.00  1  1.3424   9.0  1  48  3.9345  1  2.0000   0  10
16.00  1  1.3222   8.8  1  62  3.6990  1  0.6990  17  10
17.00  1  1.2304  10.0  1  53  3.8808  1  1.4472   4   9
17.00  1  1.5911  11.2  1  68  3.4314  0  1.6128   1  10
18.00  1  1.4472   7.5  1  65  3.5682  0  0.9031   7   8
19.00  1  1.0792  14.4  1  51  3.9191  1  2.0000   6  15
19.00  1  1.2553   7.5  0  60  3.7924  1  1.9294   5   9
24.00  1  1.3010  14.6  1  56  4.0899  1  0.4771   0   9
25.00  1  1.0000  12.4  1  67  3.8195  1  1.6435   0  10
26.00  1  1.2304  11.2  1  49  3.6021  1  2.0000  27  11
32.00  1  1.3222  10.6  1  46  3.6990  1  1.6335   1   9
35.00  1  1.1139   7.0  0  48  3.6532  1  1.1761   4  10
37.00  1  1.6021  11.0  1  63  3.9542  0  1.2041   7   9
41.00  1  1.0000  10.2  1  69  3.4771  1  1.4771   6  10
41.00  1  1.1461   5.0  1  70  3.5185  1  1.3424   0   9
51.00  1  1.5682   7.7  0  74  3.4150  1  1.0414   4  13
52.00  1  1.0000  10.1  1  60  3.8573  1  1.6532   4  10
54.00  1  1.2553   9.0  1  49  3.7243  1  1.6990   2  10
58.00  1  1.2041  12.1  1  42  3.6990  1  1.5798  22  10
66.00  1  1.4472   6.6  1  59  3.7853  1  1.8195   0   9
67.00  1  1.3222  12.8  1  52  3.6435  1  1.0414   1  10
88.00  1  1.1761  10.6  1  47  3.5563  0  1.7559  21   9
89.00  1  1.3222  14.0  1  63  3.6532  1  1.6232   1   9
92.00  1  1.4314  11.0  1  58  4.0755  1  1.4150   4  11
 4.00  0  1.9542  10.2  1  59  4.0453  0  0.7782  12  10
 4.00  0  1.9243  10.0  1  49  3.9590  0  1.6232   0  13
 7.00  0  1.1139  12.4  1  48  3.7993  1  1.8573   0  10
 7.00  0  1.5315  10.2  1  81  3.5911  0  1.8808   0  11
 8.00  0  1.0792   9.9  1  57  3.8325  1  1.6532   0   8
12.00  0  1.1461  11.6  1  46  3.6435  0  1.1461   0   7
11.00  0  1.6128  14.0  1  60  3.7324  1  1.8451   3   9
12.00  0  1.3979   8.8  1  66  3.8388  1  1.3617   0   9
13.00  0  1.6628   4.9  0  71  3.6435  0  1.7924   0   9
16.00  0  1.1461  13.0  1  55  3.8573  0  0.9031   0   9
19.00  0  1.3222  13.0  1  59  3.7709  1  2.0000   1  10
19.00  0  1.3222  10.8  1  69  3.8808  1  1.5185   0  10
28.00  0  1.2304   7.3  1  82  3.7482  1  1.6721   0   9
41.00  0  1.7559  12.8  1  72  3.7243  1  1.4472   1   9
53.00  0  1.1139  12.0  1  66  3.6128  1  2.0000   1  11
57.00  0  1.2553  12.5  1  66  3.9685  0  1.9542   0  11
77.00  0  1.0792  14.0  1  60  3.6812  0  0.9542   0  12
;
proc phreg data=Myeloma;
   model Time*VStatus(0)=LogBUN HGB Platelet Age LogWBC
                         Frac LogPBM Protein SCalc  ;
	contrast 'Effect of HGB One Unit Decrease' HGB -1 / estimate=exp ;
		contrast 'Effect of HGB One Unit Increase' HGB 1 / estimate=exp ;
			
	run;