On Aug 21, 2012, at 3:40 AM, Nanthinee Jeevanandam wrote:

I have run this model for my data set on insect survival time for
different temperature treatments in R. I understand the Wald statistic
reports on whether the variable
makes a difference to the model but there is also a p-value so i am
not sure whther to report the Wald statistic. I have pasted a copy of
my result below. Humidity is categorical (a & b) while temperature is
continuous.

coxph(formula = Surv(time, death) ~ Humidity + temp)
n= 1504, number of events= 1504

              coef   exp(coef) se(coef)          z    Pr(>|z|)
Hum.b -0.03023   0.97022  0.05695   -0.531    0.595
temp   1.19094   3.29017  0.03360   35.444   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

         exp(coef) exp(-coef) lower .95 upper .95
Humb    0.9702     1.0307    0.8678     1.085
temp      3.2902     0.3039    3.0805     3.514

Concordance= 0.918  (se = 0.013 )
Rsquare= 0.742   (max possible= 1 )
Likelihood ratio test= 2036  on 2 df,   p=0
Wald test            = 1281  on 2 df,   p=0
Score (logrank) test = 1948  on 2 df,   p=0


How do I interpret the Wald test in this case? Really appreciate the help.

The inferential tests are all in agreement. (Saying p < 2e-16 is equivalent to saying p = 0 when dealing with the limitations of floating point numbers.) I would think you would separately interpret the humidity effect, possibly saying that the differences in the two humidity conditions appears to have little influence on survival with the humidity level "b" having only a slightly lower survival rate that was not statistically significant, but that the temperature effects were (highly) significantly different than zero with the point estimate suggesting a tripling of the death rates for a one unit increase in temperature.

(I find such an effect biologically unlikely assuming the temperature is measured in any of the usual temperature scales, and wonder if you have made a coding error some place. I would need to see a much better description of the data before proceeding. Did you incorrectly create a numeric variable from factor levels? )


--
David Winsemius, MD
Alameda, CA, USA

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