I have 10 exposure variables that i would like to analyze univariate. I've applied the conditional logistic regression model to each exposure, univariate, in the program R. I then applied the summary function to each model to get the test statistic, confidence interval and p-value. The summary function prints out 3 test statistics and 3 corresponding p-values for each exposure. The output for 2 of 10 exposures looks like;

Call:

coxph(formula = Surv(rep(1, 136L), case) ~ exposure1 + strata(nam),

method = "exact")

n= 120, number of events= 33

(6 observations deleted due to missingness)

coef exp(coef) se(coef) z Pr(>|z|)

-0.1238 0.772 0.4223 -0.281 0.7681

exp(coef) exp(-coef) lower .95 upper .95

0.772 1.126 0.4781 2.432

Rsquare= 0.001 (max possible= 0.496 )

Likelihood ratio test= 0.08 on 1 df, p=0.7684

Wald test = 0.08 on 1 df, p=0.7681

Score (logrank) test = 0.08 on 1 df, p=0.7843

Call:

coxph(formula = Surv(rep(1, 136L), case) ~ exposure2 + strata(nam),

method = "exact")

n= 119, number of events= 31

(7 observations deleted due to missingness)

coef exp(coef) se(coef) z Pr(>|z|)

0.1356 1.2461 0.3897 0.327 0.842

exp(coef) exp(-coef) lower .95 upper .95

1.2461 0.1356 0.6293 2.538

Rsquare= 0.001 (max possible= 0.481 )

Likelihood ratio test= 0.11 on 1 df, p=0.7442

Wald test = 0.11 on 1 df, p=0.8421

Score (logrank) test = 0.11 on 1 df, p=0.7532

.

.

.

And so on...

I want to compare the p-values (and other values) between the 10 exposure variables. My question is: do i need to adjust the p-values? (Multiple testing)

I don't fully understand when to use multiple testing. I read that one should use it when several tests are conducted, to control the 1 error rate. Should i use it in this case?

I would very much appreciate any help or guidance.