I am doing a paired kidney analysis to look at the effect of obesity on post-transplant outcomes (e.g. death). The pairs were transplant pairs where one kidney was allocated to an obese recipient and the other to a non-obese recipient.
For the main analyisis, I used a stratified Cox model, with each pair as a stratum.
Now I am doing a dose response analysis to look at whether more severely obesed patients had a greater risk of death.
So I categorised patient cohort from "not obese VS obese" to "not obese VS obese class I or VS obese class II and III". The pairs remain the same as main analyisis, just with one more category.
My questions are:
1. From the lincom command, there is not enough evidence to conclude that obese II and III patients have a greater risk of death compared to obese class I. However, as the pair was not obese VS obese, I'm not sure if it is correct to interpret obese I VS obese II and III using lincom.
2. When comparing class I to not obese, HR=1.28, CI 1.07-1.66, p=0.008; When comparing class II and III to not obese, HR=1.42, CI 0.95-2.11, p=0.087. I think it is incorrect to say that there's an increased risk of death for obese II and III just because the point estimates are higher as the CIs are overlappping. How should I interpret it correctly?
Many thanks for any thoughts on this. Appreciate any help in advance.
Best Regards,
Bree
Below are the output.
Code:
stcox i.obese_cat i.agecat_tx i.durationcat i.hlam i.cv i.diabetes i.gfstatus , strata(groupid) hr nolog vce(robust) baselevels failure _d: death analysis time _t: (enddate-origin)/365.25 origin: time transplantdate id: id Stratified Cox regr. -- no ties No. of subjects = 3,038 Number of obs = 836,725 No. of failures = 601 Time at risk = 19741.05681 Wald chi2(13) = 145.49 Log pseudolikelihood = -255.51969 Prob > chi2 = 0.0000 (Std. Err. adjusted for 3,038 clusters in id) ----------------------------------------------------------------------------------- | Robust _t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval] ------------------+---------------------------------------------------------------- obese_cat | Not obese | 1 (base) Obese I | 1.284755 .1213985 2.65 0.008 1.067552 1.54615 Obese II and III | 1.417494 .289101 1.71 0.087 .9504223 2.1141 | agecat_tx | 18-34 | 1 (base) 35-49 | 3.26754 .914541 4.23 0.000 1.887908 5.655369 50-65 | 5.446535 1.598246 5.78 0.000 3.064372 9.680532 65+ | 8.810133 2.826903 6.78 0.000 4.697403 16.52369 | durationcat | 0-1yr | 1 (base) 1-3yr | 1.533406 .3224166 2.03 0.042 1.015505 2.315433 3yr+ | 2.151468 .455557 3.62 0.000 1.420691 3.258145 | hlam | 0 | 1 (base) 1-2 | 1.148471 .502754 0.32 0.752 .4869658 2.708579 3-4 | .8541231 .3995813 -0.34 0.736 .3414333 2.136658 5-6 | .5942201 .2849043 -1.09 0.278 .2321833 1.520771 | cv | No or missing | 1 (base) Yes or suspected | 2.161482 .3009532 5.54 0.000 1.645261 2.839673 | diabetes | No or missing | 1 (base) Yes | 1.863112 .2779249 4.17 0.000 1.390795 2.495829 | gfstatus | 0 | 1 (base) 1 | 2.990554 .5347461 6.13 0.000 2.106429 4.24577 ----------------------------------------------------------------------------------- Stratified by groupid testparm i.obese_cat ( 1) 1.obese_cat = 0 ( 2) 2.obese_cat = 0 chi2( 2) = 9.87 Prob > chi2 = 0.0072 lincom 2.obese_cat-1.obese_cat ( 1) - 1.obese_cat + 2.obese_cat = 0 ------------------------------------------------------------------------------ _t | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | .0983221 .2238869 0.44 0.661 -.3404881 .5371324 ------------------------------------------------------------------------------
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