Looking at variable interactions with foreign born status in all Hispanic women population

Variable interactions in all Hispanic women model
coef exp(coef) se(coef) z Pr(>|z|)
SE1 0.281363034 1.3249345 0.3943710 0.71344764 0.475568802859663
firstmenst1:SE1 0.170628308 1.1860498 0.4699933 0.36304414 0.716571912560819
firstmenst2:SE1 -0.477827131 0.6201294 0.5057323 -0.94482221 0.344749651004272
SE11 1.630987579 5.1089177 0.6304214 2.58713869 *0.00967766296366108
birth1:SE1 -1.666220045 0.1889600 0.6733180 -2.47464059 *0.0133370317935164
birth2:SE1 -1.500511845 0.2230160 0.7117554 -2.10818466 *0.0350150199293235
SE12 0.183944339 1.2019489 0.6841233 0.26887600 0.788025102582952
BFHS:SE1 0.001106725 1.0011073 0.0253479 0.04366143 0.965174281447233
SE13 0.392942152 1.4813327 0.2086697 1.88308191 0.0596892683729953
BIOP1:SE1 -0.651643743 0.5211884 0.3974690 -1.63948314 0.101112679271387
Cox model including age at first menstrual variable
## [1] "coxph(formula = Surv(study.entry.age, study.exit.age, observed.outcome) ~ \n    firstmenst + birth + BFHS + BIOP + SE1 * BIOP + SE1 * BFHS + \n        SE1 * firstmenst + SE1 * birth, data = ss.hispanic)"
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `factor.name = map_chr(coefficient_labels, symbol_substring, call_symbols)`.
## Caused by warning:
## ! `as_logical()` is deprecated as of rlang 0.4.0
## Please use `vctrs::vec_cast()` instead.
## This warning is displayed once every 8 hours.
Running all interactions in one cox model
factor.id factor.name factor.value HR Lower_CI Upper_CI Inv_HR Inv_Lower_CI Inv_Upper_CI p
1 firstmenst:1 firstmenst 1 0.9340159 0.47790804 1.8254259 1.0706456 0.54781737 2.0924528 0.841742407719328
2 firstmenst:2 firstmenst 2 1.5225459 0.77176250 3.0037039 0.6567946 0.33292230 1.2957354 0.225270810623344
3 birth:1 birth 1 3.6235166 1.12348295 11.6867570 0.2759750 0.08556694 0.8900892 *0.0311741083372335
4 birth:2 birth 2 3.1915950 0.93470869 10.8978108 0.3133230 0.09176155 1.0698520 0.0639969765626331
5 BFHS BFHS <continuous> 1.0480057 1.00920440 1.0882988 0.9541933 0.91886533 0.9908796 *0.0148523206742506
6 BIOP:1 BIOP 1 1.8829011 1.10812204 3.1993919 0.5310953 0.31255939 0.9024277 *0.0193082840049912
7 SE1 SE1 <continuous> 5.9316255 0.86683144 40.5894154 0.1685879 0.02463696 1.1536268 0.069628159544089
8 BIOP:1:SE1 BIOP 1:SE1 0.5431205 0.24784601 1.1901741 1.8412119 0.84021318 4.0347634 0.127255755266354
9 BFHS::SE1 BFHS :SE1 1.0029139 0.95355275 1.0548303 0.9970945 0.94801976 1.0487097 0.910034078473293
10 firstmenst:1:SE1 firstmenst 1:SE1 1.1246174 0.44661165 2.8319105 0.8891913 0.35311850 2.2390817 0.803168039918471
11 firstmenst:2:SE1 firstmenst 2:SE1 0.6300024 0.23336915 1.7007521 1.5872955 0.58797517 4.2850566 0.361844982980105
12 birth:1:SE1 birth 1:SE1 0.1972778 0.05265655 0.7391014 5.0689941 1.35299441 18.9909882 *0.0160148899965999
13 birth:2:SE1 birth 2:SE1 0.2308220 0.05717374 0.9318753 4.3323426 1.07310501 17.4905458 *0.0394888889342974

Interactions with first degree family history fem variable

# First deg history fem variable
Fhmodel_int_all <- coxph(Surv(study.entry.age, study.exit.age, observed.outcome) ~ firstmenst + birth + Fh + BIOP + SE1 * BIOP + SE1 * Fh + SE1 * firstmenst +
    SE1 * birth, data = ss.hispanic)
summary(Fhmodel_int_all)
## Call:
## coxph(formula = Surv(study.entry.age, study.exit.age, observed.outcome) ~ 
##     firstmenst + birth + Fh + BIOP + SE1 * BIOP + SE1 * Fh + 
##         SE1 * firstmenst + SE1 * birth, data = ss.hispanic)
## 
##   n= 2436, number of events= 130 
## 
##                      coef exp(coef)  se(coef)      z Pr(>|z|)  
## firstmenst1     -0.003233  0.996772  0.340174 -0.010   0.9924  
## firstmenst2      0.444118  1.559114  0.346042  1.283   0.1993  
## birth1           1.284853  3.614138  0.597440  2.151   0.0315 *
## birth2           1.172982  3.231616  0.626812  1.871   0.0613 .
## Fh1             -0.004790  0.995221  0.594386 -0.008   0.9936  
## BIOP1            0.687890  1.989513  0.269897  2.549   0.0108 *
## SE1              1.364599  3.914153  1.140805  1.196   0.2316  
## BIOP1:SE1       -0.589668  0.554511  0.398827 -1.479   0.1393  
## Fh1:SE1          0.634936  1.886902  0.934705  0.679   0.4970  
## firstmenst1:SE1  0.050699  1.052006  0.469908  0.108   0.9141  
## firstmenst2:SE1 -0.463435  0.629119  0.506003 -0.916   0.3597  
## birth1:SE1      -1.693206  0.183929  0.673584 -2.514   0.0119 *
## birth2:SE1      -1.532886  0.215912  0.712163 -2.152   0.0314 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                 exp(coef) exp(-coef) lower .95 upper .95
## firstmenst1        0.9968     1.0032   0.51173    1.9416
## firstmenst2        1.5591     0.6414   0.79127    3.0721
## birth1             3.6141     0.2767   1.12063   11.6559
## birth2             3.2316     0.3094   0.94597   11.0398
## Fh1                0.9952     1.0048   0.31044    3.1905
## BIOP1              1.9895     0.5026   1.17222    3.3766
## SE1                3.9142     0.2555   0.41839   36.6179
## BIOP1:SE1          0.5545     1.8034   0.25376    1.2117
## Fh1:SE1            1.8869     0.5300   0.30208   11.7861
## firstmenst1:SE1    1.0520     0.9506   0.41882    2.6424
## firstmenst2:SE1    0.6291     1.5895   0.23336    1.6961
## birth1:SE1         0.1839     5.4369   0.04912    0.6887
## birth2:SE1         0.2159     4.6315   0.05347    0.8719
## 
## Concordance= 0.619  (se = 0.026 )
## Likelihood ratio test= 20.69  on 13 df,   p=0.08
## Wald test            = 18.04  on 13 df,   p=0.2
## Score (logrank) test = 19.03  on 13 df,   p=0.1