Dear all,
I have a study with different outcomes represented by differences in brain areas measured at two different timepoints. The length of followup varies from subject to subject and is indicated by the variable "Months_between_MRI Diagnosis".
The areas are indicated starting from the first, "a2", while the others go progressively up to 67 (a3, a4...a67).
So the outcomes of my analysis are "deltaa2", "deltaa3"..."deltaa67".
The goal of the analysis is to understand whether the delta of each area depends on the followup time or not.
I am using the "wyoung" command to perform a p correction for multiple testing.
If I didn't consider the baseline, the command would be:
However, if I wanted to include the baseline, for each area, how could I do it?
It's a problem, because the wyoung command corrects all the regression models for the same covariates, while the baseline of the different areas are obviously different from each other.
Does anyone have an idea? I really don't know how to get out of this problem.
For convenience, below I report the dataset for the first 5 areas
I have a study with different outcomes represented by differences in brain areas measured at two different timepoints. The length of followup varies from subject to subject and is indicated by the variable "Months_between_MRI Diagnosis".
The areas are indicated starting from the first, "a2", while the others go progressively up to 67 (a3, a4...a67).
So the outcomes of my analysis are "deltaa2", "deltaa3"..."deltaa67".
The goal of the analysis is to understand whether the delta of each area depends on the followup time or not.
I am using the "wyoung" command to perform a p correction for multiple testing.
If I didn't consider the baseline, the command would be:
Code:
wyoung deltaa2-deltaa67, cmd(regress OUTCOMEVAR Gender Months_between_MRI Diagnosis) familyp(Months_between_MRI) bootstraps(1000) seed(20) replace
It's a problem, because the wyoung command corrects all the regression models for the same covariates, while the baseline of the different areas are obviously different from each other.
Does anyone have an idea? I really don't know how to get out of this problem.
For convenience, below I report the dataset for the first 5 areas
Code:
* Example generated by -dataex-. For more info, type help dataex clear input byte(id Age Gender) float(deltaa2 deltaa3 deltaa4 deltaa5 baselinea2 baselinea3 baselinea4 baselinea5) 1 30 0 .0019999924 -.02300009 -.036999952 -.23999995 2.406 2.544 1.987 2.917 2 36 0 .739 .05800011 .3919999 .26099998 1.969 2.105 1.668 1.083 3 34 0 -.12099997 -.182 .661 .038 2.637 2.274 1.796 .944 4 44 0 -.13100006 -.14399996 .9399999 .603 1.936 2.137 1.374 .83 5 49 0 .207 -.337 .7459999 -.003000028 1.783 2.322 1.403 .945 6 25 0 -.3330001 -.789 .313 -1.806 3.064 2.445 1.973 3.265 7 24 0 .12799993 -.327 1.289 .495 2.386 2.184 1.28 .905 8 15 1 -.7780001 -.1940001 .2489999 -.928 3.226 2.501 2.098 3.217 9 32 0 -.329 .51400006 .7830001 .808 2.497 1.735 1.562 .935 10 13 0 -.284 -.57399994 .3190001 .608 2.35 2.353 1.821 1.099 11 49 0 .9899999 .58000004 .916 1.46 1.646 1.771 1.396 .79 12 67 1 .14900011 .17399994 .541 -.22399995 3.412 2.567 2.179 3.151 13 47 0 -.26400006 .17299993 -.20400003 .587 2.712 2.453 2.15 2.979 14 30 1 -.7479999 -.673 .488 -2.184 3.253 2.56 2.13 3.245 15 22 0 -.7120001 -.03599991 -.406 -1.771 2.609 2.617 2.101 3.211 16 55 0 -.5170001 -.405 .23 -1.223 2.969 2.57 2.125 2.792 17 28 0 -.2819999 -.23100004 .6030001 .828 2.468 2.373 1.797 1.965 18 44 0 .622 .2019999 .525 2.658 1.987 2.293 1.431 1.117 19 35 0 -.04399993 .02899999 1.0109999 .753 2.145 2.333 1.375 1.023 20 20 1 -.3540001 -.13600005 .04399997 -.352 3.204 2.571 2.167 3.707 21 18 0 .5030001 -.4240001 .4259999 .991 1.864 2.84 1.822 1.295 22 54 0 .9620001 .17199998 1.673 1.788 2.043 2.006 1.06 .785 23 39 0 -.20200007 .03000001 .59200007 .3920001 2.272 1.983 1.575 2.005 24 21 0 -.7720001 -.414 .398 -1.82 2.865 2.237 2.006 3.356 25 24 0 .7089999 -.6439999 1.101 .5389999 2.039 2.561 1.637 1.574 26 38 1 .04400001 .4290001 .7620001 .379 2.492 1.823 1.73 1.453 27 33 0 .3289999 -.4630001 .8069999 .22400004 2.113 2.546 1.392 1.468 28 75 1 1.193 .5079999 .11899997 .309 1.88 1.753 1.633 1.648 29 38 1 -.6810001 -.57400006 .04899991 -1.1570001 2.84 2.458 2.097 3.077 30 19 1 -.664 -.4990001 .11499997 -1.177 2.615 2.619 2.267 3.2 31 44 0 -.8909999 -.3210001 .16800007 -.67 3.337 2.539 2.285 2.756 32 45 0 .213 .3359999 .772 .74 2.284 1.599 1.35 .837 33 34 0 .614 .51 1.039 1.702 2.965 2.174 .97 .945 34 12 1 .4209999 -.26199993 .02200002 -.019000005 2.077 2.373 1.856 1.575 35 53 0 1.1710001 .7800001 1.607 1.911 1.851 1.804 1.223 1.132 36 45 1 0 0 0 0 0 0 0 0 37 20 0 -1.591 -1.62 -1.253 -1.57 1.591 1.62 1.253 1.57 38 50 0 -.5209999 -.11800011 .27200004 .252 2.809 2.409 1.905 1.896 39 47 1 -.883 -.488 .8060001 -1.64 3.199 2.39 2.127 3.321 40 40 0 .424 .443 1.588 .862 2.343 1.909 1.219 1.089 41 40 1 .4859999 .6459999 .5619999 .6940001 2.479 1.967 1.689 1.69 42 63 1 .008999996 -.4620001 .357 -.934 2.945 2.69 2.268 3.273 43 34 0 0 0 0 0 0 0 0 0 44 24 0 -2.191 -2.684 -1.053 -1.124 2.191 2.684 1.053 1.124 45 63 0 -.6969999 -.5000001 -.019000055 -1.937 2.755 2.712 2.102 3.464 46 40 1 -.781 -.394 .0510001 -1.963 3.278 2.581 1.964 3.181 47 33 0 0 0 0 0 0 0 0 0 48 44 0 -.13000004 -.005999957 1.058 -.04499999 2.101 1.836 1.228 1.035 49 27 1 0 0 0 0 0 0 0 0 50 19 0 .398 -.2399999 .647 .9030001 2.059 2.38 1.573 1.409 51 52 1 .019999957 .13000005 .645 .066 2.451 2.162 1.693 1.008 52 30 0 .488 -.1919999 .915 .474 1.946 2.204 1.441 .945 53 14 0 -1.1639999 -.708 -.246 -2.251 3.4 2.573 2.047 3.414 54 66 0 .063000105 -.214 1.225 .6380001 2.35 2.053 1.158 1.177 55 63 0 -.56600004 .11599991 -.11699998 -.14500012 2.711 2.201 2.041 3.242 56 36 1 -1.102 .003 .28900003 -1.459 3.28 2.372 2.023 3.276 57 18 1 -.6940001 -.381 -.10599995 -1.207 3.001 2.347 2.074 3.15 58 37 1 -.7559999 -.7770001 -.09600002 -2.003 3.166 2.406 1.917 3.173 59 50 0 1.307 .24700004 .8360001 .54899997 1.543 2.144 1.502 1.149 60 33 0 -.6990001 -.6819999 .1060001 -1.595 2.772 2.576 2.113 3.672 61 49 0 -.452 -.12399995 .315 -.8080001 3.179 2.62 2.113 2.987 62 48 1 -.09499995 -.22099994 .1659999 .2479999 2.739 2.592 1.98 3.332 63 20 1 -.798 -.434 .08999995 -.8780001 2.966 2.339 2.108 3.234 64 72 1 .07299996 .53900003 .226 1.267 2.115 2.03 1.958 1.716 66 45 1 -.4070001 -.44300005 -.12899995 -1.443 3.188 2.595 2.125 3.444 67 53 1 -1.1020001 -.1880001 .867 -1.405 2.764 2.459 2.037 3.434 68 24 0 -.22599994 .14999995 -.021000044 .36200005 2.837 2.354 2.117 3.075 69 61 1 -.28399992 -.08000001 -.1970001 .15700006 3.125 2.465 2.501 2.852 70 43 0 -.3899999 -.697 -.21200003 -1.397 2.793 2.358 2.145 3.063 71 50 0 -.4679999 -.2589999 .3410001 -1.1519998 3.348 2.442 2.151 3.157 72 25 0 .4440001 .17399994 1.4 .356 2.147 1.886 1.399 1.252 73 16 0 1.297 .7469999 .296 1.918 1.42 1.846 1.569 1.71 74 29 0 1.4305114e-08 -.473 .9079999 .7419999 1.985 2.349 1.603 1.165 75 42 1 -.3659999 -.2980001 -.16099995 -.4830001 3.332 2.589 2.259 3.425 76 50 0 .03100011 .1570001 .842 -.10600004 2.132 1.996 1.536 1.378 77 41 0 -.396 -.659 -.26399997 -1.815 2.639 2.418 2.071 3.061 78 24 0 .28700003 -.6 1.004 .17199996 2.42 2.572 1.813 1.039 79 49 1 .10500007 -.11799993 1.1110001 .317 2.144 2.275 1.319 1.4 80 39 0 .1520001 -.434 1.0070001 .33699995 2.317 2.228 1.271 .889 81 17 1 .53999996 .05200009 .829 .7009999 2.28 1.983 1.408 1.547 82 23 1 -.003000055 .15999997 .8239999 .3749999 2.586 1.796 1.299 1.198 83 57 1 -.8339999 -.665 .02900001 -.961 3.07 2.55 2.234 3.205 84 21 1 1.475 -.1119999 .402 .53900003 1.591 2.377 1.562 .936 85 46 0 .719 .3800001 .465 .56799996 2.047 2.293 1.735 1.281 end
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