Dear all,
I am trying to assess associations between follow-up levels of risk factors with outcome (event). In order to do this first, each participant's follow-up period should be divided into a series of intervals defined by the 2 yr visits (in my case); second, the presence or absence of event by group should be documented during each interval and coupled to the time-weighted average of risk factors recorded during the interval before the development of event.
I've generated time-weighted average of some risk factors (blood pressure, lipid levels), but I am having difficulty in further analysis (I am very new to this kind of analysis). Any suggestions on how to calculate HR with time-dependent covariates?
Any help will be appreciated.
Thank you.
Data looks as below.
I am trying to assess associations between follow-up levels of risk factors with outcome (event). In order to do this first, each participant's follow-up period should be divided into a series of intervals defined by the 2 yr visits (in my case); second, the presence or absence of event by group should be documented during each interval and coupled to the time-weighted average of risk factors recorded during the interval before the development of event.
I've generated time-weighted average of some risk factors (blood pressure, lipid levels), but I am having difficulty in further analysis (I am very new to this kind of analysis). Any suggestions on how to calculate HR with time-dependent covariates?
Any help will be appreciated.
Thank you.
Data looks as below.
HTML Code:
clear input byte(studytime event baseline_age id) float(id1 days_from_baseline) double(fpg screat) int(ldl hdl sbp dbp) float(t_years sumw sumwsbp meansbp sumwdbp meandbp sumwldl meanldl sumwhdl meanhdl gr) 1 1 61 1 1 0 215 1.9 105 46 115 60 0 0 0 114.44444 0 65.77778 0 101.77778 0 47.11111 1 1 1 61 1 2 730 198 1.99 122 45 108 62 2 2 216 114.44444 124 65.77778 244 101.77778 90 47.11111 1 1 1 61 1 3 1095 99 1.6 88 46 114 64 3 5 558 114.44444 316 65.77778 508 101.77778 228 47.11111 1 1 1 61 1 4 1460 95 1.6 102 49 118 69 4 9 1030 114.44444 592 65.77778 916 101.77778 424 47.11111 1 3 0 56 2 5 0 88 1.85 109 44 110 60 0 0 0 110.22222 0 61.55556 0 98.77778 0 44 1 3 0 56 2 6 730 96 1.7 120 51 127 77 2 2 254 110.22222 154 61.55556 240 98.77778 102 44 1 3 0 56 2 7 1095 79 2.07 91 42 110 56 3 5 584 110.22222 322 61.55556 513 98.77778 228 44 1 3 0 56 2 8 1460 150 1.94 94 42 102 58 4 9 992 110.22222 554 61.55556 889 98.77778 396 44 1 2 1 76 3 9 0 41 .6 136 51 121 65 0 0 0 119.4 0 67 0 91.2 0 49.6 2 2 1 76 3 10 730 52 .52 78 55 123 73 2 2 246 119.4 146 67 156 91.2 110 49.6 2 2 1 76 3 11 1095 61 .6 100 46 117 63 3 5 597 119.4 335 67 456 91.2 248 49.6 2 4 1 79 4 12 0 149 .54 88 65 137 70 0 0 0 128.44444 0 71 0 95.88889 0 52.88889 2 4 1 79 4 13 730 98 .6 87 48 126 73 2 2 252 128.44444 146 71 174 95.88889 96 52.88889 2 4 1 79 4 14 1095 119 .6 103 48 124 67 3 5 624 128.44444 347 71 483 95.88889 240 52.88889 2 4 1 79 4 15 1460 75 .61 95 59 133 73 4 9 1156 128.44444 639 71 863 95.88889 476 52.88889 2 end [/CODE]
Comment