Hi! I have a variable gender (0=Male, 1=Female) and I am trying to run a logistic regression with "fstat" (vital status: 0=alive, 1=dead) as my dependent variable. When I run: logistic fstat gender I don't get the breakdown for males and females. Does anyone know how to get STATA to stratify for males and females?


Data:
Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input long ID double(age gender hr sysbp diasbp bmi cvd afb sho chf av3 miord mitype year los dstat lenfol fstat) byte(_st _d) int _t byte _t0 float(bmicat agecat) byte _Igender_1 float xb
2665  78 0  86 138  91   22 1 0 0 0 0 1 0 1  9 0 1293 0 1 0 1293 0 20  70 0 0
4871  63 0  89 194  81 26.4 1 1 0 0 0 0 0 1 12 0 1160 1 1 1 1160 0 25  60 0 0
 326  63 0  59 171  71 34.3 0 0 0 0 0 0 1 2  4 0 2138 0 1 0 2138 0 30  60 0 0
 287  75 0  64 148  78 32.3 1 0 0 0 0 0 1 2 12 0  353 1 1 1  353 0 30  70 0 0
 193  58 0  58 200  99 25.5 1 0 0 0 0 1 0 3  0 0 2095 0 1 0 2095 0 25  50 0 0
4429  70 0  38 151  92   19 1 1 0 0 0 0 1 1  5 0 1269 0 1 0 1269 0 15  70 0 0
1915  62 0  75 128  84 28.2 1 0 0 0 0 0 1 2  6 0 2011 0 1 0 2011 0 25  60 0 0
3447  68 0 109 200  84 26.6 0 0 0 0 0 1 1 3  9 0  606 0 1 0  606 0 25  60 0 0
3137  59 0 104 162  82 23.4 1 0 0 0 0 0 0 2  3 0 1135 0 1 0 1135 0 20  50 0 0
 835  46 0  85 130  90   29 1 0 0 0 0 0 0 3  2 0  515 0 1 0  515 0 25  40 0 0
3779  58 0 154 134  96 23.4 1 1 0 0 0 1 0 2  5 0  196 1 1 1  196 0 20  50 0 0
2716  71 0  54 119  60 24.2 0 0 0 0 0 0 0 2  1 0  213 1 1 1  213 0 20  70 0 0
 991  50 0  80 163 100 40.4 0 0 0 0 0 0 0 2 15 0  400 0 1 0  400 0 40  50 0 0
 312  54 0  84 100  56 31.1 0 0 0 0 0 1 1 2  7 0   16 1 1 1   16 0 30  50 0 0
4635  79 0  62 145  97 29.7 1 0 0 0 0 0 1 3  2 0    8 0 1 0    8 0 25  70 0 0
1858  44 0  75 150  63 31.8 1 0 0 0 0 0 1 3  5 0  598 0 1 0  598 0 30  40 0 0
4041  54 0 121 115  93 35.7 0 0 0 0 0 0 0 3  1 0    8 0 1 0    8 0 35  50 0 0
  58  52 0  71 140  62 27.4 1 0 0 0 0 0 0 2  2 0  172 0 1 0  172 0 25  50 0 0
2322  83 0  68 174 134 27.1 1 1 0 1 0 0 0 2  9 0 1894 0 1 0 1894 0 25  80 0 0
1485  46 0  97 177 125   25 0 0 0 0 0 1 0 1  9 0 1252 0 1 0 1252 0 25  40 0 0
3389  75 0  76 160  54 22.1 0 0 0 0 0 0 0 3  6 0   12 1 1 1   12 0 20  70 0 0
4753  64 0 100 151  89 34.7 1 1 0 0 0 0 0 1  4 0 1242 1 1 1 1242 0 30  60 0 0
 783  47 0  92 149 144 27.3 1 0 0 0 0 0 0 3  0 0 1322 0 1 0 1322 0 25  40 0 0
3326  75 0  57 134  84 31.6 1 1 1 1 0 0 1 2 12 0  565 1 1 1  565 0 30  70 0 0
 994  76 0  64 183 100 30.8 0 0 0 0 0 0 0 2  4 0  467 1 1 1  467 0 30  70 0 0
2606  71 0  70  74  50 26.7 0 0 0 0 0 0 0 1 12 0 2034 0 1 0 2034 0 25  70 0 0
 556  67 0  98  86  60 27.4 0 1 0 1 0 0 1 2  4 0 1891 0 1 0 1891 0 25  60 0 0
1383  46 0  72 146  72 25.6 0 0 0 0 0 0 1 1  1 0 2131 0 1 0 2131 0 25  40 0 0
 955  48 0 105 209 100 40.4 1 0 0 0 0 1 0 1  6 0 1978 0 1 0 1978 0 40  40 0 0
2864  69 0  91 123  76 23.6 0 0 0 0 0 0 1 2  4 0  402 1 1 1  402 0 20  60 0 0
2165  62 0  83 135  80 28.2 0 0 0 0 0 0 1 2  2 0  628 0 1 0  628 0 25  60 0 0
2597  61 0  67 214 109 25.6 1 0 0 0 0 0 0 3  4 0   68 0 1 0   68 0 25  60 0 0
2505  58 0  76 150  73 27.4 0 0 0 0 0 1 0 1  4 0 1314 0 1 0 1314 0 25  50 0 0
4902  64 0  76 104  70 29.3 1 0 0 0 0 0 0 2 12 0   92 0 1 0   92 0 25  60 0 0
3054  72 0 107 162 102 36.6 1 1 0 0 0 0 1 1  7 0 1127 0 1 0 1127 0 35  70 0 0
2683  76 0 104 120  80 28.6 1 0 0 0 0 1 0 3  5 0   34 1 1 1   34 0 25  70 0 0
3569  52 0  64 100  46 18.7 0 0 0 0 0 0 0 2  8 0 2142 0 1 0 2142 0 15  50 0 0
3437  57 0  63 117  69 30.2 1 0 0 0 0 1 0 3 10 0  445 1 1 1  445 0 30  50 0 0
4522  50 0  35 159  69 26.6 0 0 0 0 0 0 1 2  4 0 2172 0 1 0 2172 0 25  50 0 0
2339  76 0  86 121  23 19.1 0 0 0 0 0 1 0 1  3 0 1311 0 1 0 1311 0 15  70 0 0
1004  49 0 129 110  90 25.1 1 0 0 0 0 0 0 2  7 0 1826 0 1 0 1826 0 25  40 0 0
3072  58 0  95 180 108 28.4 1 0 0 0 0 0 0 1  5 0 2113 0 1 0 2113 0 25  50 0 0
3038  49 0 100 208 102 30.5 1 0 1 0 0 1 0 1  3 0 1246 0 1 0 1246 0 30  40 0 0
 124  65 0  67 117  50 29.4 0 0 0 0 0 0 1 1  7 0 1440 0 1 0 1440 0 25  60 0 0
1150  70 0  64 140  75 26.3 1 1 0 0 0 0 0 3  5 0  381 0 1 0  381 0 25  70 0 0
1017  75 0  55 131  80 30.7 1 0 0 1 0 0 1 1  3 0  535 0 1 0  535 0 30  70 0 0
4569  46 0 133  85  88   39 0 1 0 0 0 0 1 3  3 0 2078 0 1 0 2078 0 35  40 0 0
 507  47 0 111 140  70 27.4 1 1 0 0 0 1 0 1  3 0 1890 0 1 0 1890 0 25  40 0 0
1760  90 0  64 202  50 25.9 1 1 0 1 1 1 0 2 10 0  533 1 1 1  533 0 25  90 0 0
3645  89 0  91 162  77 26.5 1 0 0 1 0 0 0 2  4 0  393 1 1 1  393 0 25  80 0 0
2809  76 0  56 129 101 38.9 1 0 0 0 0 0 1 2  6 0 1355 0 1 0 1355 0 35  70 0 0
 266  65 0  88 170  88 26.7 1 0 0 1 0 0 1 1  6 0 1565 0 1 0 1565 0 25  60 0 0
4779  33 0  56 115  80 38.6 1 0 0 0 0 1 0 3  1 0 1451 0 1 0 1451 0 35  30 0 0
 403  46 0  85 146  57   24 1 0 0 0 0 0 0 3  5 0  999 0 1 0  999 0 20  40 0 0
2532  60 0  87 140  70 27.4 1 0 0 1 0 0 0 2  5 0  408 0 1 0  408 0 25  60 0 0
2249  85 0  78 156  84 25.8 1 0 0 1 0 1 1 1  3 0 1854 0 1 0 1854 0 25  80 0 0
4455  59 0  97 156  64 24.7 1 0 0 0 0 0 1 1  4 0 1159 1 1 1 1159 0 20  50 0 0
1360  73 0 105  96  66 34.3 1 1 0 0 0 0 1 2  8 0  541 1 1 1  541 0 30  70 0 0
2517  57 0  88 170 109 39.1 0 0 0 0 0 0 0 1 11 0 2218 0 1 0 2218 0 35  50 0 0
1544  57 0  52 156  97 23.3 1 0 0 0 0 0 1 1  4 0 2222 0 1 0 2222 0 20  50 0 0
2325 100 0  74 137  56   26 1 0 0 0 0 0 1 1  3 0 1162 1 1 1 1162 0 25 100 0 0
2528  76 0  73 198  85 27.7 1 0 0 0 0 1 0 2  1 1  444 1 1 1  444 0 25  70 0 0
4195  56 0 118 149  85 27.9 0 1 1 1 0 0 0 1  8 1  452 0 1 0  452 0 25  50 0 0
1667  55 0 100 121  80 24.8 1 0 0 1 0 0 0 2  4 0 1186 0 1 0 1186 0 20  50 0 0
4606  59 0  83 116  37 35.8 1 0 0 0 0 1 0 3  5 0   33 1 1 1   33 0 35  50 0 0
4057  81 0 159 219 103 29.6 1 0 0 0 0 1 0 2  7 1  317 1 1 1  317 0 25  80 0 0
2743  81 0 104 190  85   22 1 0 0 1 0 1 0 2  5 1    2 1 1 1    2 0 20  80 0 0
3284  63 0  80  63  60 26.4 0 0 0 0 0 1 0 2  2 0 1928 0 1 0 1928 0 25  60 0 0
4797  68 0  84 156 102 25.8 1 0 0 0 0 0 1 1  3 0 1450 0 1 0 1450 0 25  60 0 0
4411  64 0  66 124  92 24.3 1 1 0 0 0 1 0 3  5 1  411 0 1 0  411 0 20  60 0 0
2342  80 0  85 130  48 25.5 0 0 0 1 0 1 0 2  4 0    1 1 1 1    1 0 25  80 0 0
2394  76 0 100 129  66 31.2 1 0 0 0 0 0 0 1  1 1  376 0 1 0  376 0 30  70 0 0
4218  83 0  67 117  72 21.1 0 0 1 0 0 0 1 3  0 0    2 1 1 1    2 0 20  80 0 0
3171  65 0  78 179 100 25.4 1 0 1 0 0 0 1 2 10 0  412 0 1 0  412 0 25  60 0 0
2013  81 0  55 160  84 26.6 1 0 0 1 0 0 1 1  4 0  426 0 1 0  426 0 25  80 0 0
4893  40 0  88 193 100 34.9 1 0 0 0 0 0 0 2  5 0 2343 0 1 0 2343 0 30  40 0 0
3767  79 0  80  81  51 25.7 0 0 0 0 0 0 1 3  3 0    3 1 1 1    3 0 25  70 0 0
1736  62 0  66 105  61 24.2 0 0 0 0 0 0 0 3  3 0  303 0 1 0  303 0 20  60 0 0
 499  55 0  58 147  85 26.9 1 0 0 0 0 0 0 3  4 0  644 1 1 1  644 0 25  50 0 0
3557  73 0  76 142 104 35.3 1 0 0 0 0 1 0 1 23 0 2107 0 1 0 2107 0 35  70 0 0
3277  83 0 106 168 100 25.1 1 0 1 0 0 1 0 1  5 0 1350 0 1 0 1350 0 25  80 0 0
4890  58 0  76 160  78 30.6 0 0 0 0 0 0 0 3  5 0 1989 0 1 0 1989 0 30  50 0 0
3338  40 0  71 180 100 32.1 1 0 0 0 0 1 0 1  3 0 1343 0 1 0 1343 0 30  40 0 0
2021  56 0  70 136  70 39.3 1 0 0 0 0 1 0 3  3 0  529 0 1 0  529 0 35  50 0 0
 675  83 0  75 100  63   29 1 0 0 0 0 0 0 2  5 1   33 1 1 1   33 0 25  80 0 0
1508  67 0  76 170  72 21.9 1 0 0 0 0 0 1 2  5 0   19 1 1 1   19 0 20  60 0 0
3032  52 0 110 147  76 40.7 1 0 0 0 0 1 0 1  2 0 1355 0 1 0 1355 0 40  50 0 0
 927  80 0 124 170 113 40.1 1 0 0 0 0 0 0 2  3 0 1185 0 1 0 1185 0 40  80 0 0
4242  72 0 114 103  47 40.2 1 0 0 0 0 1 0 1  3 0  977 1 1 1  977 0 40  70 0 0
4157  71 0 136 138  93 32.5 1 1 0 1 0 1 0 3  9 0 1384 1 1 1 1384 0 30  70 0 0
 727  61 0  55 169  86 30.3 1 0 0 0 0 0 0 1  6 0 2164 0 1 0 2164 0 30  60 0 0
 244  84 0 100 196 103 20.6 1 0 0 0 1 0 0 1  4 0 1233 1 1 1 1233 0 20  80 0 0
4865  73 0  94 125  70 24.5 1 0 0 0 0 1 0 3  5 0 1322 0 1 0 1322 0 20  70 0 0
1894  90 0  70 179 133 28.3 1 0 0 1 0 1 0 3  9 0   39 1 1 1   39 0 25  90 0 0
 303  67 0  53 192  70 25.3 1 0 0 0 0 1 0 3  3 0   60 1 1 1   60 0 25  60 0 0
3700  75 0  64 138  53 31.7 1 0 0 1 0 0 0 2  5 0 1191 0 1 0 1191 0 30  70 0 0
1994  85 0  96 212  84 31.7 1 1 0 0 0 1 0 1  2 0  551 1 1 1  551 0 30  80 0 0
2378  58 0  76 201  94 31.6 1 0 0 0 0 0 0 3 10 0 1388 0 1 0 1388 0 30  50 0 0
 778  46 0 108  80  70 33.3 1 0 0 0 0 0 1 2  1 0 1298 0 1 0 1298 0 30  40 0 0
 525  59 0  92  89  54 24.7 1 0 0 1 0 0 1 1  6 1  466 0 1 0  466 0 20  50 0 0
end
label values bmicat bmicat
label def bmicat 15 "15-19", modify
label def bmicat 20 "20-24", modify
label def bmicat 25 "25-29", modify
label def bmicat 30 "30-34", modify
label def bmicat 35 "35-39", modify
label def bmicat 40 "40-44", modify
label values agecat agecat
label def agecat 30 "30-39", modify
label def agecat 40 "40-49", modify
label def agecat 50 "50-59", modify
label def agecat 60 "60-69", modify
label def agecat 70 "70-79", modify
label def agecat 80 "80-89", modify
label def agecat 90 "90-99", modify
label def agecat 100 "100-110", modify