*--------------------------------------------------------------------*; * Program: /jml/biostatmethods/chapter7/renal.sas *; * Source: Biostatistical Methods: The assessment of Relative Risks *; * John Wiley and Sons, 2000 *; * Author: John M. Lachin *; * Copyright (c) 2000 by John M. Lachin *; * Purpose: The SAS program and data set presented in Table 7.5 *; * for the analysis of a subset of the DCCT nephropathy *; * data used in Example 7.4. This is used to generate the *; * output presented in Table 7.6. This also includes the *; * call of PROC GENMOD presented in Example 7.7 and the *; * output presented in Table 7.7. Further it includes the *; * computaion of the sums of squares (through Proc *; * Univariate) described in Example 7.14. *; * *; * Modified by Paul Dickman to illustrate various methods *; * for estimating ratios of proportions (rather than odds *; * ratios) *; * *; * Web URL: http://www.bsc.gwu.edu/jml/biostatmethods *; *--------------------------------------------------------------------*; options pageno=1; data renal; input obsn micro24 int hbael duration sbp female; yearsdm=duration/12; label obsn='observation number' micro24='prevalence of microalbuminuria at 6 years fu' int='intensive treatment (vs conventional)' hbael='level (%) of HbA1c at baseline' duration='prior duration of diabetes' sbp='systolic blood pressure (mm Hg)' female='female (vs male) gender' ; cards; 1 0 1 9.63 178 104 1 2 0 0 7.93 175 112 0 3 1 0 11.20 126 110 1 4 1 0 10.88 116 106 0 5 0 0 8.22 168 110 1 6 1 1 12.73 71 112 0 7 0 0 8.28 107 116 1 8 0 1 9.44 79 120 1 9 0 0 7.44 176 120 1 10 0 0 8.33 47 140 0 11 1 1 9.89 135 126 1 12 0 1 12.06 117 120 0 13 0 1 9.01 35 128 1 14 0 1 10.05 82 110 1 15 1 1 9.60 70 108 1 16 0 1 10.17 159 121 1 17 0 1 10.67 43 110 0 18 0 1 8.52 142 118 0 19 1 0 8.13 99 124 1 20 0 0 8.27 105 98 1 21 1 0 8.60 126 126 0 22 1 0 9.83 139 116 0 23 0 0 8.04 97 118 0 24 0 1 9.38 113 100 0 25 1 0 10.72 115 110 0 26 0 1 7.85 48 114 0 27 0 1 9.72 151 118 0 28 0 0 7.80 19 110 0 29 0 0 8.96 84 108 1 30 0 1 8.96 80 124 0 31 0 1 7.09 109 130 1 32 0 1 8.20 90 130 0 33 0 1 8.81 92 104 1 34 0 1 8.28 112 120 1 35 0 1 11.95 38 104 1 36 0 0 14.37 147 122 1 37 1 0 7.72 119 128 0 38 0 1 9.99 119 120 1 39 1 1 9.67 180 116 0 40 0 1 7.26 16 110 0 41 0 1 7.80 154 130 0 42 0 0 10.23 124 108 1 43 1 0 11.77 57 120 1 44 0 0 9.56 44 126 0 45 1 0 12.38 96 110 1 46 0 0 10.94 99 100 1 47 0 1 9.56 143 130 0 48 0 1 8.27 116 115 0 49 0 0 9.70 56 108 0 50 0 0 7.36 168 96 1 51 0 0 8.03 162 118 0 52 0 0 8.82 114 124 1 53 0 0 7.28 157 104 0 54 1 1 10.70 135 92 0 55 0 1 9.40 77 124 1 56 0 1 11.03 165 120 0 57 0 1 8.62 142 98 1 58 0 1 7.01 71 120 0 59 1 0 12.22 67 102 1 60 0 1 8.22 147 120 0 61 1 0 10.55 101 116 0 62 0 1 9.66 78 90 1 63 0 0 10.13 77 110 1 64 1 0 9.44 84 118 0 65 0 0 9.61 167 126 1 66 0 1 8.78 137 110 0 67 0 0 7.34 173 144 0 68 0 0 9.10 177 100 1 69 0 1 7.44 113 136 1 70 0 1 9.35 59 118 0 71 1 1 7.36 138 148 0 72 0 0 8.09 146 114 1 73 0 1 9.60 147 122 1 74 1 0 9.63 131 118 0 75 1 0 8.19 107 120 0 76 0 0 6.72 116 112 0 77 0 1 9.77 67 124 1 78 0 1 7.86 99 130 0 79 1 0 10.02 157 124 0 80 0 0 9.98 105 106 0 81 0 1 7.89 172 110 1 82 0 0 7.50 133 118 0 83 0 0 7.74 135 90 1 84 0 0 8.54 146 104 1 85 0 1 11.07 82 122 1 86 1 1 10.23 84 104 1 87 1 0 8.11 55 128 0 88 0 1 10.84 84 138 0 89 1 0 9.32 170 110 1 90 0 1 10.75 102 118 1 91 0 0 8.22 97 116 0 92 0 1 9.70 131 110 1 93 0 1 9.11 145 126 0 94 0 1 9.29 129 114 0 95 0 1 8.33 122 118 1 96 1 1 11.09 27 104 0 97 0 1 8.98 112 112 1 98 1 0 10.56 97 120 0 99 0 1 8.16 166 118 1 100 0 1 8.08 172 92 1 101 1 0 10.44 72 128 1 102 0 1 8.07 109 118 1 103 1 1 7.50 147 114 0 104 1 1 8.49 168 122 1 105 0 0 10.52 35 120 0 106 0 1 9.49 154 110 0 107 0 1 12.40 119 112 1 108 0 1 8.49 173 98 1 109 0 1 9.79 35 94 1 110 0 0 7.44 104 114 0 111 0 1 8.22 133 112 0 112 0 0 7.18 17 108 0 113 0 1 9.29 132 118 0 114 0 0 9.26 151 118 1 115 0 0 10.16 96 122 0 116 0 1 7.09 153 118 0 117 0 1 8.10 163 116 1 118 1 0 10.54 46 130 0 119 0 0 9.23 70 112 1 120 0 0 10.07 112 110 1 121 1 0 8.87 138 132 0 122 0 1 7.95 55 122 0 123 0 0 6.66 141 112 0 124 1 0 8.77 168 112 0 125 0 0 9.07 111 124 1 126 0 1 8.98 116 100 0 127 0 1 8.89 33 118 0 128 0 1 10.94 88 110 0 129 0 1 8.50 118 125 0 130 1 0 12.34 122 110 1 131 0 1 12.45 117 98 1 132 1 0 8.82 130 140 0 133 0 1 10.78 120 110 0 134 0 1 8.25 143 102 0 135 0 0 9.48 135 120 0 136 0 0 8.74 155 136 0 137 0 1 7.43 145 112 0 138 0 1 7.94 112 140 0 139 0 0 11.31 101 120 1 140 1 0 10.12 110 120 0 141 0 1 7.36 165 130 0 142 0 1 9.17 154 118 1 143 0 1 8.59 163 132 0 144 1 0 8.07 93 126 0 145 0 1 10.28 104 118 0 146 0 0 8.00 84 110 1 147 0 1 8.10 63 108 0 148 0 0 10.60 163 132 0 149 0 0 7.60 76 124 0 150 0 1 9.50 152 126 1 151 0 0 10.30 125 102 0 152 0 1 8.80 127 120 1 153 1 0 9.30 136 114 1 154 0 1 10.00 175 128 0 155 0 0 9.10 140 118 1 156 1 1 12.50 91 120 0 157 0 0 9.70 132 106 1 158 1 0 11.80 157 128 0 159 0 1 7.00 64 126 0 160 0 1 9.00 141 114 0 161 0 1 8.00 46 131 0 162 1 0 12.50 99 116 1 163 1 0 7.10 89 114 0 164 0 0 8.60 139 130 1 165 0 1 12.20 76 106 1 166 0 1 11.70 118 110 1 167 1 0 11.30 99 122 1 168 0 0 6.80 41 104 1 169 0 0 10.60 82 106 1 170 0 1 8.70 101 98 1 171 0 0 7.90 136 126 0 172 0 0 10.10 127 124 0 ; proc genmod data = renal descending; model micro24 = int hbael yearsdm sbp female / dist=binomial link=logit; title 'DCCT - logistic regression'; run; ods output parameterestimates=parmest obstats=obstats; proc genmod data = renal descending; model micro24 = int hbael yearsdm sbp female / dist=binomial link=log; title 'DCCT - log link'; run; ods output close; ods output parameterestimates=parmest obstats=obstats; proc genmod data = renal descending; model micro24 = int hbael yearsdm sbp female / dist=binomial link=log intercept=-1 initial=0 0 0 0 0; title 'DCCT - log link specifying initial values'; run; ods output close; proc genmod data = renal descending; class obsn; model micro24 = int hbael yearsdm sbp female / dist=poisson link=log; title 'DCCT - Poisson regression with robust standard errors'; repeated subject=obsn / type=ind; run;