Skip to main content

Table 1 Coefficients of two-part model for alcohol consumption prediction

From: Modelling the consequences of a reduction in alcohol consumption among patients with alcohol dependence based on real-life observational data

   Logistic regression model for probability of drinking Negative binomial regression model for amount of alcohol consumed on drinking days
Parameter Level Estimate Standard error P-value Odds ratio Estimate Standard error P-value Odds ratio
Intercept   −3.5123a 0.6325 <.0001 - 3.6665b 0.2766 <.0001 -
Age (years, ref = 0)   0.006217 0.01127 0.5813 1.0062 −0.01163 0.004807 0.0156 0.9884
Sex (ref = male) Female 0.2055 0.2796 0.4624 1.2281 0.01766 0.1221 0.885 1.0178
Day (ref = Saturday) Sunday −0.05629 0.08452 0.5054 0.9453 −0.02609 0.01826 0.153 0.9742
Monday −0.4179 0.0839 <.0001 0.6584 −0.05914 0.01834 0.0013 0.9426
Tuesday −0.2794 0.08356 0.0008 0.7562 −0.0489 0.01838 0.0078 0.9523
Wednesday −0.2098 0.08446 0.013 0.8107 −0.03008 0.01845 0.103 0.9704
Thursday −0.05853 0.08508 0.4915 0.9431 −0.02463 0.01856 0.1846 0.9757
Friday −0.03683 0.08563 0.6671 0.9638 0.004004 0.01865 0.83 1.004
Log (1 + consumption) day −1   0.5019 0.01368 <.0001 1.6519 0.07999 0.004127 <.0001 1.0833
Log (1 + consumption) day −2   0.361 0.01396 <.0001 1.4348 0.05808 0.00408 <.0001 1.0598
Log (1 + consumption) day −7   0.3777 0.01312 <.0001 1.4589 0.05105 0.003695 <.0001 1.0524
Treatment during follow-up (ref = No treatment) Psychological −0.1548 0.324 0.6328 0.8566 0.1968 0.1405 0.1615 1.2175
Psychological and pharmaceutical −0.07805 0.58 0.893 0.9249 0.09311 0.2426 0.7012 1.0976
Time index   0.000461 0.000198 0.02 1.0005 0.02279 0.005067 <.0001 1.0231
Mean of baseline consumption per patient   0.000659 0.001405 0.6389 1.0007 0.01156 0.006173 0.061 1.0116
Standard deviation of baseline consumption per patient   0.00031 0.003629 0.9319 1.0003 0.0253 0.0153 0.0982 1.0256
Depressed (ref = No) Yes −0.1817 0.2674 0.4968 0.8339 0.1539 0.1148 0.1801 1.1664
Day 1   −3.9724 0.5882 <.0001 0.0188 −0.09164 0.1547 0.5537 0.9124
Day 2   −2.5372 0.5816 <.0001 0.0791 −0.1644 0.1721 0.3393 0.8484
Day 3   −0.475 0.386 0.2185 0.6219 −0.2108 0.1105 0.0565 0.8099
Day 4   −0.2679 0.4006 0.5036 0.765 −0.1374 0.1088 0.2067 0.8716
Day 5   −0.6315 0.4465 0.1573 0.5318 −0.3298 0.1207 0.0063 0.7191
Day 6   −1.0807 0.5291 0.0411 0.3394 −0.2246 0.1489 0.1314 0.7988
Day 7   −0.4915 0.5832 0.3994 0.6117 −0.2601 0.1488 0.0805 0.771
  1. aThe intercept estimate of the logistic regression is the coefficient from which the probability of drinking can be derived for the reference patient-day. On the reference patient-day, the probability of drinking is p = exp(3.5123/(1 + exp(3.5123)) = 0.0290
  2. bThe intercept estimate of the negative binomial model is the coefficient from which the mean amount of alcohol consumed can be derived for the reference patient-day. On the reference patient-day, the mean quantity of alcohol consumed, in grams, is c = exp(3.6665) = 39.11