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Table 2 Number of simulated databases with quasi-complete separation

From: How to analyze work productivity loss due to health problems in randomized controlled trials? A simulation study

Distribution of productivity loss outcome Number of Observations in each arm Truncated negative binomial distributions of productivity loss outcomes in the two arms Number of databases with quasi-complete separationa
80:15:5/60:30:10 100 Equal Scale 3
80:15:5/60:30:10 100 Unequal Scale 7
80:15:5/60:30:10 200 Equal Scale 0
80:15:5/60:30:10 200 Unequal Scale 0
60:35:5/40:50:10 100 Equal Scale 0
60:35:5/40:50:10 100 Unequal Scale 0
60:35:5/40:50:10 200 Equal Scale 0
60:35:5/40:50:10 200 Unequal Scale 0
50:40:10/30:55:15 50 Equal Scale 0
50:40:10/30:55:15 50 Unequal Scale 2
50:40:10/30:55:15 100 Equal Scale 0
50:40:10/30:55:15 100 Unequal Scale 0
50:40:10/30:55:15 200 Equal Scale 0
50:40:10/30:55:15 200 Unequal Scale 0
  1. aThe maximum likelihood estimate may not exist while running multinomial logistic regression for the three-part models. For number of observations = 50 and 5% max loss, a large number of databases with quasi-separation and thus the three-part model was not considered for these scenarios