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Table 5 Logistic regression models with six different approaches

From: Bayesian predictors of very poor health related quality of life and mortality in patients with COPD

Very poor HRQoL/death outcome Logit 1 Logit 2 Logit3 Logit 4 Logit5 Logit6
Parameter OR SE OR SE OR SE OR SE OR SE OR SE
Age at onset of COPD 0.9772 0.0178 0.9860 0.0148    0.8432* 0.0585    0.9342* 0.0273
Age at onset asthma          0.8469* 0.0560   
Year of birth        0.8579 0.0680 0.8663 0.0647 0.9163* 0.0313
Diagnosed cerebrovascular disease 2.2090 0.9669 2.4865* 0.8858 2.5121** 0.7810       
Diabetes 2.0537* 0.6558 2.2233** 0.6105 2.2204** 0.5260       
Alcohol abuse 2.1063* 0.6814 2.4580** 0.6461 1.9996** 0.4726      2.4388** 0.7556
Cancer 2.5036 1.3626 2.1930** 1.2808 2.5107* 0.8961       
Any psychiatric disease 4.6815*** 1.7979 3.3283*** 1.0637 2.5081** 0.7054 3.3262 2.4100 4.0301 2.9826 3.8206*** 1.4034
Body mass index 1.2048 0.2493 1.4440* 0.2496         
FEV1% of predicted spirometry 0.9761** 0.0071 1.4440*** 0.0580        0.9851* 0.0065
Atrial fibrillation 3.1544* 3.1544          2.6895* 1.1314
Corrected QT-time 1.0093* 0.0047           
Tests Value p Value p Value p Value p Value p Value p
N included to the model 365   547   647   75   75   389  
Probability of the model (chi2) 52.90 <0.0001 72.00 <0.0001 48.21 <0.0001 10.25 0.0166 10.28 0.0163 36.94 <0.0001
Pseudo R2 12.18%   11.69%   6.38%   12.07%   12.11%   7.88%  
Sensitivity 33.01%   24.09%   15.43%   21.05%   21.05%   21.24%  
Specificity 95.80%   95.61%   93.64%   94.64%   94.64%   94.20%  
Positive predictive value 75.56%   64.71%   47.37%   57.14%   57.14%   60.00%  
Negative predictive value 78.44%   79.03%   74.92%   77.94%   77.94%   74.50%  
Log likelihood −190.73   −271.87   −353.57   −37.32   −37.31   −215.93  
Akaike information criteria 403.46   561.74   719.14   82.65   82.61   445.87  
Bayesian information criteria 446.36   600.48   745.98   91.92   91.88   473.61  
Proportion of observations used 56.41%   84.54%   100.00%   11.59%   11.59%   60.12%  
Correct classification of used N 78.08%   77.70%   72.49%   76.00%   76.00%   73.01%  
Worst possible correct classification among all (N 647) 44.05%   65.69%   72.49%   8.81%   8.81%   43.89%  
  1. NBCMM = naïve Bayesian classification merger model. Logit1 = all variables included in the NBCMM (comparable to the NBCMM in terms of predictors included to the logit model). Logit2 = variables which had at least 80% of observations present. Logit3 = variables which had 100% of observations present (comparable to the NBCMM in terms of number of observations included). Logit4 = forward stepwise elimination with p-value 0.10 threshold. Logit5 = backward stepwise elimination with p-value 0.10 threshold (shows how well the greedy hill-descending predictor selection of naïve Bayes approach performed in comparison to the p-value selection). Logit6 = manual one-by-one dropping of the predictor variable with the poorest p-value until all logit predictors have a p-value below 0.10. *p<0.050. **p<0.010. ***p<0.001.