| All (n = 152) | Regression-based models (n = 42) | Non-regression-based models (n = 71) | Ensemble models (n = 39) |
---|---|---|---|---|
n (%) | n (%) | n (%) | n (%) | |
Predictor selection (before modelling) reported | 52 (34) | 20 (48) | 23 (32) | 9 (23) |
 A-priori | 5 | 3 | 1 | 1 |
 No selection before modelling | 3 | 1 | 2 | – |
 Univariable | 24 | 12 | 8 | 4 |
 Clinically relevant and available data | 1 | – | 1 | – |
 Dropout technique at input layer | 1 | – | 1 | – |
 Random forest with RPA | 9 | 1 | 6 | 2 |
 Other modelling approacha | 9 | 3 | 4 | 2 |
Predictor selection (during modelling) reported | 63 (41) | 25 (59) | 27 (38) | 11 (28) |
 Stepwise | 6 | 4 | 2 | – |
 Forward selection | 6 | 5 | – | 1 |
 Backward elimination | 5 | 3 | 2 | – |
 Full model approach (no selection) | 11 | 4 | 5 | 2 |
 Feed forward/backpropagation | 5 | – | 5 | – |
 Recursive partitioning analysis | 7 | – | 7 | – |
 LASSO | 5 | 5 | – | – |
 Gini index (minimised) | 7 | 1 | 4 | 2 |
 Cross validation | 4 | 2 | – | 2 |
 Otherb | 7 | 1 | 2 | 4 |
Hyperparameter tuning methods reported | 31 (21) | 4 (10) | 15 (23) | 12 (31) |
 Cross validation | 19 | 4 | 7 | 8 |
 Grid search (no further details provided) | 6 | – | 4 | 2 |
 Max tree depth | 2 | – | 1 | 1 |
 Adadelta method | 2 | – | 2 | – |
 Default software values | 2 | – | 1 | 1 |