Table 2 Methods for predictor selection before and after modelling and hyperparameter tuning for 152 developed clinical prediction models, by modelling type

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 approach^a

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

 Other^b

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