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Table 3 Sample size and number of candidate predictors informing analyses for 152 developed models, by modelling type

From: Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

 

Regression-based models (n = 42)

Non-regression-based models (n = 71)

Ensemble models (n = 39)

Reported, n (%)

Median [IQR], range

Reported, n (%)

Median [IQR], range

Reported, n (%)

Median [IQR], range

Total sample size

 Model development

42 (100)

561 [203 to 2822], 20 to 582,398

70 (99)

447 [156 to 11,901], 20 to 582,398

39 (100)

768 [203 to 1599], 20 to 582,398

 Internal validationa

20 (48)

122 [82 to 228], 47 to 291,200

35 (49)

145 [90 to 492], 47 to 291,200

24 (62)

162 [97 to 1510], 67 to 291,200

 External validation

12 (29)

511 [67 to 2300], 11 to 836,659

14 (20)

793 [59 to 1675], 11 to 836,659

11 (28)

313 [229 to 836,659], 11 to 836,659

Number of events

 Model development

20 (48)

236 [34 to 1326], 7 to 35,019

37 (52)

62 [22 to 1075], 7 to 45,797

10 (26)

37 [22 to 241], 8 to 35,019

 Internal validationa

2 (5)

41 [21 to 61], 21 to 61

3 (4)

61 [21 to 62], 21 to 62

1 (3)

61

 External validation

8 (19)

81 [18 to 327], 7 to 513

11 (15)

19 [7 to 513], 7 to 1323

5 (13)

81 [81 to 81], 7 to 513

No. candidate predictors

38 (90)

21 [15 to 34], 6 to 33,788

64 (90)

16 [12 to 25], 5 to 33,788

36 (92)

25 [14 to 37], 4 to 33,788

Events per predictorb

20 (48)

8.0 [7.1 to 23.5], 0.2 to 5836.5

35 (49)

3.4 [1.1 to 19.1], 0.2 to 5836.5

10 (26)

1.7 [1.1 to 6.0], 0.7 to 5836.5

  1. aCombines all internal validation methods, e.g., split sample, cross validation, bootstrapping
  2. bEvents per predictor for model development