Mapping of disease-specific Oxford Knee Score onto EQ-5D-5L utility index in knee osteoarthritis

Table 1 Summary for the structure and number of models tried to build a mapping algorithm from OKS to each of the five domains of EQ-5D-5L

Model class	Hyperparameters			Model structures (n)^a	Model trials (n)^b
Model class	Hyperparameter	Values	Number
Cumulative model (CM)	Parallelism	TRUE or FALSE	2	10	40
Cumulative model (CM)	Link	Logit, Probit, Cauchit, Cloglog, or Logc	5	10	40
Penalized regression	Alpha	Ridge or Lasso	2	16	64
	Criteria	AIC or BIC	2
	Link	Logit, Probit, Cauchit, or Cloglog,	4
Ordinal CART	CP	20 randomly selected values	20	80	320
	Split	Misclassification cost in absolute or quadratic terms	2
	Prune	Misclassification rate or cost	2
Ordinal forest	Nsets	50, 100, or 150	3	27	108
	Ntreeperdiv	50, 100, or 150	3
	Ntreefinal	200, 400, or 600	3
				133	532

^aRefers to the number of model structures per every model class. It is the product of multiplication of the number of the values taken by each hyperparameter, e.g., in the cumulative model, Parallelism and Link can take two and 5 different values respectively, resulting in 10 different CM structures
^bRefers to the number of model trials per every model class. It is the product of multiplication of the number of structures and the number of tried sets of predictors (4 sets). Link, link function used to transform cumulative probability into an unbounded scale; α, the term of penalty; criteria, criteria used for selecting the optimum magnitude of penalty; CP, Complexity Parameter, Split, criteria for splitting; Prune: criteria for pruning; nsets, number of score sets tried before the approximation of the optimal score set; ntreeperdiv, number of trees in the smaller forests; ntreefinal, number of trees in the final OF constructed using the optimized score set

ISSN: 1749-799X