TABLE 5.

Recommendations for Methods When Assessing Continuous Outcomes

	Recommended Uses	Recommended With Potential Alterations	Not Generally Recommended	Not Applicable
Cross-classification	Estimation at large-sample sizesa		Estimation at small sample sizesa	Variable selection
Regression (saturated model)	Estimation at large-sample sizesa		Estimation at small sample sizesa	Variable selection (partial information)
MAIHDAb	Estimation at all sizes			Variable selection (partial information)
CARTc			Estimation at all sample sizes Variable selection
CTreed	Estimation at all sample sizes		Variable selection (may be improved with cross-validation for alpha)
Random forest	Estimation at all sample sizes	Variable selection: with adjusted VIMe

^aDefining sample size as “large” or “small” is relative to the number of intersections of interest. In our scenario with 192 intersections of interest, we considered smaller sample sizes to be N = 2,000 to 5,000 and larger sample sizes as N = 50,000 and greater. A smaller number of intersections under study would allow for smaller sample sizes.

^bMultilevel analysis of individual heterogeneity and discriminatory accuracy.

^cClassification and regression trees.

^dConditional inference trees.

^eVariable importance measure.