SMHS MethodsHeterogeneity

Methods and Approaches for HTE Analytics

Methods and Approaches for HTE Analytics ****
	Meta-analysis	CART*	N of 1 trials	LGM/GMM**	QTE***	Nonparametric	Predictive risk models
Intent of the Analysis	Exploratory and confirmatory	Exploratory	Exploratory and initial testing	"Exploratory, initial testing, and confirmatory"	"Exploratory, initial testing, & confirmatory"	Exploratory and confirmatory	Initial testing and confirmatory
Data Structure	"Trial summary results, possibly with subgroup results"	Panel or cross-section	Repeated measures for a single patient: time series	Time series and panel	Panel and cross-sectional	"Panel, time series, and cross-sectional"	Panel or cross-sectional
Data Size Consideration	Advantage of combining small sample sizes	Large sample sizes	Small sample sizes	LGM: small to large sample sizes; GMM: Large sample sizes	Moderate to large sample sizes	Large sample sizes	Sample sizes depends on specific risk function
Key Strength(s)	Increase statistical power by pooling of results	Does not require assumptions around normality of distribution Can utilize different types of response variables; Possible to identify HTE across trials Possibility to measure and explain covariate's effect on treatment effect	Patient is own control; Estimates patient-specific effects	Accounting for unobserved characteristics Heterogeneous response across time	Robust to outcome outliers Heterogeneous response across quantiles	No functional form assumptions Flexible regressions	Multivariate approach to identifying risk factors or HTE Estimates patient-specific effects
Key Limitation(s)	Included studies need to be similar enough to be meaningful Assumed distribution; Selection bias	Fairly sensitive to changes in underlying data May not fully identify additive impacts of multiple variables	Requires de novo study Not applicable to all conditions or treatments	Criteria for optimization solutions not clear	"Treatment effect designed for a quantile, not a specific patient"	Computationally demanding Smoothing parameters required for kernel methods	May be more or less interpretable or useful clinically

*CART: Classification and regression tree (CART) analysis

LGM/GMM: Latent growth modeling/Growth mixture modeling.

QTE: Quantile Treatment Effect.

Standard meta-analysis like fixed and random effect models, and tests of heterogeneity, together with various plots and summaries, can be found in the R-package rmeta. Non-parametric R approaches are included in the np package.

Additional details are provided in a paper entitled From concepts, theory, and evidence of heterogeneity of treatment effects to methodological approaches: a primer.

Comparative Effectiveness Research (CER)

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• SOCR Home page: http://www.socr.umich.edu

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