SMHS MethodsHeterogeneity

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Scientific Methods for Health Sciences - Methods for Studying Heterogeneity of Treatment Effects, Case-Studies of Comparative Effectiveness Research

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.

HTE Analytics, Latent growth and growth mixture modeling (LGM/GMM)


Comparative Effectiveness Research (CER)

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