SMHS MethodsHeterogeneity
Contents
- 1 Scientific Methods for Health Sciences - Methods for Studying Heterogeneity of Treatment Effects, Case-Studies of Comparative Effectiveness Research
- 2 Methods and Approaches for HTE Analytics
- 3 HTE Analytics, Latent growth and growth mixture modeling (LGM/GMM)
- 4 Meta-analysis
- 5 Comparative Effectiveness Research (CER)
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 |
Adopted from: http://dx.doi.org/10.1186/1471-2288-12-185
- *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)
Meta-analysis
Comparative Effectiveness Research (CER)
- SOCR Home page: http://www.socr.umich.edu
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