|Features of selected approaches to analysis of HTE|
|Meta-analysis||CART||N of 1 trials||LGM/GMM*||QTE**||Nonparametric||Predictive risk models|
|Intent of HTE Analysis||· Exploratory and confirmatory||· Exploratory||· Exploratory and initial testing||· Exploratory, initial testing, and confirmatory||· Exploratory, initial testing, and 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||· Moderate to large sample sizes||· Large sample sizes||· Sample sizes depends on specific risk function|
|· GMM: Large sample sizes|
|Key Strength(s)||· Increase statistical power by pooling of results||· Does not require assumptions around normality of distribution||· Patient is own control||· Accounting for unobserved characteristics||· Robust to outcome outliers||· No functional form assumptions||· Multivariate approach to identifying risk factors or HTE|
|· Estimates patient-specific effects|
|· Heterogeneous response across quantiles||· Flexible regressions|
|·Heterogeneous response across time|
|· Possible to identify HTE across trials||· Can utilize different types of response variables|
|· Possibility to measure and explain covariate's effect on treatment effect|
|Key Limitation(s)||· Included studies need to be similar enough to be meaningful||· Fairly sensitive to changes in underlying data||· Requires de novo study||· Criteria for optimization solutions not clear||· Treatment effect designed for a quantile, not a specific patient||·Computationally demanding||· May be more or less interpretable or useful clinically|
|· Not applicable to all conditions or treatments||· Smoothing parameters required for kernel methods|
|· May not fully identify additive impacts of multiple variables|
|· Assumed distribution|
|· Selection bias|
* LGM/GMM: Latent growth modeling/Growth mixture modeling.
**QTE: Quantile treatment effect.
Willke et al.
Willke et al. BMC Medical Research Methodology 2012 12:185 doi:10.1186/1471-2288-12-185