Difference between revisions of "SMHS MethodsHeterogeneity"
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==[[SMHS| Scientific Methods for Health Sciences]] - Methods for Studying Heterogeneity of Treatment Effects, Case-Studies of Comparative Effectiveness Research == | ==[[SMHS| Scientific Methods for Health Sciences]] - Methods for Studying Heterogeneity of Treatment Effects, Case-Studies of Comparative Effectiveness Research == | ||
| − | + | ==Methods and Approaches for HTE Analytics== | |
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<center> | <center> | ||
| − | {| class="wikitable" style="text-align:center; " border="1" | + | {| class="wikitable" style="text-align:center; width:99%" border="1" |
| − | | | + | ! colspan="8" |Methods and Approaches for HTE Analytics **** |
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| − | | | + | |||Meta-analysis||CART*||N of 1 trials||LGM/GMM**||QTE***||Nonparametric||Predictive risk models |
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| − | | | + | |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 | ||
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| − | | | + | |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 |
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</center> | </center> | ||
| − | + | Adopted from: http://dx.doi.org/10.1186/1471-2288-12-185 | |
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| − | + | * *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 [http://cran.r-project.org/web/packages/rmeta R-package rmeta]. Non-parametric R approaches are included in the [http://cran.r-project.org/web/packages/np/vignettes/np.pdf np package]. | |
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| − | + | Additional details are provided in a paper entitled [http://dx.doi.org/10.1186/1471-2288-12-185 From concepts, theory, and evidence of heterogeneity of treatment effects to methodological approaches: a primer]. | |
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| − | + | ==[[SMHS_MethodsHeterogeneity_HTE |HTE Analytics, Latent growth and growth mixture modeling (LGM/GMM)]]== | |
| − | + | ==[[SMHS_MethodsHeterogeneity_MetaAnalysis |Meta-analysis]]== | |
| − | + | ==[[SMHS_MethodsHeterogeneity_CER| Comparative Effectiveness Research (CER)]]== | |
| − | + | <hr> | |
| − | + | * SOCR Home page: http://www.socr.umich.edu | |
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| − | + | {{translate|pageName=http://wiki.socr.umich.edu/index.php/SMHS_MethodsHeterogeneity_HTE}} | |
Latest revision as of 13:06, 23 May 2016
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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