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"statistical methods"

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Conclusions

Presentation: Study Size Planning for Observational Comparative Effectiveness Research
Keywords: conclusion | study size | statistical methods
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Summary Checklist (1 of 2)

Presentation: Study Size Planning for Observational Comparative Effectiveness Research
Keywords: summary checklist | study size | statistical methods
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Summary Checklist (2 of 2)

Presentation: Study Size Planning for Observational Comparative Effectiveness Research
Keywords: summary checklist | study size | statistical methods
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Introduction

Presentation: Considerations for Statistical Analysis in Observational Comparative Effectiveness Research
Keywords: comparative effectiveness | statistical methods | analysis
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Conclusion

Presentation: Considerations for Statistical Analysis in Observational Comparative Effectiveness Research
Keywords: conclusion | statistical analysis | observational comparative effectiveness research
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Introduction

Presentation: Covariate Selection for Observational Comparative Effectiveness Research
Keywords: introduction | covariate selection | confounding | causal graphs
On Data Extraction (II). Data extraction involves more than copying words and numbers from the publication to a form. Clinical domain, methodological, and statistical knowledge is needed to ensure the right information is captured. Interpretation of published data is often needed. What is reported is sometimes not what was carried out. Data extraction and evaluation of risk of bias and of applicability typically occur at the same time.
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On Data Extraction (II)

Presentation: Data Extraction
Keywords: data extraction | observational studies | risk of bias | applicability
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Study Design and Analysis (2 of 2)

Presentation: Outcome Definition and Measurement in Observational Comparative Effectiveness Research
Keywords: study design | analysis | study period | analytic considerations
This slide presents a table consisting of four columns. Column one is unlabeled; column two is labeled “Odds Ratio,”column three is labeled “Risk Ratio,” column four is labeled “Risk Difference.” Data row one, “Fixed Effect Model.” Odds ratio: Mante, 1-HaenszelPeto, Exact, Inverse variance weighted; Risk Ratio: Mantel-Haenszel, Inverse variance weighted; Risk Difference: Inverse variance weighted. Data row two, “Random Effects Model.” Odds ratio: DerSimonian and Laird. ; Risk Ratio: DerSimonian and Laird; Risk Difference: DerSimonian and Laird.
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Commonly Used Statistical Methods for Combining 2x2 Tables

Presentation: Quantitative Synthesis I
Keywords: statistical model
This figure shows a meta-regression plot. Studies are depicted as empty circles with areas proportional to their weight (influence). A line describes how the treatment effect (risk of all-cause death, y-axis) changes with the dose of vitamin E supplementation (x-axis). The line curves upward for higher doses, suggesting increased risk of death with increasing dose.
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Corresponding Univariate Meta-Regression: A Meta-analysis of Vitamin E Doses and Mortality

Presentation: Quantitative Synthesis II
Keywords: meta-analysis | meta-regression | risk difference
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Conclusion

Presentation: Sensitivity Analysis for Observational Comparative Effectiveness Research
Keywords: conclusion | sensitivity analysis
Confounding by Indication. Is a type of selection bias. Occurs when different diagnoses, severity of illness, or comorbid conditions are important reasons for physicians to assign different treatments. Is a common problem in pharmacoepidemiological studies comparing benefits. Is often difficult to adjust for, making studies with a high degree of this potential bias usually unsuitable for inclusion in a comparative effectiveness review.
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Confounding by Indication

Presentation: When To Select Observational Studies as Evidence for Comparative Effectiveness Reviews
Keywords: confounding by indication | selection bias | intervention
 
 
 
 
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