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"subgroup analyses"

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Outline of Material

Presentation: Study Size Planning for Observational Comparative Effectiveness Research
Keywords: outline | comparative effectiveness research | key considerations | study size planning
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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
Make Judgments About the Applicability of Individual Studies. For efficacy trials, clearly report characteristics that may limit applicability. Describe those characteristics in the text or in evidence tables under the heading “comments” or “limitations.” Describe how important factors would affect applicability and the expected direction and magnitude of bias.
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Make Judgments About the Applicability of Individual Studies

Presentation: Assessing Applicability
Keywords: efficacy trial | applicability | subgroup analyses | meta-regression | population
This slide presents a table of two columns, with one header row and five data rows. The header row contains titles describing the contents of each column. Column one is labeled Domain. Column two is labeled Description of Applicability Evidence for a Key Question. In row one, column one, Domain: Population. Description of Applicability Evidence for a Key Question: The population and disease stage are representative of the United States population with heart failure. In row two, Domain: Intervention. Description of Applicability Evidence for a Key Question: The intervention is plausible. In row three, Domain: Comparators. Description of Applicability Evidence for a Key Question: Watchful waiting is reasonable if the baseline treatment in both groups was standard medical therapy. Standard medical therapy is not being used in most patients. Subgroup analyses suggest that benefits are predominantly in those patients not receiving standard therapy. In row four, Domain: Outcomes. Description of Applicability Evidence for a Key Question: Although hospitalizations and survival are being evaluated, other outcomes, including harms, are not. In row five, Domain: Setting. Description of Applicability Evidence for a Key Question: The settings for the studies are large tertiary medical centers, which may overestimate the benefits of therapy in actual practice and accentuate the harms.
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Step 3. Completed Applicability Summary Table

Presentation: Assessing Applicability
Keywords: population | applicability | subgroup analysis | outcome | setting
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Comparative Effectiveness and Safety in Subpopulations

Presentation: Comparing Medications for Adults With Type 2 Diabetes
Keywords: safety | subpopulations | effectiveness | bone fractures in women | subgroup analysis
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Types of Subgroup Analyses

Presentation: Estimation and Reporting of Heterogeneity of Treatment Effects in Observational Comparative Effectiveness Research
Keywords: confirmatory | descriptive | exploratory | subgroup analyses
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General Considerations for Subgroup Analyses

Presentation: Estimation and Reporting of Heterogeneity of Treatment Effects in Observational Comparative Effectiveness Research
Keywords: general considerations | subgroup analyses | HTE | observational studies | propensity score
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Other Key Findings of the Comparative Effectiveness Review (2 of 3)

Presentation: Noninvasive Positive-Pressure Ventilation for Acute Respiratory Failure: Comparative Effectiveness
Keywords: acute respiratory failure | noninvasive positive-pressure ventilation | NPPV | outcome | strength of evidence | supportive care | finding
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Gaps in Knowledge and Future Research Needs (1 of 2)

Presentation: Noninvasive Technologies for Diagnosing Coronary Artery Disease in Women
Keywords: knowledge gaps | future research needs | coronary artery disease | CAD | women | diagnosis | noninvasive technologies
This block diagram depicts four general approaches and the specific methods that can be used to handle heterogeneity that may be encountered in a meta-analysis. The first approach is to ignore heterogeneity and combine data using a fixed effect model. The second approach is to test for heterogeneity using a chi-squared test and to not combine the data if the test is significant. In the third approach, if after assessing heterogeneity of the data it is deemed appropriate to combine results to provide an overall effect, then the random effects model is used. The fourth approach is to seek to explain heterogeneity by performing subgroup analyses or meta-regressions. The next module focuses on exploring and identifying reasons for heterogeneity.
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Dealing With Heterogeneity

Presentation: Quantitative Synthesis I
Keywords: heterogeneity | meta-analysis | method | fixed effect model | random effect model | subgroup analysis | meta-regression
Shown is a diagram that describes four ways to deal with between-study heterogeneity. From left to right, there are four options: 1)Ignore any heterogeneity, and always perform a meta-analysis with a fixed effects model; 2) Perform a statistical test for heterogeneity. If the test is nonsignificant, report a fixed effects meta-analysis. If the test is significant, do not perform a meta-analysis; 3) Consider using a random effects model, which explicitly allows for between-study heterogeneity and incorporates it in the calculations; 4) Always explore and explain between-study heterogeneity by performing subgroup analyses or meta-regression analyses.
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Dealing With Heterogeneity (I)

Presentation: Quantitative Synthesis II
Keywords: heterogeneity | meta-analysis | fixed effect model | statistical heterogeneity | random effects model | subgroup analyses | meta-regression
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