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wiki:synthesis:quantitative:methods [2022/06/30 19:58]
kevinkallmes 		wiki:synthesis:quantitative:methods [2023/09/07 12:56] (current)
jthurnham [Data Analyzed]
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 ===== Data Analyzed ===== ===== Data Analyzed =====
  
-Any study included (adjudicated, in dual screening) during [[:wiki:autolit:screening|]] and with [[wiki:autolit:extraction|Extraction]] marked "Complete" is analyzed on QNS. If dual extraction is configured for the nest, only adjudicated data is analyzed in QNS.+Any study included (adjudicated, in dual screening) during [[:wiki:autolit:screening|]] and with [[:wiki:autolit:meta_analytical_extraction|extraction]] marked "Complete" is analyzed on QNS. If dual extraction is configured for the nest, only adjudicated data is analyzed in QNS. 
  
 ===== Analyses ===== ===== Analyses =====
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 Any study with one or more arms extracted are analyzed in the summary table. The rows of the summary table correspond to individual study arms, or an intervention. Columns are estimates (singular for a study arms or pooled for interventions) of the data element being analyzed. Any study with one or more arms extracted are analyzed in the summary table. The rows of the summary table correspond to individual study arms, or an intervention. Columns are estimates (singular for a study arms or pooled for interventions) of the data element being analyzed.
- 
-=== Mean and Standard Deviation === 
  
 For continuous mean & standard deviation (SD) and dichotomous data elements, pooled estimates are computed via the Inverse Variance method. When random effects are specified, between-study variance is estimated using the Dersimonian-Laird estimator. For dichotomous data, the Haldane-Anscambe correction is conditionally applied for 0 event counts. For continuous mean & SD, missing SDs are imputed. All inferential statistics, at the arm and intervention level, are computed using Normal approximations (logit transformed, for dichotomous data). For data elements with means but not an SD configured, the arithmetic mean is computed, and no inferential statistics are computed. For continuous mean & standard deviation (SD) and dichotomous data elements, pooled estimates are computed via the Inverse Variance method. When random effects are specified, between-study variance is estimated using the Dersimonian-Laird estimator. For dichotomous data, the Haldane-Anscambe correction is conditionally applied for 0 event counts. For continuous mean & SD, missing SDs are imputed. All inferential statistics, at the arm and intervention level, are computed using Normal approximations (logit transformed, for dichotomous data). For data elements with means but not an SD configured, the arithmetic mean is computed, and no inferential statistics are computed.
- 
-=== Median === 
  
 For medians, pooled estimates & inferentials are computed as the Weighted Median of Medians, described in [[https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8013|McGrath et al.]] For medians, pooled estimates & inferentials are computed as the Weighted Median of Medians, described in [[https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8013|McGrath et al.]]
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 Direct effects are computed as Mean Differences (continuous data elements) or Odds Ratios (dichotomous data elements) between all pairs of within-study arms. Consistent indirect effects are computed using the Aitken estimator (described in [[https://journals.sagepub.com/doi/10.1177/0962280211432220|Senn et. al]]), with an pairwise-effect weighting correction for multi (3+) arm studies (described in [[https://onlinelibrary.wiley.com/doi/10.1002/sim.6236|Rücker et. al, 2014]]). For full details on computation of the Aitken estimator see [[https://onlinelibrary.wiley.com/doi/10.1002/jrsm.1058|Rücker et. al, 2012]]. When random effects are specified, between-study variance is estimated using the DerSimonian-Laird estimator. All inferential statistics, at the study (used in forest plots) and intervention level, are computed using Normal approximations (logit transformed, for dichotomous data) on the consistent treatment effects. Intervention rankings are computed with P-scores, described in [[https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-015-0060-8|Rücker and Schwarzer, 2015]]. Direct effects are computed as Mean Differences (continuous data elements) or Odds Ratios (dichotomous data elements) between all pairs of within-study arms. Consistent indirect effects are computed using the Aitken estimator (described in [[https://journals.sagepub.com/doi/10.1177/0962280211432220|Senn et. al]]), with an pairwise-effect weighting correction for multi (3+) arm studies (described in [[https://onlinelibrary.wiley.com/doi/10.1002/sim.6236|Rücker et. al, 2014]]). For full details on computation of the Aitken estimator see [[https://onlinelibrary.wiley.com/doi/10.1002/jrsm.1058|Rücker et. al, 2012]]. When random effects are specified, between-study variance is estimated using the DerSimonian-Laird estimator. All inferential statistics, at the study (used in forest plots) and intervention level, are computed using Normal approximations (logit transformed, for dichotomous data) on the consistent treatment effects. Intervention rankings are computed with P-scores, described in [[https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-015-0060-8|Rücker and Schwarzer, 2015]].
 +
 +Funnel plots are generated using all contrasts available on the selected intervention. "Comparison-adjusted" effects ([[https://doi.org/10.1371/journal.pone.0076654|Chaimani et. al]]) center all direct effects at a common value using their summary effect estimates, making trending and symmetry analysis feasible on the plot.
  
 === Sources of Computational Methods === === Sources of Computational Methods ===
wiki/synthesis/quantitative/methods.1656619109.txt.gz · Last modified: 2022/06/30 19:58 by kevinkallmes

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