The Resource Metaanalytic statistical inferences for continuous measure outcomes as a function of effect size metric and other assumptions, prepared for Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services ; prepared by University of ConnecticutHartford Hospital EvidenceBased Practice Center ; investigators, Blair T. Johnson, Tania B. HuedoMedina, (electronic resource)
Metaanalytic statistical inferences for continuous measure outcomes as a function of effect size metric and other assumptions, prepared for Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services ; prepared by University of ConnecticutHartford Hospital EvidenceBased Practice Center ; investigators, Blair T. Johnson, Tania B. HuedoMedina, (electronic resource)
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The item Metaanalytic statistical inferences for continuous measure outcomes as a function of effect size metric and other assumptions, prepared for Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services ; prepared by University of ConnecticutHartford Hospital EvidenceBased Practice Center ; investigators, Blair T. Johnson, Tania B. HuedoMedina, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of MissouriSt. Louis Libraries.This item is available to borrow from 1 library branch.
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The item Metaanalytic statistical inferences for continuous measure outcomes as a function of effect size metric and other assumptions, prepared for Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services ; prepared by University of ConnecticutHartford Hospital EvidenceBased Practice Center ; investigators, Blair T. Johnson, Tania B. HuedoMedina, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of MissouriSt. Louis Libraries.
This item is available to borrow from 1 library branch.
 Summary
 INTRODUCTION: Metaanalysis cannot proceed unless each study outcome is on the same metric and has an appropriate sampling variance estimate, the inverse of which is used as the weight in metaanalytic statistics. When comparing treatments for trials that use the same continuous measures across studies, contemporary metaanalytic practice uses the unstandardized mean difference (UMD) to model the difference between the observed means (i.e., MEMC) rather than representing effects in the standardized mean difference (SMD). A fundamental difference between the two strategies is that the UMD incorporates the observed variance of the measures as a component of the analytical weights (viz., sampling error or inverse variance) in statistically modeling the results for each study. In contrast, the SMD incorporates the measure's variance directly in the effect size itself (i.e., SMD=[ME MC]/SD) and not directly in the analytical weights. The UMD approach has been conventional even though its bias and efficiency are unknown; these have also not been compared with the SMD. Also unresolved is which of many possible available equations best optimize statistical modeling for the SMD in use with repeated measures designs (one or two groups). METHODS: Monte Carlo simulations compared available equations in terms of their bias and efficiency across the many different conditions established by crossing: (1) number of studies in the metaanalysis (k = 10, 20, 50, and 100); (2) mean study sample sizes (5 values of N ranging from small to very large); (3) the ratio of the withinstudy observed measure variances for experimental and control groups and at pretest and posttest (ratios: 1:1, 2:1, and 4:1); (4) the posttest mean of each pseudo experimental group to achieve 3 parametric effect sizes (p= 0.25, 0.50, and 0.80); (5) normal versus nonnormal distributions (4 levels); and (6) the betweenstudies variance (#2= 0, 0.04, 0.08, 0.16, and 0.32). For the second issue, (7) the correlation between the two conditions was manipulated (prepost = 0, 0.25, 0.50, and 0.75). RESULTS AND CONCLUSIONS: This investigation provides guidance for statistical practice in relation to metaanalysis of studies that compare two groups at one point in time, or that examine repeated measures for one or two groups. Simulations showed that neither standardized or unstandardized effect size indexes had an advantage in terms of bias or efficiency when distributions are normal, when there is no heterogeneity among effects, and when the observed variances of the experimental and control groups are equal. In contrast, when conditions deviate from these ideals, the SMD yields better statistical inferences than UMDs in terms of bias and efficiency. Under high skewness and kurtosis, neither metric has a marked advantage. In general, the standardized index presents the least bias under most conditions and is more efficient than the unstandardized index. Finally, the results comparing estimations of the SMD and its variance suggest that some are preferable to others under certain conditions. The current results imply that the choice of effect size metrics, estimators, and sampling variances can have substantial impact on statistical inferences even under such commonly observed circumstances as normal sampling distributions, large numbers of studies, and studies with large samples, and when effects exhibit heterogeneity. Although using the SMD may make clinical inferences more difficult, use of the SMD does permit inferences about effect size magnitude. The Discussion considers clinical interpretation of results using the SMD and addresses limitations of the current project
 Language
 eng
 Note

 "April 2013."
 Title from PDF title page
 Label
 Metaanalytic statistical inferences for continuous measure outcomes as a function of effect size metric and other assumptions
 Title
 Metaanalytic statistical inferences for continuous measure outcomes as a function of effect size metric and other assumptions
 Statement of responsibility
 prepared for Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services ; prepared by University of ConnecticutHartford Hospital EvidenceBased Practice Center ; investigators, Blair T. Johnson, Tania B. HuedoMedina
 Language
 eng
 Summary
 INTRODUCTION: Metaanalysis cannot proceed unless each study outcome is on the same metric and has an appropriate sampling variance estimate, the inverse of which is used as the weight in metaanalytic statistics. When comparing treatments for trials that use the same continuous measures across studies, contemporary metaanalytic practice uses the unstandardized mean difference (UMD) to model the difference between the observed means (i.e., MEMC) rather than representing effects in the standardized mean difference (SMD). A fundamental difference between the two strategies is that the UMD incorporates the observed variance of the measures as a component of the analytical weights (viz., sampling error or inverse variance) in statistically modeling the results for each study. In contrast, the SMD incorporates the measure's variance directly in the effect size itself (i.e., SMD=[ME MC]/SD) and not directly in the analytical weights. The UMD approach has been conventional even though its bias and efficiency are unknown; these have also not been compared with the SMD. Also unresolved is which of many possible available equations best optimize statistical modeling for the SMD in use with repeated measures designs (one or two groups). METHODS: Monte Carlo simulations compared available equations in terms of their bias and efficiency across the many different conditions established by crossing: (1) number of studies in the metaanalysis (k = 10, 20, 50, and 100); (2) mean study sample sizes (5 values of N ranging from small to very large); (3) the ratio of the withinstudy observed measure variances for experimental and control groups and at pretest and posttest (ratios: 1:1, 2:1, and 4:1); (4) the posttest mean of each pseudo experimental group to achieve 3 parametric effect sizes (p= 0.25, 0.50, and 0.80); (5) normal versus nonnormal distributions (4 levels); and (6) the betweenstudies variance (#2= 0, 0.04, 0.08, 0.16, and 0.32). For the second issue, (7) the correlation between the two conditions was manipulated (prepost = 0, 0.25, 0.50, and 0.75). RESULTS AND CONCLUSIONS: This investigation provides guidance for statistical practice in relation to metaanalysis of studies that compare two groups at one point in time, or that examine repeated measures for one or two groups. Simulations showed that neither standardized or unstandardized effect size indexes had an advantage in terms of bias or efficiency when distributions are normal, when there is no heterogeneity among effects, and when the observed variances of the experimental and control groups are equal. In contrast, when conditions deviate from these ideals, the SMD yields better statistical inferences than UMDs in terms of bias and efficiency. Under high skewness and kurtosis, neither metric has a marked advantage. In general, the standardized index presents the least bias under most conditions and is more efficient than the unstandardized index. Finally, the results comparing estimations of the SMD and its variance suggest that some are preferable to others under certain conditions. The current results imply that the choice of effect size metrics, estimators, and sampling variances can have substantial impact on statistical inferences even under such commonly observed circumstances as normal sampling distributions, large numbers of studies, and studies with large samples, and when effects exhibit heterogeneity. Although using the SMD may make clinical inferences more difficult, use of the SMD does permit inferences about effect size magnitude. The Discussion considers clinical interpretation of results using the SMD and addresses limitations of the current project
 Cataloging source
 DNLM
 http://library.link/vocab/creatorName
 Johnson, Blair T
 Funding information
 Contract No. 290200710067I
 NLM call number
 WA 950
 http://library.link/vocab/relatedWorkOrContributorName

 HuedoMedina, Tania B
 United States
 University of ConnecticutHartford Hospital Evidencebased Practice Center
 Series statement

 Methods research reports
 AHRQ publication
 Series volume
 no. 13EHC075EF
 http://library.link/vocab/subjectName

 MetaAnalysis as Topic
 Statistics as Topic
 Models, Statistical
 Research Design
 United States
 United States
 Label
 Metaanalytic statistical inferences for continuous measure outcomes as a function of effect size metric and other assumptions, prepared for Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services ; prepared by University of ConnecticutHartford Hospital EvidenceBased Practice Center ; investigators, Blair T. Johnson, Tania B. HuedoMedina, (electronic resource)
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 "April 2013."
 Title from PDF title page
 Bibliography note
 Includes bibliographical references
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 OCM1bookssj0001004716
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 unknown
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 remote
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 (WaSeSS)bookssj0001004716
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 Metaanalytic statistical inferences for continuous measure outcomes as a function of effect size metric and other assumptions, prepared for Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services ; prepared by University of ConnecticutHartford Hospital EvidenceBased Practice Center ; investigators, Blair T. Johnson, Tania B. HuedoMedina, (electronic resource)
 Note

 "April 2013."
 Title from PDF title page
 Bibliography note
 Includes bibliographical references
 Control code
 OCM1bookssj0001004716
 Dimensions
 unknown
 Specific material designation
 remote
 System control number
 (WaSeSS)bookssj0001004716
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