Standardization and other approaches to meta-analyze differences in means.

Hopkins WG; Rowlands DS

Standardization and other approaches to meta-analyze differences in means.

dc.citation.volume	Early View
dc.contributor.author	Hopkins WG
dc.contributor.author	Rowlands DS
dc.coverage.spatial	England
dc.date.accessioned	2024-06-18T19:47:40Z
dc.date.available	2024-06-18T19:47:40Z
dc.date.issued	2024-05-18
dc.description.abstract	Meta-analysts often use standardized mean differences (SMD) to combine mean effects from studies in which the dependent variable has been measured with different instruments or scales. In this tutorial we show how the SMD is properly calculated as the difference in means divided by a between-subject reference-group, control-group, or pooled pre-intervention SD, usually free of measurement error. When combining mean effects from controlled trials and crossovers, most meta-analysts have divided by either the pooled SD of change scores, the pooled SD of post-intervention scores, or the pooled SD of pre- and post-intervention scores, resulting in SMDs that are biased and difficult to interpret. The frequent use of such inappropriate standardizing SDs by meta-analysts in three medical journals we surveyed is due to misleading advice in peer-reviewed publications and meta-analysis packages. Even with an appropriate standardizing SD, meta-analysis of SMDs increases heterogeneity artifactually via differences in the standardizing SD between settings. Furthermore, the usual magnitude thresholds for standardized mean effects are not thresholds for clinically important differences. We therefore explain how to use other approaches to combining mean effects of disparate measures: log transformation of factor effects (response ratios) and of percent effects converted to factors; rescaling of psychometrics to percent of maximum range; and rescaling with minimum clinically important differences. In the absence of clinically important differences, we explain how standardization after meta-analysis with appropriately transformed or rescaled pre-intervention SDs can be used to assess magnitudes of a meta-analyzed mean effect in different settings.
dc.description.confidential	false
dc.format.pagination	1-17
dc.identifier.author-url	https://www.ncbi.nlm.nih.gov/pubmed/38761102
dc.identifier.citation	Hopkins WG, Rowlands DS. (2024). Standardization and other approaches to meta-analyze differences in means.. Stat Med. Early View. (pp. 1-17).
dc.identifier.doi	10.1002/sim.10114
dc.identifier.eissn	1097-0258
dc.identifier.elements-type	journal-article
dc.identifier.issn	0277-6715
dc.identifier.uri	https://mro.massey.ac.nz/handle/10179/69886
dc.language	eng
dc.publisher	John Wiley and Sons Ltd
dc.publisher.uri	https://onlinelibrary.wiley.com/doi/10.1002/sim.10114
dc.relation.isPartOf	Stat Med
dc.rights	(c) 2024 The Author/s
dc.rights	CC BY-NC-ND 4.0
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject	Cochrane
dc.subject	bias
dc.subject	change‐score SD
dc.subject	factor effect
dc.subject	meta‐analysis
dc.subject	standardized mean difference
dc.title	Standardization and other approaches to meta-analyze differences in means.
dc.type	Journal article
pubs.elements-id	489024
pubs.organisational-group	College of Health