Addressing continuous data measured with different instruments for participants excluded from trial analysis: a guide for systematic reviewers

Shanil Ebrahim; Bradley C Johnston; Elie A Akl; Reem A Mustafa; Xin Sun; Stephen D Walter; Diane Heels-Ansdell; Pablo Alonso-Coello; Gordon H Guyatt

doi:10.1016/j.jclinepi.2013.11.014

Addressing continuous data measured with different instruments for participants excluded from trial analysis: a guide for systematic reviewers

J Clin Epidemiol. 2014 May;67(5):560-70. doi: 10.1016/j.jclinepi.2013.11.014. Epub 2014 Mar 5.

Authors

Shanil Ebrahim¹, Bradley C Johnston², Elie A Akl³, Reem A Mustafa⁴, Xin Sun⁵, Stephen D Walter⁶, Diane Heels-Ansdell⁶, Pablo Alonso-Coello⁷, Gordon H Guyatt⁸

Affiliations

¹ Department of Clinical Epidemiology and Biostatistics, McMaster University, 1200 Main St. West, Hamilton, Ontario, Canada, L8S 4K1; Department of Anesthesia, McMaster University, 1200 Main St. West, Hamilton, Ontario, Canada, L8S 4K1; Stanford Prevention Research Center, Department of Medicine, Stanford University, 1265 Welch Road, Stanford, California, USA, 94305; Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, 555 University Ave, Toronto, Ontario, Canada, M5G 1X8. Electronic address: shanil.ebrahim@mcmaster.ca.
² Department of Clinical Epidemiology and Biostatistics, McMaster University, 1200 Main St. West, Hamilton, Ontario, Canada, L8S 4K1; Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, 555 University Ave, Toronto, Ontario, Canada, M5G 1X8; Child Health Evaluative Sciences, The Hospital for Sick Children Research Institute, 555 University Ave, Toronto, Ontario, Canada, M5G 1X8; Institute for Health Policy, Management and Evaluation, University of Toronto, 155 College St., Toronto, Ontario, Canada, M5T 3M6.
³ Department of Clinical Epidemiology and Biostatistics, McMaster University, 1200 Main St. West, Hamilton, Ontario, Canada, L8S 4K1; Department of Internal Medicine, American University of Beirut, Riad-El-Solh Beirut 1107 2020, Beirut, Lebanon; Department of Medicine, State University of New York at Buffalo, 462 Grider St., Buffalo, New York, USA, 14215.
⁴ Department of Clinical Epidemiology and Biostatistics, McMaster University, 1200 Main St. West, Hamilton, Ontario, Canada, L8S 4K1; Department of Medicine, University of Missouri-Kansas City, 5100 Rockhill Road, Kansas City, Missouri, USA, 64110.
⁵ Department of Clinical Epidemiology and Biostatistics, McMaster University, 1200 Main St. West, Hamilton, Ontario, Canada, L8S 4K1; Center for Clinical Epidemiology and Evidence-based Medicine, Xinqiao Hospital, 183 Xinqiaozheng Street, Chongqing, China.
⁶ Department of Clinical Epidemiology and Biostatistics, McMaster University, 1200 Main St. West, Hamilton, Ontario, Canada, L8S 4K1.
⁷ Department of Clinical Epidemiology and Biostatistics, McMaster University, 1200 Main St. West, Hamilton, Ontario, Canada, L8S 4K1; Iberoamerican Cochrane Centre, CIBERESP-IIB Sant Pau, Sant Antoni M. Claret 167, Barcelona, Spain 08025.
⁸ Department of Clinical Epidemiology and Biostatistics, McMaster University, 1200 Main St. West, Hamilton, Ontario, Canada, L8S 4K1; Department of Medicine, McMaster University, 1200 Main St. West, Hamilton, Canada, L8S 4K1.

PMID: 24613497
DOI: 10.1016/j.jclinepi.2013.11.014

Abstract

Background: We previously developed an approach to address the impact of missing participant data in meta-analyses of continuous variables in trials that used the same measurement instrument. We extend this approach to meta-analyses including trials that use different instruments to measure the same construct.

Methods: We reviewed the available literature, conducted an iterative consultative process, and developed an approach involving a complete-case analysis complemented by sensitivity analyses that apply a series of increasingly stringent assumptions about results in patients with missing continuous outcome data.

Results: Our approach involves choosing the reference measurement instrument; converting scores from different instruments to the units of the reference instrument; developing four successively more stringent imputation strategies for addressing missing participant data; calculating a pooled mean difference for the complete-case analysis and imputation strategies; calculating the proportion of patients who experienced an important treatment effect; and judging the impact of the imputation strategies on the confidence in the estimate of effect. We applied our approach to an example systematic review of respiratory rehabilitation for chronic obstructive pulmonary disease.

Conclusions: Our extended approach provides quantitative guidance for addressing missing participant data in systematic reviews of trials using different instruments to measure the same construct.

Keywords: Continuous outcomes; Lost to follow-up; Meta-analysis; Missing participant data; Risk of bias; Systematic reviews.

MeSH terms

Guidelines as Topic*
Humans
Meta-Analysis as Topic*
Research Design*
Review Literature as Topic*