Handling trial participants with missing outcome data when conducting a meta-analysis : A systematic survey of proposed approaches
Akl, Elie A. (McMaster University (Canadà))
Kahale, Lara A. (American University of Beirut Medical Center)
Agoritsas, Thomas (McMaster University (Canadà))
Brignardello-Petersen, Romina (University of Toronto)
Busse, Jason (McMaster University (Canadà))
Carrasco-Labra, Alonso (McMaster University (Canadà))
Ebrahim, Shanil (Stanford University (Estats Units))
Johnston, Bradley C. (University of Toronto)
Neumann, Ignacio (McMaster University (Canadà))
Solà, Ivan (Institut d'Investigació Biomèdica Sant Pau)
Sun, Xin (McMaster University (Canadà))
Vandvik, Per (The Norwegian Knowledge Centre for the Health Services)
Zhang, Yuqing (McMaster University (Canadà))
Alonso-Coello, Pablo (Institut d'Investigació Biomèdica Sant Pau)
Guyatt, Gordon (McMaster University (Canadà))
Universitat Autònoma de Barcelona. Departament de Medicina

Data:	2015
Resum:	Background: When potentially associated with the likelihood of outcome, missing participant data represents a serious potential source of bias in randomized trials. Authors of systematic reviews frequently face this problem when conducting meta-analyses. The objective of this study is to conduct a systematic survey of the relevant literature to identify proposed approaches for how systematic review authors should handle missing participant data when conducting a meta-analysis. Methods: We searched MEDLINE and the Cochrane Methodology register from inception to August 2014. We included papers that devoted at least two paragraphs to discuss a relevant approach for missing data. Five pairs of reviewers, working independently and in duplicate, selected relevant papers. One reviewer abstracted data from included papers and a second reviewer verified them. We summarized the results narratively. Results: Of 9,138 identified citations, we included 11 eligible papers. Four proposed general approaches for handling dichotomous outcomes, and all recommended a complete case analysis as the primary analysis and additional sensitivity analyses using the following imputation methods: based on reasons for missingness (n = 3), relative to risk among followed up (n = 3), best-case scenario (n = 2), and worst-case scenario (n = 3). Three of these approaches suggested taking uncertainty into account. Two papers proposed general approaches for handling continuous outcomes, and both proposed a complete case analysis as the reference analysis and the following imputation methods as sensitivity analyses: based on reasons for missingness (n = 2), based on the mean observed in the same trial or other trials (n = 1), and based on informative missingness differences in means (n = 1). The remaining eligible papers did not propose general approaches but addressed specific statistical issues. Conclusions: All proposed approaches for handling missing participant data recommend conducting a complete case analysis for the primary analysis and some form of sensitivity analysis to evaluate robustness of results. Although these approaches require further testing, they may guide review authors in addressing missing participant data.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Meta-analysis ; Missing participant data ; Systematic reviews
Publicat a:	Systematic Reviews, Vol. 4 Núm. 1 (july 2015) , p. 98, ISSN 2046-4053

DOI: 10.1186/s13643-015-0083-6
PMID: 26202162

7 p, 422.9 KB

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