Literature DB >> 26099485

Reporting and handling missing outcome data in mental health: a systematic review of Cochrane systematic reviews and meta-analyses.

Loukia M Spineli1, Nikolaos Pandis2,3, Georgia Salanti1.   

Abstract

OBJECTIVES: The purpose of the study was to provide empirical evidence about the reporting of methodology to address missing outcome data and the acknowledgement of their impact in Cochrane systematic reviews in the mental health field.
METHODS: Systematic reviews published in the Cochrane Database of Systematic Reviews after January 1, 2009 by three Cochrane Review Groups relating to mental health were included.
RESULTS: One hundred ninety systematic reviews were considered. Missing outcome data were present in at least one included study in 175 systematic reviews. Of these 175 systematic reviews, 147 (84%) accounted for missing outcome data by considering a relevant primary or secondary outcome (e.g., dropout). Missing outcome data implications were reported only in 61 (35%) systematic reviews and primarily in the discussion section by commenting on the amount of the missing outcome data. One hundred forty eligible meta-analyses with missing data were scrutinized. Seventy-nine (56%) of them had studies with total dropout rate between 10 and 30%. One hundred nine (78%) meta-analyses reported to have performed intention-to-treat analysis by including trials with imputed outcome data. Sensitivity analysis for incomplete outcome data was implemented in less than 20% of the meta-analyses.
CONCLUSIONS: Reporting of the techniques for handling missing outcome data and their implications in the findings of the systematic reviews are suboptimal.
Copyright © 2014 John Wiley & Sons, Ltd.

Keywords:  intention-to-treat analysis; last observation carried forward; missing outcome data; psychiatric trials; systematic reviews

Mesh:

Year:  2015        PMID: 26099485     DOI: 10.1002/jrsm.1131

Source DB:  PubMed          Journal:  Res Synth Methods        ISSN: 1759-2879            Impact factor:   5.273


  9 in total

1.  Continuous(ly) missing outcome data in network meta-analysis: A one-stage pattern-mixture model approach.

Authors:  Loukia M Spineli; Chrysostomos Kalyvas; Katerina Papadimitropoulou
Journal:  Stat Methods Med Res       Date:  2021-01-06       Impact factor: 3.021

2.  Comparative efficacy and acceptability of first-generation and second-generation antidepressants in the acute treatment of major depression: protocol for a network meta-analysis.

Authors:  Toshi A Furukawa; Georgia Salanti; Lauren Z Atkinson; Stefan Leucht; Henricus G Ruhe; Erick H Turner; Anna Chaimani; Yusuke Ogawa; Nozomi Takeshima; Yu Hayasaka; Hissei Imai; Kiyomi Shinohara; Aya Suganuma; Norio Watanabe; Sarah Stockton; John R Geddes; Andrea Cipriani
Journal:  BMJ Open       Date:  2016-07-08       Impact factor: 2.692

Review 3.  Dealing with missing outcome data in meta-analysis.

Authors:  Dimitris Mavridis; Ian R White
Journal:  Res Synth Methods       Date:  2019-06-09       Impact factor: 5.273

4.  Pattern-mixture model in network meta-analysis of binary missing outcome data: one-stage or two-stage approach?

Authors:  Loukia M Spineli; Katerina Papadimitropoulou; Chrysostomos Kalyvas
Journal:  BMC Med Res Methodol       Date:  2021-01-07       Impact factor: 4.615

5.  How robust are findings of pairwise and network meta-analysis in the presence of missing participant outcome data?

Authors:  Loukia M Spineli; Chrysostomos Kalyvas; Katerina Papadimitropoulou
Journal:  BMC Med       Date:  2021-12-21       Impact factor: 8.775

6.  Prevalence of Overweight and Obesity in People With Severe Mental Illness: Systematic Review and Meta-Analysis.

Authors:  Medhia Afzal; Najma Siddiqi; Bilal Ahmad; Nida Afsheen; Faiza Aslam; Ayaz Ali; Rubab Ayesha; Maria Bryant; Richard Holt; Humaira Khalid; Kousar Ishaq; Kamrun Nahar Koly; Sukanya Rajan; Jobaida Saba; Nilesh Tirbhowan; Gerardo A Zavala
Journal:  Front Endocrinol (Lausanne)       Date:  2021-11-25       Impact factor: 5.555

7.  A systematic survey shows that reporting and handling of missing outcome data in networks of interventions is poor.

Authors:  Loukia M Spineli; Juan J Yepes-Nuñez; Holger J Schünemann
Journal:  BMC Med Res Methodol       Date:  2018-10-24       Impact factor: 4.615

8.  Allowing for uncertainty due to missing and LOCF imputed outcomes in meta-analysis.

Authors:  Dimitris Mavridis; Georgia Salanti; Toshi A Furukawa; Andrea Cipriani; Anna Chaimani; Ian R White
Journal:  Stat Med       Date:  2018-10-22       Impact factor: 2.373

9.  Comparison of exclusion, imputation and modelling of missing binary outcome data in frequentist network meta-analysis.

Authors:  Loukia M Spineli; Chrysostomos Kalyvas
Journal:  BMC Med Res Methodol       Date:  2020-02-28       Impact factor: 4.615

  9 in total

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