PMID- 27749930
OWN - NLM
STAT- MEDLINE
DCOM- 20170531
LR  - 20221207
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 11
IP  - 10
DP  - 2016
TI  - A Systematic Review of Methods for Handling Missing Variance Data in 
      Meta-Analyses of Interventions in Type 2 Diabetes Mellitus.
PG  - e0164827
LID - 10.1371/journal.pone.0164827 [doi]
LID - e0164827
AB  - AIMS: Meta-analysis is of critical importance to decision makers to assess the 
      comparative efficacy and safety of interventions and is integral to health 
      technology assessment. A major problem for the meta-analysis of continuous 
      outcomes is that associated variance data are not consistently reported in trial 
      publications. The omission of studies from a meta-analysis due to incomplete 
      reporting may introduce bias. The objectives of this study are to summarise and 
      describe the methods used for handling missing variance data in meta-analyses in 
      populations with type 2 diabetes mellitus (T2DM). METHODS: Electronic databases, 
      Embase, MEDLINE, and the Cochrane Library (accessed June 2015), were 
      systematically searched to identify meta-analyses of interventions in patients 
      with T2DM. Eligible studies included those which analysed the change in HbA1c 
      from baseline. RESULTS: Sixty-seven publications reporting on meta-analyses of 
      change in HbA1c from baseline in T2DM were identified. Approaches for dealing 
      with missing variance data were reported in 41% of publications and included 
      algebraic calculation, trial-level imputation, and no imputation. CONCLUSIONS: 
      Meta-analysis publications typically fail to report standardised approaches for 
      dealing with missing variance data. While no particular imputation method is 
      favoured, authors are discouraged from using a no-imputation approach. Instead, 
      authors are encouraged to explore different approaches using sensitivity analyses 
      and to improve the quality of reporting by documenting the methods used to deal 
      with missing variance data.
FAU - Batson, Sarah
AU  - Batson S
AD  - DRG Abacus, Bicester, United Kingdom.
FAU - Burton, Hannah
AU  - Burton H
AD  - DRG Abacus, Bicester, United Kingdom.
LA  - eng
PT  - Journal Article
PT  - Meta-Analysis
PT  - Review
PT  - Systematic Review
DEP - 20161017
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
RN  - 0 (Glycated Hemoglobin A)
RN  - 0 (hemoglobin A1c protein, human)
SB  - IM
MH  - Analysis of Variance
MH  - Databases, Factual
MH  - Diabetes Mellitus, Type 2/*pathology
MH  - Glycated Hemoglobin/analysis
MH  - Humans
PMC - PMC5066955
COIS- We have the following interests. This study was funded by DRG Abacus. Batson and 
      Burton are employed by DRG Abacus. There are no patents, products in development 
      or marketed products to declare. This does not alter our adherence to all the 
      PLOS ONE policies on sharing data and materials, as detailed online in the guide 
      for authors.
EDAT- 2016/10/18 06:00
MHDA- 2017/06/01 06:00
PMCR- 2016/10/17
CRDT- 2016/10/18 06:00
PHST- 2016/07/17 00:00 [received]
PHST- 2016/09/30 00:00 [accepted]
PHST- 2016/10/18 06:00 [pubmed]
PHST- 2017/06/01 06:00 [medline]
PHST- 2016/10/18 06:00 [entrez]
PHST- 2016/10/17 00:00 [pmc-release]
AID - PONE-D-16-28537 [pii]
AID - 10.1371/journal.pone.0164827 [doi]
PST - epublish
SO  - PLoS One. 2016 Oct 17;11(10):e0164827. doi: 10.1371/journal.pone.0164827. 
      eCollection 2016.