Dataset Information

A Systematic Review of Methods for Handling Missing Variance Data in Meta-Analyses of Interventions in Type 2 Diabetes Mellitus.

ABSTRACT:

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.

SUBMITTER: Batson S

PROVIDER: S-EPMC5066955 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Systematic Review of Methods for Handling Missing Variance Data in Meta-Analyses of Interventions in Type 2 Diabetes Mellitus.

Batson Sarah S Burton Hannah H

PloS one 20161017 10

<h4>Aims</h4>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 ...[more]

PMID: 27749930

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Project description:BackgroundWell-constructed systematic reviews and meta-analyses are key tools in evidenced-based healthcare. However, a common problem with performing a meta-analysis is missing data, such as standard deviations (SD). An increasing number of methods are utilised to calculate or impute missing SDs, allowing these studies to be included in analyses. The aim of this review was to investigate the methods reported and utilised for handling missing change SDs in meta-analyses, using the topic of exercise therapy in heart failure patients as a model.MethodsA systematic search of PubMed, EMBASE and Cochrane Library from 1 January 2014 to 31st March 2018 was conducted for meta-analyses of exercise based trials in heart failure. Studies were eligible to be included if they performed a meta-analysis of change in exercise capacity in heart failure patients after a training intervention.ResultsTwenty two publications performed a meta-analysis on the effect of exercise therapy on exercise capacity in heart failure patients. Eleven (50%) publications did not directly report the approach for dealing with missing change SDs. Approaches reported and utilised to deal with missing change SDs included imputation, actual and approximate algebraic recalculation using study level summary statistics and exclusion of studies.ConclusionChange SDs are often not reported in trial papers and while in the first instance meta-analysts should attempt to obtain missing data from trial authors, this information is frequently not forthcoming. Meta-analysts are then forced to make a decision on how these trials and missing data are to be handled. Whilst not one approach is favoured for dealing with this matter, authors need to clearly report the approach to be utilised for missing change SDs. Where change SDs are imputed meta-analyst are encouraged to explore several options and have a sound rationale as to the choice, and where data is imputed, sensitivity analysis should be conducted.

Project description:BackgroundTissue micro-arrays (TMAs) are increasingly used to generate data of the molecular phenotype of tumours in clinical epidemiology studies, such as studies of disease prognosis. However, TMA data are particularly prone to missingness. A variety of methods to deal with missing data are available. However, the validity of the various approaches is dependent on the structure of the missing data and there are few empirical studies dealing with missing data from molecular pathology. The purpose of this study was to investigate the results of four commonly used approaches to handling missing data from a large, multi-centre study of the molecular pathological determinants of prognosis in breast cancer.Patients and methodsWe pooled data from over 11,000 cases of invasive breast cancer from five studies that collected information on seven prognostic indicators together with survival time data. We compared the results of a multi-variate Cox regression using four approaches to handling missing data - complete case analysis (CCA), mean substitution (MS) and multiple imputation without inclusion of the outcome (MI-) and multiple imputation with inclusion of the outcome (MI+). We also performed an analysis in which missing data were simulated under different assumptions and the results of the four methods were compared.ResultsOver half the cases had missing data on at least one of the seven variables and 11 percent had missing data on 4 or more. The multi-variate hazard ratio estimates based on multiple imputation models were very similar to those derived after using MS, with similar standard errors. Hazard ratio estimates based on the CCA were only slightly different, but the estimates were less precise as the standard errors were large. However, in data simulated to be missing completely at random (MCAR) or missing at random (MAR), estimates for MI+ were least biased and most accurate, whereas estimates for CCA were most biased and least accurate.ConclusionIn this study, empirical results from analyses using CCA, MS, MI- and MI+ were similar, although results from CCA were less precise. The results from simulations suggest that in general MI+ is likely to be the best. Given the ease of implementing MI in standard statistical software, the results of MI+ and CCA should be compared in any multi-variate analysis where missing data are a problem.

Dataset Information

A Systematic Review of Methods for Handling Missing Variance Data in Meta-Analyses of Interventions in Type 2 Diabetes Mellitus.

Aims

Methods

Results

Conclusions

Publications

A Systematic Review of Methods for Handling Missing Variance Data in Meta-Analyses of Interventions in Type 2 Diabetes Mellitus.

Similar Datasets

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