Dataset Information

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

ABSTRACT:

Background

To provide empirical evidence about prevalence, reporting and handling of missing outcome data in systematic reviews with network meta-analysis and acknowledgement of their impact on the conclusions.

Methods

We conducted a systematic survey including all published systematic reviews of randomized controlled trials comparing at least three interventions from January 1, 2009 until March 31, 2017.

Results

We retrieved 387 systematic reviews with network meta-analysis. Description of missing outcome data was available in 63 reviews. Intention-to-treat analysis was the most prevalent method (71%), followed by missing outcome data investigated as secondary outcome (e.g., acceptability) (40%). Bias due to missing outcome data was evaluated in half the reviews with explicit judgments in 18 (10%) reviews. Only 88 reviews interpreted their results acknowledging the implications of missing outcome data and mostly using the network meta-analysis results on missing outcome data as secondary outcome. We were unable to judge the actual strategy applied to deal with missing outcome data in 65% of the reviews due to insufficient information. Six percent of network meta-analyses were re-analyzed in sensitivity analysis considering missing outcome data, while 4% explicitly justified the strategy for dealing with missing outcome data.

Conclusions

The description and handling of missing outcome data as well as the acknowledgment of their implications for the conclusions from network meta-analysis are deemed underreported.

SUBMITTER: Spineli LM

PROVIDER: S-EPMC6201503 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Spineli Loukia M LM Yepes-Nuñez Juan J JJ Schünemann Holger J HJ

BMC medical research methodology 20181024 1

<h4>Background</h4>To provide empirical evidence about prevalence, reporting and handling of missing outcome data in systematic reviews with network meta-analysis and acknowledgement of their impact on the conclusions.<h4>Methods</h4>We conducted a systematic survey including all published systematic reviews of randomized controlled trials comparing at least three interventions from January 1, 2009 until March 31, 2017.<h4>Results</h4>We retrieved 387 systematic reviews with network meta-analysi ...[more]

PMID: 30355280

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Project description:The aim of this review was to establish the frequency with which trials take into account missingness, and to discover what methods trialists use for adjustment in randomised controlled trials with longitudinal measurements. Failing to address the problems that can arise from missing outcome data can result in misleading conclusions. Missing data should be addressed as a means of a sensitivity analysis of the complete case analysis results. One hundred publications of randomised controlled trials with longitudinal measurements were selected randomly from trial publications from the years 2005 to 2012. Information was extracted from these trials, including whether reasons for dropout were reported, what methods were used for handing the missing data, whether there was any explanation of the methods for missing data handling, and whether a statistician was involved in the analysis. The main focus of the review was on missing data post dropout rather than missing interim data. Of all the papers in the study, 9 (9%) had no missing data. More than half of the papers included in the study failed to make any attempt to explain the reasons for their choice of missing data handling method. Of the papers with clear missing data handling methods, 44 papers (50%) used adequate methods of missing data handling, whereas 30 (34%) of the papers used missing data methods which may not have been appropriate. In the remaining 17 papers (19%), it was difficult to assess the validity of the methods used. An imputation method was used in 18 papers (20%). Multiple imputation methods were introduced in 1987 and are an efficient way of accounting for missing data in general, and yet only 4 papers used these methods. Out of the 18 papers which used imputation, only 7 displayed the results as a sensitivity analysis of the complete case analysis results. 61% of the papers that used an imputation explained the reasons for their chosen method. Just under a third of the papers made no reference to reasons for missing outcome data. There was little consistency in reporting of missing data within longitudinal trials.

Project description:BackgroundRetaining participants in cohort studies with multiple follow-up waves is difficult. Commonly, researchers are faced with the problem of missing data, which may introduce biased results as well as a loss of statistical power and precision. The STROBE guidelines von Elm et al. (Lancet, 370:1453-1457, 2007); Vandenbroucke et al. (PLoS Med, 4:e297, 2007) and the guidelines proposed by Sterne et al. (BMJ, 338:b2393, 2009) recommend that cohort studies report on the amount of missing data, the reasons for non-participation and non-response, and the method used to handle missing data in the analyses. We have conducted a review of publications from cohort studies in order to document the reporting of missing data for exposure measures and to describe the statistical methods used to account for the missing data.MethodsA systematic search of English language papers published from January 2000 to December 2009 was carried out in PubMed. Prospective cohort studies with a sample size greater than 1,000 that analysed data using repeated measures of exposure were included.ResultsAmong the 82 papers meeting the inclusion criteria, only 35 (43%) reported the amount of missing data according to the suggested guidelines. Sixty-eight papers (83%) described how they dealt with missing data in the analysis. Most of the papers excluded participants with missing data and performed a complete-case analysis (n=54, 66%). Other papers used more sophisticated methods including multiple imputation (n=5) or fully Bayesian modeling (n=1). Methods known to produce biased results were also used, for example, Last Observation Carried Forward (n=7), the missing indicator method (n=1), and mean value substitution (n=3). For the remaining 14 papers, the method used to handle missing data in the analysis was not stated.ConclusionsThis review highlights the inconsistent reporting of missing data in cohort studies and the continuing use of inappropriate methods to handle missing data in the analysis. Epidemiological journals should invoke the STROBE guidelines as a framework for authors so that the amount of missing data and how this was accounted for in the analysis is transparent in the reporting of cohort studies.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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