A review of the reporting and handling of missing data in cohort studies with repeated assessment of exposure measures. 2012

Amalia Karahalios, and Laura Baglietto, and John B Carlin, and Dallas R English, and Julie A Simpson
Cancer Epidemiology Centre, Cancer Council Victoria, Carlton, VIC, Australia.

BACKGROUND Retaining 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. METHODS A 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. RESULTS Among 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. CONCLUSIONS This 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.

UI MeSH Term Description Entries
D010352 Patient Dropouts Discontinuance of care received by patient(s) due to reasons other than full recovery from the disease. Dropout, Patient,Dropouts, Patient,Patient Dropout
D010358 Patient Participation Patient involvement in the decision-making process in matters pertaining to health. Patient Activation,Patient Empowerment,Patient Engagement,Patient Involvement,Patient Participation Rates,Activation, Patient,Empowerment, Patient,Engagement, Patient,Involvement, Patient,Participation Rate, Patient,Participation Rates, Patient,Participation, Patient,Patient Participation Rate
D010506 Periodicals as Topic Works about publications issued at stated, more or less regular, intervals. Journals as Topic,Magazines,Newsletters,Magazine,Newsletter
D011643 Publishing "The business or profession of the commercial production and issuance of literature" (Webster's 3d). It includes the publisher, publication processes, editing and editors. Production may be by conventional printing methods or by electronic publishing. Electronic Publishing,Publishing, Electronic,Electronic Publishings,Publishings, Electronic
D012107 Research Design A plan for collecting and utilizing data so that desired information can be obtained with sufficient precision or so that an hypothesis can be tested properly. Experimental Design,Data Adjustment,Data Reporting,Design, Experimental,Designs, Experimental,Error Sources,Experimental Designs,Matched Groups,Methodology, Research,Problem Formulation,Research Methodology,Research Proposal,Research Strategy,Research Technics,Research Techniques,Scoring Methods,Adjustment, Data,Adjustments, Data,Data Adjustments,Design, Research,Designs, Research,Error Source,Formulation, Problem,Formulations, Problem,Group, Matched,Groups, Matched,Matched Group,Method, Scoring,Methods, Scoring,Problem Formulations,Proposal, Research,Proposals, Research,Reporting, Data,Research Designs,Research Proposals,Research Strategies,Research Technic,Research Technique,Scoring Method,Source, Error,Sources, Error,Strategies, Research,Strategy, Research,Technic, Research,Technics, Research,Technique, Research,Techniques, Research
D003627 Data Interpretation, Statistical Application of statistical procedures to analyze specific observed or assumed facts from a particular study. Data Analysis, Statistical,Data Interpretations, Statistical,Interpretation, Statistical Data,Statistical Data Analysis,Statistical Data Interpretation,Analyses, Statistical Data,Analysis, Statistical Data,Data Analyses, Statistical,Interpretations, Statistical Data,Statistical Data Analyses,Statistical Data Interpretations
D005500 Follow-Up Studies Studies in which individuals or populations are followed to assess the outcome of exposures, procedures, or effects of a characteristic, e.g., occurrence of disease. Followup Studies,Follow Up Studies,Follow-Up Study,Followup Study,Studies, Follow-Up,Studies, Followup,Study, Follow-Up,Study, Followup
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D001499 Bayes Theorem A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result. Bayesian Analysis,Bayesian Estimation,Bayesian Forecast,Bayesian Method,Bayesian Prediction,Analysis, Bayesian,Bayesian Approach,Approach, Bayesian,Approachs, Bayesian,Bayesian Approachs,Estimation, Bayesian,Forecast, Bayesian,Method, Bayesian,Prediction, Bayesian,Theorem, Bayes
D015331 Cohort Studies Studies in which subsets of a defined population are identified. These groups may or may not be exposed to factors hypothesized to influence the probability of the occurrence of a particular disease or other outcome. Cohorts are defined populations which, as a whole, are followed in an attempt to determine distinguishing subgroup characteristics. Birth Cohort Studies,Birth Cohort Study,Closed Cohort Studies,Cohort Analysis,Concurrent Studies,Historical Cohort Studies,Incidence Studies,Analysis, Cohort,Cohort Studies, Closed,Cohort Studies, Historical,Studies, Closed Cohort,Studies, Concurrent,Studies, Historical Cohort,Analyses, Cohort,Closed Cohort Study,Cohort Analyses,Cohort Studies, Birth,Cohort Study,Cohort Study, Birth,Cohort Study, Closed,Cohort Study, Historical,Concurrent Study,Historical Cohort Study,Incidence Study,Studies, Birth Cohort,Studies, Cohort,Studies, Incidence,Study, Birth Cohort,Study, Closed Cohort,Study, Cohort,Study, Concurrent,Study, Historical Cohort,Study, Incidence

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