Journal of clinical epidemiology
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Errors in the electronic search strategy of a systematic review may undermine the integrity of the evidence base used in the review. We studied the frequency and types of errors in reviews published by the Cochrane Collaboration. ⋯ When the MEDLINE search strategy used in a systematic review is reported in enough detail to allow assessment, errors are commonly revealed. Additional peer review steps are needed to ensure search quality and freedom from errors.
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In most situations, simple techniques for handling missing data (such as complete case analysis, overall mean imputation, and the missing-indicator method) produce biased results, whereas imputation techniques yield valid results without complicating the analysis once the imputations are carried out. Imputation techniques are based on the idea that any subject in a study sample can be replaced by a new randomly chosen subject from the same source population. Imputation of missing data on a variable is replacing that missing by a value that is drawn from an estimate of the distribution of this variable. ⋯ But single imputation results in too small estimated standard errors, whereas multiple imputation results in correctly estimated standard errors and confidence intervals. In this article we explain why all this is the case, and use a simple simulation study to demonstrate our explanations. We also explain and illustrate why two frequently used methods to handle missing data, i.e., overall mean imputation and the missing-indicator method, almost always result in biased estimates.
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To propose and test a simple instrument based on seven criteria of study design to distinguish effectiveness (pragmatic) from efficacy (explanatory) trials. ⋯ When applied in a standardized manner, our proposed criteria can provide a valid and simple tool to distinguish effectiveness from efficacy studies. The applicability of systematic reviews can improve when analysts place more emphasis on the generalizability of included studies. In addition, clinicians can also use our criteria to determine the external validity of individual studies, given an appropriate population of interest.
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To illustrate the effects of different methods for handling missing data--complete case analysis, missing-indicator method, single imputation of unconditional and conditional mean, and multiple imputation (MI)--in the context of multivariable diagnostic research aiming to identify potential predictors (test results) that independently contribute to the prediction of disease presence or absence. ⋯ In multivariable diagnostic research complete case analysis and the use of the missing-indicator method should be avoided, even when data are missing completely at random. MI methods are known to be superior to single imputation methods. For our example study, the single imputation methods performed equally well, but this was most likely because of the low overall number of missing values.