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We continue our review of issues related to measurement error and misclassification in epidemiology. We further describe methods of adjusting for biased estimation caused by measurement error in continuous covariates, covering likelihood methods, Bayesian methods, moment reconstruction, moment-adjusted imputation, and multiple imputation. We then describe which methods can also be used with misclassification of categorical covariates. Methods of adjusting estimation of distributions of continuous variables for measurement error are then reviewed. Illustrative examples are provided throughout these sections. We provide lists of available software for implementing these methods and also provide the code for implementing our examples in the Supporting Information. Next, we present several advanced topics, including data subject to both classical and Berkson error, modeling continuous exposures with measurement error, and categorical exposures with misclassification in the same model, variable selection when some of the variables are measured with error, adjusting analyses or design for error in an outcome variable, and categorizing continuous variables measured with error. Finally, we provide some advice for the often met situations where variables are known to be measured with substantial error, but there is only an external reference standard or partial (or no) information about the type or magnitude of the error.

Measurement error and misclassification of variables frequently occur in epidemiology and involve variables important to public health. Their presence can impact strongly on results of statistical analyses involving such variables. However, investigators commonly fail to pay attention to biases resulting from such mismeasurement. We provide, in two parts, an overview of the types of error that occur, their impacts on analytic results, and statistical methods to mitigate the biases that they cause. In this first part, we review different types of measurement error and misclassification, emphasizing the classical, linear, and Berkson models, and on the concepts of nondifferential and differential error. We describe the impacts of these types of error in covariates and in outcome variables on various analyses, including estimation and testing in regression models and estimating distributions. We outline types of ancillary studies required to provide information about such errors and discuss the implications of covariate measurement error for study design. Methods for ascertaining sample size requirements are outlined, both for ancillary studies designed to provide information about measurement error and for main studies where the exposure of interest is measured with error. We describe two of the simpler methods, regression calibration and simulation extrapolation (SIMEX), that adjust for bias in regression coefficients caused by measurement error in continuous covariates, and illustrate their use through examples drawn from the Observing Protein and Energy (OPEN) dietary validation study. Finally, we review software available for implementing these methods. The second part of the article deals with more advanced topics.

© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim The food frequency questionnaire (FFQ) is known to be prone to measurement error. Researchers have suggested excluding implausible energy reporters (IERs) of FFQ total energy when examining the relationship between a health outcome and FFQ-reported intake to obtain less biased estimates of the effect of the error-prone measure of exposure; however, the statistical properties of stratifying by IER status have not been studied. Under certain assumptions, including nondifferential error, we show that when stratifying by IER status, the attenuation of the estimated relative risk in the stratified models will be either greater or less in both strata (implausible and plausible reporters) than for the nonstratified model, contrary to the common belief that the attenuation will be less among plausible reporters and greater among IERs. Whether there is more or less attenuation depends on the pairwise correlations between true exposure, observed exposure, and the stratification variable. Thus exclusion of IERs is inadvisable but stratification by IER status can sometimes help. We also address the case of differential error. Examples from the Observing Protein and Energy Nutrition Study and simulations illustrate these results.

Adult stem cells are finding increased therapeutic potential not least in tissue regeneration protocols. The cell sources being proposed for such protocols include embryonic, umbilical cord blood (CB) and adult bone marrow (BM). Although embryonic sources are controversial, CB and marrow are available immediately. The appropriate cells of use in these sources are considered to be extremely rare and a characterisation of the starting cell source is important for the development of adult stem cell protocols and ex vivo expansion. Umbilical CB and BM mononuclear cells were labelled for the antigens CD34, CD133, CD117, CD164, Thy-1 or CD38, and additional intracellular CD34 antigen. Three dimensional flow-cytometric analyses were carried out together with dual laser confocal microscope analysis for antigen profile expression. Variable levels of immaturity were detected on CB and BM populations using internal and external CD34 antigen. For CB and BM cells, internal CD34 (intCD34+) could be detected on co-expressing CD133+ cells before expression of external CD34 antigen (extCD34+). CD38 co-expression analysis also showed that a small but distinct group of cells expressing low CD38 and no external CD34 antigen could be detected. Additional phenotyping of these cells using CD117, Thy-1, CD164 and CD133 demonstrated variable primitive status detectable within the external CD34- population. Newly harvested primary CB and BM populations were shown to contain not only cellular populations of known standard sequential maturity but also populations of more extreme rarity. The presence of cells which lacked extracellular CD34 antigen, in both CB and BM, but which possessed CD133, has important implications for purification of human stem cells in clinical applications.