BACKGROUND Random errors in the measurement of risk factors lead to bias in the exposure-disease association. OBJECTIVES This study aimed to examine the extent of underestimation in the association of total cholesterol (TC), high density lipoprotein cholesterol (HDL-C) and triglyceride (TG) with cardiovascular disease (CVDs) in the Tehran Lipid and Glucose Study. PATIENTS AND METHODS Of 632...

Measure errors in economic data are pervasive and nontrivial in size. The presence of measurement errors causes biased and inconsistent parameter estimates and leads to erroneous conclusions to various degrees in economic analysis. The importance of measurement errors in analyzing the empirical implications of economic theories is highlighted in Milton Friedman’s seminal book on the consumption...

The greater use of microeconomic and survey based data in addressing key financial stability related questions is a natural outcome of the recent financial crisis. Amongst other benefits, the use of such data enables a more precise understanding of the differing attitudes and responses of individual agents such as households to financial shocks. However, some difficulties can arise with the use...

The greater use of microeconomic and survey based data in addressing key financial stability related questions is a natural outcome of the recent financial crisis. Amongst other benefits, the use of such data enables a more precise understanding of the differing attitudes and responses of individual agents such as households to financial shocks. However, some difficulties can arise with the use...

AIMS To estimate the combined contribution of serum total cholesterol, blood pressure and cigarette smoking to coronary heart disease (CHD) risk after adjustment for regression dilution bias. METHODS AND RESULTS Six thousand, five hundred and thirteen middle-aged British men without CHD were followed for major CHD events over 10 years. The population attributable risk fraction (PARF) was pred...

Cole et al. in this issue 2 propose that MI may also be useful in dealing with a second problem rife in epidemiology: exposure measurement error, which typically causes underestimation of exposure–disease associations (regression dilution bias). 3 They coin the acronym MIME (multiple imputation for measurement error) and show that this method can indeed remove regression dilution bias. How wide...