Pamela A Shaw1, Veronika Deffner2, Ruth H Keogh3, Janet A Tooze4, Kevin W Dodd5, Helmut Küchenhoff2, Victor Kipnis5, Laurence S Freedman6. 1. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia. Electronic address: shawp@upenn.edu. 2. Department of Statistics, Statistical Consulting Unit StaBLab, Ludwig-Maximilians- Universität, Munich, Germany. 3. Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK. 4. Department of Biostatistical Sciences, Wake Forest School of Medicine, Medical Center Blvd, Winston-Salem, NC. 5. Division of Cancer Prevention, Biometry Research Group, National Cancer Institute, Bethesda, MD. 6. Information Management Services, Inc, Rockville, MD; Biostatistics Unit, Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Tel Hashomer, Israel.
Abstract
PURPOSE: Variables in observational studies are commonly subject to measurement error, but the impact of such errors is frequently ignored. As part of the STRengthening Analytical Thinking for Observational Studies Initiative, a task group on measurement error and misclassification seeks to describe the current practice for acknowledging and addressing measurement error. METHODS: Task group on measurement error and misclassification conducted a literature survey of four types of research studies that are typically impacted by exposure measurement error: (1) dietary intake cohort studies, (2) dietary intake population surveys, (3) physical activity cohort studies, and (4) air pollution cohort studies. RESULTS: The survey revealed that while researchers were generally aware that measurement error affected their studies, very few adjusted their analysis for the error. Most articles provided incomplete discussion of the potential effects of measurement error on their results. Regression calibration was the most widely used method of adjustment. CONCLUSIONS: Methods to correct for measurement error are available but require additional data regarding the error structure. There is a great need to incorporate such data collection within study designs and improve the analytical approach. Increased efforts by investigators, editors, and reviewers are needed to improve presentation of research when data are subject to error.
PURPOSE: Variables in observational studies are commonly subject to measurement error, but the impact of such errors is frequently ignored. As part of the STRengthening Analytical Thinking for Observational Studies Initiative, a task group on measurement error and misclassification seeks to describe the current practice for acknowledging and addressing measurement error. METHODS: Task group on measurement error and misclassification conducted a literature survey of four types of research studies that are typically impacted by exposure measurement error: (1) dietary intake cohort studies, (2) dietary intake population surveys, (3) physical activity cohort studies, and (4) air pollution cohort studies. RESULTS: The survey revealed that while researchers were generally aware that measurement error affected their studies, very few adjusted their analysis for the error. Most articles provided incomplete discussion of the potential effects of measurement error on their results. Regression calibration was the most widely used method of adjustment. CONCLUSIONS: Methods to correct for measurement error are available but require additional data regarding the error structure. There is a great need to incorporate such data collection within study designs and improve the analytical approach. Increased efforts by investigators, editors, and reviewers are needed to improve presentation of research when data are subject to error.
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