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Shaw PA, Gustafson P, Carroll RJ, Deffner V, Dodd KW, Keogh RH, Kipnis V, Tooze JA, Wallace MP, Küchenhoff H, Freedman LS. STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2-More complex methods of adjustment and advanced topics. Stat Med 2020; 39:2232-2263. [PMID: 32246531 PMCID: PMC7272296 DOI: 10.1002/sim.8531] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2018] [Revised: 02/27/2020] [Accepted: 02/28/2020] [Indexed: 12/24/2022]
Abstract
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.
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Affiliation(s)
- Pamela A Shaw
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Paul Gustafson
- Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Raymond J Carroll
- Department of Statistics, Texas A&M University, College Station, Texas, USA
- School of Mathematical and Physical Sciences, University of Technology Sydney, Broadway, New South Wales, Australia
| | - Veronika Deffner
- Statistical Consulting Unit StaBLab, Department of Statistics, Ludwig-Maximilians-Universität, Munich, Germany
| | - Kevin W Dodd
- Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland, USA
| | - Ruth H Keogh
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Victor Kipnis
- Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland, USA
| | - Janet A Tooze
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Michael P Wallace
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada
| | - Helmut Küchenhoff
- Statistical Consulting Unit StaBLab, Department of Statistics, Ludwig-Maximilians-Universität, Munich, Germany
| | - Laurence S Freedman
- Biostatistics and Biomathematics Unit, Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Tel Hashomer, Israel
- Information Management Services Inc., Rockville, Maryland, USA
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Bennett DA, Landry D, Little J, Minelli C. Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology. BMC Med Res Methodol 2017; 17:146. [PMID: 28927376 PMCID: PMC5606038 DOI: 10.1186/s12874-017-0421-6] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Accepted: 09/03/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Several statistical approaches have been proposed to assess and correct for exposure measurement error. We aimed to provide a critical overview of the most common approaches used in nutritional epidemiology. METHODS MEDLINE, EMBASE, BIOSIS and CINAHL were searched for reports published in English up to May 2016 in order to ascertain studies that described methods aimed to quantify and/or correct for measurement error for a continuous exposure in nutritional epidemiology using a calibration study. RESULTS We identified 126 studies, 43 of which described statistical methods and 83 that applied any of these methods to a real dataset. The statistical approaches in the eligible studies were grouped into: a) approaches to quantify the relationship between different dietary assessment instruments and "true intake", which were mostly based on correlation analysis and the method of triads; b) approaches to adjust point and interval estimates of diet-disease associations for measurement error, mostly based on regression calibration analysis and its extensions. Two approaches (multiple imputation and moment reconstruction) were identified that can deal with differential measurement error. CONCLUSIONS For regression calibration, the most common approach to correct for measurement error used in nutritional epidemiology, it is crucial to ensure that its assumptions and requirements are fully met. Analyses that investigate the impact of departures from the classical measurement error model on regression calibration estimates can be helpful to researchers in interpreting their findings. With regard to the possible use of alternative methods when regression calibration is not appropriate, the choice of method should depend on the measurement error model assumed, the availability of suitable calibration study data and the potential for bias due to violation of the classical measurement error model assumptions. On the basis of this review, we provide some practical advice for the use of methods to assess and adjust for measurement error in nutritional epidemiology.
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Affiliation(s)
- Derrick A. Bennett
- Nuffield Department of Population Health, University of Oxford, Big Data Institute, Old Road Campus, Roosevelt Drive, Headington, Oxford, OX3 7LF UK
| | - Denise Landry
- School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Canada
| | - Julian Little
- School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Canada
| | - Cosetta Minelli
- Population Health & Occupational Disease, National Heart and Lung Institute, Imperial College London, London, UK
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Freedman LS, Commins JM, Willett W, Tinker LF, Spiegelman D, Rhodes D, Potischman N, Neuhouser ML, Moshfegh AJ, Kipnis V, Baer DJ, Arab L, Prentice RL, Subar AF. Evaluation of the 24-Hour Recall as a Reference Instrument for Calibrating Other Self-Report Instruments in Nutritional Cohort Studies: Evidence From the Validation Studies Pooling Project. Am J Epidemiol 2017; 186:73-82. [PMID: 28402488 DOI: 10.1093/aje/kwx039] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Accepted: 08/02/2016] [Indexed: 12/26/2022] Open
Abstract
Calibrating dietary self-report instruments is recommended as a way to adjust for measurement error when estimating diet-disease associations. Because biomarkers available for calibration are limited, most investigators use self-reports (e.g., 24-hour recalls (24HRs)) as the reference instrument. We evaluated the performance of 24HRs as reference instruments for calibrating food frequency questionnaires (FFQs), using data from the Validation Studies Pooling Project, comprising 5 large validation studies using recovery biomarkers. Using 24HRs as reference instruments, we estimated attenuation factors, correlations with truth, and calibration equations for FFQ-reported intakes of energy and for protein, potassium, and sodium and their densities, and we compared them with values derived using biomarkers. Based on 24HRs, FFQ attenuation factors were substantially overestimated for energy and sodium intakes, less for protein and potassium, and minimally for nutrient densities. FFQ correlations with truth, based on 24HRs, were substantially overestimated for all dietary components. Calibration equations did not capture dependencies on body mass index. We also compared predicted bias in estimated relative risks adjusted using 24HRs as reference instruments with bias when making no adjustment. In disease models with energy and 1 or more nutrient intakes, predicted bias in estimated nutrient relative risks was reduced on average, but bias in the energy risk coefficient was unchanged.
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Subar AF, Kushi LH, Lerman JL, Freedman LS. Invited Commentary: The Contribution to the Field of Nutritional Epidemiology of the Landmark 1985 Publication by Willett et al. Am J Epidemiol 2017; 185:1124-1129. [PMID: 28535308 DOI: 10.1093/aje/kwx072] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 03/20/2017] [Indexed: 12/15/2022] Open
Abstract
The semiquantitative food frequency questionnaire (FFQ) has been the primary source of dietary exposure data in epidemiology for decades. Although frequency instruments had been evaluated before the 1985 publication "Reproducibility and Validity of a Semiquantitative Food Frequency Questionnaire" by Willett et al. (Am J Epidemiol. 1985;122(1):51-65), that paper was the prototype for the development and validation of what was then a highly innovative method for collecting dietary data. This approach was adopted in nearly all subsequent cohort studies of diet and disease. The paper also catalyzed an extended scientific discourse regarding methods for validation, energy adjustment, and measurement error. It is now well established that data from FFQs and other self-reported dietary assessment instruments have both value and error and that this error should be considered in the analysis and interpretation of findings, including sensitivity analyses in which adjustment for measurement error is explored. Advances in technology make it feasible to consider collecting multiple granular short-term instruments such as recalls or records over time in addition to FFQs among all participants in large cohort studies; both provide valuable information. Without a doubt, the 1985 publication by Willett et al. provided the foundation that propelled the field of nutritional epidemiology forward, and it continues to be relevant today.
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