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Quantifying the potential impact of measurement error in an investigation of autism spectrum disorder (ASD)
  1. Karyn Heavner1,
  2. Craig Newschaffer2,
  3. Irva Hertz-Picciotto3,
  4. Deborah Bennett3,
  5. Igor Burstyn2,4
  1. 1Drexel University School of Public Health, Philadelphia, Pennsylvania, USA
  2. 2A.J. Drexel Autism Institute, Drexel University School of Public Health, Drexel University, Philadelphia, Pennsylvania, USA
  3. 3Department of Public Health Sciences, University of California at Davis, Davis, California, USA
  4. 4Department of Environmental and Occupational Health, Drexel University School of Public Health, Drexel University, A.J. Drexel Autism Institute, Philadelphia, Pennsylvania, USA
  1. Correspondence to Dr Karyn Heavner, Drexel University School of Public Health, Department of Environmental and Occupational Health, Nesbit Hall, 3215 Market St, Philadelphia, PA 19104, USA; karynkh{at}aol.com

Abstract

The Early Autism Risk Longitudinal Investigation (EARLI), an ongoing study of a risk-enriched pregnancy cohort, examines genetic and environmental risk factors for autism spectrum disorders (ASDs). We simulated the potential effects of both measurement error (ME) in exposures and misclassification of ASD-related phenotype (assessed as Autism Observation Scale for Infants (AOSI) scores) on measures of association generated under this study design. We investigated the impact on the power to detect true associations with exposure and the false positive rate (FPR) for a non-causal correlate of exposure (X2, r=0.7) for continuous AOSI score (linear model) versus dichotomised AOSI (logistic regression) when the sample size (n), degree of ME in exposure, and strength of the expected (true) OR (eOR)) between exposure and AOSI varied. Exposure was a continuous variable in all linear models and dichotomised at one SD above the mean in logistic models. Simulations reveal complex patterns and suggest that: (1) There was attenuation of associations that increased with eOR and ME; (2) The FPR was considerable under many scenarios; and (3) The FPR has a complex dependence on the eOR, ME and model choice, but was greater for logistic models. The findings will stimulate work examining cost-effective strategies to reduce the impact of ME in realistic sample sizes and affirm the importance for EARLI of investment in biological samples that help precisely quantify a wide range of environmental exposures.

  • Epidemiological Methods
  • Research Design in Epidemiology
  • MCH

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