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What to Do When Accumulated Exposure Affects Health but Only Its Duration Was Measured? A Case of Linear Regression

Author

Listed:
  • Igor Burstyn

    (Department of Environmental and Occupational Health, Dornsife School of Public Health, Drexel University, Philadelphia, PA 19104, USA)

  • Francesco Barone-Adesi

    (Department of Pharmaceutical Sciences, University of Eastern Piedmont, Novara 28100, Italy)

  • Frank de Vocht

    (Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK)

  • Paul Gustafson

    (Department of Statistics, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada)

Abstract

Background : We considered a problem of inference in epidemiology when cumulative exposure is the true dose metric for disease, but investigators are only able to measure its duration on each subject. Methods : We undertook theoretical analysis of the problem in the context of a continuous response caused by cumulative exposure, when duration and intensity of exposure follow log-normal distributions, such that analysis by linear regression is natural. We present a Bayesian method to adjust duration-only analysis to incorporate partial knowledge about the relationship between duration and intensity of exposure and illustrate this method in the context of association of smoking and lung function. Results : We derive equations that (a) describe under what circumstances bias arises when duration of exposure is used as a proxy of cumulative exposure, (b) quantify the degree of such bias and loss of precision, and (c) describe how knowledge about relationship of duration and intensity of exposure can be used to recover an estimate of the effect of cumulative exposure when only duration was observed on every subject. Conclusions : Under our assumptions, when duration and intensity of exposure are either independent or positively correlated, we can be more confident in qualitatively interpreting the direction of effects that arise from use of duration of exposure per se. We can use external information on the relationship between duration and intensity of exposure (namely: correlation and variance of intensity), even if intensity of exposure is not available at the individual level, to make reliable inferences about the magnitude of effect of cumulative exposure on the outcome.

Suggested Citation

  • Igor Burstyn & Francesco Barone-Adesi & Frank de Vocht & Paul Gustafson, 2019. "What to Do When Accumulated Exposure Affects Health but Only Its Duration Was Measured? A Case of Linear Regression," IJERPH, MDPI, vol. 16(11), pages 1-16, May.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:11:p:1896-:d:235217
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    References listed on IDEAS

    as
    1. Nan Xuan Lin & Stuart Logan & William Edward Henley, 2013. "Bias and Sensitivity Analysis When Estimating Treatment Effects from the Cox Model with Omitted Covariates," Biometrics, The International Biometric Society, vol. 69(4), pages 850-860, December.
    2. Igor Burstyn & Yunwen Yang & A. Robert Schnatter, 2014. "Effects of Non-Differential Exposure Misclassification on False Conclusions in Hypothesis-Generating Studies," IJERPH, MDPI, vol. 11(10), pages 1-16, October.
    3. Paul Gustafson & Nhu D. Le, 2002. "Comparing the Effects of Continuous and Discrete Covariate Mismeasurement, with Emphasis on the Dichotomization of Mismeasured Predictors," Biometrics, The International Biometric Society, vol. 58(4), pages 878-887, December.
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