| Titre : | The limitations due to exposure detection limits for regression models. (2006) |
| Auteurs : | Enrique-F SCHISTERMAN ; . AIYI LIU ; Albert VEXLER ; Brian-W WHITCOMB ; National Institutes of Health. National Institute of Child Health and Human Development. Division of Epidemiology Statistics and Prevention Research. Rockville. MD. USA |
| Type de document : | Article |
| Dans : | American journal of epidemiology (vol. 163, n° 4, 2006) |
| Pagination : | 374-383 |
| Langues: | Anglais |
| Mots-clés : | Homme ; Exposition ; Epidémiologie ; Estimation ; Simulation ; Biais |
| Résumé : | [BDSP. Notice produite par INIST-CNRS R0xj1921. Diffusion soumise à autorisation]. Biomarker use in exposure assessment is increasingly common, and consideration of related issues is of growing importance. Exposure quantification may be compromised when measurement is subject to a lower threshold. Statistical modeling of such data requires a decision regarding the handling of such readings. Various authors have considered this problem. In the context of linear regression analysis, Richardson and Ciampi (Am J Epidemiol 2003 ; 157 : 355-63) proposed replacement of data below a threshold by a constant equal to the expectation for such data to yield unbiased estimates. Use of such an imputation has some limitations ; distributional assumptions are required, and bias reduction in estimation of regression parameters is asymptotic, thereby presenting concerns about small studies. In this paper, the authors propose distribution-free methods for managing values below detection limits and evaluate the biases that may result when exposure measurement is constrained by a lower threshold. The authors utilize an analytical approach and a simulation study to assess the effects of the proposed replacement method on estimates. These results may inform decisions regarding analytical plans for future studies and provide a possible explanation for some amount of the discordance seen in extant literature. |
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