Literature DB >> 24352593

Accounting for outcome misclassification in estimates of the effect of occupational asbestos exposure on lung cancer death.

Jessie K Edwards, Stephen R Cole, Haitao Chu, Andrew F Olshan, David B Richardson.   

Abstract

In studies of the health effects of asbestos, lung cancer death is subject to misclassification. We used modified maximum likelihood to explore the effects of outcome misclassification on the rate ratio of lung cancer death per 100 fiber-years per milliliter of cumulative asbestos exposure in a cohort study of textile workers in Charleston, South Carolina, followed from 1940 to 2001. The standard covariate-adjusted estimate of the rate ratio was 1.94 (95% confidence interval: 1.55, 2.44), and modified maximum likelihood produced similar results when we assumed that the specificity of outcome classification was 0.98. With sensitivity assumed to be 0.80 and specificity assumed to be 0.95, estimated rate ratios were further from the null and less precise (rate ratio = 2.17; 95% confidence interval: 1.59, 2.98). In the present context, standard estimates for the effect of asbestos on lung cancer death were similar to estimates accounting for the limited misclassification. However, sensitivity analysis using modified maximum likelihood was needed to verify the robustness of standard estimates, and this approach will provide unbiased estimates in settings with more misclassification.

Entities:  

Keywords:  asbestos; bias; sensitivity and specificity

Mesh:

Substances:

Year:  2013        PMID: 24352593      PMCID: PMC3927979          DOI: 10.1093/aje/kwt309

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  27 in total

1.  Accounting for misclassified outcomes in binary regression models using multiple imputation with internal validation data.

Authors:  Jessie K Edwards; Stephen R Cole; Melissa A Troester; David B Richardson
Journal:  Am J Epidemiol       Date:  2013-04-04       Impact factor: 4.897

2.  Exposures and mortality among chrysotile asbestos workers. Part II: mortality.

Authors:  J M Dement; R L Harris; M J Symons; C M Shy
Journal:  Am J Ind Med       Date:  1983       Impact factor: 2.214

3.  Exposures and mortality among chrysotile asbestos workers. Part I: exposure estimates.

Authors:  J M Dement; R L Harris; M J Symons; C M Shy
Journal:  Am J Ind Med       Date:  1983       Impact factor: 2.214

4.  Relationship of mortality to measures of environmental asbestos pollution in an asbestos textile factory.

Authors:  J Peto; R Doll; C Hermon; W Binns; R Clayton; T Goffe
Journal:  Ann Occup Hyg       Date:  1985

5.  A prospective study of 1152 hospital autopsies: II. Analysis of inaccuracies in clinical diagnoses and their significance.

Authors:  H M Cameron; E McGoogan
Journal:  J Pathol       Date:  1981-04       Impact factor: 7.996

6.  The effect of diagnostic misclassification on non-cancer and cancer mortality dose response in A-bomb survivors.

Authors:  R Sposto; D L Preston; Y Shimizu; K Mabuchi
Journal:  Biometrics       Date:  1992-06       Impact factor: 2.571

7.  Validation of death certificate diagnosis for coronary heart disease: the Atherosclerosis Risk in Communities (ARIC) Study.

Authors:  S A Coady; P D Sorlie; L S Cooper; A R Folsom; W D Rosamond; D E Conwill
Journal:  J Clin Epidemiol       Date:  2001-01       Impact factor: 6.437

8.  Inaccuracy of death certificate diagnoses in malignancy: an analysis of 1,405 autopsied cases.

Authors:  F Gobbato; F Vecchiet; D Barbierato; M Melato; R Manconi
Journal:  Hum Pathol       Date:  1982-11       Impact factor: 3.466

9.  Asbestos and cancer: a cohort followed up to death.

Authors:  P E Enterline; J Hartley; V Henderson
Journal:  Br J Ind Med       Date:  1987-06

10.  Accuracy of cancer death certificates and its effect on cancer mortality statistics.

Authors:  C Percy; E Stanek; L Gloeckler
Journal:  Am J Public Health       Date:  1981-03       Impact factor: 9.308

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  9 in total

1.  Sensitivity Analyses for Misclassification of Cause of Death in the Parametric G-Formula.

Authors:  Jessie K Edwards; Stephen R Cole; Richard D Moore; W Christopher Mathews; Mari Kitahata; Joseph J Eron
Journal:  Am J Epidemiol       Date:  2018-08-01       Impact factor: 4.897

2.  Bias in estimating the cross-sectional smoking, alcohol, obesity and diabetes associations with moderate-severe periodontitis in the Atherosclerosis Risk in Communities study: comparison of full versus partial-mouth estimates.

Authors:  Aderonke A Akinkugbe; Veeral M Saraiya; John S Preisser; Steven Offenbacher; James D Beck
Journal:  J Clin Periodontol       Date:  2015-07-14       Impact factor: 8.728

3.  Misclassification of the actual causes of death and its impact on analysis: A case study in non-small cell lung cancer.

Authors:  Kay See Tan
Journal:  Lung Cancer       Date:  2019-05-16       Impact factor: 5.705

4.  Misclassification in administrative claims data: quantifying the impact on treatment effect estimates.

Authors:  Michele Jonsson Funk; Suzanne N Landi
Journal:  Curr Epidemiol Rep       Date:  2014-12

5.  Maternal major depression disorder misclassification errors: Remedies for valid individual- and population-level inference.

Authors:  Arthur H Owora
Journal:  Brain Behav       Date:  2022-05-19       Impact factor: 3.405

6.  Causal inference in the face of competing events.

Authors:  Jacqueline E Rudolph; Catherine R Lesko; Ashley I Naimi
Journal:  Curr Epidemiol Rep       Date:  2020-07-12

7.  Bayesian adjustment of gastric cancer mortality rate in the presence of misclassification.

Authors:  Nastaran Hajizadeh; Mohamad Amin Pourhoseingholi; Ahmad Reza Baghestani; Alireza Abadi; Mohammad Reza Zali
Journal:  World J Gastrointest Oncol       Date:  2017-04-15

8.  Missingness in the Setting of Competing Risks: from missing values to missing potential outcomes.

Authors:  Bryan Lau; Catherine Lesko
Journal:  Curr Epidemiol Rep       Date:  2018-03-19

9.  A Bayesian Approach to Account for Misclassification and Overdispersion in Count Data.

Authors:  Wenqi Wu; James Stamey; David Kahle
Journal:  Int J Environ Res Public Health       Date:  2015-08-28       Impact factor: 3.390

  9 in total

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