Exposure Measurement Errors, Risk Estimate and Statistical Power in Case-Control Studies Using Dichotomous Analysis of a Continuous Exposure Variable

Delpizzo, Vincent; Borghesi, Joanna L

doi:10.1093/ije/24.4.851

Cited by 18 publications

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“…flncrease in risk per unit change in exposure level. The three levels are coded 1, 2, or 3 and the variable is treated as continuous.S0Rs for the year of hire variable were as follows: < 1955, OR = 1-0; 1955-64, OR = 1-66 (95% CI 0-97 to 2-83); 1965-74, OR = 0-92 (95% CI 0-50 to 1-67); > 1975, OR = 1-97 (95% CI = 092 to[4][5][6][7][8][9][10][11][12][13][14][15][16][17][18].…”

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confidence: 99%

Occupational exposure to magnetic fields in relation to mortality from brain cancer among electricity generation and transmission workers.

et al. 1997

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Objective-To investigate whether the risks of mortality from brain cancer are related to occupational exposure to magnetic fields. Methods-A total of 112 cases of primary brain cancer were identified from a cohort of 84 018 male and female employees of the (then) Central Electricity Generating Board and its privatised successor companies. Individual cumulative occupational exposures to magnetic fields were estimated by linking available computerised job history data with magnetic field measurements collected over 675 person-workshifts. Estimated exposure histories of the case workers were compared with those of 654 control workers drawn from the cohort (nested case-control study), by means of conditional logistic regression. Results-For exposure assessments based on arithmetic means, the risk of mortality from brain cancer for subjects with an estimated cumulative exposure to magnetic fields of 5'4-13-4 uT.y v subjects with lower exposures (0.0-5.3 pT.y) was 1 04 (95% confidence interval (95% CI) 0-60 to 1-80). The corresponding relative risk in subjects with higher exposures (> 13 5 pT.y) was 095 (95% CI 0 54 to

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Occupational exposure to magnetic fields in relation to mortality from brain cancer among electricity generation and transmission workers.

et al. 1997

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“…Although this is generally the case, 6 other error structures and alternative model assumptions can actually lead to a bias in effect estimates away from the null. 32,33 Our simulation experiment focused on the potential impact of inaccurate reporting of true condom-related behaviors and use of imperfect diagnostic tests in studies of the effectiveness of condoms in preventing STD infection. An area of similar concern arises concerning reliance on self-reported compliance with treatment protocols in evaluations of the impact of microbicides, with and without condoms, on STD infection risk.…”

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confidence: 99%

The Impact of Inaccurate Reporting of Condom Use and Imperfect Diagnosis of Sexually Transmitted Disease Infection in Studies of Condom Effectiveness

Devine¹,

Aral²

2004

Sexually Transmitted Diseases

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“…Although a variety of approaches have been presented to adjust sample-size estimates for this uncertainty, a simulation-based assessment of the study, similar to those used in the examples in this article, can be a highly informative means of examining the adequacy of sample-size estimates and other design attributes prior to data collection (Delpizzo & Borghesi, 1995). In addition, even in the absence of measurement error, simulation results can provide highly informative sensitivity assessments reflecting assumptions on parameters important in designing efficient epidemiologic studies.…”

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confidence: 99%

The Impact of Ignoring Measurement Error when Estimating Sample Size for Epidemiologic Studies

Devine¹

2003

Eval Health Prof

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The author presents two examples illustrating the bias in sample-size estimates that can result from ignoring measurement error among study variables. The first example examines the impact of ignoring misclassification of the study's outcome variable on the accuracy of sample-size estimates. In addition, the author outlines a simple yet effective means of adjusting sample-size estimates to account for outcome misclassification. In the second example, the author illustrates the potential for severe underestimation of required sample size in studies using linear regression to evaluate associations between the outcome of interest and an independent variable subject to classical measurement error. The author concludes with a discussion of pertinent literature that might be helpful to study planners interested in adjusting sample-size estimates to account for measurement errors in both outcome and predictor variables.

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Exposure Measurement Errors, Risk Estimate and Statistical Power in Case-Control Studies Using Dichotomous Analysis of a Continuous Exposure Variable

Abstract: When sufficient empirical data are available, computer modelling may give a more accurate estimate of the effects of measurement errors than algebraic corrections.

Cited by 18 publications

References 0 publications

Occupational exposure to magnetic fields in relation to mortality from brain cancer among electricity generation and transmission workers.

Occupational exposure to magnetic fields in relation to mortality from brain cancer among electricity generation and transmission workers.

The Impact of Inaccurate Reporting of Condom Use and Imperfect Diagnosis of Sexually Transmitted Disease Infection in Studies of Condom Effectiveness

The Impact of Ignoring Measurement Error when Estimating Sample Size for Epidemiologic Studies

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