The specific aim of this project was the development of new measurement error methods which would be applicable to retrospective cohort and cross sectional studies typically found in occupational epidemiology. The methods were to be easy to use and understand. The usefulness of the methods was demonstrated through an analysis of an important occupational data set, the ACE study of the relationship between health and occupational exposure to anticancer drugs. Work was also performed on developing a user friendly computer software system to implement these methods so as to encourage routine use by occupational epidemiologists. The major findings of the study were that exposure measurement error is often an important source of bias in occupational epidemiology and that methods are available to correct for these biases. The second significant finding was that the fully parametric maximum likelihood method is efficient and consistent if an empirically verified measurement error model is correctly specified. Semiparametric estimating equations are locally efficient and can be used to check for sensitivity of the results to measurement error model misspecification. Thirdly, it was determined that automatic differentiation is an easy to use indispensable tool for developing an error free code for finding the root of nonlinear equations, such as parametric estimating equations, or for finding the maximum of the log likelihood.
Links with this icon indicate that you are leaving the CDC website.
The Centers for Disease Control and Prevention (CDC) cannot attest to the accuracy of a non-federal website.
Linking to a non-federal website does not constitute an endorsement by CDC or any of its employees of the sponsors or the information and products presented on the website.
You will be subject to the destination website's privacy policy when you follow the link.
CDC is not responsible for Section 508 compliance (accessibility) on other federal or private website.
For more information on CDC's web notification policies, see Website Disclaimers.
CDC.gov Privacy Settings
We take your privacy seriously. You can review and change the way we collect information below.
These cookies allow us to count visits and traffic sources so we can measure and improve the performance of our site. They help us to know which pages are the most and least popular and see how visitors move around the site. All information these cookies collect is aggregated and therefore anonymous. If you do not allow these cookies we will not know when you have visited our site, and will not be able to monitor its performance.
Cookies used to make website functionality more relevant to you. These cookies perform functions like remembering presentation options or choices and, in some cases, delivery of web content that based on self-identified area of interests.
Cookies used to track the effectiveness of CDC public health campaigns through clickthrough data.
Cookies used to enable you to share pages and content that you find interesting on CDC.gov through third party social networking and other websites. These cookies may also be used for advertising purposes by these third parties.
Thank you for taking the time to confirm your preferences. If you need to go back and make any changes, you can always do so by going to our Privacy Policy page.