Course Website Locator: epi207-01

Harvard School of Public Health

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Fall 1 2009

Dr. J. Robins, Dr. M. Hernan
2.5 credits
Lectures. Two 2-hour sessions and one 2-hour lab each week.

Provides an in-depth investigation of statistical methods for drawing causal inferences from observational studies. Informal epidemiologic concepts such as confounding, selection bias, overall effects, direct effects, and intermediate variables will be formally defined within the context of a counterfactual causal model and with the help of causal diagrams. Methods for the analysis of the causal effects of time-varying exposures in the presence of time dependent covariates that are simultaneously confounders and intermediate variables will be emphasized. These methods include g-computation algorithm estimators, inverse probability weighted estimators of marginal structural models, g-estimation of structural nested models. As a practicum, students will reanalyze data sets using the above methods.
Course Activities: Class discussion, homework, practicum and final examination.

Course Note: EPI204, BIO210 and EPI289, or BIO233, or signature of instructor required; familiarity with logistic regression and survival analysis is expected; lab time will be announced at first meeting. (5.06)

Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Fall 1 2008

Dr. J. Robins, Dr. M. Hernan
2.5 credits
Lectures. Two 2-hour sessions and one 2-hour lab each week.

Provides an in-depth investigation of statistical methods for drawing causal inferences from observational studies. Informal epidemiologic concepts such as confounding, selection bias, overall effects, direct effects, and intermediate variables will be formally defined within the context of a counterfactual causal model and with the help of causal diagrams. Methods for the analysis of the causal effects of time-varying exposures in the presence of time dependent covariates that are simultaneously confounders and intermediate variables will be emphasized. These methods include g-computation algorithm estimators, inverse probability weighted estimators of marginal structural models, g-estimation of structural nested models. As a practicum, students will reanalyze data sets using the above methods.
Course Activities: Class discussion, homework, practicum and final examination.

Course Note: EPI204, BIO210 and EPI289, or BIO233, or signature of instructor required; familiarity with logistic regression and survival analysis is expected; lab time will be announced at first meeting. (5.06)

Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Fall 1 2007

Dr. J. Robins, Dr. M. Hernan
2.5 credits
Lectures. Two 2-hour sessions and one 2-hour lab each week.

Provides an in-depth investigation of statistical methods for drawing causal inferences from observational studies. Informal epidemiologic concepts such as confounding, selection bias, overall effects, direct effects, and intermediate variables will be formally defined within the context of a counterfactual causal model and with the help of causal diagrams. Methods for the analysis of the causal effects of time-varying exposures in the presence of time dependent covariates that are simultaneously confounders and intermediate variables will be emphasized. These methods include g-computation algorithm estimators, inverse probability weighted estimators of marginal structural models, g-estimation of structural nested models. As a practicum, students will reanalyze data sets using the above methods.
Course Activities: Class discussion, homework, practicum and final examination.
Course Note: EPI204, BIO210 and EPI289, or BIO233, or signature of instructor required; familiarity with logistic regression and survival analysis is expected; lab time will be announced at first meeting. (5.06)

Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Fall 2006

Dr. J. Robins, Dr. M. Hernan
2.5 credits
Lectures. Two 2-hour sessions and one 2-hour lab each week.

Provides an in-depth investigation of statistical methods for drawing causal inferences from observational studies. Informal epidemiologic concepts such as confounding, selection bias, overall effects, direct effects, and intermediate variables will be formally defined within the context of a counterfactual causal model and with the help of causal diagrams. Methods for the analysis of the causal effects of time-varying exposures in the presence of time dependent covariates that are simultaneously confounders and intermediate variables will be emphasized. These methods include g-computation algorithm estimators, inverse probability weighted estimators of marginal structural models, g-estimation of structural nested models. As a practicum, students will reanalyze data sets using the above methods.
Course Activities: Class discussion, homework, practicum and final examination.
Course Note: EPI204, BIO210 and EPI289, or BIO233, or signature of instructor required; familiarity with logistic regression and survival analysis is expected; lab time will be announced at first meeting. (5.06)

Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

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