Course Website Locator: bio223-01

Harvard School of Public Health

The following course websites match your request:

Spring 2010

Dr. Lee-Jen Wei
5 credits
Lectures. Two 2-hour sessions each week. One 1-hour optional lab each week.

This course will cover topics in both discrete data analysis (25% of class) and applied survival analysis (75% of class). The course will begin with a review of sampling plans and contingency table for discrete data. Further topics in discrete data analysis will include logistic regression, exact inference, and conditional logistic regression. This short survey of discrete data topics will provide a natural transition to analysis of survival data. Survival topics include: hazard, survivor, and cumulative hazard functions, Kaplan-Meier and actuarial estimation of the survival distribution, comparison of survival using log rank and other tests, regression models including the Cox proportional hazards model and accelerated failure time model, adjustment for time-varying covariates, and use of parametric distributions (exponential, Weibull) in survival analysis. Class material will include presentation of statistical methods for estimation and testing, along with current software (SAS, Stata, Splus) for implementing analyses of discrete data and survival data. Applications to real data will be emphasized.
Course Note: BIO 210, BIO 213, or BIO 230 required, or signature of instructor.

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.

Spring 2009

Dr. Lee-Jen Wei
5 credits
Lectures. Two 2-hour sessions each week. One 1-hour optional lab each week.

This course will cover topics in both discrete data analysis (25% of class) and applied survival analysis (75% of class). The course will begin with a review of sampling plans and contingency table for discrete data. Further topics in discrete data analysis will include logistic regression, exact inference, and conditional logistic regression. This short survey of discrete data topics will provide a natural transition to analysis of survival data. Survival topics include: hazard, survivor, and cumulative hazard functions, Kaplan-Meier and actuarial estimation of the survival distribution, comparison of survival using log rank and other tests, regression models including the Cox proportional hazards model and accelerated failure time model, adjustment for time-varying covariates, and use of parametric distributions (exponential, Weibull) in survival analysis. Class material will include presentation of statistical methods for estimation and testing, along with current software (SAS, Stata, Splus) for implementing analyses of discrete data and survival data. Applications to real data will be emphasized.
Course Note: BIO 210, BIO 213, or BIO 230 required, or signature of instructor.

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.

Spring 2008

E. Goetghebeur, E. Weller Toolan
5 credits
Lectures. Two 2-hour sessions each week. One 1-hour optional lab each week.

This course will cover topics in both discrete data analysis (25% of class) and applied survival analysis (75% of class). The course will begin with a review of sampling plans and contingency table for discrete data. Further topics in discrete data analysis will include logistic regression, exact inference, and conditional logistic regression. This short survey of discrete data topics will provide a natural transition to analysis of survival data. Survival topics include: hazard, survivor, and cumulative hazard functions, Kaplan-Meier and actuarial estimation of the survival distribution, comparison of survival using log rank and other tests, regression models including the Cox proportional hazards model and accelerated failure time model, adjustment for time-varying covariates, and use of parametric distributions (exponential, Weibull) in survival analysis. Class material will include presentation of statistical methods for estimation and testing, along with current software (SAS, Stata, Splus) for implementing analyses of discrete data and survival data. Applications to real data will be emphasized.
Course Note: BIO 210, BIO 213, or BIO 230 required, or signature of instructor. (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.

Spring 2007

E. Goetghebeur, E. Weller Toolan
5 credits
Lectures. Two 2-hour sessions each week. One 1-hour optional lab each week.

This course will cover topics in both discrete data analysis (25% of class) and applied survival analysis (75% of class). The course will begin with a review of sampling plans and contingency table for discrete data. Further topics in discrete data analysis will include logistic regression, exact inference, and conditional logistic regression. This short survey of discrete data topics will provide a natural transition to analysis of survival data. Survival topics include: hazard, survivor, and cumulative hazard functions, Kaplan-Meier and actuarial estimation of the survival distribution, comparison of survival using log rank and other tests, regression models including the Cox proportional hazards model and accelerated failure time model, adjustment for time-varying covariates, and use of parametric distributions (exponential, Weibull) in survival analysis. Class material will include presentation of statistical methods for estimation and testing, along with current software (SAS, Stata, Splus) for implementing analyses of discrete data and survival data. Applications to real data will be emphasized.
Course Note: BIO 210, BIO 213, or BIO 230 required, or signature of instructor. (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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