# 9 Results

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This course provides a broad understanding of the application of biostatistics in a regulatory context. Reviews the relevant regulations and guidance documents. Includes topics such as basic study design, target population, comparison groups, and endpoints. Addresses analysis issues with emphasis on the regulatory aspects, including issues of missing data and informative censoring. Discusses safety monitoring, interim analysis and early termination of trials with a focus on regulatory implications.

Subject:
Mathematics
Material Type:
Full Course
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Provider:
Johns Hopkins Bloomberg School of Public Health
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JHSPH OpenCourseWare
Author:
Mary Foulkes
Simon Day
09/15/2008
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Introduces the theory and application of modern, computationally-based methods for exploring and drawing inferences from data. Covers re-sampling methods, non-parametric regression, prediction, and dimension reduction and clustering. Specific topics include Monte Carlo simulation, bootstrap cross-validation, splines, local weighted regression, CART, random forests, neural networks, support vector machines, and hierarchical clustering. De-emphasizes proofs and replaces them with extended discussion of interpretation of results and simulation and data analysis for illustration.

Subject:
Mathematics
Material Type:
Full Course
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Provider:
Johns Hopkins Bloomberg School of Public Health
Provider Set:
JHSPH OpenCourseWare
Author:
Irizarry, Rafael
07/05/2018
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Presents fundamental concepts in applied probability, exploratory data analysis, and statistical inference, focusing on probability and analysis of one and two samples. Topics include discrete and continuous probability models; expectation and variance; central limit theorem; inference, including hypothesis testing and confidence for means, proportions, and counts; maximum likelihood estimation; sample size determinations; elementary non-parametric methods; graphical displays; and data transformations.

Subject:
Mathematics
Material Type:
Full Course
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Provider:
Johns Hopkins Bloomberg School of Public Health
Provider Set:
JHSPH OpenCourseWare
Author:
Brian Caffo
07/05/2018
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This course presents construction of sampling frames, area sampling, methods of estimation, stratified sampling, subsampling, and sampling methods for surveys of human populations. Students use STATA or another comparable package to implement designs and analyses of survey data.

Subject:
Mathematics
Material Type:
Full Course
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Provider:
Johns Hopkins Bloomberg School of Public Health
Provider Set:
JHSPH OpenCourseWare
Author:
Saifuddin Ahmed
01/15/2009
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Statistical Reasoning in Public Health II provides an introduction to selected important topics in biostatistical concepts and reasoning through lectures, exercises, and bulletin board discussions. The course builds on the material in Statistical Reasoning in Public Health I , extending the statistical procedures discussed in that course to the multivariate realm, via multiple regression methods. New topics, such as methods for clinical diagnostic testing, and univariate, bivariate, and multivariate techniques for survival analysis will also be covered. These topics will be reinforced with many "real-life" examples drawn from recent biomedical literature. While there are some formulae and computational elements to the course, the emphasis is again on interpretation and concepts.

Subject:
Mathematics
Material Type:
Full Course
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Provider:
Johns Hopkins Bloomberg School of Public Health
Provider Set:
JHSPH OpenCourseWare
Author:
07/05/2018
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This course introduces the basic concepts and methods of statistics with applications in the experimental biological sciences. Demonstrates methods of exploring, organizing, and presenting data, and introduces the fundamentals of probability. Presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests. Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data.

Subject:
Mathematics
Material Type:
Full Course
Homework/Assignment
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Provider:
Johns Hopkins Bloomberg School of Public Health
Provider Set:
JHSPH OpenCourseWare
Author:
Broman, Karl
07/05/2018
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This course introduces the basic concepts and methods of statistics with applications in the experimental biological sciences. Demonstrates methods of exploring, organizing, and presenting data, and introduces the fundamentals of probability. Presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests. Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data.

Subject:
Mathematics
Material Type:
Full Course
Lecture Notes
Syllabus
Provider:
Johns Hopkins Bloomberg School of Public Health
Provider Set:
JHSPH OpenCourseWare
Author:
Broman, Karl
07/05/2018
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This course presents quantitative approaches to theory construction in the context of multiple response variables, with models for both continuous and categorical data. Topics include the statistical basis for causal inference; principles of path analysis; linear structural equation analysis incorporating measurement models; latent class regression; and analysis of panel data with observed and latent variable models. Draws examples from the social sciences, including the status attainment approach to intergenerational mobility, behavior genetics models of disease and environment, consumer satisfaction, functional impairment and disability, and quality of life.

Subject:
Mathematics
Material Type:
Full Course
Lecture Notes
Syllabus
Provider:
Johns Hopkins Bloomberg School of Public Health
Provider Set:
JHSPH OpenCourseWare
Author:
Quian-Li Xue
William Eaton
09/15/2007
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Presents quantitative approaches to measurement in the psychological and social sciences. Topics include the principles of psychometrics, including reliability and validity; the statistical basis for latent variable analysis, including exploratory and confirmatory factor analysis and latent class analysis; and item response theory. Draws examples from the social sciences, including stress and distress, social class and socioeconomic status, personality; consumer satisfaction, functional impairment and disability, quality of life, and the measurement of overall health status. Intended for doctoral students.

Subject:
Mathematics
Material Type:
Full Course
Lecture Notes
Syllabus
Provider:
Johns Hopkins Bloomberg School of Public Health
Provider Set:
JHSPH OpenCourseWare
Author:
Eaton, William
Garrett-Mayer, Elizabeth
Leoutsakos, Jeannie-Marie