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If you have a disability that requires special testing accommodations or other classroom modifications, you need to notify both the instructor and Disability Resources and Services no later than the second week of the term. You may be asked to provide documentation of your disability to determine the appropriateness of accommodations.
The office is located in William Pitt Union. Computer Methods in Clinical Research 1. Dataset manipulation, descriptive statistics, and the graphical presentation of data will be presented using a standard statistical package.
Click here for course syllabus. Clinical Research Methods 3. Topics include study design, data analysis and interpretation, and determination of appropriate methodologies to answer different research questions.
Bias and confounding in observational research, the clinical value of diagnostic tests, appropriate use of cross-sectional, case control and cohort study designs, and various statistical modeling used in clinical research will be presented. Topics include data description and summarization, basic probability theory, estimation, and hypothesis testing with emphasis on one- and two-sample comparisons involving continuous and categorical data.
Linear regression and analysis of variance will be introduced. Trainees will develop their analytic skills through the analysis and discussion of large clinical studies.
Topics covered include multiple linear regression, regression diagnostics, ANOVA, analysis of covariance, confounding, mediation, moderation, and model selection.
At the completion of the course, trainees should be able to understand the appropriate uses of ANOVA and linear regression, to assess their appropriateness and adequacy, to analyze simple datasets taken from the fields of medicine and public health, and to summarize results from regression models via written communication.
The course focuses on regression methods for binary data and on the basics of maximum likelihood inference. At the completion of the course, trainees should be able to understand how logistic regression can be used to address a variety of epidemiologic and clinical questions; to interpret models and assess their appropriateness and adequacy; to develop analytic skills through the analysis of datasets taken from the fields of medicine and public health; and to develop oral and written communication skills through the description of analytic strategies and the summarization and interpretation of results.
It is intended for physicians in fellowship training programs and other researchers with a limited background in statistics. The course focuses on descriptive methods for survival data, survival analysis, and issues pertaining to time-dependent covariates. Analysis of Correlated Data 1.
The first half of the course lectures will focus on models for continuous data, including mixed effects models, fixed effects models, and generalized estimating equations.
The second half of lectures will extend to analysis in the generalized linear model setting binary outcomes, count data, etc. We will show students how to investigate data graphically and descriptively before beginning statistical modeling and will introduce students to topics on missing data, group trajectory modeling, and sample size estimation.
We will use homework assignments and articles from multilevel and longitudinal studies to facilitate learning of concepts discussed in class. Measurement in Clinical Research 1. Specific objectives are to analyze methods for testing psychometric properties reliability and validity of psychological instruments and physiological instruments; to evaluate the adequacy of selected scaling methodologies used in research; to apply knowledge of instrumentation to the description of a psychosocial instrument and a physiological instrument for a research proposal; and to synthesize course content with statistical criteria for scale evaluation and make decisions regarding scale revision.
The domain sampling model is presented as the major theory of measurement error, with the parallel test model presented as a special case of the domain sampling model.
The construct, criterion, and content validity of psychosocial instruments are explored, and methods for evaluating each of these relative to specific instruments are presented.
A variety of scaling methodologies, as well as the principles involved in the design and formatting of questionnaires, will be discussed.
Survey Design and Data Analysis 1. The skills include identifying and developing specific survey objectives, designing survey studies, sampling respondents, developing reliable and valid self-administered questionnaires, and administering surveys.
The techniques of analyzing survey data include both classic methods such as factor analysis and advanced methods such as item response theory. A majority of lectures will focus on survey research, constructing surveys, response set, survey administration methods, questionnaire construction and programming surveys, sampling and power calculation, maximizing response rates, data coding and entry, reliability and validity, survey data analysis, factor analysis and item response theory.
The students will be introduced to the internet based survey and the computerized adaptive testing to broaden their scope of the current survey design and collection. I will use manuscripts of survey data and protocols of completed studies to facilitate learning of concepts discussed in class. Advanced Grant Writing Part I 3.
Trainees will learn the phases of the research process from conception to design and, ultimately, to implementation of the research. Through a combination of group sessions and independent work, trainees will use a research topic of their choice to develop their own research proposal in the form of an NIH grant application.
The application will include sections on specific aims, background and significance, previous work, and methods. In addition, trainees will review and critique the work of their peers.
Mentor must be identified prior to enrollment.
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