Contextualizing results in the context of health sciences problems and research questions is stressed throughout the course. Most assignments will be completed using statistical computing software. Power and sample size computations for time-to-event data will also be introduced. As time allows, other topics will be introduced including parametric survival models, frailty models and/or models incorporating competing risks. The Cox proportional hazards regression model is presented in detail, along with some extensions of this model. Methods widely used in the health sciences are covered, including Kaplan-Meier (empirical) estimate of the survival function and its associated statistical tests. survival) data, covering methods for estimation, hypothesis testing, and regression methods for censored data with covariates. This course introduces students to analysis of time-to-event (i.e. BSTA 511/611: Estimation & Hypothesis Testing for Applied BiostatisticsĪlso offered as BSTA 613 for doctoral students. Most homework assignments for this course require the use of statistical software. We will also learn some machine learning techniques other than logistic regression model. Students will have the opportunity to be exposed to other analysis methods, such as Poisson regression and multinomial logistic regression, etc. Similar to linear regression in Bsta 512/Bsta 612, topics for logistic regression will include parameter interpretation, statistical adjustment, variable selection techniques and model fit assessment. Students will learn logistic regression, and relate results back to those found with stratified analyses. This course covers topics in categorical data analysis such as cross tabulation statistics, statistics for matched samples, and methods to assess confounding and interaction via stratified tables. Computer applications are included as part of the course to introduce students to basic data management, reading output from computer pack-ages, interpreting and summarizing results.ĭoctoral students register for the BSTA 611 section.Ĭategorical Data Analysis is the third course in the required sequence for applied Biostatistics (Bsta 511, Bsta 512, Bsta 513 or Bsta 611, Bsta 612, Bsta 613). The course focuses on understanding when to use basic statistical methods, how to compute test statistics and how to interpret and communicate the results. Students will be introduced to one-way analysis of variance (ANOVA), correlation, and simple linear regression. Both normal theory and nonparametric approaches will be studied including one- and two-sample tests of population means and tests of independence for two-way tables. Confidence intervals and hypothesis testing will be studied with emphasis on applying these methods to relevant situations. Basic probability concepts will be explored to establish the basis for statistical inference. The course begins by covering methods of summarizing data through graphical displays and numerical measures. This course covers a broad range of basic statistical methods used in the health sciences. More Information About the Integrative Project For example, students may write a high-quality written paper using the findings from a statistical analysis performed in support of a research project that is separate from their Practice Experience. We recommend (but do not require) that the IP paper build upon work conducted in the Practice Experience. Appropriate types of written products vary by program, type of practice experience (if the two are integrated), and the student’s career goals. The paper will take the form of a substantial written product such as a program evaluation, policy or economic analysis, grant proposal, health promotion or community engagement program plan, publishable manuscript, or other written product that demonstrates integration of three foundational (one must include Foundational Competency #6) and three program competencies. Through the IP, a high-quality written product is produced, which we call the “IP paper.” Through the IP paper, students demonstrate their academic learning and public health practice skills through the synthesis of foundational and program competencies and application of those competencies to complex public health issues. The key culminating step for each MPH student is the Integrative Project (IP).
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