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Biostatistics

The certificate in Biostatistics is designed for professionals who are interested in the techniques needed to analyze data and draw conclusions in health-rated fields such as clinical trials, health informatics, and epidemiology.

Individuals may customize a certificate by working with an advisor to develop a plan that meets their needs.

Each certificate requires completion of four classes, as noted in the following list.

  • Categorical Data Analysis
  • Modeling with Discrete Data
  • Nonparametric Statistics
  • Clinical Trials

The following additional introductory courses are available and may be necessary for individuals with limited background:

  • Basic Statistics
  • Introduction to R
  • Introduction to SAS

Each course is fifteen hours including online lectures, discussion boards and/or chat sessions taken over a five-week period. Each course may be taken individually and qualify for CEUs.

For more information, contact Greg Evershed (gmecqa@rit.edu) at (585) 475-5442.

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Categorical Data Analysis

Brief Description: There are many situations in which the variable that you are interested in analyzing is categorical in nature. The usual analysis tools such as t-tests, ANOVA, regression, etc. do not apply in these cases. We must use techniques that were developed specifically for the analysis of categorical data. This course develops statistical methods specifically for categorical variables. These techniques with examples will be presented using the SAS computing package.

Topics:

  • Introduction and uses for categorical variables
  • Numerical summary measures such as proportions and rates
  • Graphical summaries for categorical variables 
  • One sample hypothesis tests and confidence intervals for proportions
  • Two sample hypothesis tests for proportions including the use of measures of association and odds ratios
  • K-sample hypothesis tests for proportions including the use of measures of association and odds ratios
  • Adjustment for potential confounders

Target participants: Analysts, managers, scientists, engineers and/or anyone else interested in learning more about the proper way to use SAS to analyze categorical data

Prerequisites: Participants should have a basic understanding of hypothesis testing and the SAS computing package

This course can be used for the Professional Certificate in Biostatistics or a customized certificate.

Modeling with Discrete Data

Brief Description: There are many situations in which statistical models are needed. When the variable that you would like to model is continuous, the usual modelling techniques such as ANOVA and regression are used. When the variable that you would like to model is discrete, these techniques no longer apply. We must use techniques that were developed specifically for model building with discrete response variables. These techniques are part of the family of generalized linear models or GLIMs for short. This course develops statistical methods specifically for model building when the response variable is discrete. This includes model selection and assessment. These techniques with examples will be presented using the SAS computing package.

Topics:

  • Introduction and uses for GLIMs
  • Poisson and Negative Binomial Regression
  • Binary Logistic Regression
  • Model Building and Selection
  • Multinomial Logistic Regression
  • Model Illustration and Assessment

Target participants: Analysts, managers, scientists, engineers and/or anyone else interested in learning more about the proper way to use SAS to build models in situations where the response variable is discrete.

Prerequisites: Participants should have a basic understanding of model building and the SAS computing package

This course can be used for the Professional Certificate in Biostatistics or a customized certificate.

Nonparametric Statistics

Brief Description: There are many situations in statistics where we rely on the normal distribution. This includes t-tests, ANOVA, regression, etc. What if the data does not exhibit the characteristics for a normal distribution? What if it is skewed or has extreme values? For these cases, the above techniques based on the normality assumption are no longer valid. Instead we can use techniques based on the ranks of the data instead of the data itself. This course develops statistical methods equivalent to the techniques above based on ranks.  This includes the rank equivalents for one and two sample tests, ANOVA, correlation, etc. These techniques with examples will be presented using the SAS computing package.

Topics:

  • a review of hypothesis testing and confidence-interval construction
  • Sign and Wilcoxon signed-rank tests
  • Mann-Whitney and Friedman tests
  • Kruskal-Wallis test
  • Runs tests
  • Chi-square tests
  • Rank correlation
  • Rank order tests
  • Kolmogorov-Smirnov statistics

Target participants: Analysts, managers, scientists, engineers and/or anyone else interested in learning more about the proper way to use SAS to build models in situations where the response variable is discrete.

Prerequisites: Participants should have a basic understanding of model building and the SAS computing package

This course can be used for the Professional Certificate in Biostatistics or a customized certificate.

Introduction to Clinical Trials

Brief Description: Clinical Trials play a major role in drug development and the creation of medical devices. There are specific techniques used to develop and run these types of studies. In this course, we will introduce the idea of a clinical trial, including the history and phases. We will also discuss the development of the protocol, including criteria for the selection of participants, treatments, and endpoints, randomization procedures, sample size determination, data analysis, and study interpretation. Finally, we will discuss the statistical analysis plan (SAP) and the clinical study report. All statistical aspects of the course including determining sample size, data analysis and interpretation will be accomplished using the SAS computing package.

Topics:

  • History of clinical trials
  • Phases of a clinical trial
  • Protocol development
  • Sample Size Determination
  • Statistical Analysis Plan (SAP)
  • Data Analysis and Interpretation
  • Clinical Study Report

Target participants: Analysts, managers, scientists, statisticians and/or anyone else interested in learning more about the clinical trial process, especially the responsibilities of the statistician.

Prerequisites: Participants should have a basic understanding of data analysis and the SAS computing package

This course can be used for the Professional Certificate in Biostatistics or a customized certificate.

 

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