Course Description and Objectives

G910 is one credit hour introductory level biostatistics course for dental students. The objectives of this course are

1)      To enable students to read and evaluate the dental literature by:

a)      understanding statistical terms and expressions,

b)      evaluating the study design and associated biases,

c)      determining if correct statistical procedures have been used, and

d)     determining if conclusions are appropriate with regard to the results.

2)      To enable students to design their research projects, beginning with their thesis.

3)  To enable students to collaborate with statisticians on complex study designs and statistical analyses.      

Textbook

Basic and Clinical Biostatistics  by Beth Dawson-Saunders and Robert G. Trapp

Prerequisites

None

Syllabus

Study Design

   Observational and experimental studies, advantages and disadvantages of different study

   Designs

Exploring and Presenting Data

Types of data, tabular and graphical presentation of data, examples of misleading charts and graphs

Summarizing Data

Measures of central tendency, measures of dispersion, measures to use with nominal data,  measures to describe relationships between two characteristics

Probability, Sampling and Probability Distributions

Meaning, definition and rule of probability, population and samples, random variables and probability distributions of random variables, normal distribution

Statistical Inference

Basic concept in test of hypothesis, confidence interval, steps in conducting a test of hypothesis

Estimating and Comparing Means

Student’s t-distribution and standard normal distribution, one-sample t-test, paired t-test, two-sample t-test and non-parametric alternatives, sample size determination

Analysis of Variance (ANOVA)

One-way ANOVA, multiple comparison procedures, multiway ANOVA and non-parametric ANOVA

Analysis of Categorical Data

       Binomial distribution, one sample test for proportion, comparing proportions for two or

       more independent groups, comparing proportions in paired groups

Correlation and Regression

      Overview and measures of correlation, simple linear regression, interpretation and inference

      for regression coefficients, estimation and prediction, model diagnosis

Multiple Linear Regression and Logistic Regression

      Model fittings, interpretation and inference for regression coefficients, estimation and

      prediction, model diagnosis

Evaluating Diagnostic Procedures

       Sensitivity, specificity, positive and negative predictive values, ROC curves