ALS434 - Clinical Biostatistics

ALS434 - Clinical Biostatistics

This course is taught by  Mark Ghamsary. Mark is the Director of the Biostatistics Program at the Department of Biostatistics and Epidemiology at Loma Linda University School of Public Health.

Description:

This half-course provides a basic primer in statistical methods commonly used in the design of clinical trials. Topics covered are expected include data reporting and descriptive statistics, probability, estimation, hypothesis testing (parametric, non-parametric, and categorical), multisample inference, regression and correlation. Sample size and power estimation methods will be developed for various hypothesis testing scenarios.

Prerequisites:

A basic knowledge of mathematical methods at the level of the Math ramp-up course is necessary. Additional exposure to concepts in probability and statistics is desirable but not a hard pre-requisite.

Objectives:

After completing this course students will be able to:

1.      Provide methods of presentation with numerical and graphical presentation on continuous and discrete data sets.

2.      Cover simple probability theory and provide examples of more common applications in clinical setting.

3.      Analyze discrete probability distributions (binomial, Poisson and Hyper geometric).

4.      Analyze continuous probability distributions (normal, chi-square and exponential).

5.      Construct the confidence intervals for the mean, proportion, and variance(One and two sample

6.      Construct the test of hypotheses about the mean, proportion, variance (one and two samples).

7.      Compute the measure of associations, odds ratio, hazard ratio, and the χ² statistics.

8.      Do analysis of variance for independent, as well as repeated measure data.

9.       Compute the sample size determination and power analysis.

10.   Compute the Bayesian probability and its application in medical field

11.   Analyze the simple and multiple linear regressions.

12.   Handle regression model building and examine the underlying assumptions.

13.   Perform survival data analysis for clinical trials.

14.   Understand to run Logistic regression, Poisson regression and Cox Regression

15.   Use SAS software in all clinical researches.

 Statistical Software: SAS will be used in all homework and labs

Homework: There will be about 10 sets of homework assignments. In order to succeed you need to review your notes, read the sections in our text on which we are working.