1. COURSE DESCRIPTION
The course focuses on descriptive and inferential statistics as applied to medical practice.
The course starts with descriptive measures and probability concepts. Conditional probability and Bayes theory are given due emphasis to compute validity indicators for clinical and laboratory tests, i.e. sensitivity, specificity and predictive values for single and multiple tests.
The students are trained to draw statistical inferences by two main methods these are: Estimation and Hypothesis testing. Chi-square variants are discussed with relevant clinical examples.
Students are trained to use computer software in solving assigned exercises. The standard text book used in this course is W. Daniel Biostatistics. This text book uses Mini tab soft ware computer program. The students are provided with necessary Mini tab software at the beginning of the course to be used during the course in solving practical exercises and in data analysis.
2. GENERAL OBJECTIVES
1) organize, summarize and present data related to health sciences.
2) Define representative and appropriate sample from a certain population using the appropriate sampling technique.
3) Estimate population parameters (means and proportions) with high reliability based on the information contained in the sample.
4) Test any hypothesis about the population parameters.
5) Reach decisions about large body of data by examining only a small part of the data.
6) Draw scientific conclusions from data.
First Exam = 30%
Second Exam = 30%
Final Exam = 40%
5. RECOMMENDED TEXT BOOKS
Wayne Daniel, Biostatistics: A Foundation for Analysis in the Health Sciences, Eighth Edition, 2005, John Wiley, Newyork
WEEK TOPIC .
1 Definitions of terms
2 Descriptive statistics: Frequency distribution
Measures of central tendency
3 Descriptive statistics: Measures of variability
4 Probability concepts
5 Bayes Theorem: Validity indicators
Positive and negative predictive values
6 First term exam.
7 Probability distribution: Binomial distribution
Normal, standard normal distribution
8 Sampling distributions
9 Estimation of population parameters (mean, proportion)
10 Estimation - difference between two means, two proportions
11 Second term exam.
12 Hypothesis testing: Introduction
13 Hypothesis testing: One-sample t test
Hypothesis testing: Proportion test (Z test)
14 Hypothesis testing: Difference between two means t-test
Hypothesis testing: Paired t-test
Hypothesis testing: Difference between two proportions
15 Chi-square tests
Chi-square test: Relative risk, Odds ratio