18BT31 Biostatistics syllabus for BT

INTRODUCTION TO STATISTICS AND STUDY DESIGN:

Introduction, graphical representation of data. Measures of central tendency, dispersion. Significance of statistics to biological problems, experimental studies; randomized controlled studies, historically controlled studies, factorial design, cluster design,; completely randomized block design, analysis and interpretation.

DESCRIPTIVE STATISTICS AND OBSERVATIONAL STUDY DESIGN:

Types of variables, measure of spread, logarithmic transformations, multivariate data. Basics of study design, cohort studies, case control studies, outcomes, odd ratio and relative risks. Principles of statistical inference: Parameter estimation, hypothesis testing. variables; categorical data, binomial distribution, Normal distribution

COMPARISON OF MEANS:

Test statistics; t-test, F distribution, independent and dependent sample comparison, Wilcoxon Signed Rank Test, Wilcoxon Mann-Whitney Test, ANOVA. Correlation and simple linear regression: Introduction, Karl Pearson correlation coefficient, Spearman Rank correlation coefficient, simple linear regression, regression model fit, Multiple linear regression and linear models: Introduction, Multiple linear regression model, ANOVA table for multiple linear regression model, assessing model fit, polynomials and interactions. Oneway and Two-way ANOVA tables, F-tests.

DESIGN AND ANALYSIS OF EXPERIMENTS:

Random block design, multiple sources of variation, correlated data and random effects regression, model fitting. Completely randomized design, stratified design. Biological study designs. Optimization strategies with case studies.

SAS PROGRAMMING:

Basic syntax: variables, strings, arrays, decision making, input methods. SAS data set operations: Read raw data; write, merging, subsetting, sort, format data sets, output delivery system. SAS representations (Histogram, bar chart, pie chart, scatter plot). SAS basic statistical procedure (Arithmetic mean, Standard deviation, T-tests, correlation analysis, frequency distribution, linear regression, Chi square test, one way ANOVA, Hypothesis testing).

Course Outcomes:

At the end of the course the student will be able to:

• Study and design various statistical problems
• Foundation to tackle live problems in various spheres of bioscience and bioengineering.
• Demonstrate strong basics in statistics and numerical analysis

Question paper pattern:

• The question paper will have ten full questions carrying equal marks.
• Each full question will be for 20 marks.
• There will be two full questions (with a maximum of four sub- questions) from each module.
• Each full question will have sub- question covering all the topics under a module.
• The students will have to answer five full questions, selecting one full question from each module.

Textbooks

1 Biostatistics Alvin E. Lewis McGraw-Hill Professional Publishing 2013

2 Statistics and Numerical Methods in BASIC for Biologists J.D. Lee and T.D. Lee Van Nostr and Reinhold Company 1982

3 Statistical Analysis of Gene Expression Microarray Data T.P. Chapman CRC 2003

4 SAS Essentials: Mastering SAS for Data Analytics Alan C. Elliott , Wayne A. Woodward John Wiley & Sons 2nd Edition, 2015

Reference Books

1 Numerical Methods of Statistics (Cambridge Series in Statistical and Probabilistic Mathematics) John F. Monahan Cambridge University Press 2011

2 Numerical Methods for Engineers and Scientists Joe D. Hoffman CRC Press 2ndEdition, 2001

3 Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health) Warren J. Ewens Gregory Grant Springer 2005