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BP801T BIOSTATISTICS
Monitoring, detecting, and preventing adverse drug reactions, ensuring safe and effective use of medications through:
– Adverse event reporting and analysis
– Risk management and mitigation
– Regulatory compliance and safety surveillance”
Course Content
Unit Title
Content
UNIT-I
- Introduction: Statistics, Biostatistics, Frequency distribution
- Measures of central tendency: Mean, Median, Mode- Pharmaceutical examples
- Measures of dispersion: Dispersion, Range, standard deviation, Pharmaceutical problems
- Correlation: Definition, Karl Pearson’s coefficient of correlation, Multiple correlation Pharmaceuticals examples
UNIT-II
- Regression: Curve fitting by the method of least squares, fitting the lines y= a + bx and x = a+by, Multiple regression, standard error of regression– Pharmaceutical Examples
- Probability: Definition of probability, Binomial distribution, Normal distribution, Poisson’s distribution, properties- problems
Sample, Population, large sample, small sample, Null hypothesis, alternative hypothesis, sampling, essence of sampling, types of sampling, Error-I type, Error-II type, Standard
error of mean (SEM)- Pharmaceutical examples - Parametric test: t-test(Sample, Pooled or Unpaired and Paired) , ANOVA, (One way and Two way), Least Significance difference
UNIT-III
- Non Parametric tests: Wilcoxon Rank Sum Test, Mann-WhitneyU test, Kruskal-Wallis test, Friedman Test
- Introduction to Research: Need for research, Need for design of Experiments, Experiential Design Technique, plagiarism
- Graphs: Histogram, Pie Chart, Cubic Graph, response surface plot, Counter Plot graph
- Designing the methodology: Sample size determination and Power of a study, Report writing and presentation of data, Protocol, Cohorts studies, Observational studies, Experimental studies, Designing clinical trial, various phases
UNIT-IV
- Blocking and confounding system for Two-level factorials
- Regression modeling: Hypothesis testing in Simple and Multiple regression models
- Introduction to Practical components of Industrial and Clinical Trials Problems: Statistical Analysis Using Excel, SPSS, MINITAB®, DESIGN OF EXPERIMENTS, R Online Statistical Software’s to Industrial and Clinical trial approach
UNIT-V
- Design and Analysis of experiments:
- Factorial Design: Definition, 22, 23design. Advantage of factorial design
- Response Surface methodology: Central composite design, Historical design, Optimization Techniques
Learning Objectives
- Methods to generate safety data during pre clinical, clinical and post approval phases of drugs’ life cycle
- ICH guidelines for ICSR, PSUR, expedited reporting, pharmacovigilance planning
- CIOMS requirements for ADR reporting
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