Unit 1:
Introduction to Biostatistics, Definition and scope of biostatistics, Descriptive vs. inferential statistics, Levels of measurement.
Unit 2:
Study Designs in Biomedical Research; Observational studies (cross-sectional, case-control, cohort), Experimental studies (randomized controlled trials), Study biases and confounding factors.
Unit 3:
Data Collection and Management; Data sources and sampling techniques, Data collection instruments, Data cleaning and quality control.
Unit 4:
Descriptive Statistics; Measures of central tendency and dispersion Frequency distributions and graphical presentation of data, Probability distributions.
Unit 5:
Inferential Statistics; Hypothesis testing, Confidence intervals, Parametric vs. non-parametric tests.
Unit 6:
Regression Analysis; Simple linear regression, Multiple linear regression, Logistic regression.
Unit 7:
Survival Analysis; Kaplan-Meier method, Cox proportional hazards model.
Unit 8:
Statistical Software Applications; Introduction to statistical software packages (e.g., R, SAS, SPSS), Data manipulation and analysis using software tools.
Unit 9:
Interpreting and Presenting Statistical Results; Reporting statistical findings, Visualization techniques, Interpreting p-values and confidence intervals.

Preamble

Biostatistics and Research Methodology is a graduate-level course designed to provide students with the foundational knowledge and skills necessary to understand and apply statistical methods in biomedical and public health research. The course will cover basic statistical concepts, study design, data collection, analysis techniques, and interpretation of results. Emphasis will be placed on practical applications through hands-on exercises and real-world examples.

Course outcome

CO1: To understand fundamental statistical concepts and their relevance in biomedical research.
CO2: To learn about different types of study designs and their strengths and limitations.
CO3: To acquire skills in data collection, management, and quality assurance.
CO4: To gain proficiency in using statistical software for data analysis.
CO5: To interpret statistical results and communicate findings effectively.

Program outcome (PO)

PO1: Utilize scientific principles and methodologies to design innovative solutions for data analysis, experimentation, and product development for challenges in translational research.
PO2: Recognize the importance of environmental sustainability in translational research and strive to minimize adverse environmental impacts.
PO3: Engage in ethical conduct, leadership, active listening, constructive feedback, and interpersonal communication to facilitate productive collaborations and knowledge exchange.
PO4: Acquire fundamental and advanced knowledge and skills in project management, financial planning, and entrepreneurship relevant to translational research ventures and initiatives.
3 = High Affinity, 2 = Medium Affinity, 1 = Low Affinity, – = No Affinity

Program Specific Outcome (PSO)

PSO1: Addresses the complexity of interdisciplinary sciences in biological and medical contexts.
PSO2: Deals with regulatory affairs in medicine, covering topics such as ethical considerations and regulatory frameworks.
PSO3: Covers compounds as drugs and their efficacy, involving pharmacology and drug development.
PSO4: Explores the intersection of bioinformatics and artificial intelligence in biology and medicine.
PSO5: Deals with technology in personalizing medicine, involving precision medicine approaches.
PSO6: Focuses on communicating and disseminating science and medicine to the public, involving science communication and public outreach efforts

Singh, Y.K., 2006. Fundamental of research methodology and statistics. New Age International.