| Course Name | Statistical Methods in Diagnostics |
| Course Code | 25CMD502 |
| Program | M. Sc. in Advanced Clinical and Molecular Diagnostics |
| Semester | 1 |
| Credits | 3 |
| Campus | Faridabad |
Unit 1
Fundamentals of Biostatistics and Diagnostic Study Design, Introduction to biostatistics in diagnostics, Types of data: nominal, ordinal, interval, ratio, Descriptive statistics: mean, median, variance, standard deviation, Study designs in diagnostics: cross-sectional, cohort, case-control, Sampling methods and sample size calculation.
Unit 2
Probability, Distributions, and Hypothesis Testing, Probability theory and rules, Normal, binomial, and Poisson distributions, Central Limit Theorem, Hypothesis formulation, types of errors, p-values, Parametric vs non-parametric tests (t-test, chi-square, ANOVA, Mann-Whitney U).
Unit 3
Diagnostic Accuracy and Test Evaluation Metrics, Sensitivity, specificity, accuracy, precision, Positive Predictive Value (PPV) and Negative Predictive Value (NPV), Likelihood ratios and diagnostic odds ratios, Prevalence and its impact on test performance, Application to binary and continuous test outcomes.
Unit 4
ROC Curve Analysis and Model Performance Evaluation, Receiver Operating Characteristic (ROC) curve: concepts and construction, Area Under the Curve (AUC) and its interpretation, Comparison of diagnostic tests using ROC, Cut-off point optimization and Youden Index, Precision-Recall curves and calibration plots.
Unit 5
Regression and Predictive Modeling in Diagnostics, Linear and logistic regression basics, Multivariate modeling: feature selection, interaction terms, Model validation: internal and external, Introduction to survival analysis in diagnostic context (Kaplan-Meier, Cox regression), predictive models for diagnostic applications using real-world datasets.
Unit 6
Statistical Software, Case Studies, and Reporting Standards, Hands-on data analysis using R/SPSS/GraphPad, Case studies from infectious diseases, cancer, and genetic diagnostics, STARD guidelines for reporting diagnostic accuracy studies, Critical appraisal of diagnostic literature, Ethical use and interpretation of diagnostic statistics.
(45 classes)
Preamble
Statistical Methods in Diagnostics provides a foundation in statistical tools and techniques used in biomedical research and diagnostics. The course focuses on experimental design, data interpretation, and statistical validation of diagnostic tests. Students will gain hands-on experience using software tools and statistical methods for sensitivity, specificity, ROC analysis, and predictive modelling.
Course outcomes
CO1: To apply biostatistics and diagnostic study designs, including data types and sampling methods.
CO2: To understand probability, distributions, and hypothesis testing, including parametric and non-parametric tests.
CO3: To evaluate diagnostic test accuracy using sensitivity, specificity, and predictive values.
CO4: To interpret ROC curves, AUC, and optimize diagnostic test cut-off points.
CO5: To apply regression and predictive modeling techniques, including survival analysis in diagnostics.
CO6: To use statistical software for data analysis, case studies, and adhere to reporting standards.
Program outcome
PO1: Bioscience Knowledge
PO2: Problem Analysis
PO3: Design/Development of Solutions
PO4: Conduct Investigations of complex problems
PO5: Modern tools usage
PO6: Bioscientist and Society
PO7: Environment and Sustainability
PO8: Ethics
PO9: Individual & Team work
PO10: Communication
PO11: Project management & Finance
PO12: Lifelong learning
0 – No affinity; 1 – low affinity; 2 – Medium affinity; 3 – High affinity
| CO | PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 |
| CO1 | 3 | 3 | 2 | 2 | 1 | 1 | 0 | 0 | 0 | 2 | 1 | 2 |
| CO2 | 3 | 3 | 2 | 2 | 1 | 1 | 0 | 0 | 0 | 2 | 1 | 2 |
| CO3 | 3 | 3 | 2 | 3 | 2 | 1 | 0 | 0 | 0 | 2 | 1 | 2 |
| CO4 | 3 | 3 | 2 | 3 | 2 | 1 | 0 | 0 | 0 | 2 | 1 | 2 |
| CO5 | 3 | 3 | 3 | 3 | 3 | 1 | 0 | 0 | 0 | 2 | 1 | 2 |
| CO6 | 3 | 2 | 3 | 3 | 3 | 2 | 0 | 0 | 0 | 3 | 2 | 3 |
Program-specific outcome
PSO 1 – Emerging technologies in clinical diagnostics
PSO 2 – Biomolecules in Medicine
PSO 3 – Molecular dysregulation in diseases
PSO 4 – Molecular technology in diagnosis and therapy
PSO 5 – Applying lab discoveries to clinical practice
PSO 6 – Microorganisms in Medicine
PSO 7 – Statistical methods to interpret and validate diagnostic results
PSO 8 – Integrate molecular diagnostics into personalized medicine
PSO 9 – Compounds as biomarkers and its specificity
PSO 10 – Bioinformatics and biological data use
0 – No affinity; 1 – low affinity; 2 – Medium affinity; 3 – High affinity
| CO | PSO1 | PSO2 | PSO3 | PSO4 | PSO5 | PSO6 | PSO7 | PSO8 | PSO9 | PSO10 |
| CO1 | 2 | 1 | 1 | 1 | 1 | 0 | 3 | 0 | 0 | 2 |
| CO2 | 2 | 2 | 1 | 1 | 1 | 0 | 3 | 0 | 0 | 2 |
| CO3 | 2 | 1 | 1 | 2 | 2 | 0 | 3 | 0 | 0 | 2 |
| CO4 | 2 | 1 | 1 | 3 | 2 | 0 | 3 | 0 | 0 | 2 |
| CO5 | 2 | 2 | 2 | 3 | 3 | 0 | 3 | 1 | 0 | 2 |
| CO6 | 1 | 1 | 1 | 1 | 1 | 0 | 3 | 2 | 0 | 3 |
Textbook:
Zhou XH, Obuchowski NA, McClish DK. Statistical Methods in Diagnostic Medicine. 2nd ed. Hoboken (NJ): Wiley; 2011.
Reference Book:
Altman DG. Practical Statistics for Medical Research. London: Chapman and Hall/CRC; 1991.
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