Fundamentals of Biostatistics: Definition, historical developments, its applications, WhatRole Does Biostatistics Play in Public Health, nature of data, data summarization.
Estimation theory probability and random variables, sampling distributions, point and confidence interval estimation, hypothesis testing (one sample, Comparing Means among Two (or More) Independent Populations, Comparing Proportions Between Two Independent Populations), power and sample size estimation
Measures in epidemiology (measures of frequency, association and risk): Prevalence, Incidence, Risk, Odds of disease, Incidence time, Incidence rate, Relationship between prevalence, rate and risk, measures of morbidity and mortality, measures of association. Routine data to measure disease occurrence, age standardization, direct method of Standardization, indirect method of standardization, cumulative rate, cumulative risk, proportional incidence.
Validity and reliability of measures of exposure and outcome: Diagnostic tests. Sensitivity, Specificity, predictive value method for selecting a positivity criterion, receiver operator characteristic (ROC) curve, Intra and Inter-observer reliability, Kappa measure of agreement.
Confounding and Effect Modification: what is confounding factor? Properties of confounding factor, Statistical Interaction/Effect Modification
Advanced Biostatistical Data Analysis: generalized linear models, generalized linear mixed models, semiparametric and nonparametric regression, neural networks, and Bayesian data analysis; theory and practice in the health sciences