Category: Biostatistics

  • Analysis Lab
    • Some basic analysis tools
  • Biostatistics
    • The first three chapters cover basic concepts of Statistics focusing on the collection, presentation and summarization of data. Chapter four deals with the basic demographic methods and health service statistics giving greater emphasis to indices relating to the hospital. In chapters five and six elementary probability and sampling methods are presented with practical examples. A relatively comprehensive description of statistical inference on means and proportions is given in chapters seven and eight. The last chapter of this lecture note is about linear correlation and regression.
  • Biostatistics Case Studies
    • Examples of real data with analyses and interpretation.
  • Biostatistics Lecture Series
    • The day-to-day collaboration between the researchers in Public Health and Biostatistics at the School reveals unified topics that cut across many applications. This series of presentations: 1. Introduces the topics that show empirically to be most important in these collaborations; and 2. Emphasizes concepts over details, through recent applications in Public Health.
  • Dataplot
    • Documentation, instruction, and downloads for Dataplot, a software system for scientific visualization, statistical analysis, and non-linear modeling.
  • Epidemiology and Biostatistics
    • This course teaches the basic skills needed to critique the medical literature by providing a fundamental understanding of epidemiology and biostatistics. Includes lectures, slides, assignments, quizzes and outlines for small group sessions.
  • Essentials of Probability and Statistical Inference IV
    • Introduces the theory and application of modern, computationally-based methods for exploring and drawing inferences from data. Covers re-sampling methods, non-parametric regression, prediction, and dimension reduction and clustering. Specific topics include Monte Carlo simulation, bootstrap cross-validation, splines, local weighted regression, CART, random forests, neural networks, support vector machines, and hierarchical clustering. De-emphasizes proofs and replaces them with extended discussion of interpretation of results and simulation and data analysis for illustration.
  • Methods in Biostatistics I
    • Presents fundamental concepts in applied probability, exploratory data analysis, and statistical inference, focusing on probability and analysis of one and two samples. Topics include discrete and continuous probability models; expectation and variance; central limit theorem; inference, including hypothesis testing and confidence for means, proportions, and counts; maximum likelihood estimation; sample size determinations; elementary non-parametric methods; graphical displays; and data transformations.
  • Online Statistics: An Interactive Multimedia Course of Study
    • An introductory-level statistics book. The material is presented both as a standard textbook and as a multimedia presentation. The book features interactive demonstrations and simulations, case studies, and an analysis lab
  • Open Source Epidemiologic Statistics for Public Health
    • Provides statistics for counts and measurements in descriptive and analytic studies, stratified analysis with exact confidence limits, matched pair and person-time analysis, sample size and power calculations, random numbers, sensitivity, specificity and other evaluation statistics, R x C tables, chi-square for dose-response, and links to other useful sites.
  • Simulations/Demonstrations
    • Java applets that demonstrate various statistical concepts.
  • Statistical Reasoning I
    • Statistical Reasoning in Public Health provides an introduction to selected important topics in biostatistical concepts and reasoning through lectures, exercises, and bulletin board discussions. It represents an introduction to the field and provides a survey of data and data types. Specific topics include tools for describing central tendency and variability in data; methods for performing inference on population means and proportions via sample data; statistical hypothesis testing and its application to group comparisons; issues of power and sample size in study designs; and random sample and other study types. While there are some formulae and computational elements to the course, the emphasis is on interpretation and concepts.
  • Statistical Reasoning II
    • Statistical Reasoning in Public Health II provides an introduction to selected important topics in biostatistical concepts and reasoning through lectures, exercises, and bulletin board discussions. The course builds on the material in Statistical Reasoning in Public Health I , extending the statistical procedures discussed in that course to the multivariate realm, via multiple regression methods. New topics, such as methods for clinical diagnostic testing, and univariate, bivariate, and multivariate techniques for survival analysis will also be covered. These topics will be reinforced with many "real-life" examples drawn from recent biomedical literature. While there are some formulae and computational elements to the course, the emphasis is again on interpretation and concepts.
  • Statistics in Psychosocial Research: Measurement
    • Presents quantitative approaches to measurement in the psychological and social sciences. Topics include the principles of psychometrics, including reliability and validity; the statistical basis for latent variable analysis, including exploratory and confirmatory factor analysis and latent class analysis; and item response theory. Draws examples from the social sciences, including stress and distress, social class and socioeconomic status, personality; consumer satisfaction, functional impairment and disability, quality of life, and the measurement of overall health status. Intended for doctoral students.
  • Supercourse Statistics Course
    • This course is an introduction to stochastic processes as applied to the biomedical sciences. Among the topics which will be discussed are: epidemiology models for incidence, prevalence and mortality, backward and forward recurrence times and their relationship to length biased sampling, Poisson processes, birth and death processes, Markov chains and semi-Markov processes.
  • The R-Project
    • Provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.