74948 - Statistics for Healthcare

Learning outcomes

To introduce you to probability theory and classical statistical methodology, with applications to healthcare. To show you how to apply and interpret the results of statistical analyses in your
future role as a health/healthcare researcher or manager. To prepare you for Econometrics, the second course in Quantitative Methods in Health and Healthcare.

Course contents

By the end of this course, you should be able to: 1. explain why risk and uncertainty are fundamental to research and decision-making in healthcare; 2. explain some of the fundamental concepts of probability theory, the difference between a population and a sample, experiments, observational studies and sampling error; 3. explain the difference between qualitative and quantitative variables and describe the main discrete and continuous random variables that are relevant for classical statistics. Define and be able to interpret the probability mass and density function, measures of central tendency and dispersion, including the expected value, variance and standard
deviation; 4. explain what is meant by making causal inferences, why randomised controlled experiments may permit us to make them, and the challenges of making causal inferences with
observational data sets; 5. explain, and be able to carry out, numerical and graphical analysis of data in a sample
using descriptive statistics; 6. explain what is meant by the sampling distribution of a statistic, confidence intervals,
hypothesis tests, p-values and test statistics. Be able to test and interpret hypotheses for population means and proportions; 7. explain what is meant by simple regression and multiple regression; 8. using software such as Stata or MS Excel, carry out suitable exploratory analyses using numerical, graphical and tabular descriptive methodologies and test hypotheses using
experimental and observational data sets. Be able to interpret the results of such analyses, explain the statistical methodology underlying the results and interpret the results in a manner which may be understood by non-statisticians.

Readings/Bibliography

An introduction to statistical methodology for health economics may be found in: Chapter 3 of 'Statistical Tools for Health Economics’ in 'The Economics of Health and Health Care', S. Folland, A. C. Goodman and M. Stano 2017, 2014, 2013, 2010
(8, 7e, 7, 6 Editions), Routledge/Pearson/Prentice Hall.

To supplement the introductory material in Classes 1 to 3 of this course: Chapter 1, 'Sample Space and Probability’ in Introduction to Probability, D. Bertsekas and J. Tsitsiklis 2008. Athena Scientific.

To accompany most of the topics in the course, you could choose any basic text in introductory statistics. For example, Probability, Statistics and Econometrics, O.B. Linton 2017. Academic Press. Statistics for Business and Economics, Global Edition, 9th Edition. P. Newbold et al. 2019. Pearson.

For a long-standing and excellent text on the practical application of statistical methods in medical research, see: Practical Statistics for Medical Research. D. Altman 1991. 1st Edition. Chapman and Hall.

Teaching methods

Lectures, case studies and workshops using Stata.

Assessment methods

Written examination at the end of the course.

Teaching tools

The e-learning platform will provide access to lecture slides, case studies, data sets and solutions. Normally lessons will also be recorded and made available to support your studies. Weekly office hours will be held and comments and grading of the exams will be available in online feedback meetings.

Office hours

See the website of Martin Forster