# 74948 - Statistics for Healthcare

### SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.  ## Learning outcomes

At the end of the course the student is provided with the essential concepts of statistics and probability, with the dual objective of illustrating the basic tools for the exploratory analysis of health-related data and introducing the founding elements of econometrics.

## Course contents

(Updated with minor edits on 18 September 2021)

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.

*** Chapter 3, '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.

***  Chapter 1, 'Sample Space and Probability' in Introduction to Probability, D. Bertsekas and J. Tsitsiklis 2008. Athena Scientific. (Free download available).

*** Probability, Statistics and Econometrics, O.B. Linton 2017. Academic Press. (University of Bologna e-book).

*** Statistics for Business and Economics, Global Edition. P. Newbold W. Carlson, B. Thorne 2019. 9th edition. (University of Bologna e-book).

*** Practical Statistics for Medical Research. D. Altman 1991. 1st Edition. Chapman and Hall.

## Teaching methods

Lectures, computer-based workshops and tutor classes.

## Assessment methods

Examination with multiple choice, short answer and short essay questions.

## Teaching tools

Lectures, computer-based workshops and tutor classes.

## Office hours

See the website of Martin Forster