7: Probability and Risk Analysis — Read

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Introduction

The public health applications of epidemiology, the branch of medicine concerned with identifying patterns and potential causes of disease and health in populations, were every day in the news during the Covid-19 pandemic. From contact tracing to reproductive rate of the SARS-Cov-2 virus to numbers of hospital beds and nurses available in ICU units across the country, to the discussions and debates over how the virus is spread, no doubt you have learned much about the critical role epidemiology continues to play.

Four-panel cartoon showing a doctor and a patient. The doctor states that it doesn't look good for the patient, who has a lot of measurements and variables, because variables are the #1 risk factor for outcomes. When the patient asks if that is just causality, the doctor replies that causality is the leading cause of death, and the patient having any family history "in general" is bad news. — Figure \(\PageIndex{1}\): “Health data,” https://xkcd.com/2620/

This chapter is about probability and will introduce you to risk analysis (Fig. \(\PageIndex{1}\)), used to “… characterize the nature and magnitude of risks to human health for various populations…”, a foundational topic in biostatistics and epidemiology. The epiR package will be introduced and code examples provided for descriptive epidemiology and again for statistical inference (Chapter 9).

7.1: Epidemiology definitions
List of definitions for key terms used in epidemiology.
7.2: Epidemiology basics
Introduction to epidemiology, the study of the causes and distribution of health-related events in a population. Includes discussion of prevalence rates, incidence rates, and person-time.
7.3: Conditional probability and evidence-based medicine
Calculating the probability of multiple independent events. Introduction to conditional probability. Interpreting diagnostic test results with conditional probability, and introduction to the concept of evidence-based medicine.
7.4: Epidemiology relative risk and absolute risk, explained
How absolute and relative risk reductions are calculated in an epidemiological context, with confidence intervals. Discussion of the Number needed to treat statistic. Includes worked examples.
7.5: Odds ratio
Odds ratio as a way to quantify the strength of association between an event happening in one group and in another. When to use odd ratios versus relative risk or hazard ratios.
7.6: Confidence intervals
Confidence intervals for proportions, absolute risk ratio, and number needed to treat. Standard error and confidence intervals for odds ratio.
7.7: Chapter 7 References and Suggested Readings

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Contents

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Introduction