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Elliot McClenaghan

Elliot McClenaghan profile page

Epidemiologist and PhD Candidate

at London School of Hygiene and Tropical Medicine

Elliot is a statistical epidemiologist and doctoral candidate in pharmacoepidemiology in the department of Medical Statistics and the Electronic Health Records group at London School of Hygiene and Tropical Medicine (LSHTM), where his research focuses on optimal causal inference and target trial emulation methodologies for treatment effects in cystic fibrosis. Previously, he worked as a research fellow in the Department of Infectious Disease Epidemiology, working on COVID-19 transmission and seroprevalence studies. Before that he was a medical statistician at the UK Cystic Fibrosis Registry, where he designed and conducted post-authorization drug safety studies and was lead statistician of a global registry collaboration on the impact of COVID-19 in people with cystic fibrosis.

Education

Newcastle University

Queen’s University Belfast

Awards & Certifications

Doctoral scholarship to undertake his PhD research as part of an industry–academic partnership between LSHTM and GlaxoSmithKline (GSK)

Accreditations

BSc Biomedical Sciences recognised by Newcastle University

MPH Masters in Public Health (quantitative focus) recognised by Queen’s University Belfast

Areas of Expertise

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Published Content

Total: 13

A graph representing normally distributed sample data compared to a hypothesized or population value.

The One Sample T Test

In this article, we will explore some of the theory behind the one sample t test, assumptions of the test, interpretation and a worked example.

Page of a calendar showing a month with three consecutive Saturdays circled.

The Friedman Test

The Friedman test can be used to compare repeated measures or samples, such as following a person's biological functions over time. In this article, we consider its assumptions, when to use it and go through a worked example.

Smiling ladies of different weights representing samples of two populations.

The Z Test

If you want to compare means of continuous variables between two groups or to a hypothesized value, you might need a z test. In this article, we explore the two types of z test, assumptions of the test, interpretation and a worked example.

An illustrated graph of height versus age, using images of people growing up rather than data points.

Pearson Correlation

In this article, we will explore the theory, assumptions and interpretation of Pearson’s correlation, including a worked example of how to calculate Pearson’s correlation coefficient, often referred to as Pearson’s r.

Scatter graph showing the correlation between students' chemistry and math scores.

Spearman Rank Correlation

In this article, we will explore the theory, assumptions and interpretation of Spearman’s rank correlation, a flexible statistical tool that assesses the strength and direction of the relationship between two quantitative, ranked variables.

Illustration of data that may be analyzed by Fisher's exact test, here icons represent males and females voting for and against.

The Fisher’s Exact Test

In this article, we explore the theory, assumptions and interpretation of the Fisher’s exact test, used to investigate associations between two categorical, binary variables with small sample sizes, and take you through a worked example.

A title reading "An Introduction to Bayesian Statistics"

An Introduction to Bayesian Statistics

Bayesian statistics has emerged as a powerful methodology for making decisions from data in the applied sciences. Bayesian brings a new way of thinking to statistics, in how it deals with probability, uncertainty and drawing inferences from an analysis.

Post-Hoc Tests in bold, dark blue writing on a white background.

Post-Hoc Tests in Statistical Analysis

In this article, we review the function of post-hoc tests in statistical analysis, how to interpret them and when to use them (and not use them).

The Wilcoxon Signed-Rank Test.

The Wilcoxon Signed-Rank Test

The Wilcoxon signed rank test, which is also known as the Wilcoxon signed rank sum test and the Wilcoxon matched pairs test, is a non-parametric statistical test used to compare two dependent samples (in other words, two groups consisting of data points that are matched or paired). In this article, we explain how and when this test should be used.

The Kruskal Wallis Test

The Kruskal–Wallis Test

The Kruskal–Wallis test is a statistical test used to compare two or more groups for a continuous or discrete variable. This article explores what the Kruskal–Wallis test is, what it tells us and when it should be used.

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