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Doctoral student in epidemiological methods / applied medical statistics

Published 24 March 2022 The doctoral project is a project in epidemiological methods / applied medical statistics in the field of infectious diseases. The overall aim of the project is to investigate socioeconomic and demographic gradients in the risk of severe infectious diseases through epidemiological studies, with a special focus on sepsis and covid-19, to study direct and indirect effects and

https://www.lupop.lu.se/article/doctoral-student-epidemiological-methods-applied-medical-statistics - 2025-04-15

Comparison of the performances of survival analysis regression models for analysis of conception modes and risk of type-1 diabetes among 1985–2015 Swedish birth cohort

Published 16 May 2022 The goal is to examine the risk of conception mode-type-1 diabetes using different survival analysis modelling approaches and examine if there are differentials in the risk of type-1 diabetes between children from fresh and frozen-thawed embryo transfers. We aimed to compare the performances and fitness of different survival analysis regression models with the Cox proportiona

https://www.lupop.lu.se/article/comparison-performances-survival-analysis-regression-models-analysis-conception-modes-and-risk-type - 2025-04-15

Reflection on modern methods: cause of death decomposition of cohort survival comparisons

By anna [dot] axmon [at] med [dot] lu [dot] se (Anna Axmon) - published 8 June 2021 This study extends TCAL by disentangling causes of death contributions. The strength of the approach is that it allows identification of mortality differences in cohorts with members still alive, as well as identification of which ages and causes of death contribute to mortality differentials between populations. R

https://www.lupop.lu.se/article/reflection-modern-methods-cause-death-decomposition-cohort-survival-comparisons - 2025-04-15

Can synthetic controls improve causal inference in interrupted time series evaluations of public health interventions?

By anna [dot] axmon [at] med [dot] lu [dot] se (Anna Axmon) - published 9 June 2021 Advances in synthetic control methods bring new opportunities to conduct rigorous research in evaluating public health interventions. However, incorporating synthetic controls in interrupted time series studies may not always nullify important threats to validity nor improve causal inference. Read the paper at http

https://www.lupop.lu.se/article/can-synthetic-controls-improve-causal-inference-interrupted-time-series-evaluations-public-health - 2025-04-15

Reflection on modern methods: Statistics education beyond ‘significance’: novel plain English interpretations to deepen understanding of statistics and to steer away from misinterpretations

By anna [dot] axmon [at] med [dot] lu [dot] se (Anna Axmon) - published 10 June 2021 Concerns have been expressed over standards of statistical interpretation. Results with P <0.05 are often referred to as ‘significant’ which, in plain English, implies important. This leads some people directly into the misconception that this provides proof that associations are clinically relevant. Read the pape

https://www.lupop.lu.se/article/reflection-modern-methods-statistics-education-beyond-significance-novel-plain-english - 2025-04-15

Reflection on modern methods: demystifying robust standard errors for epidemiologists

By anna [dot] axmon [at] med [dot] lu [dot] se (Anna Axmon) - published 17 June 2021 Standard errors are usually calculated based on assumptions underpinning the statistical model used in the estimation. However, there are situations in which some assumptions of the statistical model including the variance or covariance of the outcome across observations are violated, which leads to biased standar

https://www.lupop.lu.se/article/reflection-modern-methods-demystifying-robust-standard-errors-epidemiologists - 2025-04-15

Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations

By anna [dot] axmon [at] med [dot] lu [dot] se (Anna Axmon) - published 11 June 2021 Photo: Pixabay / Gerd Altmann Directed acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require conditioning when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparenc

https://www.lupop.lu.se/article/use-directed-acyclic-graphs-dags-identify-confounders-applied-health-research-review-and - 2025-04-15