Addressing complex structures of measurement error arising in the exposure assessment in occupational epidemiology using a Bayesian hierarchical approach.

- Sometimes measurement errors are rediculously complex.

- You can use Bayesian ideas to do some magic and obtain unbiased results somehow.

- Here: Correcting measurement errors in Survival models for data of the Wismut cohort.

- Requires a lot of customization of a MCMC sampler with computational tricks.

Distributed non-disclosive validation of predictive models by a modified ROC-GLM

- Validate your ML models on remote locations without looking at the data.

- Utilizes SDC and Differential privacy to estimate ROC curves.

A Bayesian hierarchical approach to account for evidence and uncertainty in the modeling of infectious diseases: An application to COVID-19

- Combine the signal from different uncertain sources to get a reliable estimate of latent variables.

- Incorporates uncertainty of possibly contradicting data sources.

- Estimates disease dynamics to infer NPIs effects during COVID.

- Data of 20 European countries.

Using a Bayesian hierarchical approach to study the association between non-pharmaceutical interventions and the spread of Covid-19 in Germany

- An application of the methodology from above on German federal state level data.

- Some modifications.

- A lot of different NPIs were estimated.

Colorectal Cancer Screening for Persons With a Positive Family History

- Evaluation and statistical analysis of the FARKOR program.

- Analysis of detection rates of colorectal cancer in persons Aged 25 to 50.

- You may want to look at this affiliated publication about benefits and harms of screenings.