Discusses taxonomy of research designs and choosing appropriate design for implementation research, with examples of cluster-randomized trials and before/after studies.
Explores the significance of randomization in protein mass spectrometry and proteomics, highlighting its role in minimizing bias and ensuring research validity.
Explores the challenges of observational studies, emphasizing the importance of randomization and sensitivity analysis in drawing valid conclusions from 'found data'.
Explores the challenges of inferring epidemiological parameters from clinical data, focusing on COVID-19 and the complexities of estimating infection fatality ratios.
