Statistical analysis and review

In this blogpost, we’ll take a look at how such analyses can be conducted, with a step-by-step explanation of the process.

Biomedical research often deals with complex phenomena, and missing values can throw a wrench into our data analyses. This blogpost explains Multiple Imputation by Chained Equations (MICE), which…

Read this blogpost to understand why Bayesian methods must be used to analyze longitudinal data and which of the Bayesian methods work well.

Many biomedical problems require intricate, multifaceted models. Bayesian statistics can handle these complexities seamlessly, allowing you to create models that reflect the real-world intricacies…

Bayesian information borrowing allows you to leverage existing knowledge from related studies or domains. Read this post to understand Bayesian information borrowing in detail, and steps and…

In this blogpost, we've examined how machine learning benefits various aspects of clinical trial management, from planning the study to overseeing participants and handling data.

In this blog post, we have broken down the concept of joint modeling in simple terms and explained its significance for biomedical researchers.

This infographic presents five key things biomedical researchers need to know about correlation analysis in order to produce reliable results.

Analyzing time-to-event data is crucial in understanding the progression and outcomes of diseases, treatment effectiveness, and more. Here are some important considerations for statistical…