Calculating Number Needed to Treat/Harm (NNT/H) with Odds Ratio

We learned how to convert the pooled odds ratio from a random-effects model and subsequently calculate the number needed to treat (NNT) or harm (NNH). It’s important to understand that without knowing the event proportions in either the treatment or control groups, we cannot accurately estimate the absolute risk reduction for an individual study or for a meta-analysis. Fascinating indeed! Everyday is a school day! 🙌

Approaches to Calculating Number Needed to Treat (NNT) with Meta-Analysis

Here, we have demonstrated three different methods for calculating NNT with meta-analysis data. I learned a lot from this experience, and I hope you find it enjoyable and informative as well. Thank you, @wwrighID, for initiating the discussion and providing a pivotal example by using the highest weight control event proportion to back-calculate ARR and, eventually, NNT. I also want to express my gratitude to @DrToddLee for contributing a brilliant method of pooling a single proportion from the control group for further estimation. Special thanks to @MatthewBJane, the meta-analysis maestro, for guiding me toward the correct equation to calculate event proportions, with weight estimated by the random effect model. 🙏

#IDWeek2023 Posts/Tweets Analysis

Immersed in gratitude and inspiration at #IDWeek2023 🌐! A massive thank you to everyone who contributed - your posts were a beacon of warmth and wisdom. 🌟 Celebrating the triumphs of award recipients 🏆, your remarkable achievements propel us all forward! Enlightened by the groundbreaking insights from new trials, we are reminded to remain humble and passionate in our continuous quest for knowledge. Together, we will continue unveiling the realms of Infectious Disease, advancing with unity and purpose!

An Educational Stroll With Stan - Part 4

What an incredible journey it has been! I’m thoroughly enjoying working with Stan codes, even though I don’t yet grasp all the intricacies. We’ve already tackled simple linear and logistic regressions and delved into the application of Bayes’ theorem. Now, let’s turn our attention to the fascinating world of Mixed-Effect Models, also known as Hierarchical Models