A LEGENDARY way to do observational data analysis at scale

The South Australian branch was pleased to have A/Prof Nicole Pratt to present at the august monthly virtual branch meeting.  A/Prof Pratt is an expert in biostatistics and pharmaco-epidemiology, specializing in the development of methodologies to study the effects of medicines and medical devices in linked health-care datasets.  She gave a talk on A LEGENDARY way to do observational data analysis at scale.

The talk was interesting and well-presented. A/Prof Pratt started with information on OHDSI (Observational Health Data Sciences and Informatics) coordinating center which works as a platform for international researchers to work collaboratively on a large-scale observational health dataset. In this distributive network, source data has transferred to standardized de-identified patient level data and you can perform analytics and produce results for publishing. She talked about ATLAS software and cohort Pathway package to perform collaborative studies and gave example of a hypertension study where data from 11 different databases in 4 countries (N= 250 million) was analyzed for different treatment pathways to treat hypertension. 

Later in her talk, A/Prof Pratt informed us that OHDSI collaborative launched a Large-Scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) research initiative, aiming to generate evidence on the effects of medical interventions using observational healthcare databases and addressing the missing evidence from clinical trials. She defined ten principles of LEGEND, prescribing the generation and dissemination of evidence on many research questions at once, comparing all treatments for a disease for many outcomes, thus preventing publication bias and avoiding p-hacking. The Best-practice methods addressed measured confounding and control questions (questions where the answer is known) quantify potential residual bias. She gave an example of a study published in Lancet where they have looked at comprehensive comparative effectiveness and safety of first-line antihypertensive drug class, where every treatments contrast for every health outcomes (52 outcomes) with 9 different data sources. This study has enhanced the evidence around first line hypertension. She concluded her talk mentioning how OHDSI framework helping in generating evidence in a network of databases to assess consistency, by sharing open source analytics code to enhance transparency and reproducibility, but without sharing patient-level information, ensuring patient privacy. 

There were quite few questions from the audience after the talk, finishing the meeting at 7:10 pm.

By Aarti Gulyani