February Talk: A Student Summary

The talk’s title is “A New Approach to the Generalizability of Randomized Trials” presented by Dr. Anders Huitfeldt.

To extrapolate causal effects from one setting (the study population of a RCT) to another setting (a clinically relevant target population) we need to justify some parameters. For example, we have to see if the conditional effect parameter in the target population is equal to the corresponding parameter in the study population. Known as the “effect homogeneity”. The effect homogeneity parameter occurs not only between the study population and the target population but also between two groups in the RCT’s population. The principle of extrapolating causal effects is that, because we have a randomized trial in the study population, we know what happens in this population if everyone takes the drug and what if they don’t take the drug. In addition, because the drug is not available in the target population we know what happens if they don’t take the drug. Then, we target to use the above information in combination with homogeneity assumption to predict what happens if someone in the target population don’t take the drug. However, the different definition of “effect homogeneity” leads to a different empirical prediction. Traditional approaches for it are: Effect Measure Modification, Forest plots, Cochran’s Q, I2. These approaches contain some shortcomings which can be reduced (eg. No biological interpretation). That is why a method which can be used to determine choice between effect measures is necessary. The COST parameter would be a new class of causal models for the purpose. The COST parameter has many advantages as effect quality measuring because of (1) a clear biological interpretation, (2) the effect of a drug is determined by gene, (3) Baseline risk independence. Finally, there is still controversial among methodologists about using COST parameter as an approach to determining the appropriate choice of scale if effect homogeneity is considered in terms of measures of effect.

January Talk: A Student Summary

The title of the talk is “Understanding Population-based Migraine Through Genome-wide Genetics” by Daniel Chasman from Brigham and Women’s Hospital.

Neurological disorders is becoming a global burden and ranks 2nd for number of years lost to disability. History of diabetes and hypertension, postmenopausal hormone use, physical activities, alcohol consumption, and smoking status are more frequent in people with migraines. Aging is a very important factor in migraine development. In the WGHS data, 3 SNPs investigated the relationship with migraine. Among them, PRDM16 rs22651899 increases the risk and TRPM8 rs10166942 decrease the risk of migraine, while LRP1 rs11172113 was not associated with migraine. After the first implementation of genetic analysis in 2009 with 3 SNPs, the number of SNPs that are included in the analysis increased gradually through each study, and reach out to 44 genome-wide significant loci in a large population study called IHGC 2016 with 59,042 participants. The genetic risk score (GRS) has been calculated to investigate the shared genetic contribution of ischemic stroke and migraine. In observational studies, migraine with aura is a risk factor for ischemic stroke. The causality of the relationship between migraines and coronary artery disease (CAD), MI, angina and atrial fibrillation have been assessed using Mendelian Randomization (MR). The confirmation was drawn from CAD, MI, angina. Some loci with likely vascular function show concordant susceptibility between migraine, dissection but inverse susceptibility with stroke/ CAD. The higher degree of heterogeneity in migraine genetics makes a more complex underlying biology investigation of this form of the disease. In conclusion, there is a long road ahead in Science to determine the matrix of migraine, SNPs, and other diseases.