I’m a clinical cardiologist with the aforementioned expectation of high quality evidence from large trials with important endpoints. But I have no training in epidemiology whatsoever, nor am I any expert in EBM.
I agree with Dr. Rind that, wrt TTR silencers, we are at the level of evidence generation to prove that it “works”. And I agree that is a separate question than the ICER analyses that should follow. In other words, we are still establishing that vutisiran is an agent we “should” use; whether it is one we can “afford” to use (with or without tafamidis) is an important and practical, but non-scientific and societal, question.
However, on first principles, I would consider any post hoc analysis of subgroups to be hypothesis generating. I’m not sure how that principle would be nullified in this case. Furthermore, (I don’t have the Helios paper in front of me) the OP notes that the effect in the “combo” subgroup was NOT statistically significant vs placebo. So I’m not sure how one can establish the observed benefit of combo use as “proven “ given the nature and extent of the data in question.
I agree that the study looked at some instances of adding vutisiran to tafamdis, which should not be considered equivalent to adding tafamdis to vutisiran. From a mechanism standpoint, it makes sense that, if TTR synthesis is effectively “silenced”, there would not be very much circulating unstable TTR remaining that requires “stabilizing “.
As a sub-human who avoids all drugs, why not try and discover what is causing heart issues? There always has to be specific reasons. You know that many of these trials will be structured and manipulated to present the best results even if they are not the down and dirty truth. There will never be a drug to cure heart disease just like there will never be a drug to cure cancer. No golden goose = no golden eggs.
I have three questions. 1, would a Baysian analysis say more about the subgroups? Particularly if one had reason to think that 2 drugs are better than one. 2, To what extent does 'Simpson paradox' enter into objections to this sort of subgroup analysis and to what extent would creating a hierarchical model help? 3, are t- tests relevant to subgroup analysis?
These are sincere questions. I'm trying to learn. References to read and learn would be great too!
1) Since the concern of the cardiologists is an inherent skepticism that two drugs are better than one, I doubt their Bayesian priors would have gone in that direction. My understanding is that there are a number of Bayesian approaches to subgroup analysis -- I don't know if one method is considered "best". 2) I don't think you need to worry about Simpson's Paradox when looking at an RCT for a connection between the intervention and the outcome, whether or not you are looking at subgroups. Random chance variation and small numbers is the main problem with subgroup analyses based on a priori groups in an RCT. 3) It's fairly common when arguing that there is a subgroup effect that relates to mean effect between two subgroups to perform a t-test on those two means to see if they are more different than would be expected by chance alone.
I’m a clinical cardiologist with the aforementioned expectation of high quality evidence from large trials with important endpoints. But I have no training in epidemiology whatsoever, nor am I any expert in EBM.
I agree with Dr. Rind that, wrt TTR silencers, we are at the level of evidence generation to prove that it “works”. And I agree that is a separate question than the ICER analyses that should follow. In other words, we are still establishing that vutisiran is an agent we “should” use; whether it is one we can “afford” to use (with or without tafamidis) is an important and practical, but non-scientific and societal, question.
However, on first principles, I would consider any post hoc analysis of subgroups to be hypothesis generating. I’m not sure how that principle would be nullified in this case. Furthermore, (I don’t have the Helios paper in front of me) the OP notes that the effect in the “combo” subgroup was NOT statistically significant vs placebo. So I’m not sure how one can establish the observed benefit of combo use as “proven “ given the nature and extent of the data in question.
I agree that the study looked at some instances of adding vutisiran to tafamdis, which should not be considered equivalent to adding tafamdis to vutisiran. From a mechanism standpoint, it makes sense that, if TTR synthesis is effectively “silenced”, there would not be very much circulating unstable TTR remaining that requires “stabilizing “.
As a sub-human who avoids all drugs, why not try and discover what is causing heart issues? There always has to be specific reasons. You know that many of these trials will be structured and manipulated to present the best results even if they are not the down and dirty truth. There will never be a drug to cure heart disease just like there will never be a drug to cure cancer. No golden goose = no golden eggs.
I have three questions. 1, would a Baysian analysis say more about the subgroups? Particularly if one had reason to think that 2 drugs are better than one. 2, To what extent does 'Simpson paradox' enter into objections to this sort of subgroup analysis and to what extent would creating a hierarchical model help? 3, are t- tests relevant to subgroup analysis?
These are sincere questions. I'm trying to learn. References to read and learn would be great too!
1) Since the concern of the cardiologists is an inherent skepticism that two drugs are better than one, I doubt their Bayesian priors would have gone in that direction. My understanding is that there are a number of Bayesian approaches to subgroup analysis -- I don't know if one method is considered "best". 2) I don't think you need to worry about Simpson's Paradox when looking at an RCT for a connection between the intervention and the outcome, whether or not you are looking at subgroups. Random chance variation and small numbers is the main problem with subgroup analyses based on a priori groups in an RCT. 3) It's fairly common when arguing that there is a subgroup effect that relates to mean effect between two subgroups to perform a t-test on those two means to see if they are more different than would be expected by chance alone.