I think, besides the conclusions, we don't know all the factors and variables involved in those studies. Here is a biased opinion obtained from an AI; some points may be worth considering.
----------------------
Small Studies vs. Large Studies
Intensity of Treatment in Small Studies: It's possible that in smaller studies, doctors may have applied more intensive or varied therapeutic strategies alongside magnesium. This could have influenced the outcomes, making it difficult to isolate the effect of IV-Mg alone.
Heterogeneity of Treatment Protocols: Small studies might have variations in treatment protocols, patient demographics, and clinical settings, which could lead to different outcomes compared to larger, more standardized trials.
Personalized Care: In smaller studies, the level of personalized care might be higher, potentially affecting patient outcomes in ways not solely attributable to the treatment being studied.
Potential Biases and Fallacies
Overconfidence in Large Studies: The article suggests a strong preference for large trials, assuming they provide definitive answers. However, large studies can also have biases. For instance, they might overlook subgroups of patients who could benefit from a treatment that doesn't work for the larger population.
Generalization Issues: Large studies often aim for broad applicability, which might miss specific contexts where a treatment could be effective. The diverse patient population in a massive trial like ISIS-4 may dilute the impact of a treatment that could be beneficial in a more targeted patient group.
Statistical Significance vs. Clinical Significance: The article focuses heavily on statistical outcomes. While statistical significance is important, it doesn't always translate to clinical significance. A treatment showing a small statistical effect in a large trial might still be clinically meaningful in certain situations.
Underestimation of Small Study Benefits: The author seems to dismiss the positive outcomes of smaller studies as noise or publication bias. While these are valid concerns, it's also possible that small studies could detect real effects in specific contexts that larger studies might miss.
Publication Bias in Large Trials: Just as small studies might suffer from publication bias, large studies can too. There's often significant pressure to publish positive results in large, expensive trials, which can lead to selective reporting or interpretation of data.
Ignoring the Evolution of Medical Understanding: Medicine evolves over time. What is considered an effective treatment can change with new evidence. Dismissing earlier studies outright, rather than understanding them as steps in the evolving medical knowledge, might be an oversight.
Conclusion
While the ISIS-4 trial provides valuable insights, it's important to recognize that both small and large studies have their roles and limitations. Medical research should consider a balance of evidence from various sources, acknowledging that different study sizes and types can offer unique insights into patient care. It's crucial to maintain a nuanced view, understanding that what works in a large, diverse population might not apply to every individual patient, and vice versa.
At the risk of incurring the wrath of statisticians, I would suggest that the use of statistical analysis is often used to create a false impression of mathematical certainty to unwarranted conclusions. The application of common sense will often be a better guide to the true significance of the differences between experimental groups. I also often see a lack of attention being paid to the quality of the data. For example, many studies in the cardiology field give figures for "cardiovascular death" and get the bulk of their information from notoriously inaccurate death certificates. And it is very difficult, if not impossible, to grade the degree of improvement or relief from symptoms that should also be a consideration in clinical patient care. These are but a few of the limitations that need to be considered in the evaluation of medical studies.
I’m suspicious when the effect reverses. To conclude clearly that Mg didn’t work, I’d have to see replication of the larger study. I’ve seen too many studies where the results were skewed, intentionally or unintentionally in a manner that is not easy to discover.
After discussing this article with my son, he replied with a question, "If small studies can have a lot of positive error (saying something is more effective than it is), seems like they could also have a lot of negative error (saying that something is not effective when it actually is). Wonder how much good things are MISSED because never tried against a wider audience? Anyone have an answer?
Except that it is more likely a new intervention will NOT work, than it is that it will work. Studies tend to exaggerate benefits, and underestimate side effects.
Another important point that can be deduced from these studies is that "statistical significance" should be disregarded as a measure of the worthiness of conclusions drawn from the data. Unless I have misread the information provided, all the smaller studies that showed the benefits of magnesium infusion were statistically significant. My suggestion has always been to look at the raw data and the percentages for each outcome under investigation and decide for yourself whether the differences observed are of any practical significance. Of course one should take into consideration sample size and a number of other factors in reaching a conclusion.
Not to take issue with the main point of your article, but a demonstrative statement like, "...IV-Mg does not reduce death. Period." is a red flag in and of itself in the ever-evolving world of clinical science. The critique does not include an analysis of the potential impact of different study time frames and also of subject selection. Now perhaps a more thorough critique would dismiss those and other variables as important. Nevertheless, absolutism is not a healthy approach to take in any serious scientific discussion.
One other important lesson lies in your statement “Doctors speculated that Mg might produce this amazing effect by coronary dilation, reduction of arrhythmias, favorable platelet effects, and a host of other means.”
In other words, a drug was found to have a possible effect, and then people started to invent explanations. This is working backwards. If you really understood the cause(s) of death following an MI, you should be able to formulate a testable hypothesis as to which treatments would address those causes, and then you would test those treatments.
Similarly, composite end points reflect multiple possible outcomes of the same disease process, and there’s no guarantee that all of those outcomes reflect the same pathophysiology. For example, (not that I’m a cardiologist) arrhythmias might arise from injured cells sending off inappropriate electrical signals, whereas congestive heart failure might result from dead cells no longer contributing to the workload. So, testing one drug against multiple endpoints combined into a composite endpoint makes no sense if those various endpoints don’t all reflect the same pathophysiology Al process.
Every post at Cardiology Trials thus far has been an eye-opener. Congrats on starting that site.
It is not “naive” to want larger trials that test hard (or harder) endpoints. It is simply that the balance of regulatory teeth and financial incentive is now skewed towards industry’s benefit. So we get composites with benefits driven by lab values or QOL (absent proper blinding).
What we need is for the regulator to actually regulate. It can be argued, however, that believing such a development to be possible would represent peak naïveté.
Such clear, succinct writing with vivid examples. Thank you for all you are doing to broaden our understanding of trials and the evolution of medical practice as we see it today.
Wonderful examples that show how trials with small end point levels and/or multiple end points (with differing degrees of " hardness") can be terribly misleading. Now apply the same reasoning and analysis to the drug trials that supposedly justify the use of statins to reduce the risks of cardiovascular events.
There is another lesson to be learned. David Spiegelhalter showed that the standard methods for doing meta-analysis are flawed. These methods (DerSimonean and Laird) pretend that the variance of random effects is estimated without error. Spiegelhalter re-ran the meta-analysis using a Bayesian random effects model and showed a lack of evidence for Mg efficacy then because of the properly wider uncertainty interval. Statistical methods matter.
Great article. How do we change the paradigm of research so that we can concentrate on large scale, multi-institutional collective efforts to do more meaningful research rather than adding noise with the plethora of small-sized - underpowered - studies?
Or maybe Isis was wrong
I think, besides the conclusions, we don't know all the factors and variables involved in those studies. Here is a biased opinion obtained from an AI; some points may be worth considering.
----------------------
Small Studies vs. Large Studies
Intensity of Treatment in Small Studies: It's possible that in smaller studies, doctors may have applied more intensive or varied therapeutic strategies alongside magnesium. This could have influenced the outcomes, making it difficult to isolate the effect of IV-Mg alone.
Heterogeneity of Treatment Protocols: Small studies might have variations in treatment protocols, patient demographics, and clinical settings, which could lead to different outcomes compared to larger, more standardized trials.
Personalized Care: In smaller studies, the level of personalized care might be higher, potentially affecting patient outcomes in ways not solely attributable to the treatment being studied.
Potential Biases and Fallacies
Overconfidence in Large Studies: The article suggests a strong preference for large trials, assuming they provide definitive answers. However, large studies can also have biases. For instance, they might overlook subgroups of patients who could benefit from a treatment that doesn't work for the larger population.
Generalization Issues: Large studies often aim for broad applicability, which might miss specific contexts where a treatment could be effective. The diverse patient population in a massive trial like ISIS-4 may dilute the impact of a treatment that could be beneficial in a more targeted patient group.
Statistical Significance vs. Clinical Significance: The article focuses heavily on statistical outcomes. While statistical significance is important, it doesn't always translate to clinical significance. A treatment showing a small statistical effect in a large trial might still be clinically meaningful in certain situations.
Underestimation of Small Study Benefits: The author seems to dismiss the positive outcomes of smaller studies as noise or publication bias. While these are valid concerns, it's also possible that small studies could detect real effects in specific contexts that larger studies might miss.
Publication Bias in Large Trials: Just as small studies might suffer from publication bias, large studies can too. There's often significant pressure to publish positive results in large, expensive trials, which can lead to selective reporting or interpretation of data.
Ignoring the Evolution of Medical Understanding: Medicine evolves over time. What is considered an effective treatment can change with new evidence. Dismissing earlier studies outright, rather than understanding them as steps in the evolving medical knowledge, might be an oversight.
Conclusion
While the ISIS-4 trial provides valuable insights, it's important to recognize that both small and large studies have their roles and limitations. Medical research should consider a balance of evidence from various sources, acknowledging that different study sizes and types can offer unique insights into patient care. It's crucial to maintain a nuanced view, understanding that what works in a large, diverse population might not apply to every individual patient, and vice versa.
At the risk of incurring the wrath of statisticians, I would suggest that the use of statistical analysis is often used to create a false impression of mathematical certainty to unwarranted conclusions. The application of common sense will often be a better guide to the true significance of the differences between experimental groups. I also often see a lack of attention being paid to the quality of the data. For example, many studies in the cardiology field give figures for "cardiovascular death" and get the bulk of their information from notoriously inaccurate death certificates. And it is very difficult, if not impossible, to grade the degree of improvement or relief from symptoms that should also be a consideration in clinical patient care. These are but a few of the limitations that need to be considered in the evaluation of medical studies.
I’m suspicious when the effect reverses. To conclude clearly that Mg didn’t work, I’d have to see replication of the larger study. I’ve seen too many studies where the results were skewed, intentionally or unintentionally in a manner that is not easy to discover.
After discussing this article with my son, he replied with a question, "If small studies can have a lot of positive error (saying something is more effective than it is), seems like they could also have a lot of negative error (saying that something is not effective when it actually is). Wonder how much good things are MISSED because never tried against a wider audience? Anyone have an answer?
Except that it is more likely a new intervention will NOT work, than it is that it will work. Studies tend to exaggerate benefits, and underestimate side effects.
I would say that is certainly true.
Another important point that can be deduced from these studies is that "statistical significance" should be disregarded as a measure of the worthiness of conclusions drawn from the data. Unless I have misread the information provided, all the smaller studies that showed the benefits of magnesium infusion were statistically significant. My suggestion has always been to look at the raw data and the percentages for each outcome under investigation and decide for yourself whether the differences observed are of any practical significance. Of course one should take into consideration sample size and a number of other factors in reaching a conclusion.
Not to take issue with the main point of your article, but a demonstrative statement like, "...IV-Mg does not reduce death. Period." is a red flag in and of itself in the ever-evolving world of clinical science. The critique does not include an analysis of the potential impact of different study time frames and also of subject selection. Now perhaps a more thorough critique would dismiss those and other variables as important. Nevertheless, absolutism is not a healthy approach to take in any serious scientific discussion.
One other important lesson lies in your statement “Doctors speculated that Mg might produce this amazing effect by coronary dilation, reduction of arrhythmias, favorable platelet effects, and a host of other means.”
In other words, a drug was found to have a possible effect, and then people started to invent explanations. This is working backwards. If you really understood the cause(s) of death following an MI, you should be able to formulate a testable hypothesis as to which treatments would address those causes, and then you would test those treatments.
Similarly, composite end points reflect multiple possible outcomes of the same disease process, and there’s no guarantee that all of those outcomes reflect the same pathophysiology. For example, (not that I’m a cardiologist) arrhythmias might arise from injured cells sending off inappropriate electrical signals, whereas congestive heart failure might result from dead cells no longer contributing to the workload. So, testing one drug against multiple endpoints combined into a composite endpoint makes no sense if those various endpoints don’t all reflect the same pathophysiology Al process.
I hate autocorrect. That last line should read “...pathophysiological process.”
Every post at Cardiology Trials thus far has been an eye-opener. Congrats on starting that site.
It is not “naive” to want larger trials that test hard (or harder) endpoints. It is simply that the balance of regulatory teeth and financial incentive is now skewed towards industry’s benefit. So we get composites with benefits driven by lab values or QOL (absent proper blinding).
What we need is for the regulator to actually regulate. It can be argued, however, that believing such a development to be possible would represent peak naïveté.
Such clear, succinct writing with vivid examples. Thank you for all you are doing to broaden our understanding of trials and the evolution of medical practice as we see it today.
You have a barrel of red and white marbles of which we think ~8.5% are red. (All the information I know from first study)
Able draws 754 and ~5.6% are red
Brenda draws 740 and ~11.6% are red.
I am not looking at Able as a superior red marble avoider, unless he drew less than 5.4% red.
8.5% +/- 3 * sqrt of (8.5%*(1-8.5%)/N)
A different way to look at data.
https://x.com/jdkromkowski/status/1752055169661960336?s=46&t=1Drt5a50QkS8KZQv_GYarQ
From the beginning people fooled by randomness.
Wonderful examples that show how trials with small end point levels and/or multiple end points (with differing degrees of " hardness") can be terribly misleading. Now apply the same reasoning and analysis to the drug trials that supposedly justify the use of statins to reduce the risks of cardiovascular events.
There is another lesson to be learned. David Spiegelhalter showed that the standard methods for doing meta-analysis are flawed. These methods (DerSimonean and Laird) pretend that the variance of random effects is estimated without error. Spiegelhalter re-ran the meta-analysis using a Bayesian random effects model and showed a lack of evidence for Mg efficacy then because of the properly wider uncertainty interval. Statistical methods matter.
So we want to rally know things is the question. It seems more often than not, what we really want is to sell things.
Great article. How do we change the paradigm of research so that we can concentrate on large scale, multi-institutional collective efforts to do more meaningful research rather than adding noise with the plethora of small-sized - underpowered - studies?
Appreciate the references and grounded example. Intuitive and sensible.