Basic general doubt about Mendelian randomization

Good morning everyone;

I wanted to ask you a question surely very basic but I hope you can help me to solve it.

My thought is the following:

I have a mendelian randomization study on e.g. LDL

My doubt arises about the external validity of the causal results obtained for the outcome X

And the question is the following;

Would the causal results be applicable to a population completely different or significantly different from the study population?

I believe that it would not be applicable since those different variables could function, let’s say, as “enzymes” that are the true culprits of the casual effect. Is this so, or am I wrong?

Could you help me to understand this? Thank you very much

This article should help answer your question

Swanson, Sonja A.a,b; Tiemeier, Henninga,c,d; Ikram, M. Arfana; Hernán, Miguel A.b,e,f. Nature as a Trialist?: Deconstructing the Analogy Between Mendelian Randomization and Randomized Trials. Epidemiology 28(5):stuck_out_tongue: 653-659, September 2017. | DOI: 10.1097/EDE.0000000000000699


Thank you very much :green_heart:, I have read the document 3 times and I do not see in my head the answer to my question​:frowning:.

My doubt is : I am not sure if using the same exposures in the Mendelian study we would get the same results if the population under study is the population of a biobank X or another database.

That is, if the entire population 1 is subjected to contamination, I expect that the high exposure will be shown to be causal because the population will be endothelially impaired.

But if population 2 is not subjected to contamination in any case, I understand it possible that the high exposure will not show causality.

That is, I would conclude that the generalization is contingent just like an RCT about which populations are similar.

Am I thinking correctly?

Oxygen, matches, and fire?

I understand your question better now. Yes, you are correct. Transportability of a study effect requires several assumptions, including the same distribution of effect modifiers between the populations. In your example, it sounds like there is an effect modifier present in one group that is not present in the other so the effect would not be transportable.

This paper has more about the assumptions for transportability: Degtiar, I., & Rose, S. (2023). A review of generalizability and transportability. Annual Review of Statistics and Its Application, 10(1), 501–524.

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If we’re starting from a consensus that LDL is on the causal path toward atherosclerosis and if you’re asking about the impact of LDL in populations that lack other CV risk factors, then I have some articles you might be interested in and can dig them up for you if you’d like. On the other hand, if the purpose of this post is to cast doubt on the role of LDL in atherogenesis, I don’t see much point in providing more articles. A person who isn’t already convinced by the overwhelming existing evidence isn’t likely to be convinced by any additional articles I might provide. I’ll be guided by your response.

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Speaking in general terms, regardless of how much evidence any model accumulates, I think there should be some amount of skepticism to minimize confirmation bias. If one only looks for confirmatory evidence then that is all one finds even if there is evidence to the contrary and looking for gaps in a model is what progresses science.