Today Mendelian Randomization (MR) is usually implemented as a form of instrumental variable (IV) estimation.
Aware that valid #IVestimation requires strong assumptions, MR advocates often retreat to the position that MR numerical estimates need not be taken seriously...
Their position: The goal of MR is to "test causality". MR studies aren't designed to yield a valid numerical IV estimate of causal effect. MR studies are designed to answer a yes/no question: Is the causal null hypothesis true?
But retreating to *null testing* is problematic...
1) If the goal of MR is simply to test the null, then why use #IVestimation at all? MR papers should just report the association between the genetic trait (proposed IV) and the outcome.
This is the routine approach in RCTs to test the null. It’s called intent-to-treat approach.
2) If the goal of MR is simply to test the null, we don't need #IVestimation but we still need an IV.
That is, all criticisms of genetic traits as IVs continue to apply even if the goal is redefined as "just testing the null." See recent discussion here
3) "Retreating to null testing" raises other serious problems when, as in most MR studies, the underlying exposure is time-varying. Even if the genetic trait were a true IV!
1/ One day everyone will recognize #selectionbias due to a #collider and the world will be a better place.
This time observational studies found a higher risk of omicron reinfection after a 3rd dose of #COVID19 vaccine. As usual, alarms went off.
Can you see the obvious bias?
2/ Those who receive a booster and get infected are, on average, more susceptible to infection than those who don't receive a booster and get infected.
So no surprise than those who receive a booster and get infected are more likely to get reinfected.
1/ Our findings on a fourth dose (2nd booster) of the Pfizer-BioNTech #COVID19 vaccine are now published.
Compared with 3 doses only, a fourth dose had 68% effectiveness against COVID-19 hospitalization during the Omicron era in persons over 60 years of age.
@ProfMattFox 1/
The odds ratio from a case-control study is an unbiased estimator of the
a. odds ratio in the underlying cohort when we sample controls among non-cases
b. rate ratio in the underlying cohort when we use with incidence density sampling
No rare outcome assumption required.
@ProfMattFox 2/
Because the odds ratio is approximately equal to the risk ratio when the outcome is rare, the odds ratio from a case-control study approximates the risk ratio in the underlying cohort when we sample controls among non-cases and the outcome is rare.
But...
@ProfMattFox 3/
... for an unbiased estimator of the risk ratio (regardless of the outcome being rare), we need a case-base design, not a classical case-control design.
Of course, all of the above only applies to time-fixed treatments or exposures.