Sensitivity Analyses for Unmeasured Variables

[@ernestsia7798]

Hi Leslie, I'm a medical student in Indonesia in the progress of conducting relatively "new" research about travel health, this video helps me a lot since the research contains many independent variables and has not yet found any control variables. I'm planning to use sensitivity analysis as a way to cover the weakness of this research. Thank you Leslie :)

[@achmadsamjunanto6410]

is there any cutoff, of how much the associations are called strong or not? to qualitatively change results.

[@sethjchandler]

Great exposition; where might one find the code that simulates the values of U?

[@lesliemyint1865]

I recommend the tipr package in R!

[@baptisteboukebous501]

Hey Thanks for this video! What function did you use to plot the graph, using Tipr? Thanks

[@lesliemyint1865]

I didn't actually use tipr in making this video (learned about it later). I used custom code available here: lmyint.github.io/causal_fall_2020/sensitivity-analyses-for-unmeasured-variables.html

[@wataru_fukuokaya]

@@lesliemyint1865 Thank you for your very informative video. Can the codes on your homepage be applicable to Cox regression model?

[@lesliemyint1865]

@@wataru_fukuokaya Yes, that code uses a useful general approach: simulation of the unmeasured confounder. It simulates an unmeasured variable U that is a common cause of treatment and outcome (and is independent of the measured confounders). In this way, you can use U in any subsequent analysis as if it were a measured confounder. The code creates multiple U's with different strengths of association with the treatment and outcome. When you use U in your analysis to estimate causal effects (e.g., Cox regression), you can include these U's in the model to see how your estimates change.

[@alakashakiru7680]

I have a question around PS. Why does weighting minimize bias more than stratification or matching methods? Second, does the choices of these techniques should be guided by the research question?

[@lesliemyint1865]

Stratification (subclassification) "coarsens" the grouping (less similar units get grouped together), and with matching, not all units can get a good match. In general, the choice of technique should be guided by features of the dataset.

[@bevansmith3210]

Hi Leslie, I understand that we vary the strength by varying the coefficients. But what does U look like? Is it just some arbitrary normal distribution with a mean and std dev? Thanks :)

[@lesliemyint1865]

Yes exactly! In general, when people want to assess the impact of a continuous U, assuming a normal distribution for U is common. Then the mean and SD of U depend on parameters governing the relationships between U and treatment and U and outcome.

[@bevansmith3210]

@@lesliemyint1865 Thanks Leslie

[@yulinliu850]

👍👍👍