In the last couple of posts (starting here), I’ve tried to unpack some of the ideas that sit underneath TMLE: viewing parameters as functionals of a distribution, thinking about sampling as a perturbation, and understanding how influence functions describe the leading behavior of estimation error. In the second post, I showed through simulation how errors in nuisance estimation can interact with sampling variability, but typically have a smaller effect than the main sampling fluctuation itself. This brings us to the central idea behind TMLE.
[Read More]Getting to the bottom of TMLE: influence functions and perturbations
I first encountered TMLE—sometimes spelled out as targeted maximum likelihood estimation or targeted minimum-loss estimate—about twelve or so years ago when Mark var der Laan, one of the original developers who literally wrote the book, gave a talk at NYU. It sounded very cool and seemed quite revolutionary and important, but it was really challenging to follow all of the details. Following that talk, I tried to tackle some of the literature, but quickly found that it as a challenge to penetrate. What struck me most was not the algorithmic complexity (which it certainly had), but much of the language and terminology, and the underlying math.
[Read More]An IV study design to estimate an effect size when randomization is not ethical
An investigator I frequently consult with seeks to estimate the effect of a palliative care treatment protocol for patients nearing end-stage disease, compared to a more standard, though potentially overly burdensome, therapeutic approach. Ideally, we would conduct a two-arm randomized clinical trial (RCT) to create comparable groups and obtain an unbiased estimate of the intervention effect. However, in this case, it may be considered unethical to randomize patients to a non-standard protocol.
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