An Illustrated Guide to TMLE, Part III: Properties, Theory, and Learning More

The is the third and final post in a three-part series to help beginners and/or visual learners understand Targeted Maximum Likelihood Estimation (TMLE). In this section, I discuss more statistical properties of TMLE, offer a brief explanation for the theory behind TMLE, and provide resources for learning more. Properties of TMLE 📈 To reiterate a point from Parts I and II, a main motivation for TMLE is that it allows the use of machine learning algorithms while still yielding asymptotic properties for inference.

An Illustrated Guide to TMLE, Part II: The Algorithm

The second post of a three-part series to help beginners and/or visual learners understand Targeted Maximum Likelihood Estimation (TMLE). This section walks through the TMLE algorithm for the mean difference in outcomes for a binary treatment and binary outcome. This post is an expansion of a printable “visual guide” available on my Github. I hope it helps analysts who feel out-of-practice reading mathematical notation follow along with the TMLE algorithm.

An Illustrated Guide to TMLE, Part I: Introduction and Motivation

The introductory post of a three-part series to help beginners and/or visual learners understand Targeted Maximum Likelihood Estimation (TMLE). This section contains a brief overview of the targeted learning framework and motivation for semiparametric estimation methods for inference, including causal inference. Table of Contents This blog post series has three parts: Part I: Motivation TMLE in three sentences 🎯 An Analyst’s Motivation for Learning TMLE 👩🏼‍💻 Is TMLE Causal Inference?

Become a Superlearner! An Illustrated Guide to Superlearning

Why use one machine learning algorithm when you could use all of them?! This post contains a step-by-step walkthrough of how to build a superlearner prediction algorithm in R. HTML Image as link A Visual Guide… Over the winter, I read Targeted Learning by Mark van der Laan and Sherri Rose. This “visual guide” I made for Chapter 3: Superlearning by Rose, van der Laan, and Eric Polley is a condensed version of the following tutorial.

Customizable correlation plots in R

TL;DR If you’re ever felt limited by correlogram packages in R, this post will show you how to write your own function to tidy the many correlations into a ggplot2-friendly form for plotting. By the end, you will be able to run one function to get a tidied data frame of correlations: formatted_cors(mtcars) %>% head() %>% kable() measure1 measure2 r n p sig_p p_if_sig r_if_sig mpg mpg 1.

Rethinking Conditional and Iterated Expectations with Linear Regression Models

An “aha!” moment: the day I realized I should rethink all the probability theorems using linear regressions. TL;DR You can a regress an outcome on a grouping variable plus any other variable(s) and the unadjusted and adjusted group means will be identical. We can see this in a simple example using the palmerpenguins data: #remotes::install_github("allisonhorst/palmerpenguins") library(palmerpenguins) library(tidyverse) library(gt) # use complete cases for simplicity penguins <- drop_na(penguins) penguins %>% # fit a linear regression for bill length given bill depth and species # make a new column containing the fitted values for bill length mutate(preds = predict(lm(bill_length_mm ~ bill_depth_mm + species, data = .

Lessons learned: my top five coding 'tricks' during the NYC COVID-19 outbreak

In non-coronavirus times, I am the biostatistician for a team of NYC pulmonologists and intensivists. When the pandemic hit NYC in mid-March, I immediately became a 100% 200% COVID-19 statistician. I received many analysis requests, though not all of them from official investigators: My family recently learned I am the statistician for my hospital’s pulmonologists and now I get COVID-19 analysis requests from them, too pic.twitter.com/wlHmUaBh6Y — Kat Hoffman (@rkatlady) April 10, 2020 Jokes aside, I was really, really busy during the outbreak.

Patient Treatment Timelines for Longitudinal Survival Data

I am a biostatistician at a research university, and I often find myself working with longitudinal survival data. As with any data analysis, I need to examine the quality of my data before deciding which statistical methods to implement. This post contains reproducible examples for how I prefer to visually explore survival data containing longitudinal exposures or covariates. I create a “treatment timeline” for each patient, and the end product looks something like this:

A short and sweet tutorial on using `sl3` for superlearning

Background In September 2019, I gave an R-Ladies NYC presentation about using the package sl3 to implement the superlearner algorithm for predictions. You can download the slides for it here. This post is a modification to the original demo I gave. For a better background on what the superlearner algorithm is, please see my more recent blog post. Step 0: Load your libraries, set a seed, and load the data You’ll likely need to install sl3 from the tlverse github page, as it was not yet on CRAN at the time of writing this post.

Become a Superlearner

Using sl3 to build ensemble learning models in R