Posts

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:

Stats on Drugs: An Interview with a Pharmaceutical CRO Biostatistician

Earlier this year I wrote my first blog post, “A Day in the Life of a Biostatistician,” documenting the granular details of my work as an early career academic research biostatistician. I’m excited to announce I am turning that post into a “day in the life” series in which I interview other biostatisticians with differing roles. My hope is that it will enlighten anyone interested in the field of biostatistics, and especially help undergraduate and current biostatistics Masters students make informed decisions about their careers.

Tips and Tricks from the New York R Conference

In early May I attended the New York R Conference. There were 24 speakers, including my coworker at Weill Cornell Medicine, Elizabeth Sweeney! Each person did a 20-minute presentation on some way they use R for their work and/or hobbies. There was a ton of information, and even though not all of it was directly useful for my workflow as a statistical consultant in an academic setting, I really enjoyed being around so many people who love R.

A Day in the Life of a Biostatistician

It seems fitting that my first blog post is on a topic that I tried and failed to find via Google search a few years ago. I’ll back up for a second. A few years ago I was a recent college graduate, and trying hard to “figure out my life.” My major was biochemistry, which is one of those degrees where 99%* of people just keep on going to school.

Outputting Rmarkdown Headers within Functions

When doing long, identical analyses on different data sets or variables, it can be useful to have one function which outputs your analyses in an Rmarkdown friendly (ie., with headers) format. This is a simple example of how multiple mini-analyses can be combined into one run-all function containing headers. Let’s say we have two separate data sets, dat1 and dat2, and we want to look do two analyses on each data set.

Data Wrangling with dplyr

A Presentation for Weill Cornell Medicine’s Biostatistics Computing Club Image courtesy of Allison Horst’s Twitter: @allison_horst Introduction Why dplyr? Powerful but efficient Consistent syntax Fast Function chaining Works well with entire tidyverse suite Efficiency* Simple syntax Function chaining Ability to analyze external databases Works well with other packages in tidyverse suite ggplot2 tidyr stringr forcats purrr *if you start dealing with data sets with > 1 million rows, data.