Devin Incerti

Data science, health economics, epidemiology, R, open source, new dad, ex-athlete

[Devin Incerti @DevinIncerti]

@joeymattingly @brootmcq @mikepaulden @djvanness @DARTHworkgroup @AnkurFactorial @DrWmPadula @juliaslejko We're moving code over to Python so hopefully it won't be considered too wild in the near future 😁

[Devin Incerti @DevinIncerti]

External controls are intriguing but bias is a concern. Across 14 old studies, we found that trial controls survived longer---even after PS adjustment---than external controls (HR=0.90). We propose a meta-analytic framework to adjust for such bias.

https://t.co/WlgQulEjBG

[Devin Incerti @DevinIncerti]

Big thanks to the glmnet team for making it possible to model start/stop survival data and adjust for left-truncation. https://t.co/Z0G0Rv5EEX

[Devin Incerti @DevinIncerti]

Happy to see our paper w/@EsMagoo,@robtibshirani & all on prediction modeling with high-dimensional left-truncated and right-censored survival data published in Statistics in Medicine. Need to be especially careful using C-index for model evaluation. https://t.co/jJMv4Sf73S

[Devin Incerti @DevinIncerti]

We think this type of data will be increasingly common as datasets that link genomic information with clinical outcomes become more common. All code for our analysis is available in our GitHub repo.https://t.co/67h8PjGcRZ

[Devin Incerti @DevinIncerti]

@JonathanBriody @JeroenPJansen @ISPORorg Thanks so much @JonathanBriody, I'm glad you found the materials helpful! And hope you will consider R for your cost effectiveness modeling 😃

[Devin Incerti @DevinIncerti]

New version of hesim (0.5.1) on CRAN. Updates include new summary methods for checking/summarizing model inputs, enhancements to make it easier to build large transition matrices, and improved documentation, among others.

https://t.co/ZVlD7Rqy6x

[Devin Incerti @DevinIncerti]

Looking forward to teaching an @ISPORorg short course (Aug 11/12) on cost-effectiveness modeling with R with @JeroenPJansen. We'll build models in both base R and hesim and have made our course materials available online.

https://t.co/IlFdnckslf

https://t.co/sI0SxF6wP4

[Devin Incerti @DevinIncerti]

@ajhatswell @waq0r Agree that the main advantage of R is the stats integration

[Devin Incerti @DevinIncerti]

@waq0r @ajhatswell Certainly if you want someone to be able to run something interactively on the web than a few hours isn't going to cut it. It can also be a pain to debug if run time is on the order of hours. Likewise sensitivity analyses get annoying with slow run times.

[Devin Incerti @DevinIncerti]

@felbalazard @OWKINscience @Inria @Univ_Paris Much appreciated @felbalazard. Thank your for putting together such a great event!

[Devin Incerti @DevinIncerti]

@rhta16 @feralaes @leatl4 Thanks @rhta16, really excited to be joining!

[Devin Incerti @DevinIncerti]

@djvanness There is a pretty simple example in the Gelman and Hill textbook that uses simulation. I think it would extend in a straightforward way to survey weights. The alternative is to use the delta method to get standard errors, but I think simulation is easier.

[Devin Incerti @DevinIncerti]

@brootmcq Could also be the quality/clarity of the code that was written. Definitely worth learning Python though!

[Devin Incerti @DevinIncerti]

It's still slower than simulating from a parametric model because we use the inverse CDF method for random number generation and the quantile function must be computed numerically, but the performance penalty is relatively small.

[Devin Incerti @DevinIncerti]

Some new benchmarks for microsimulations parameterized with spline-based survival models in our #rstats hesim package. #Rcpp makes it surprisingly fast: in realistic settings, run time is half a minute compared to 3.5 hours with other packages! https://t.co/OdvKQHgXIw

[Devin Incerti @DevinIncerti]

@brootmcq @JeroenPJansen Thanks @brootmcq!

[Devin Incerti @DevinIncerti]

New preprint with @JeroenPJansen describing our hesim R package for fast simulation of cost-effectiveness models. Accompanies release of v0.5.0 on CRAN with many new features.

https://t.co/6uqrg5w5o2

[Sarah McGough @EsMagoo]

Uncovering immortal time bias when fitting prognostic survival models in a large clinico-genomic database. Here, our assessment w/ @DevinIncerti, @robtibshirani & all (pre-print).

https://t.co/K5Abe8Q4wI