# DoseFinding

## New phase I methods in escalation

Introducing new methods in escalation for TPI, mTPI and Neuenschwander et al.'s design for phase I trials, plus MCMC-based CRM methods.

## trialr and escalation

The trialr package, the escalation package, how they work together, and how they will grow.

## Fitting the Emax Model in R

Emax is an awesome, flexible non-linear model for estimating dose-response curves. Come learn how to fit it in R.

## Real data on lots of dose-finding trials

We collected efficacy and toxicity outcomes from 122 published dose-finding trials. This project introduces the data and our insights.

## Sample sizes in phase I

Empirically, what sample size is used in dose-escalation trials?

## Dataset containing outcomes from dose-finding trials in cancer

Descriptive data and dose-level outcomes from 122 manuscripts reporting results of dose-finding clinical trials in cancer.

## Simulation or enumeration with dose-finding designs?

Simulation is the popular approach but brute-force enumeration is more accurate and can even be quicker.

## Dose-paths in the escalation package

Enumerate every possible dose selection decision for all dose-finding models implemented in the escalation package.

## The escalation package

_escalation_ is an R package for dose-escalation clinical trials, providing a consistent, extensible, modular approach.

## CRM Simulation Checklist

During 2019, I was working on simulations using CRM designs in several different trials. I found I would frequently get the designs mixed up: “We are targeting 20% toxicity in this trial, right? No, that was the other trial…we are targeting 33% here…” Similarly, once or twice, I got to the stage where I wanted to run simulations only to discover we had not specified some important design aspect, like when the trial should stop.