Approximate Tolerance and Prediction in Non-normal Models with Application to Clinical Trial Recruitment and End-of-study Success

11/23/2020
by   Geoffrey S Johnson, et al.
0

A prediction interval covers a future observation from a random process in repeated sampling, and is typically constructed by identifying a pivotal quantity that is also an ancillary statistic. Outside of normality it can sometimes be challenging to identify an ancillary pivotal quantity without assuming some of the model parameters are known. A common solution is to identify an appropriate transformation of the data that yields normally distributed observations, or to treat model parameters as random variables and construct a Bayesian predictive distribution. Analogously, a tolerance interval covers a population percentile in repeated sampling and poses similar challenges outside of normality. The approach we consider leverages a link function that results in a pivotal quantity that is approximately normally distributed and produces tolerance and prediction intervals that work well for non-normal models where identifying an exact pivotal quantity may be intractable. This is the approach we explore when modeling recruitment interarrival time in clinical trials, and ultimately, time to complete recruitment.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

04/24/2017

On Prediction and Tolerance Intervals for Dynamic Treatment Regimes

We develop and evaluate tolerance interval methods for dynamic treatment...
11/03/2019

Bayesian adaptive N-of-1 trials for estimating population and individual treatment effects

This article presents a novel adaptive design algorithm that can be used...
11/19/2018

Sampling on Social Networks from a Decision Theory Perspective

Some of the most used sampling mechanisms that propagate through a socia...
03/23/2022

On predictive inference for intractable models via approximate Bayesian computation

Approximate Bayesian computation (ABC) is commonly used for parameter es...
09/28/2021

Constructing Prediction Intervals Using the Likelihood Ratio Statistic

Statistical prediction plays an important role in many decision processe...
05/18/2016

ABC random forests for Bayesian parameter inference

This preprint has been reviewed and recommended by Peer Community In Evo...
11/09/2020

Approaches to Linear Mixed Effects Models with Sign Constraints

Linear Mixed Effects (LME) models have been widely applied in clustered ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.