A Bounded Integer Model for Rating and Composite Scale Data.

ABSTRACT: Rating and composite scales are commonly used to assess treatment efficacy. The two main strategies for modelling such endpoints are to treat them as a continuous or an ordered categorical variable (CV or OC). Both strategies have disadvantages, including making assumptions that violate the integer nature of the data (CV) and requiring many parameters for scales with many response categories (OC). We present a method, called the bounded integer (BI) model, which utilises the probit function with fixed cut-offs to estimate the probability of a certain score through a latent variable. This method was successfully implemented to describe six data sets from four different therapeutic areas: Parkinson's disease, Alzheimer's disease, schizophrenia, and neuropathic pain. Five scales were investig

SUBMITTER: Wellhagen GJ 

PROVIDER: S-EPMC6554249 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature