Many-levelled continuation ratio models for frequency of alcohol and drug use data

TL;DR

This paper introduces a novel continuation ratio ordinal model tailored for frequency of alcohol and drug use data, effectively capturing complex patterns and bounded responses that traditional models struggle with.

Contribution

The paper proposes a new continuation ratio ordinal model that assigns each possible response value its own category, enabling precise modeling of bounded and multimodal substance use data.

Findings

Model better captures complex drinking patterns.

Outperforms binomial, hurdle-negative binomial, and beta-binomial models.

Demonstrated on 28-day alcohol use survey data.

Abstract

Studies of alcohol and drug use are often interested in the number of days that people use the substance of interest over an interval, such as 28 days before a survey date. Although count models are often used for this purpose, they are not strictly appropriate for this type of data because the response variable is bounded above. Furthermore, if some peoples' substance use behaviors are characterized by various weekly patterns of use, summaries of substance days-of-use used over longer periods can exhibit multiple modes. These characteristics of substance days-of-use data are not easily fitted with conventional parametric model families. We propose a continuation ratio ordinal model for substance days-of-use data. Instead of grouping the set of possible response values into a small set of ordinal categories, each possible value is assigned its own category. This allows the exact numeric distribution implied by the predicted ordinal response to be recovered. We demonstrate the proposed model using survey data reporting days of alcohol use over 28-day intervals. We show the continuation ratio model is better able to capture the complexity in the drinking days dataset compared to binomial, hurdle-negative binomial and beta-binomial models.