Work

Using machine learning to model dose–response relationships

Year: 2016

Type: article

Abstract: RATIONALE, AIMS AND OBJECTIVES: Establishing the relationship between various doses of an exposure and a response variable is integral to many studies in health care. Linear parametric models, widely used for estimating dose-response relationships, have several limitations. This paper employs the optimal discriminant analysis (ODA) machine-learning algorithm to determine the degree to which exposure dose can be distinguished based on the distribution of the response variable. By framing the dose-response relationship as a classification problem, machine learning can provide the same functionality as conventional models, but can additionally make individual-level predictions, which may be helpful in practical applications like establishing responsiveness to prescribed drug regimens. METHOD: Using data from a study measuring the responses of blood flow in the forearm to the intra-arterial administration of isoproterenol (separately for 9 black and 13 white men, and pooled), we compare the results estimated from a generalized estimating equations (GEE) model with those estimated using ODA. RESULTS: Generalized estimating equations and ODA both identified many statistically significant dose-response relationships, separately by race and for pooled data. Post hoc comparisons between doses indicated ODA (based on exact P values) was consistently more conservative than GEE (based on estimated P values). Compared with ODA, GEE produced twice as many instances of paradoxical confounding (findings from analysis of pooled data that are inconsistent with findings from analyses stratified by race). CONCLUSIONS: Given its unique advantages and greater analytic flexibility, maximum-accuracy machine-learning methods like ODA should be considered as the primary analytic approach in dose-response applications.

Source: Journal of Evaluation in Clinical Practice

DOI: 10.1111/jep.12573

Authors: Ariel Linden, Paul R Yarnold, Brahmajee K Nallamothu

Institutions: University of Michigan, IMRA America (United States), University of South Carolina, NutritionQuest (United States), Michigan Medicine

Language: English

Topic: Statistical Methods in Epidemiology

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Cancer Institute