# Sequential Re-estimation Learning of Optimal Individualized Treatment Rules Among Ordinal Treatments with Application to Recommended Intervals Between Blood Donations

## TL;DR

This paper introduces a sequential re-estimation learning method for optimal individualized treatment rules with ordinal treatments, applied to blood donation intervals, improving safety and sustainability by integrating multi-marker data and variable selection.

## Contribution

It develops a novel sequential re-estimation approach that effectively incorporates treatment orderings and variable selection for personalized treatment decisions in ordinal settings.

## Key findings

- Outperforms existing methods with lower misclassification rates.
- Achieves higher utility values in simulations.
- Optimizes blood donation intervals for safety and donor retention.

## Abstract

Personalized medicine has gained much popularity recently as a way of providing better healthcare by tailoring treatments to suit individuals. Our research, motivated by the UK INTERVAL blood donation trial, focuses on estimating the optimal individualized treatment rule (ITR) in the ordinal treatment-arms setting. Restrictions on minimum lengths between whole blood donations exist to safeguard donor health and quality of blood received. However, the evidence-base for these limits is lacking. Moreover, in England, the blood service is interested in making blood donation both safe and sustainable by integrating multi-marker data from INTERVAL and developing personalized donation strategies. As the three inter-donation interval options in INTERVAL have clear orderings, we propose a sequential re-estimation learning method that effectively incorporates "treatment" orderings when identifying optimal ITRs. Furthermore, we incorporate variable selection into our method for both linear and nonlinear decision rules to handle situations with (noise) covariates irrelevant for decision-making. Simulations demonstrate its superior performance over existing methods that assume multiple nominal treatments by achieving smaller misclassification rates and larger value functions. Application to a much-in-demand donor subgroup shows that the estimated optimal ITR achieves both the highest utilities and largest proportions of donors assigned to the safest inter-donation interval option in INTERVAL.