  This paper presents the Burdened Bayesian Logistic Regression Model (BBLRM),
an enhancement to the Bayesian Logistic Regression Model (BLRM) for
dose-finding in phase I oncology trials. Traditionally, the BLRM determines the
maximum tolerated dose (MTD) based on dose-limiting toxicities (DLTs). However,
clinicians often perceive model-based designs like BLRM as complex and less
conservative than rule-based designs, such as the widely used 3+3 method. To
address these concerns, the BBLRM incorporates non-DLT adverse events (nDLTAEs)
into the model. These events, although not severe enough to qualify as DLTs,
provide additional information suggesting that higher doses might result in
DLTs. In the BBLRM, an additional parameter $\delta$ is introduced to account
for nDLTAEs. This parameter adjusts the toxicity probability estimates, making
the model more conservative in dose escalation. The $\delta$ parameter is
derived from the proportion of patients experiencing nDLTAEs within each cohort
and is tuned to balance the model's conservatism. This approach aims to reduce
the likelihood of assigning toxic doses as MTD while involving clinicians more
directly in the decision-making process. The paper includes a simulation study
comparing BBLRM with the traditional BLRM across various scenarios. The
simulations demonstrate that BBLRM significantly reduces the selection of toxic
doses as MTD without compromising, and sometimes even increasing, the accuracy
of MTD identification. These results suggest that integrating nDLTAEs into the
dose-finding process can enhance the safety and acceptance of model-based
designs in phase I oncology trials.
