Bayesian Kernel Machine Regression (BKMR) is widely used in environmental health research to model complex, nonlinear, and interactive relationships in high-dimensional datasets. However, using a fixed posterior inclusion probability (PIP) threshold can lead to inconsistent test size control, influenced by the coefficient of variation (CV) and sample size. This study introduces a dynamic thresholding approach that adapts to these dataset characteristics, improving the sensitivity and reliability of BKMR analyses. A four-parameter logistic regression model was developed to estimate the 95th percentile of PIP as a function of the log-transformed CV and sample size. Simulations were performed across a broad range of CV values and sample sizes to evaluate test size performance for fixed and dynamic thresholds. The dynamic threshold was validated with independent simulated datasets and applied to the 2011–2014 NHANES data. The dynamic threshold consistently maintained nominal test sizes near five percent, outperforming the fixed threshold, which exhibited substantial variability. Validation with empirical data identified cadmium, manganese, and lead as significant contributors to cognitive performance, with cadmium emerging as the most influential. The dynamic threshold approach improves the precision of variable selection in BKMR, offering a more reliable method to analyze complex exposure-response relationships in environmental health research.

The authors have declared no competing interest.

This study did not receive any funding

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

The code and simulation data will be made available upon reasonable request to the authors.