Withdrawn - 06. Modeling Longitudinal Microbiome Data with Application to a Melanoma Clinical Trial Evaluating Immune Checkpoint Inhibitor Treatment Response (NCT05102773)

The microbiome has increasingly been shown to play a fundamental role in human health. As microbiome research expands and microbial communities are better characterized there is growing interest in leveraging these complex data to reveal mechanistic insights and identify predictive biomarkers of disease. However, the high dimensional, and often zero-inflated nature of the data presents a unique statistical challenge – particularly in the context of longitudinal studies. Here, utilizing data from a prospective, longitudinal study of gut microbiome samples from patients with metastatic melanoma treated with immune checkpoint inhibitors (ICIs), we assess the efficacy of several zero-inflated mixed models for prediction of ICI response (RECIST at 12 weeks) and toxicity (≥ CTCAE grade 1). Gut microbiome samples from NCT05102773 (n=41, averaging 2.1 samples/patient) were processed and analyzed by metagenomic sequencing. Zero-inflated mixed models were fit on a subset of microbes using the NBZIMM R package, which supports modeling features tailored to microbiome data, including zero inflation. Both Gaussian models (with relative abundance data) and negative binomial models (with estimated raw counts) were evaluated. Model variations included the addition of a library size offset and correlation structure specification, including AR(1), CAR(1), and ARMA. Model metrics, such as MSE and AIC were calculated and used to evaluate model fit. The zero-inflated Gaussian mixed model with an independent correlation structure consistently had the lowest MSE and lowest AIC of the models tested, indicating its utility as a model for longitudinal microbiome samples, balancing accuracy and parsimony. Further work is needed to assess the model validity and utility across longitudinal microbiome datasets.

microbiome

cancer

longitudinal

zero-inflated models

correlation structures

mixed-effects 

Beginner

Knowledge

Women in Statistics and Data Science 2025