# ViVo: A Temporal Modeling Framework That Boosts Statistical Power and Minimizes Animal Usage

## TL;DR

ViVo is a new framework that improves statistical power in tumor studies by modeling tumor growth rates, reducing the need for large animal samples.

## Contribution

ViVo introduces Tumor Growth Rate matrices and an exponential modeling approach to enhance statistical power and reduce animal usage.

## Key findings

- Exponential fits showed high agreement with raw tumor data (median R² = 0.937).
- ViVo outperformed traditional methods in detecting treatment effects with small sample sizes.
- The framework allows inclusion of early euthanized animals, improving statistical consistency.

## Abstract

Preclinical tumor studies are often limited by high variability
and small-sample sizes, reducing statistical power, and masking treatment
effects. We present an exponential framework that estimates tumor
growth rates (r) independently of initial burden
and introduces Tumor Growth Rate (TGR) matrices to map treatment effects
across time windows. Applied to a public xenograft data set and four
additional mouse models, exponential fits achieved high agreement
with raw data (median R
2 = 0.937). When
resampling matched group sizes (n = 3–7),
our approach consistently outperformed conventional end point and
daily nonparametric analyses, revealing treatment effects that standard
comparisons missed. The framework also predicts tumor weights for
animals euthanized early, enabling their inclusion in final analyses
and enhancing statistical consistency while advancing the 3Rs principles.
To ensure broad adoption, we developed ViVo, an open-source
web platform (gcanudo-barreras.github.io/ViVo-Platform/) that provides accessible, standardized analysis of in vivo tumor
kinetics and therapeutic efficacy.