From Awareness to Action in Experimental Design

Animal physiological and behavioral research is subject to many confounding influences that can be difficult to control, potentially compromising experimental findings. Without proper experimental design and analysis, nuisance factors can systematically undermine data quality and reproducibility, often in underappreciated ways.

In this article, we will:

• Examine sources of confounding effects in animal studies

• Present methods for addressing these factors and improving outcomes

• Explore how multifactor relational analysis can identify factors that were not previously identified

Characterized Confounding Factors

Confounding factors in animal research range from well-recognized to frequently overlooked. The following lists some characterized (or known) sources:

• Procedural: Handling, experimenter effects, and habituation, are well-established sources of variability in mouse behavioral research. The gender of the researcher performing the assessment not only affects stress and pain responses, (Sorge et al 2014) but also alters mouse responses to pharmacological treatment. Georgiou et al., (2022)

• Temporal: Gene expression, circadian rhythms, immune activity, and behavior are all continuously shifting across time, developmental stage, and disease state. Nonoptimal time point selection has been shown to meaningfully reduce sensitivity of biological signals in gene expression research (Zhang et al., PNAS, 2014), and the same logic extends across physiological and behavioral endpoints.

• Environmental: Housing, enrichment, lighting, and sensory environment, are potent sources of variability in mouse studies. The Journal of the American Association for Laboratory Animal Science dedicated an entire issue to this subject, with a compelling and succinct foreword that frames the challenge.

• Animal-intrinsic: Strain, sex, age, health status, and microbiome composition, represent some of the most consistent and biologically consequential sources of variation in mouse research.

Addressing Confounding Factors

With the long list of factors described above, how should researchers approach experimental design and analysis? The simplest approach is to understand the factor, then either reduce its impact or control it directly.

Take these two examples:

• Animal handling introduces experimenter variability and disrupts normative animal behavior and physiology. Potential solution: Adopt non-invasive monitoring to minimize direct animal interaction, allowing researchers to observe biology in a closer to undisturbed state and increasing confidence in data accuracy.

• Episodic observation misses optimal time points for data collection. Potential solution: Ensure optimal time point sampling through pilot studies or take advantage of technologies that enable continuous monitoring of relevant outputs, directly addressing this risk.

But what are some more systematic approaches?

Characterization-driven Design

A systematic approach to addressing nuisance factors involves thorough characterization and reporting of relevant variables, followed by modification of experimental design or analysis where feasible. Transparent reporting of these decisions is essential for interpretation and reproducibility. The ARRIVE recommendations for improving animal research provide a strong framework for understanding which factors to consider, how to report them, and how to address them. Richter et al. (2011) demonstrate a practical application of this approach. In this study the authors recognized that cage and husbandry variables were impacting results. In response they randomized experimental groups across these variables.

Many insights arise from purposeful observation during the early phases of a study, which serve as pilot assessments. Such approaches predominantly rely on explicit identification and measurement of candidate confounding variables, either through experimental design (e.g., randomization, blocking, stratification) or analytic adjustment (e.g., regression, ANCOVA, propensity-based methods).

While useful when relevant factors are correctly anticipated and measured, these methods cannot address signals from unanticipated sources. Furthermore, hypothesis-driven or sensitivity analyses focused on suspected variables require both foresight and additional experimentation.

Validating Measures

A rigorous approach to addressing possible confounds is ensuring that the measures used faithfully represent the biological process you are studying. Like the approaches outlined above, this requires prework. Validation of every measure may be challenged if large numbers of animals are required to accomplish this. However, a compelling argument may be made that inconclusive results due to a lack of such characterization may ultimately result in the use of more animals.

In his 2017 article Hanno Würbel makes a strong case for validation while presenting a thoughtful discussion on these tensions. Such concerns raise the possibility that large well-managed consortia that share data or adoption of purpose-built pre-validated platforms may be of interest to reduce this burden.

Identifying Confounders that Have Never Been Considered

Multifactor relational analysis approaches (e.g., Principal Component Analysis, Multiple Factor Analysis, clustering) are valuable tools for identifying potential confounders. When first exposed to these approaches, readers often wrongly conclude that these methods can exclude unwanted or irrelevant factors from analysis.

In practice, these methods identify groups of individuals within a study that are responding similarly across multiple dimensions. These individuals may not appear to share characteristics, but further characterization may reveal previously unrecognized similarities, and with them, unconsidered contributors to variance in the system. One can then design the experiment or analysis appropriately to improve outcomes.

These approaches expose unexpected relationships among commonly used measures, revealing that variables assumed to be independent may in fact share underlying processes rather than representing distinct biological processes (Crusio, W. E, 2001). As Jolliffe and Cadima (2016) note, PCA identifies correlation structure, redundancy, and latent dimensions, but it is not a tool for correcting bias.

Furthermore, these methods are only as good as the data fed into them and do not preclude following the ARRIVE guidelines. The more information and relevant measures recorded, and the greater the rigor of experimental design, the better equipped these methods are to surface potential confounders.

Turning Awareness into Better Experimental Design

Rigorous approaches exist to improve interpretability by identifying and managing confounding influences. The core principles are:

• Identification and documentation of plausible confounders, guided by prior knowledge, pilot observations, and reporting frameworks.

• Purposeful handling of known variables through study design, using blocking, restriction, or targeted randomization as appropriate.

• Early observation and refinement, using careful assessment during initial study phases to inform design decisions.

• Use of multivariate analyses as diagnostic tools, capable of revealing latent structure, redundancy, and unanticipated sources of variation.

• Transparent reporting of design choices and analytical decisions, enabling informed interpretation, reproducibility, and generalization.

These principles emphasize that improving clarity depends less on any method but on sustained attention to structure, context, and documentation.

A System Built to Address Biological Complexity

Biological systems are inherently complex, and interpreting animal studies is rarely straightforward. Innovative digital monitoring technologies are beginning to address these common challenges in targeted ways. By minimizing direct animal handling, these tools reduce experimenter-introduced variability and allow researchers to observe animals in a more undisturbed state. Continuous monitoring directly addresses the risk of missing optimal time points by capturing biology as it unfolds rather than in episodic snapshots that may not reflect meaningful signals. And AI-assisted multifactor analysis can surface relationships and sources of variation that were never anticipated in the original experimental design, revealing confounders that other approaches would miss entirely.

JAX's Envision™ brings these capabilities together in a single platform. Learn how Envision is helping researchers move from awareness to action.

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