Computational Analysis of Lifespan Experiment Reproducibility.

Michael Petrascheck,Dana L Miller

doi:10.3389/fgene.2017.00092

Michael Petrascheck, Dana L Miller

Open Access

https://doi.org/10.3389/fgene.2017.00092

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Abstract

Independent reproducibility is essential to the generation of scientific knowledge. Optimizing experimental protocols to ensure reproducibility is an important aspect of scientific work. Genetic or pharmacological lifespan extensions are generally small compared to the inherent variability in mean lifespan even in isogenic populations housed under identical conditions. This variability makes reproducible detection of small but real effects experimentally challenging. In this study, we aimed to determine the reproducibility of C. elegans lifespan measurements under ideal conditions, in the absence of methodological errors or environmental or genetic background influences. To accomplish this, we generated a parametric model of C. elegans lifespan based on data collected from 5,026 wild-type N2 animals. We use this model to predict how different experimental practices, effect sizes, number of animals, and how different “shapes” of survival curves affect the ability to reproduce real longevity effects. We find that the chances of reproducing real but small effects are exceedingly low and would require substantially more animals than are commonly used. Our results indicate that many lifespan studies are underpowered to detect reported changes and that, as a consequence, stochastic variation alone can account for many failures to reproduce longevity results. As a remedy, we provide power of detection tables that can be used as guidelines to plan experiments with statistical power to reliably detect real changes in lifespan and limit spurious false positive results. These considerations will improve best-practices in designing lifespan experiment to increase reproducibility.

Highlights

Over the last few years, science has been plagued by a reproducibility crisis (Editors, 2005; Loannidis, 2005; Baker, 2016)
We provide a series of power calculation tables to be used as general guidelines to plan and execute C. elegans lifespan experiments with adequate power of detection (POD), though our approach is applicable to any organism for which a robust set of mortality data is available to derive the parameters of the Gompertz equation
We derived a parametric model based on the Gompertz equation that allowed us to simulate thousands of lifespan experiments in silico

Summary

Introduction

Over the last few years, science has been plagued by a reproducibility crisis (Editors, 2005; Loannidis, 2005; Baker, 2016). One way to address these reproducibility problems would be to list the numerous controversies and to attempt to identify the individual underlying causes and to provide a possible explanation (see note #1 in Supplementary Materials). This would be a long and arduous task resulting in largely speculative explanation and provide little in terms to resolve future controversies. An alternative way would be to assume that these controversies arise mostly

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