Dataset Information

Interaction Analysis of Longevity Interventions Using Survival Curves.

ABSTRACT: A long-standing problem in ageing research is to understand how different factors contributing to longevity should be expected to act in combination under the assumption that they are independent. Standard interaction analysis compares the extension of mean lifespan achieved by a combination of interventions to the prediction under an additive or multiplicative null model, but neither model is fundamentally justified. Moreover, the target of longevity interventions is not mean life span but the entire survival curve. Here we formulate a mathematical approach for predicting the survival curve resulting from a combination of two independent interventions based on the survival curves of the individual treatments, and quantify interaction between interventions as the deviation from this prediction. We test the method on a published data set comprising survival curves for all combinations of four different longevity interventions in Caenorhabditis elegans. We find that interactions are generally weak even when the standard analysis indicates otherwise.

SUBMITTER: Nowak S

PROVIDER: S-EPMC5872032 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Interaction Analysis of Longevity Interventions Using Survival Curves.

Nowak Stefan S Neidhart Johannes J Szendro Ivan G IG Rzezonka Jonas J Marathe Rahul R Krug Joachim J

Biology 20180106 1

A long-standing problem in ageing research is to understand how different factors contributing to longevity should be expected to act in combination under the assumption that they are independent. Standard interaction analysis compares the extension of mean lifespan achieved by a combination of interventions to the prediction under an additive or multiplicative null model, but neither model is fundamentally justified. Moreover, the target of longevity interventions is not mean life span but the ...[more]

PMID: 29316622

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Project description:Objectives: Post Mortem Human Surrogate (PMHS) experiments are used for describing tolerance and improve safety. For nearside impacts, the United States Standard Federal Motor Vehicle Safety Standards (FMVSS-214) used PMHS tests and binary regression methods to achieve these goals. Since this promulgation, Parametric Statistical Survival Modeling (PSSM) has become a de facto standard for developing injury risk curves (IRCs). This study is focused on pelvic injuries from side impacts. The objectives are as follows. Analyze impactor-based intact PMHS tests and develop IRCs at different AIS levels using the force metric and examine the effectiveness of other force-related variables on IRCs.Methods: Impactor-driven pelvic tests conducted using whole body PMHS were selected from published studies. The dataset had 63 tests. Peak force, 3-ms clip force, and impulse were used to develop IRCs for Abbreviated Injury Scores (AIS) AIS2+ and AIS3+, i.e., groups A and B. Brier Score Metric (BSM) was used for ranking metrics. 95% confidence intervals were computed, Normalized Confidence Interval Sizes (NCIS) were determined, and quality of the IRCs were obtained.Results: Impulse best described the underlying response of the pelvis. BSMs were the lowest for the impulse for both groups. At 10% and 50% probabilities, impulses were 71 Ns and 125 Ns for group A and 79 Ns and 160 Ns for group B; peak forces were 3.8 kN and 7.1 kN and 4 kN and 10 kN for groups A and B; and clip forces were 2.7 kN and 6.5 kN and 3.6 kN and 8.6 kN, for groups A and B. NCIS at discrete probability levels, qualities of risk curves, and individual IRCs are given.Conclusion: This study underscores the importance of using impulse to describe pelvis injury criteria in lateral impacts. These findings are applicable to anthropomorphic test devices, as matched pair tests are done to determine dummy-based injury criteria/injury assessment risk curves (IARCs). Although IRCs have been developed for WorldSID, it may be appropriate to use impulse-based IARCs. Because THOR is a potential device for automated vehicle environments, it may be appropriate to develop THOR-based IARCS. The present IRCs act as fundamental human-based injury criteria. These responses can also be used in human body and subsystem computational models.

Dataset Information

Interaction Analysis of Longevity Interventions Using Survival Curves.

Publications

Interaction Analysis of Longevity Interventions Using Survival Curves.

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