Dataset Information

Application of Survival Analysis and Multistate Modeling to Understand Animal Behavior: Examples from Guide Dogs.

ABSTRACT: Epidemiology is the study of patterns of health-related states or events in populations. Statistical models developed for epidemiology could be usefully applied to behavioral states or events. The aim of this study is to present the application of epidemiological statistics to understand animal behavior where discrete outcomes are of interest, using data from guide dogs to illustrate. Specifically, survival analysis and multistate modeling are applied to data on guide dogs comparing dogs that completed training and qualified as a guide dog, to those that were withdrawn from the training program. Survival analysis allows the time to (or between) a binary event(s) and the probability of the event occurring at or beyond a specified time point. Survival analysis, using a Cox proportional hazards model, was used to examine the time taken to withdraw a dog from training. Sex, breed, and other factors affected time to withdrawal. Bitches were withdrawn faster than dogs, Labradors were withdrawn faster, and Labrador × Golden Retrievers slower, than Golden Retriever × Labradors; and dogs not bred by Guide Dogs were withdrawn faster than those bred by Guide Dogs. Multistate modeling (MSM) can be used as an extension of survival analysis to incorporate more than two discrete events or states. Multistate models were used to investigate transitions between states of training to qualification as a guide dog or behavioral withdrawal, and from qualification as a guide dog to behavioral withdrawal. Sex, breed (with purebred Labradors and Golden retrievers differing from F1 crosses), and bred by Guide Dogs or not, effected movements between states. We postulate that survival analysis and MSM could be applied to a wide range of behavioral data and key examples are provided.

SUBMITTER: Asher L

PROVIDER: S-EPMC5532385 | biostudies-other | 2017

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:Colorectal cancer (CRC) has high incidence and is associated with high case fatality. In France, the 5-year survival, pooled across all cancer stages at diagnosis, ranges from 57% in men to 60% in women. About one third of patients diagnosed with CRC will develop a metachronous recurrence during the following years. It is of paramount importance to accurately identify factors associated with the increased risk of progression and death, in order to develop effective follow-up and treatment strategies. However, to accurately assess the role of patients’ specific characteristics in the progression of cancer several methodological challenges need to be overcome. One difficulty, common to prognostic studies of cancer, concerns the need to separate the effects of prognostic factors on different clinical endpoints, such as disease recurrence vs recurrence-free death. Another difficulty, encountered in prognostic studies, is that the cause of death is not available or not accurately coded. Yet, some patients are likely to die of causes not related to the disease of primary interest, especially in cancers with longer survival and in those that affect older subjects. Until recently, the existing statistical methodology was not able to simultaneously, deal with both difficulties, i.e. to account for (i) possibly different effects of prognostic factors on death vs recurrence, and (ii) unknown causes of death. However, this challenge has been addressed by the recent development of the Markov relative survival model (MRS) , which extends the Markov multi-state model to incorporate relative survival modelling. Simulations demonstrate that MRS is able to accurately estimate different effects of prognostic factors on the risk of each of several events, including separate effects on disease-specific vs other causes of death. To date, the MRS had not been applied in clinical or epidemiological studies. The aim of this study was to assess the potential advantages of the new multi-state relative survival model (MRS), proposed by Huszti et al. (2012), in a prognostic cancer study. To this end, we compared the MRS results with those obtained with two more conventional analyses, based on Cox’s proportional hazards model, and the multi-state Markov model proposed by Alioum and Commenges (2001). The three models were applied to explore the impact of prognostic factors on cancer-specific mortality and recurrence, in a large population-based French registry of colorectal cancer, with up to 25 years of follow-up.

Project description:Understanding of other's actions as goal-directed is considered a fundamental ability underlying cognitive and social development in human infants. A number of studies using the habituation-dishabituation paradigm have shown that the ability to discern intentional relations, in terms of goal-directedness of an action towards an object, appears around 5 months of age. The question of whether non-human species can perceive other's actions as goal-directed has been more controversial, however there is mounting evidence that at least some primates species do. Recently domestic dogs have been shown to be particularly sensitive to human communicative cues and more so in cooperative and intentional contexts. Furthermore, they have been shown to imitate selectively. Taken together these results suggest that dogs may perceive others' actions as goal-directed, however no study has investigated this issue directly. In the current study, adopting an infant habituation-dishabituation paradigm, we investigated whether dogs attribute intentions to an animate (a human) but not an inanimate (a black box) agent interacting with an object. Following an habituation phase in which the agent interacted always with one of two objects, two sets of 3 trials were presented: new side trials (in which the agent interacted with the same object as in the habituation trial but placed in a novel location) and new goal trials (in which the agent interacted with the other object placed in the old location). Dogs showed a similar pattern of response to that shown in infants, looking longer in the new goal than new side trials when they saw the human agent interact with the object. No such difference emerging with the inanimate agent (the black box). Results provide the first evidence that a non-primate species can perceive another individual's actions as goal-directed. We discuss results in terms of the prevailing mentalisitic and non-mentalistic hypotheses regarding goal-attribution.

Project description:ObjectiveIn oncology, extrapolation of clinical outcomes beyond trial duration is traditionally achieved by parametric survival analysis using population-level outcomes. This approach may not fully capture the benefit/risk profile of immunotherapies due to their unique mechanisms of action. We evaluated an alternative approach-dynamic modeling-to predict outcomes in patients with advanced renal cell carcinoma. We compared standard parametric fitting and dynamic modeling for survival estimation of nivolumab and everolimus using data from the phase III CheckMate 025 study.MethodsWe developed two statistical approaches to predict longer-term outcomes (progression, treatment discontinuation, and survival) for nivolumab and everolimus, then compared these predictions against follow-up clinical trial data to assess their proximity to observed outcomes. For the parametric survival analyses, we selected a probability distribution based on its fit to observed population-level outcomes at 14-month minimum follow-up and used it to predict longer-term outcomes. For dynamic modeling, we used a multivariate Cox regression based on patient-level data, which included risk scores, and probability and duration of response as predictors of longer-term outcomes. Both sets of predictions were compared against trial data with 26- and 38-month minimum follow-up.ResultsBoth statistical approaches led to comparable fits to observed trial data for median progression, discontinuation, and survival. However, beyond the trial duration, mean survival predictions differed substantially between methods for nivolumab (30.8 and 51.5 months), but not everolimus (27.2 and 29.8 months). Longer-term follow-up data from CheckMate 025 and phase I/II studies resembled dynamic model predictions for nivolumab.ConclusionsDynamic modeling can be a good alternative to parametric survival fitting for immunotherapies because it may help better capture the longer-term benefit/risk profile and support health-economic evaluations of immunotherapies.

Dataset Information

Application of Survival Analysis and Multistate Modeling to Understand Animal Behavior: Examples from Guide Dogs.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets