Hudson Christopher D, Huxley Jonathan N, Green Martin J
School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Sutton Bonington, Leicestershire, United Kingdom.
PLoS One. 2014 Aug 7;9(8):e103426. doi: 10.1371/journal.pone.0103426. eCollection 2014.
The ever-growing volume of data routinely collected and stored in everyday life presents researchers with a number of opportunities to gain insight and make predictions. This study aimed to demonstrate the usefulness in a specific clinical context of a simulation-based technique called probabilistic sensitivity analysis (PSA) in interpreting the results of a discrete time survival model based on a large dataset of routinely collected dairy herd management data. Data from 12,515 dairy cows (from 39 herds) were used to construct a multilevel discrete time survival model in which the outcome was the probability of a cow becoming pregnant during a given two day period of risk, and presence or absence of a recorded lameness event during various time frames relative to the risk period amongst the potential explanatory variables. A separate simulation model was then constructed to evaluate the wider clinical implications of the model results (i.e. the potential for a herd's incidence rate of lameness to influence its overall reproductive performance) using PSA. Although the discrete time survival analysis revealed some relatively large associations between lameness events and risk of pregnancy (for example, occurrence of a lameness case within 14 days of a risk period was associated with a 25% reduction in the risk of the cow becoming pregnant during that risk period), PSA revealed that, when viewed in the context of a realistic clinical situation, a herd's lameness incidence rate is highly unlikely to influence its overall reproductive performance to a meaningful extent in the vast majority of situations. Construction of a simulation model within a PSA framework proved to be a very useful additional step to aid contextualisation of the results from a discrete time survival model, especially where the research is designed to guide on-farm management decisions at population (i.e. herd) rather than individual level.
日常生活中常规收集和存储的数据量不断增长，为研究人员提供了许多获得见解和进行预测的机会。本研究旨在证明一种名为概率敏感性分析（PSA）的基于模拟的技术在特定临床背景下的有用性，该技术用于解释基于常规收集的奶牛群管理大数据集的离散时间生存模型的结果。来自12515头奶牛（来自39个牛群）的数据被用于构建一个多层次离散时间生存模型，其中结果是奶牛在给定的两天风险期内怀孕的概率，以及在相对于风险期的不同时间框架内记录的跛行事件的有无作为潜在解释变量。然后构建了一个单独的模拟模型，使用PSA来评估模型结果的更广泛临床意义（即牛群跛行发病率对其整体繁殖性能的潜在影响）。尽管离散时间生存分析揭示了跛行事件与怀孕风险之间存在一些相对较大的关联（例如，在风险期14天内发生跛行病例与该风险期内奶牛怀孕风险降低25%相关），但PSA显示，在现实临床情况的背景下，在绝大多数情况下，牛群的跛行发病率极不可能对其整体繁殖性能产生有意义的影响。在PSA框架内构建模拟模型被证明是一个非常有用的额外步骤，有助于对离散时间生存模型的结果进行情境化，特别是在研究旨在指导群体（即牛群）而非个体层面的农场管理决策时。
