Survival analysis: a tool in the study of post-harvest diseases in peaches

Survival analysis is applied when the time until the occurrence of an event is of interest. Such data are routinely collected in plant diseases, although applications of the method are uncommon. The objective of this study was to use two studies on post-harvest diseases of peaches, considering two harvests together and the existence of random effect shared by fruits of a same tree, in order to describe the main techniques in survival analysis. The nonparametric Kaplan-Meier method, the log-rank test and the semi-parametric Cox's proportional hazards model were used to estimate the effect of cultivars and the number of days after full bloom on the survival to the brown rot symptom and the instantaneous risk of expressing it in two consecutive harvests. The joint analysis with baseline effect, varying between harvests, and the confirmation of the tree effect as a grouping factor with random effect were appropriate to interpret the phenomenon (disease) evaluated and can be important tools to replace or complement the conventional analysis, respecting the nature of the variable and the phenomenon.

生存分析适用于关注事件发生前时间跨度的研究场景。尽管该方法的实际应用尚不多见，但植物病害研究中常会采集此类数据。本研究以两项桃采后病害相关研究为对象，将两次采收的数据合并分析，并考虑同一植株果实所共有的随机效应，旨在阐述生存分析的核心技术方法。本研究采用非参数卡普兰-迈耶法（Kaplan-Meier method）、对数秩检验（log-rank test）以及半参数考克斯比例风险模型（Cox's proportional hazards model），在两次连续采收的场景下，估算品种以及盛花后天数对褐腐病（brown rot）症状发生存活率的影响，以及该症状出现的瞬时风险。针对采收间存在差异的基线效应开展联合分析，并验证以随机效应分组因子形式呈现的植株效应，能够合理阐释所研究的病害现象；该方法可在尊重变量与现象本质的前提下，作为替代或补充常规分析的重要工具。