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Abstract:
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Time-to-event outcomes are common in agricultural sciences. For example, how long it takes until flowering is one of the critical research questions for plant scientists. Despite the popularity of survival analysis in medical studies for past decades, application in agricultural sciences has been less discussed. The main strength of this method is their ability to handle missing data over time, namely, right-censored data. Even well-designed experimental data may encounter drop-out, which can be ignored in methods such as analysis of variance (t-test). Survival models also tend to have greater statistical power to detect a significant treatment effect than methods for binary response such as logistic regression. The goal of this study is to review basic concepts of survival analysis, importantly to discuss the benefit of this method when it comes to agricultural research applications. Cox regression and alternative regression models are compared to demonstrate the advantage (or disadvantage) of each method through both simulation study and real data examples.
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