Survival Analysis as a Basis to Test Hypotheses When Using Quantitative Ordinal Scale Disease Severity Data

Abstract

Disease severity in plant pathology is often measured by the amount of a plant or plant part that exhibits disease symptoms. This is typically assessed using a numerical scale, which allows for a standardized, convenient, and quick method of rating. These scales, known as "quantitative ordinal scales" (QOS), divide the percentage scale into a predetermined number of intervals. There are various ways to analyze this ordinal data, with traditional methods involving the use of mid-point conversion to represent the interval. However, this may not be precise enough, as it is only an estimate of the true value. In this case, the data may be considered "interval-censored," meaning that we have some knowledge of the value but not an exact measurement. This type of uncertainty is known as "censoring" and techniques that address censoring, such as survival analysis (SA), use all available information and account for this uncertainty. To investigate the pros and cons of using SA with QOS measurements, we conducted a simulation based on three pathosystems. The results showed that SA almost always outperformed the mid-point conversion with data analyzed using a t-test, particularly when data was not normally distributed. The mid-point conversion is currently a standard procedure. In certain cases, the mid-point approach required a 400% increase in sample size in order to achieve the same power as the SA method. We conclude that SA is a valuable method for enhancing the power of hypothesis testing when analyzing QOS severity data.