Survival analysis as a basis to test hypotheses when using quantitative ordinal scale disease severity data

Authors: CHIANG, KUO-SZU - National Chung-Hsing University; CHANG, Y.M. - Tunghai University; LIU, H.I. - Ming Chi University Of Technology; LEE, J.Y. - Feng Chia University; JARROUDI, MOUSSA - Universite De Liege; Bock, Clive

Submitted to: Phytopathology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/16/2023
Publication Date: 2/19/2024
Citation: Chiang, K., Chang, Y., Liu, H., Lee, J., Jarroudi, M., Bock, C.H. 2024. Survival analysis as a basis to test hypotheses when using quantitative ordinal scale disease severity data. Phytopathology. 114(2). https://doi.org/10.1094/PHYTO-02-23-0055-R.
DOI: https://doi.org/10.1094/PHYTO-02-23-0055-R

Interpretive Summary: Disease severity in plant pathology is often measured by the amount of a plant or plant part that have disease symptoms. Various numeric scales may be used, one type being a "quantitative ordinal scale" (QOS) that divides the percentage scale into a predetermined number of classes, each describing consecutive intervals on the percentage scale. Various methods are available to analyze these class-based data, some of which may lack precision. As the data is ordinal, and is only an estimate of the true value (binned in a class), it can be considered "interval-censored", meaning that we have limited knowledge of the value but, it is not exact. Such uncertainty is termed "censoring" and techniques to address censoring are available. One such is survival analysis (SA). In a simulation study, we tested SA using QOS to other methods of analysis used on these data. Other methods required an up to 400% increase in sample size to achieve the same power as the SA method. Based on these findings, we conclude that SA is a valuable method for enhancing the power of hypothesis testing when analyzing QOS disease severity data. Future research should investigate the wider use of survival analysis techniques in plant pathology and their potential applications in the discipline.

Technical 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 generally outperforms the mid-point conversion with data are 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. However, as the mean severity increases, fewer additional samples are needed (approximately an additional 100%) regardless of the assessment method used. Based on these findings, we conclude that SA is a valuable method for enhancing the power of hypothesis testing when analyzing QOS severity data. Future research should investigate the wider use of survival analysis techniques in plant pathology and their potential applications in the discipline.