A discussion on disease severity index values. Part II: using the disease severity index for null hypothesis testing

Authors: CHIANG, KUO-SZU - National Chung-Hsing University; LIU, HSUAN - National Chung-Hsing University; TSAI, J.W. - National Chung-Hsing University; TSAI, J.R. - Academia Sinica; Bock, Clive

Submitted to: Annals of Applied Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/23/2017
Publication Date: 10/18/2017
Citation: Chiang, K., Liu, H.I., Tsai, J., Tsai, J., Bock, C.H. 2017. A discussion on disease severity index values. Part II: using the disease severity index for null hypothesis testing. Annals of Applied Biology. 171(3):490-504. https://doi.org/10.1111/aab.12396.
DOI: https://doi.org/10.1111/aab.12396

Interpretive Summary: Disease severity indices (DSI) are used to summarize disease severity and is most often used with data based on a special type of ordinal scale comprising a series of consecutive ranges of defined numeric intervals (generally based on the percent area of symptoms). Plant pathologists and other professionals use this data to calculate DSIs to compare treatments. We explored the effects of different ordinal scales having either equal or unequal intervals and using either the score or midpoint value of the interval on hypothesis testing for treatment comparison. Using simulation and meta-analysis we found that using quantitative ordinal rating grades or the midpoint for the ranges of disease severity yielded very comparable results with respect to the power of the hypothesis tests. However, the principal factor determining the power of the hypothesis test is the nature of the intervals, not the selection of values for ordinal scale intervals (i.e., not the mid-point or ordinal grade). Furthermore, although using the percent scale is preferable, the results of this study provide a framework for developing improved research methods where the use of ordinal scales in conjunction with a DSI is either preferred or a necessity for comparing disease severities.

Technical Abstract: A disease severity index (DSI) is a single number for summarizing a large amount of information on disease severity. It has been used to indicate the performance of a cultivar in regard to disease resistance at a particular location, to relate disease severity to yield loss, to determine the effectiveness of fungicides etc. The DSI has most often been used with data based on a special type of ordinal scale comprising a series of consecutive ranges of defined numeric intervals, generally based on the percent area of symptoms presenting on the specimen(s). Plant pathologists and other professionals use such ordinal scale data in conjunction with a DSI (%) for treatment comparisons. The objective of this work is to explore the effects both of different scales (i.e., those having equal or unequal classes, or different widths of intervals) and of the selection of values for scale intervals (i.e., the ordinal grade for the category or the midpoint value of the interval) on the null hypothesis test for the treatment comparison. A two-stage simulation approach was employed to approximate the real mechanisms governing the disease-severity sampling design. Subsequently, a meta-analysis was performed to compare the effects of two treatments, which demonstrated that using quantitative ordinal rating grades or the midpoint conversion for the ranges of disease severity yielded very comparable results with respect to the power of hypothesis testing. However, the principal factor determining the power of the hypothesis test is the nature of the intervals, not the selection of values for ordinal scale intervals (i.e., not the mid-point or ordinal grade). Although using the percent scale is always preferable, the results of this study provide a framework for developing improved research methods where the use of ordinal scales in conjunction with a DSI is either preferred or a necessity for comparing disease severities.