scholarly journals Quantitative Ordinal Scale Estimates of Plant Disease Severity: Comparing Treatments Using a Proportional Odds Model

2020 ◽  
Vol 110 (4) ◽  
pp. 734-743 ◽  
Author(s):  
K. S. Chiang ◽  
H. I. Liu ◽  
Y. L. Chen ◽  
M. El Jarroudi ◽  
C. H. Bock
Keyword(s):  
Disease Severity ◽  
Hypothesis Test ◽  
Standard Procedure ◽  
Simulation Method ◽  
Superior Performance ◽  
Ordinal Scale ◽  
Proportional Odds ◽  
Ordinal Scales ◽  
Proportional Odds Models ◽  
Percent Area

Studies in plant pathology, agronomy, and plant breeding requiring disease severity assessment often use quantitative ordinal scales (i.e., a special type of ordinal scale that uses defined numeric ranges); a frequently used example of such a scale is the Horsfall-Barratt scale. Parametric proportional odds models (POMs) may be used to analyze the ratings obtained from quantitative ordinal scales directly, without converting ratings to percent area affected using range midpoints of such scales (currently a standard procedure). Our aim was to evaluate the performance of the POM for comparing treatments using ordinal estimates of disease severity relative to two alternatives, the midpoint conversions (MCs) and nearest percent estimates (NPEs). A simulation method was implemented and the parameters of the simulation estimated using actual disease severity data from the field. The criterion for comparison of the three approaches was the power of the hypothesis test (the probability to reject the null hypothesis when it is false). Most often, NPEs had superior performance. The performance of the POM was never inferior to using the MC at severity <40%. Especially at low disease severity (≤10%), the POM was superior to using the MC method. Thus, for early onset of disease or for comparing treatments with severities <40%, the POM is preferable for analyzing disease severity data based on quantitative ordinal scales when comparing treatments and at severities >40% is equivalent to other methods.

scholarly journals Proportional odds model applied to mapping of disease resistance genes in plants

2000 ◽  
Vol 23 (1) ◽  
pp. 223-227 ◽  
Author(s):  
Maria Helena Spyrides-Cunha ◽  
Clarice G.B. Demétrio ◽  
Luis E.A. Camargo
Keyword(s):  
Disease Resistance ◽  
Disease Severity ◽  
Resistance Genes ◽  
Proportional Odds Model ◽  
Disease Resistance Genes ◽  
Ordinal Scale ◽  
Proportional Odds ◽  
Response Variable ◽  
Resistance Qtl ◽  
Single Marker

Molecular markers have been used extensively to map quantitative trait loci (QTL) controlling disease resistance in plants. Mapping is usually done by establishing a statistical association between molecular marker genotypes and quantitative variations in disease resistance. However, most statistical approaches require a continuous distribution of the response variable, a requirement not always met since evaluation of disease resistance is often done using visual ratings based on an ordinal scale of disease severity. This paper discusses the application of the proportional odds model to the mapping of disease resistance genes in plants amenable to expression as ordinal data. The model was used to map two resistance QTL of maize to Puccinia sorghi. The microsatellite markers bngl166 and bngl669, located on chromosomes 2 and 8, respectively, were used to genotype F2 individuals from a segregating population. Genotypes at each marker locus were then compared by assessing disease severity in F3 plants derived from the selfing of each genotyped F2 plant based on an ordinal scale severity. The residual deviance and the chi-square score statistic indicated a good fit of the model to the data and the odds had a constant proportionality at each threshold. Single-marker analyses detected significant differences among marker genotypes at both marker loci, indicating that these markers were linked to disease resistance QTL. The inclusion of the interaction term after single-marker analysis provided strong evidence of an epistatic interaction between the two QTL. These results indicate that the proportional odds model can be used as an alternative to traditional methods in cases where the response variable consists of an ordinal scale, thus eliminating the problems of heterocedasticity, non-linearity, and the non-normality of residuals often associated with this type of data.

scholarly journals Effects of Quantitative Ordinal Scale Design on the Accuracy of Estimates of Mean Disease Severity

Agronomy ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 565 ◽  
Author(s):  
Hung I. Liu ◽  
Jia Ren Tsai ◽  
Wen Hsin Chung ◽  
Clive H. Bock ◽  
Kuo Szu Chiang
Keyword(s):  
Disease Severity ◽  
Plant Disease ◽  
Mean Squared Error ◽  
Simulation Method ◽  
Ordinal Scale ◽  
Disease Assessment ◽  
The Mean ◽  
Scale Design ◽  
Number Of Classes ◽  
Assessment Scales

Estimates of plant disease severity are crucial to various practical and research-related needs in agriculture. Ordinal scales are used for categorizing severity into ordered classes. Certain characteristics of quantitative ordinal scale design may affect the accuracy of the specimen estimates and, consequently, affect the accuracy of the resulting mean disease severity for the sample. The aim of this study was to compare mean estimates based on various quantitative ordinal scale designs to the nearest percent estimates, and to investigate the effect of the number of classes in an ordinal scale on the accuracy of that mean. A simulation method was employed. The criterion for comparison was the mean squared error of the mean disease severity for each of the different scale designs used. The results indicate that scales with seven or more classes are preferable when actual mean disease severities of 50% or less are involved. Moreover, use of an amended 10% quantitative ordinal scale with additional classes at low severities resulted in a more accurate mean severity compared to most other scale designs at most mean disease severities. To further verify the simulation results, estimates of mean severity of pear scab on samples of leaves from orchards in Taiwan demonstrated similar results. These observations contribute to the development of plant disease assessment scales to improve the accuracy of estimates of mean disease severities.

scholarly journals Understanding the ramifications of quantitative ordinal scales on accuracy of estimates of disease severity and data analysis in plant pathology

2021 ◽  
Author(s):  
Kuo-Szu Chiang ◽  
Clive H. Bock
Keyword(s):  
Plant Pathology ◽  
Disease Severity ◽  
Statistical Power ◽  
Plant Diseases ◽  
Ordinal Scale ◽  
Disease Assessment ◽  
Ordinal Scales ◽  
Scale Design ◽  
Data Design ◽  
Scale Data

AbstractThe severity of plant diseases, traditionally defined as the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases but is prone to error. Plant pathologists face many situations in which the measurement by nearest percent estimates (NPEs) of disease severity is time-consuming or impractical. Moreover, rater NPEs of disease severity are notoriously variable. Therefore, NPEs of disease may be of questionable value if severity cannot be determined accurately and reliably. In such situations, researchers have often used a quantitative ordinal scale of measurement—often alleging the time saved, and the ease with which the scale can be learned. Because quantitative ordinal disease scales lack the resolution of the 0 to 100% scale, they are inherently less accurate. We contend that scale design and structure have ramifications for the resulting analysis of data from the ordinal scale data. To minimize inaccuracy and ensure that there is equivalent statistical power when using quantitative ordinal scale data, design of the scales can be optimized for use in the discipline of plant pathology. In this review, we focus on the nature of quantitative ordinal scales used in plant disease assessment. Subsequently, their application and effects will be discussed. Finally, we will review how to optimize quantitative ordinal scales design to allow sufficient accuracy of estimation while maximizing power for hypothesis testing.

scholarly journals Mapping EQ-5D-3L from the Knee Injury and Osteoarthritis Outcome Score (KOOS)

2019 ◽  
Vol 29 (1) ◽  
pp. 265-274
Author(s):  
Ali Kiadaliri ◽  
Monica Hernández Alava ◽  
Ewa M. Roos ◽  
Martin Englund
Keyword(s):  
Disease Severity ◽  
Knee Injury ◽  
Cruciate Ligament ◽  
Simulated Data ◽  
Probit Model ◽  
Original Data ◽  
Cost Utility ◽  
Superior Performance ◽  
Outcome Score ◽  
Mapping Models

Abstract Purpose To develop a mapping model to estimate EQ-5D-3L from the Knee Injury and Osteoarthritis Outcome Score (KOOS). Methods The responses to EQ-5D-3L and KOOS questionnaires (n = 40,459 observations) were obtained from the Swedish National anterior cruciate ligament (ACL) Register for patients ≥ 18 years with the knee ACL injury. We used linear regression (LR) and beta-mixture (BM) for direct mapping and the generalized ordered probit model for response mapping (RM). We compared the distribution of the original data to the distributions of the data generated using the estimated models. Results Models with individual KOOS subscales performed better than those with the average of KOOS subscale scores (KOOS5, KOOS4). LR had the poorest performance overall and across the range of disease severity particularly at the extremes of the distribution of severity. Compared with the RM, the BM performed better across the entire range of disease severity except the most severe range (KOOS5 < 25). Moving from the most to the least disease severity was associated with 0.785 gain in the observed EQ-5D-3L. The corresponding value was 0.743, 0.772 and 0.782 for LR, BM and RM, respectively. LR generated simulated EQ-5D-3L values outside the feasible range. The distribution of simulated data generated from the BM model was almost identical to the original data. Conclusions We developed mapping models to estimate EQ-5D-3L from KOOS facilitating application of KOOS in cost-utility analyses. The BM showed superior performance for estimating EQ-5D-3L from KOOS. Further validation of the estimated models in different independent samples is warranted.

IDEMPOTENT UNINORMS ON FINITE ORDINAL SCALES

2009 ◽  
Vol 17 (01) ◽  
pp. 1-14 ◽  
Author(s):  
BERNARD DE BAETS ◽  
JÁNOS FODOR ◽  
DANIEL RUIZ-AGUILERA ◽  
JOAN TORRENS
Keyword(s):  
Ordinal Scale ◽  
Neutral Element ◽  
Ordinal Scales ◽  
One To One

In this paper we characterize all idempotent uninorms defined on a finite ordinal scale. It is proved that any such discrete idempotent uninorm is uniquely determined by a decreasing function from the set of scale elements not greater than the neutral element to the set of scale elements not smaller than the neutral element, and vice versa. Based on this one-to-one correspondence, the total number of discrete idempotent uninorms on a finite ordinal scale of n + 1 elements is equal to 2n.

A Two-stage Multilevel Randomized Response Technique With Proportional Odds Models and Missing Covariates

2020 ◽  
pp. 004912412091495
Author(s):  
Shu-Hui Hsieh ◽  
Shen-Ming Lee ◽  
Chin-Shang Li
Keyword(s):  
Missing At Random ◽  
Missing Covariates ◽  
Randomized Response ◽  
Personal Privacy ◽  
Proportional Odds ◽  
Two Stage ◽  
Inverse Probability ◽  
Wide Range ◽  
Proportional Odds Models ◽  
Income Data

Surveys of income are complicated by the sensitive nature of the topic. The problem researchers face is how to encourage participants to respond and to provide truthful responses in surveys. To correct biases induced by nonresponse or underreporting, we propose a two-stage multilevel randomized response (MRR) technique to investigate the true level of income and to protect personal privacy. For a wide range of applications, we present a proportional odds model for two-stage MRR data and apply inverse probability weighting and multiple imputation methods to deal with covariates on some subjects that are missing at random. A simulation study is conducted to investigate the effects of missing covariates and to evaluate the performance of the proposed methods. The practicality of the proposed methods is illustrated with the regular monthly income data collected in the Taiwan Social Change Survey. Furthermore, we provide an estimate of personal regular monthly mean income.

scholarly journals Predicting preference-based utility values using partial proportional odds models

2014 ◽  
Vol 7 (1) ◽  
Author(s):  
Roberta Ara ◽  
Ben Kearns ◽  
Ben A vanHout ◽  
John E Brazier
Keyword(s):  
Proportional Odds ◽  
Utility Values ◽  
Proportional Odds Models

Aerodynamic Design Modeling and Simulation by Developing RFID Middleware

2014 ◽  
Vol 543-547 ◽  
pp. 1467-1470
Author(s):  
Shao Song Wan ◽  
Jian Cao ◽  
Cong Yan
Keyword(s):  
Modeling And Simulation ◽  
Graphical User Interface ◽  
Control Process ◽  
Simulation Method ◽  
Parametric Modeling ◽  
Superior Performance ◽  
Aerodynamic Design ◽  
Simulation Test ◽  
Test Environment ◽  
Rfid Middleware

In this paper, a parametric modeling and simulation method is proposed, which provides a virtual simulation test environment for photovoltaic radar. A graphical user interface (GUI) is developed using VC++6.0. With this system, the integration of CAD/CFD application is achieved. Application examples show that, this system can significantly improve the efficiency of aerodynamic design. Finally, it is proved that the control process of developing RFID middleware simulator has superior performance and expected effect.

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