| 【Objective】As for repeated-measured qualitative data on ischemia stroke, mostprevious studies focused on the cross-sectional analysis. However, the longitudinalanalysis for this kind of data is lacking, so are systematic and comprehensivestatistical methods. This paper, is intended to explore the syndrome variationregularity of ischemia stroke to provide new tactics for data analysis by researchingthe repeated-measured qualitative data on ischemia stroke, which can help unveil themechanism of ischemia stroke and guide clinical intervention. This paper also aims tosummarize relevant analytical methods for studying the variation regularity ofrepeated-measured qualitative data on other diseases. In addition, this paper conductsitem selection and optimization of the stroke syndrome factor evaluation scale. Theoverall purpose of this study is to provide a basis and support for statistical methodsfor clinical research and practice.【Content】This study performs a large-scale statistical analysis in two aspects:the syndrome variation regularity and item selection of the evaluation scale. To bespecific, this paper explores the variation regularity of six different syndromes ofischemia stroke (including the wind syndrome, fire syndrome, phlegm syndrome,blood stasis syndrome, qi-deficiency syndrome and yin-deficiency syndrome) andseeks influential factors based on continuous and dynamic data. The patients areclassified by the first syndromes and6syndromes at each time point respectively inorder to explore the syndrome variation regularity and the influential factors ofischemia stroke in different classes, help clinicians find the best time for medicalintervention, and explore analytical methods for variation regularity of repeated-measured qualitative data. In addition, this paper conducts item selection of the strokesyndrome factor evaluation scale which contains97symptoms, including dizziness,upset, fever, and discusses the application of item response theory in item selection ofthe evaluation scale.This study, focused on the drawbacks of variation regularity research onrepeated-measured qualitative data of ischemia stroke, conducts analysis of variationregularity of repeated-measured qualitative data on ischemia stroke, and appliesitem response theory to the item selection of stroke syndrome factor evaluation scalethrough programming language of SAS software and Mplus software.【Methods】The paper makes full use of various analytical methods of statistics,especially the generalized estimating equation, latent class analysis, latent transition analysis, item response theory. Based on the basic research conducted by DongzhimenHospital Affiliated to Beijing University of Chinese Medicine (“Research on theDiagnostic Criteria and the Efficacy Evaluation System of the symptoms of IschemicStroke”, the project supported by the National Basic Research Program of China,Grant No.:2003CB517102, and “Research on the key techniques in clinical efficacyevaluation that displays the therapeutic advantages of traditional Chinese medicine”,the project supported by the National Science and Technology Major Project,“Significant New Drug Creation”, Grant No.:2009ZX09502-028), we have come toregard the observation time as a classification variable and a continuous variablerespectively in the research on the syndrome variation regularity of ischemia stroke,and adopt the GEE to explore the influential factors and syndrome variation regularityof993ischemia stroke patients. Afterwards, according to the tactic of combiningLCA with GEE, and combining LTA with GEE, we consider the observation time aclassification variable and a continuous variable respectively to explore both theinfluential factors which cause different syndromes of ischemia stroke patients indifferent classes and the syndrome variation regularity. In the study of item selectionof stroke syndrome factor evaluation scale, the item response theory is adopted. Theitem information function, discrimination parameter, item characteristic curve andpractical theory of TCM are used to select items, eliminate items that are relativelyless informative, construct a logistic curve regression equation, compare the predictedprobabilities gained by adopting the two main parameter estimation methods of IRT(the maximum likelihood estimation method and the Bayes estimation method) withthe real frequency, and find the best parameter estimation method according to theresidual sum of squares and the correlation coefficient.【Results】The paper attempts to eliminate the drawbacks in the existinganalytical methods of repeated-measured qualitative data on ischemia stroke, proposesome tactics for research on syndrome variation regularity of ischemia stroke and itemselection of stroke syndrome factor evaluation scale, and present them in the mostsuitable way through programming by SAS and Mplus. To be specific, the results andmajor innovations of this paper are summarized as follows.(1) The inner correlation of subjects is taken into consideration, andrepeated-measured qualitative data on ischemia stroke are analyzed using GEE. Theobservation time is considered a classification variable which focuses on the influenceof each time point compared with the starting point (standing in the local point ofview) and a continuous variable which considers the variation regularity of occurrenceprobability with time (standing in the global point of view) respectively. GEE isadopted to explore the influential factors, discuss the syndrome variation regularity ofischemia stroke, and predict the chance that a patient may develop a specificsyndrome at each time point through a fitting equation. GEE can be adopted to analyze repeated-measured qualitative data on ischemia stroke which lacksindependence.(2) An analytical strategy for syndrome variation regularity of ischemia stroke ispresented based on the fact that the first syndromes are of great clinical significance.By combining LCA with GEE, we classify the patients based on the first6syndromesby LCA. According to the fitting indexes, patients are preferably classified into twogroups, with379and614patients in each group respectively. Afterwards, we considerthe observation time a classification variable and a continuous variable respectively,explore the influential factors and the syndrome variation regularity of ischemiastroke in different classes using GEE, and predict the prospects that a patient in eachclass may develop a certain syndrome at each time point through a fitting equation.The result shows that the syndrome incidence and the variation regularity of the twogroups are different.(3) An analytical strategy for syndrome variation regularity of ischemia stroke ispresented based on the6syndromes at each time point. By combining LTA with GEE,we classify the patients by the6syndromes at each time point using LTA. The fittingindexes show that it is the best when seven classes are classified, with498,251,87,63,52,26and16patients in each class respectively. This paper regards theobservation time as a classification variable and a continuous variable respectively,afterwards, explore the influential factors, the syndrome variation regularity and thetransition probability by analyzing the two classes that account for the largestproportions using GEE, and predict the probability that a patient in each class maydevelop a particular syndrome at each time point through a fitting equation. The resultsuggests that the syndrome incidence and the variation regularity in each class vary.(4) The IRT is adopted to acquire difficulty parameter, discrimination parameter,information function, test scores of each syndrome and ability parameter estimates ofpatients, and to draw the item characteristic curve and the test characteristic curve forstroke syndrome factor evaluation scale. Items are selected by item informationfunction, discrimination parameter, item characteristic curve and practical theory ofTCM. As a result, eight items that provide little information (including f6, f13, h24,h25, t10, q18, y11and y12) are eliminated, which account for8.25%of the total. Alogistic curve regression equation is constructed using item parameters so that thechance of each patient having each item can be obtained by inputting the abilityparameter estimate of each patient. Afterwards, the predicted probabilities acquired byadopting the two most-frequently-used parameter estimation methods of IRT (themaximum likelihood estimation method and the Bayes estimation method) arecompared with the real frequency. According to the residual sum of squares and thecorrelation coefficient, we are able to draw the conclusion that the results of the abovetwo methods are consistent, and the MLE method is marginally better than the Bayes method.【Conclusions】The paper explores repeated-measured qualitative data ofischemia stroke and achieves some desirable results by resolving the issue of sampleclassification for qualitative data and repeatedly-measured quantitative data withmultiple response variables that are syndromes. The inner correlation of subjects istaken into consideration in this study so that the statistical inference is highly reliable.What’s more, it lays the foundation for studying the variation regularity ofrepeated-measured qualitative data of other diseases. This paper discusses thevariation regularity of each class after sample clustering, which helps guide clinicalintervention at different stages for patients suffering from ischemia stroke in differentclasses, and improve the curative effect. In addition, this paper uses the item responsetheory, which is mainly used in the field of psychological measurement presently, initem selection of stroke syndrome factor evaluation scale. It is proved that the result isfeasible, which indicates that the application of IRT is extended. |
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