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Classification and Regression Tree and Computer Adaptive Testing in Cardiac Rehabilitation: Instrument Validation Study.

ABSTRACT: BACKGROUND:There is a need for shorter-length assessments that capture patient questionnaire data while attaining high data quality without an undue response burden on patients. Computerized adaptive testing (CAT) and classification and regression tree (CART) methods have the potential to meet these needs and can offer attractive options to shorten questionnaire lengths. OBJECTIVE:The objective of this study was to test whether CAT or CART was best suited to reduce the number of questionnaire items in multiple domains (eg, anxiety, depression, quality of life, and social support) used for a needs assessment procedure (NAP) within the field of cardiac rehabilitation (CR) without the loss of data quality. METHODS:NAP data of 2837 CR patients from a multicenter Cardiac Rehabilitation Decision Support System (CARDSS) Web-based program was used. Patients used a Web-based portal, MyCARDSS, to provide their data. CAT and CART were assessed based on their performances in shortening the NAP procedure and in terms of sensitivity and specificity. RESULTS:With CAT and CART, an overall reduction of 36% and 72% of NAP questionnaire length, respectively, was achieved, with a mean sensitivity and specificity of 0.765 and 0.817 for CAT, 0.777 and 0.877 for classification trees, and 0.743 and 0.40 for regression trees, respectively. CONCLUSIONS:Both CAT and CART can be used to shorten the questionnaires of the NAP used within the field of CR. CART, however, showed the best performance, with a twice as large overall decrease in the number of questionnaire items of the NAP compared to CAT and the highest sensitivity and specificity. To our knowledge, our study is the first to assess the differences in performance between CAT and CART for shortening questionnaire lengths. Future research should consider administering varied assessments of patients over time to monitor their progress in multiple domains. For CR professionals, CART integrated with MyCARDSS would provide a feedback loop that informs the rehabilitation progress of their patients by providing real-time patient measurements.

SUBMITTER: Peute L

PROVIDER: S-EPMC7055848 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Classification and Regression Tree and Computer Adaptive Testing in Cardiac Rehabilitation: Instrument Validation Study.

Peute Linda L Scheeve Thom T Jaspers Monique M

Journal of medical Internet research 20200130 1

<h4>Background</h4>There is a need for shorter-length assessments that capture patient questionnaire data while attaining high data quality without an undue response burden on patients. Computerized adaptive testing (CAT) and classification and regression tree (CART) methods have the potential to meet these needs and can offer attractive options to shorten questionnaire lengths.<h4>Objective</h4>The objective of this study was to test whether CAT or CART was best suited to reduce the number of q ...[more]

PMID: 32012065

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Project description:BackgroundIn the typical setting of gene-selection problems from high-dimensional data, e.g., gene expression data from microarray or next-generation sequencing-based technologies, an enormous volume of high-throughput data is generated, and there is often a need for a simple, computationally-inexpensive, non-parametric screening procedure than can quickly and accurately find a low-dimensional variable subset that preserves biological information from the original very high-dimensional data (dimension p > 40,000). This is in contrast to the very sophisticated variable selection methods that are computationally expensive, need pre-processing routines, and often require calibration of priors.ResultsWe present a tree-based sequential CART (S-CART) approach to variable selection in the binary classification setting and compare it against the more sophisticated procedures using simulated and real biological data. In simulated data, we analyze S-CART performance versus (i) a random forest (RF), (ii) a fully-parametric Bayesian stochastic search variable selection (SSVS), and (iii) the moderated t-test statistic from the LIMMA package in R. The simulation study is based on a hierarchical Bayesian model, where dataset dimensionality, percentage of significant variables, and substructure via dependency vary. Selection efficacy is measured through false-discovery and missed-discovery rates. In all scenarios, the S-CART method is seen to consistently outperform SSVS and RF in both speed and detection accuracy. We demonstrate the utility of the S-CART technique both on simulated data and in a control-treatment mouse study. We show that the network analysis based on the S-CART-selected gene subset in essence recapitulates the biological findings of the study using only a fraction of the original set of genes considered in the study's analysis.ConclusionsThe relatively simple-minded gene selection algorithms like S-CART may often in practical circumstances be preferred over much more sophisticated ones. The advantage of the "greedy" selection methods utilized by S-CART and the likes is that they scale well with the problem size and require virtually no tuning or training while remaining efficient in extracting the relevant information from microarray-like datasets containing large number of redundant or irrelevant variables.AvailabilityThe MATLAB 7.4b code for the S-CART implementation is available for download from https://neyman.mcg.edu/posts/scart.zip.

Project description:Praziquantel (PZQ) is an effective chemotherapy for schistosomiasis mansoni and a mainstay for its control and potential elimination. However, it does not prevent against reinfection, which can occur rapidly in areas with active transmission. A guide to ranking the risk factors for Schistosoma mansoni reinfection would greatly contribute to prioritizing resources and focusing prevention and control measures to prevent rapid reinfection. The objective of the current study was to explore the relationship among the socioeconomic, demographic, and epidemiological factors that can influence reinfection by S. mansoni one year after successful treatment with PZQ in school-aged children in Northeastern Minas Gerais state Brazil. Parasitological, socioeconomic, demographic, and water contact information were surveyed in 506 S. mansoni-infected individuals, aged 6 to 15 years, resident in these endemic areas. Eligible individuals were treated with PZQ until they were determined to be negative by the absence of S. mansoni eggs in the feces on two consecutive days of Kato-Katz fecal thick smear. These individuals were surveyed again 12 months from the date of successful treatment with PZQ. A classification and regression tree modeling (CART) was then used to explore the relationship between socioeconomic, demographic, and epidemiological variables and their reinfection status. The most important risk factor identified for S. mansoni reinfection was their "heavy" infection at baseline. Additional analyses, excluding heavy infection status, showed that lower socioeconomic status and a lower level of education of the household head were also most important risk factors for S. mansoni reinfection. Our results provide an important contribution toward the control and possible elimination of schistosomiasis by identifying three major risk factors that can be used for targeted treatment and monitoring of reinfection. We suggest that control measures that target heavily infected children in the most economically disadvantaged households would be most beneficial to maintain the success of mass chemotherapy campaigns.

Project description:ObjectivesTo build an item response theory-based computer adaptive test (CAT) for balance from 3 traditional, fixed-form balance measures: Berg Balance Scale (BBS), Performance-Oriented Mobility Assessment (POMA), and dynamic gait index (DGI); and to examine whether the CAT's psychometric performance exceeded that of individual measures.DesignSecondary analysis combining 2 existing datasets.SettingCommunity based.ParticipantsCommunity-dwelling older adults (N=187) who were aged ≥65 years (mean age, 75.2±6.8y, 69% women).InterventionsNot applicable.Main outcome measuresThe BBS, POMA, and DGI items were compiled into an initial 38-item bank. The Rasch partial credit model was used for final item bank calibration. CAT simulations were conducted to identify the ideal CAT. CAT score accuracy, reliability, floor and ceiling effects, and validity were examined. Floor and ceiling effects and validity of the CAT and individual measures were compared.ResultsA 23-item bank met model expectations. A 10-item CAT was selected, showing a very strong association with full item bank scores (r=.97) and good overall reliability (.78). Reliability was better in low- to midbalance ranges as a result of better item targeting to balance ability when compared with the highest balance ranges. No floor effect was noted. The CAT ceiling effect (11.2%) was significantly lower than the POMA (40.1%) and DGI (40.3%) ceiling effects (P<.001 per comparison). The CAT outperformed individual measures, being the only test to discriminate between fallers and nonfallers (P=.007), and being the strongest predictor of self-reported function.ConclusionsThe balance CAT showed excellent accuracy, good overall reliability, and excellent validity compared with individual measures, being the only measure to discriminate between fallers and nonfallers. Prospective examination, particularly in low-functioning older adults and clinical populations with balance deficits, is recommended. Development of an improved CAT based on an expanded item bank containing higher difficulty items is also recommended.

Dataset Information

Classification and Regression Tree and Computer Adaptive Testing in Cardiac Rehabilitation: Instrument Validation Study.

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Classification and Regression Tree and Computer Adaptive Testing in Cardiac Rehabilitation: Instrument Validation Study.

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