Missing Responses Research Articles

Validation of the Japanese version of the Body Image Scale for bladder cancer patients

The Body Image Scale (BIS) is a 10-item tool that measures the body images of cancer patients. This study aims to validate the Japanese version of the BIS for bladder cancer patients. A multicenter cross-sectional survey was used to identify the participants, which included Japanese bladder cancer patients. The percentage of missing responses, internal consistency, and known-group validity were evaluated. The correlations between the BIS and two HRQOL instruments (the Bladder Cancer Index and the SF-12) were assessed to determine convergent validity. Among 397 patients, 221 patients were treated by transurethral resection of bladder tumor (TURBT) endoscopically, 49 patients underwent cystectomy with neobladder, and 127 patients underwent cystectomy involving stoma. The percentage of missing responses in the BIS ranged from 8.1 to 15.6%. Cronbach's α coefficient was 0.924. Higher BIS scores indicate negative body image, and the median BIS score for patients with native bladders after TURBT (0.5) was significantly lower than those of the patients with neobladder (4.0) and stoma formation (7.0), which indicated the discriminatory ability of the BIS. Each domain of the Bladder Cancer Index and the role summary score of the SF-12 correlated to the BIS scores, which confirmed the convergent validity. A range of BIS scores were identified among patients who reported similar physical summary scores and mental summary scores of the SF-12. This study confirmed the reliability and validity of the Japanese version of the BIS for bladder cancer patients.

Evaluating the Effects of Missing Data Handling Methods on Scale Linking Accuracy.

For large-scale assessments, data are often collected with missing responses. Despite the wide use of item response theory (IRT) in many testing programs, however, the existing literature offers little insight into the effectiveness of various approaches to handling missing responses in the context of scale linking. Scale linking is commonly used in large-scale assessments to maintain scale comparability over multiple forms of a test. Under a common-item nonequivalent group design (CINEG), missing data that occur to common items potentially influence the linking coefficients and, consequently, may affect scale comparability, test validity, and reliability. The objective of this study was to evaluate the effect of six missing data handling approaches, including listwise deletion (LWD), treating missing data as incorrect responses (IN), corrected item mean imputation (CM), imputing with a response function (RF), multiple imputation (MI), and full information likelihood information (FIML), on IRT scale linking accuracy when missing data occur to common items. Under a set of simulation conditions, the relative performance of the six missing data treatment methods under two missing mechanisms was explored. Results showed that RF, MI, and FIML produced less errors for conducting scale linking whereas LWD was associated with the most errors regardless of various testing conditions.

Development of the First Health-Related Quality of Life Questionnaires in Arabic for Women With Polycystic Ovary Syndrome (Part III): Scoring System Interpretation.

Our team developed the first highly reliable, validated, easily comprehensible, and self-administered polycystic ovary syndrome quality of life (PCOSQOL)-42 and PCOSQOL-47 questionnaires for unmarried and married women with polycystic ovary syndrome (PCOS), respectively. Using such scales needs a scoring system that covers the responses to each item per domain and overcomes the missing responses. we developed a scoring system for PCOSQOL-42 and PCOSQOL-47 to interpret the items' responses at any similar population. The scoring was inspired by the 5-point Likert scale that was used during the creation of PCOSQOL-42 and PCOSQOL-47, where each item represents the woman's experience in the last two weeks before seeking consultation, i.e., Never=5 or no effect, on the health-related quality of life (HRQOL), (Seldom=4), (Quite often=3), (Very often=2), and (Always=1 or the maximal effect on the QOL). The sum of the total points in each item per domain was evaluated. Then we divided the results by the number of the items that had been scored only to get the final domain score as a (mean ± SD). The ultimate or final score per the questionnaire was gained from the sum of individual domain scores divided by the number of domains that had been evaluated. Ultimately, the first interval values (from 1 to < 3 points) represent marked effects on HRQOL; second interval values (from 3 to < 4 points) represent the marginal effect on HRQOL; third interval values (from 4 to < 5 points) represent the minimal effect on HRQOL; fourth interval (5 points) represents no effect on HRQOL. The lower the score, the greater the negative impact on HRQOL. Although all domains in both questionnaires showed a marked reduction in HRQOL, women in either cohort were more concerned with their body image dissatisfaction and psychological distress induced by PCOS than their reproductive concerns. The mean score calculated for the questionnaire had a greater negative impact than the emotions subscale and was similar to the subscale for infertility. All mean points per item and domain values indicate a marked effect (< 3 points) on QOL using PCOSQOL-42 and PCOSQOL-47. All values referred to a marked reduction in PCOSQOL-47 points, whether item or domain-wise. The current scoring system provides an easy way to interpret the responses in both questionnaires and overcome the missing responses in any item per domain. There was a marked impact on all domains of HRQOL using both questionnaires, with a special impact on body image and psychological concerns. The responses of women in either cohort to the sexual and reproductive items were extremely high, reflecting the scope of this problem in the life of women with PCOS.

RAMP: response-aware multi-task learning with contrastive regularization for cancer drug response prediction.

The accurate prediction of cancer drug sensitivity according to the multiomics profiles of individual patients is crucial for precision cancer medicine. However, the development of prediction models has been challenged by the complex crosstalk of input features and the resistance-dominant drug response information contained in public databases. In this study, we propose a novel multidrug response prediction framework, response-aware multitask prediction (RAMP), via a Bayesian neural network and restrict it by soft-supervised contrastive regularization. To utilize network embedding vectors as representation learning features for heterogeneous networks, we harness response-aware negative sampling, which applies cell line-drug response information to the training of network embeddings. RAMP overcomes the prediction accuracy limitation induced by the imbalance of trained response data based on the comprehensive selection and utilization of drug response features. When trained on the Genomics of Drug Sensitivity in Cancer dataset, RAMP achieved an area under the receiver operating characteristic curve > 89%, an area under the precision-recall curve > 59% and an $\textrm{F}_1$ score > 52% and outperformed previously developed methods on both balanced and imbalanced datasets. Furthermore, RAMP predicted many missing drug responses that were not included in the public databases. Our results showed that RAMP will be suitable for the high-throughput prediction of cancer drug sensitivity and will be useful for guiding cancer drug selection processes. The Python implementation for RAMP is available at https://github.com/hvcl/RAMP.

Powerful nonparametric checks for parametric single‐index quantile models with missing responses

This paper considers the lack‐of‐fit test of parametric single‐index quantile models when the response variable is missing at random. The model's coefficients are estimated by an estimation method suitable for the quantile regression coefficients of the missing data. Simultaneously, an algorithm for solving the central subspace of the multidimensional quantile regression model with missing responses is proposed. Based on the central quantile regression subspace, we construct two‐dimensional reduction adaptive‐to‐model test statistics suitable for randomly missing response variables to avoid the curse of dimensionality. Under the null hypothesis and local alternative hypothesis, the asymptotic properties of the test statistics are obtained. The proposed testing methods are shown to be consistent and able to detect local alternative hypothetical models converging to the null model at the rate of order . A consistent bootstrap method is proposed to determine the critical values, and its asymptotic properties are established. The simulation results show that the proposed method is superior to existing methods in terms of both empirical size and power in the case of multidimensional and even high‐dimensional covariables. The ACTG Protocol 175 data set is analyzed to demonstrate the application of the testing procedures. Supplementary materials for this article are available online.

Physiological recovery of tree water relations upon drought release-response of mature beech and spruce after five years of recurrent summer drought.

As climate change progresses, the frequency and duration of drought stress events are increasing. While the mechanisms of drought acclimation of trees has received considerable attention in recent years, the recovery processes remain critically understudied. We used a unique throughfall exclusion experiment in a mature temperate mixed forest consisting of the more isohydric Norway spruce and more anisohydric European beech, to study the recovery and resilience after drought release. We hypothesized that pre-dawn water potential (ΨPD) of both species will increase within 1 day after watering, while the recovery of stomatal conductance (gs) and the reversal of osmoregulation will be significantly delayed in the more isohydric spruce. Furthermore, we hypothesized that the xylem sap flow density (udaily) will not fully recover within the growing season due to the strong drought impact. After 5 years of summer drought, trees showed significantly reduced ΨPD, udaily and increased osmoregulation in leaves, but only isohydric spruce displayed increased leaf abscisic acid concentrations. In line with our hypothesis, ΨPD and gs recovered within 1 day in beech. Conversely, isohydric spruce showed delayed increases in ΨPD and gs. The delay in recovery of spruce was partially related to the replenishment of the stem water reservoir, as indicated by the missing response of udaily at the crown base compared with DBH level upon watering. However, udaily fully recovered only in the next growing season for beech and was still reduced in spruce. Nevertheless, in both species, osmotic acclimations of leaves were reversed within several weeks. While both species displayed full resilience to drought stress in water-related physiology, the recovery time was in several cases, e.g., udaily, ΨPD and gs, shorter for beech than for spruce. With future increases in the frequency of drought events under ongoing climate change, tree species that recover more quickly will be favored.

Identifying psychiatric diagnosis from missing mood data through the use of log-signature features.

The availability of mobile technologies has enabled the efficient collection of prospective longitudinal, ecologically valid self-reported clinical questionnaires from people with psychiatric diagnoses. These data streams have potential for improving the efficiency and accuracy of psychiatric diagnosis as well predicting future mood states enabling earlier intervention. However, missing responses are common in such datasets and there is little consensus as to how these should be dealt with in practice. In this study, the missing-response-incorporated log-signature method achieves roughly 74.8% correct diagnosis, with f1 scores for three diagnostic groups 66% (bipolar disorder), 83% (healthy control) and 75% (borderline personality disorder) respectively. This was superior to the naive model which excluded missing data and advanced models which implemented different imputation approaches, namely, k-nearest neighbours (KNN), probabilistic principal components analysis (PPCA) and random forest-based multiple imputation by chained equations (rfMICE). The log-signature method provided an effective approach to the analysis of prospectively collected mood data where missing data was common and should be considered as an approach in other similar datasets. Because of treating missing responses as a signal, its superiority also highlights that missing data conveys valuable clinical information.

Software Application Profile: PXStools—an R package of tools for conducting exposure-wide analysis and deriving polyexposure risk scores

Abstract Motivation Investigating the aggregate burden of environmental factors on human traits and diseases requires consideration of the entire ‘exposome’. However, current studies primarily focus on a single exposure or a handful of exposures at a time, without considering how multiple exposures may be simultaneously associated with each other or with the phenotype. Polyexposure risk scores (PXS) have been shown to predict and stratify risk for disease beyond or complementary to genetic and clinical risk. PXStools provides an analytical package to standardize exposome-wide studies as well as derive and validate polyexposure risk scores. Implementation PXStools is a package for the statistical R. General features The package allows users to (i) conduct exposure-wide association studies; (ii) derive and validate polyexposure risk scores with and without accounting for exposure interactions, using new approaches in regression modelling (hierarchical lasso);(iii) compare goodness of fit between models with and without multiple exposures; and (iv) visualize results. A data frame with a unique identifier, phenotype and exposures is needed as the only input. Various customizations are allowed including data preprocessing (removing missing or unwanted responses), covariates adjustment, multiple hypothesis correction and model specification (linear, logistic, survival). Availability The PXStools source code is freely available on Github at [https://github.com/yixuanh/PXStools].

Out-of-Level Cognitive Testing of Children with Special Educational Needs

Abstract: Children with special educational needs in the area of learning (SEN-L) have severe learning disabilities and often exhibit substantial cognitive impairments. Therefore, standard assessment instruments of basic cognitive abilities designed for regular school children are frequently too complex for them and, thus, unable to provide reliable proficiency estimates. The present study evaluated whether out-of-level testing with the German version of the Cognitive Abilities Test using test versions developed for younger age groups might suit the needs of these children. Therefore, N = 511 children with SEN-L and N = 573 low achieving children without SEN-L attending fifth grades in Germany were administered four tests measuring reasoning and verbal comprehension that were designed for fourth graders. The results showed that children with SEN-L exhibited significantly more missing responses than children without SEN-L. Moreover, three of the four tests were still too difficult for them. Importantly, no substantial differential response functioning was found for children with and without SEN-L. Thus, out-of-level testing might represent a feasible strategy to assess basic cognitive functioning in children with SEN-L. However, comparative interpretations would require additional norms or linked test versions that place results from out-of-level tests on a common metric.

Multiple Imputation to Salvage Partial Respondents: Analysis of the Forgotten Joint Score-12 After Total Hip Arthroplasty.

Missing responses are common when Asian patients complete the Forgotten Joint Score-12 (FJS-12), which is widely used to evaluate total hip arthroplasty (THA). We aimed to provide orthopaedic researchers with a solution for handling missing values in such patient-reported outcome measures (PROMs). Patients who had undergone primary THA between 1998 and 2016 (n = 1,021) were investigated in 2020. The FJS-12 and 9 other PROMs, including questions related to Asian lifestyle activities, were administered. Risk factors for missing FJS-12 items were investigated. Partial respondents were matched with complete respondents; then, in each pair, the items not completed by the partial respondent were deleted from the responses of the complete respondent. Predictive mean matching (PMM) was performed in an attempt to recover the deleted items, using 65 sets of imputation models. After the missing values had been imputed, we explored patient characteristics that affected the FJS-12, using data from all complete and partial respondents. A total of 652 patients responded to the survey (393 complete and 193 partial respondents). Partial respondents were older, more often female, and less active. Older respondents were more likely to skip items involving the bed, while those who reported a better ability to sit in the seiza style (traditional Japanese floor sitting) were more likely to skip items about chair sitting. The imputed FJS-12 value exhibited excellent reliability (intraclass correlation coefficient for agreement with the true scores, 0.985). FJS-12 values of complete respondents were significantly higher than those of respondents with 4 to 11 missing items (51.6 versus 32.8, p < 0.001). Older age was associated with higher FJS-12 values, which was revealed only via analysis of the multiply imputed data sets (p < 0.001). Analysis of only complete FJS-12 responses after THA resulted in a nonresponse bias, preferentially excluding older, female, and less active individuals and those with a traditional floor living style. Multiple imputation could provide a solution to scoring and analyzing PROMs with missing responses by permitting the inclusion of partial respondents. Therapeutic Level III . See Instructions for Authors for a complete description of levels of evidence.

Nonlinear interaction detection through partial dimension reduction with missing response data

In this paper, we are concerned with nonlinear interaction detection based on partial dimension reduction with missing response data. The covariates are grouped through linear combinations in a general class of semi-parametric models to detect their joint interaction effects. The joint interaction effects are estimated by a profile least squares approach with the help of the inverse probability weighted technique. The asymptotic properties of the resulting estimate for the central partial mean subspace are established. In addition, a Wald type test is proposed to detect the interactions between the covariates. A BIC-type criterion is applied to determine the structural dimension of the central partial mean subspace and its consistency is also obtained. Simulations are conducted to examine the finite sample performances of our proposed method and a real data set is analyzed for illustration.

Some performance considerations when using multi-armed bandit algorithms in the presence of missing data.

When comparing the performance of multi-armed bandit algorithms, the potential impact of missing data is often overlooked. In practice, it also affects their implementation where the simplest approach to overcome this is to continue to sample according to the original bandit algorithm, ignoring missing outcomes. We investigate the impact on performance of this approach to deal with missing data for several bandit algorithms through an extensive simulation study assuming the rewards are missing at random. We focus on two-armed bandit algorithms with binary outcomes in the context of patient allocation for clinical trials with relatively small sample sizes. However, our results apply to other applications of bandit algorithms where missing data is expected to occur. We assess the resulting operating characteristics, including the expected reward. Different probabilities of missingness in both arms are considered. The key finding of our work is that when using the simplest strategy of ignoring missing data, the impact on the expected performance of multi-armed bandit strategies varies according to the way these strategies balance the exploration-exploitation trade-off. Algorithms that are geared towards exploration continue to assign samples to the arm with more missing responses (which being perceived as the arm with less observed information is deemed more appealing by the algorithm than it would otherwise be). In contrast, algorithms that are geared towards exploitation would rapidly assign a high value to samples from the arms with a current high mean irrespective of the level observations per arm. Furthermore, for algorithms focusing more on exploration, we illustrate that the problem of missing responses can be alleviated using a simple mean imputation approach.

Treatment effect identification using two-level designs with partially ignorable missing data

Missingness is often present in treatment effect inference studies with response datasets. However, the existing methods could not deal with the partially ignorable missingness (PIM) of response data from many time points. These data often have two characteristics: (a) missing in the framework of counterfactual outcomes; (b) missing responses arising from dropouts. One challenge is to identify the treatment effect in three-way arrays of outcomes with missing data.Thus, this paper proposes a novel method, PIM estimator (PIME), for inferring the differential effect of treatment. This method aims to estimate the average treatment effect using the three-way outcome array of a two-level design. This method can identify the selection bias and quantify it with the difference in outcome means between complete and incomplete cases. With the sensitivity analysis of dropout fractions, the results suggest there exists a minimum point of the upper bound of the difference estimation with the specified models. The results with real applications show that the proposed method is efficient for the treatment effect estimation. The proposed method was compared with the related methods (with dropouts imputed or removed). The results indicate that patients with higher blood glucose levels are more likely to drop out than the others.

The TMD-7 as a Brief Measure for Assessing Temporomandibular Disorder.

The aim of this cross-sectional prospective study was to determine the internal consistency of the TMD-7, and compare prevalence of TMD symptoms in an adult population. Upon presenting to the orthodontic screening appointment, a total of 440 subjects (316 females and 124 males) were asked to complete the TMD-7 questionnaire. A total of 108 of the participants were later excluded from the study either due to the duplicate or missing responses. The final sample consisted of data from 332 participants (232 females and 100 males), aged between 18 and 64 (mean age: 42.9 ± 9.0) years. Cronbach's α statistics were calculated to assess internal consistency. Comparisons between genders, among age categories, and between subjects with versus without prior orthodontic treatment were performed using Wilcoxon ranks sum and Kruskal-Wallis tests. Comparisons for differences in the individual TMD-7 item ratings were performed using Mantel-Haenszel chi-square tests for ordered categorical responses. The calculated Cronbach's α for TMD-7 scale was 0.77. No statistically significant differences were found in the TMD-7 scale score or the individual TMD-7 item ratings between age categories (p = 0.993). Females had significantly higher TMD-7 scale score and higher ratings for headache, pain in jaw, pain in neck, pain in forehead, difficulty opening mouth, and difficulty while eating (p < 0.05). No statistically significant differences were found in the TMD-7 scale score or the individual TMD-7 item ratings between subjects with versus without previous orthodontic treatment (p = 0.075). The TMD-7 tool has good internal consistency and can be used reliably for assessment of TMD symptoms in adults. The use of this tool revealed no significant differences between age groups or between subjects with or without previous orthodontic treatment. However, a significant female gender predisposition for TMD symptoms in the adulthood was determined.

Validity and Reliability of the Persian Version of Reflux Symptom Score-12 in Patients with Laryngopharyngeal Reflux Disease

Cross cultural adaptation of the reflux symptom score-12 (RSS-12) into Persian language and to evaluate its validity and reliability in the assessment of patients with laryngopharyngeal reflux disease (LPRD). A cross-sectional and prospective cohort design. A standard forward and backward translation was followed to cross-culturally adapt the RSS-12 into Persian language. To study discriminative validity, the RSS-12p was administrated to 63 patients with LPRD (40 men and 23 women; mean age: 39.26 ± 9.79 years) and 50 healthy volunteers (31 men and 19 women; mean age: 37.24 ± 10.28 years). The patients completed the reflux symptom index (RSI) to assess construct validity. The test-retest reliability was investigated in 31 patients (time interval=7 days). There were no missing responses and floor or ceiling effects. The assessing of discriminative validity showed that the questionnaire was able to discriminate between patients with LPRD and healthy participants (P<0.001). Construct validity was confirmed by the Pearson correlation between the RSS-12p and the RSI (rp= 0.87; P<0.00). The internal consistency was confirmed with Cronbach α 0.85 and 0.72 for the RSS-12p and quality of life (QoL), respectively. Test-retest reliability was excellent (ICCagreement=0.98 for the RSS-12p and 0.94 for QoL). The Persian version of RSS-12 is a valid and reliable self-administered questionnaire for assessing LPRD in Persian-speaking patients.

How Prostate Cancer Patients are Surveyed may Influence Self-Reported Sexual Function Responses

BackgroundThe side effects of prostate cancer treatment include decreases in sexual function, hence, the way patient reported outcomes are collected may affect the quantity and quality of responses. AimTo determine the effect that different survey modes (email, telephone, or mail) had on the quantity of missing data and self-reported function following treatment. MethodsMen newly diagnosed with prostate cancer and enrolled in the Victorian Prostate Cancer Outcomes Registry formed the study population. The Expanded Prostate Cancer Index Composite (EPIC-26) survey instrument was administered approximately 1 year after their initial treatment. EPIC-26 measures self-reported function in the sexual, urinary, bowel, and hormonal domains. Multivariable regression models were used to examine effects of survey mode, adjusting for age, residence, socioeconomic status, diagnosing institute type, risk group and primary treatment modality. OutcomesThe percentage of patients for whom a domain score could not be calculated due to missing responses and the functional score within each domain. ResultsRegistry staff attempted to reach 8,586 men eligible to complete the EPIC-26. Of these, 4,301 (50%) returned the survey via email, 1,882 (22%) completed by telephone, and 197 (2.3%) by mail. 2,206 (26%) were uncontactable or did not respond. Email responders had the highest proportion answering all 26 questions (95% vs 87% by phone and 67% by mail). The sexual function score was unable to be calculated due to missing responses for 1.3% of email responders, 8.8% by phone, and 8.1% by mail. After adjustment for patient and disease factors, phone responders were almost 6 times more likely than email responders to have a missing score in this domain, odds ratio = 5.84 (95% confidence interval: 4.06–8.40). The adjusted mean functional score (out of 100) was higher for those responding by phone than email or mail across all domains. The largest adjusted difference between phone and email was observed in the hormonal domain (mean difference 4.5, 95% confidence interval: 3.5–5.4), exceeding the published minimally important difference for this score. Clinical ImplicationsStudies that ask questions regarding sexual health and use multi-modal data collection methods should be aware that this potentially affects their data and consider adjusting for this factor in their analyses. Strengths and limitationsA large study sample utilizing a widely available survey instrument. Patient specific reasons for non-response were not explored. ConclusionCompletion mode effects should be considered when analyzing responses to sexual function questions in an older, male population.Papa N, Bensley JG, Perera M, et al. How Prostate Cancer Patients are Surveyed may Influence Self-Reported Sexual Function Responses. J Sex Med 2022;19:1442–1450.

Quantile regression for nonignorable missing data with its application of analyzing electronic medical records.

Over the past decade, there has been growing enthusiasm for using electronic medical records (EMRs) for biomedical research. Quantile regression estimates distributional associations, providing unique insights into the intricacies and heterogeneity of the EMR data. However, the widespread nonignorable missing observations in EMR often obscure the true associations and challenge its potential for robust biomedical discoveries. We propose a novel method to estimate the covariate effects in the presence of nonignorable missing responses under quantile regression. This method imposes no parametric specifications on response distributions, which subtly uses implicit distributions induced by the corresponding quantile regression models. We show that the proposed estimator is consistent and asymptotically normal. We also provide an efficient algorithm to obtain the proposed estimate and a randomly weighted bootstrap approach for statistical inferences. Numerical studies, including an empirical analysis of real-world EMR data, are used to assess the proposed method's finite-sample performance compared to existingliterature.

Neurologists' Evaluations of Experience and Effectiveness of Teleneurology Encounters.

Background and Objectives: To better understand neurologists' assessments of the experiences and effectiveness of teleneurology encounters. Methods: After completing an audio-video telehealth visit with verbally consenting patients, neurologists recorded their evaluations of the encounter. Data were analyzed using standard quantitative and qualitative techniques. Results: The study included unique encounters between 187 patients and 11 neurologists. The mean patient age was 49 ± 17.5 years. Two thirds of patients (66.8%, 125/187) were female. One third of patients (33.2%; 62) were new patients. The most common patient complaints were headache (69/187, 36.9%), focal and generalized numbness or tingling (21, 11.2%), memory difficulty (15, 8%), spine-related symptoms (12, 6.4%), and vertigo (11, 5.9%). Neurologists reported that they completed a virtual examination that provided enough information for medical decision-making in 94.9% of encounters (169/178, 9 missing responses). Fourteen of 25 examination elements important for medical decision-making could be performed sufficiently during virtual encounters. Examination assistance was needed for 16.4% (30/183) of patients, who were, on average, 17.3 years older than those who did not require assistance (62.9 years vs. 45.6 years, p = 0.0002). In 19.1% (34/178) of encounters, neurologists learned clinically relevant information from seeing patients in their homes. Neurologists' assessments of the effectiveness of encounters were not related to the presence (97.2%, 35/36 effective) or absence (95%, 134/141 effective) of technical difficulties (p = 0.5729) in 177 encounters (10 missing responses). Discussion: Neurologists reported that nearly 95% of teleneurology encounters were effective despite limitations of the virtual examination, occasional need for patient assistance, and technical difficulties.

Comparing the prediction performance of item response theory and machine learning methods on item responses for educational assessments.

To obtain more accurate and robust feedback information from the students’ assessment outcomes and to communicate it to students and optimize teaching and learning strategies, educational researchers and practitioners must critically reflect on whether the existing methods of data analytics are capable of retrieving the information provided in the database. This study compared and contrasted the prediction performance of an item response theory method, particularly the use of an explanatory item response model (EIRM), and six supervised machine learning (ML) methods for predicting students’ item responses in educational assessments, considering student- and item-related background information. Each of seven prediction methods was evaluated through cross-validation approaches under three prediction scenarios: (a) unrealized responses of new students to existing items, (b) unrealized responses of existing students to new items, and (c) missing responses of existing students to existing items. The results of a simulation study and two real-life assessment data examples showed that employing student- and item-related background information in addition to the item response data substantially increases the prediction accuracy for new students or items. We also found that the EIRM is as competitive as the best performing ML methods in predicting the student performance outcomes for the educational assessment datasets.

Prevalence of adverse childhood experiences and their association with suicidal ideation and non-suicidal self-injury among incarcerated methamphetamine users in Japan

BackgroundAdverse childhood experiences (ACEs) are distressing and/or traumatic events that occur during childhood that increase the risk of negative health outcomes in adulthood. ObjectiveThis study estimated the prevalence of ACEs in a nationwide sample of Japanese methamphetamine users in prison and examined associations among ACEs, suicidal ideation, and non-suicidal self-injury. Participants and settingParticipants were 636 inmates (418 male and 218 female) who were newly incarcerated in Japan for Stimulants Control Act violations. MethodsFirst, 699 participants completed an anonymous self-report questionnaire. Of these, 636 participants who did not have any missing responses were included in the analysis. After calculating descriptive statistics, the associations between ACEs and suicidal behaviors were assessed using binary logistic regression analyses. ResultsResults showed that 76.1 % of the participants reported at least one ACE before the age of 18, and female participants reported a significantly higher number of adversities than their male counterparts. The most common ACEs were parental death or divorce, followed by psychological abuse. Logistic regression analyses revealed that ACE scores significantly increased the risk of suicidal ideation (SI; adjusted odd ratio [AOR] = 1.18, p < .001) and non-suicidal self-injury (NSSI; AOR = 1.18, p < .001) after controlling for possible confounding variables. ConclusionsFindings suggest the importance of early prevention and intervention for traumatic experiences, and have implications for the recommendation of gender-responsive, trauma-focused interventions, especially for female inmates in the criminal justice system, to break the intergenerational chain of abuse. Future research directions and treatment are discussed.