RESUMEN

INTRODUCTION: Obtaining a categorical general surgery residency position is recognized as a highly challenging process, and many aspiring surgeons find themselves matching into a preliminary position. The American Board of Surgery In-Training Examination (ABSITE) is relevant as a discriminator, as it is the only national evaluation metric that compares residents between programs. This study examines the correlation between ABSITE performance and the likelihood of obtaining a categorical position for non-designated preliminary surgery residents. METHODS: Retrospective analysis of preliminary residents who completed the ABSITE between 2011 and 2021 at a single academic training program. RESULTS: 108 preliminary residents were included. Among preliminary residents who were successful in securing a categorical position, the average ABSITE percentile was 59 (SD = 26.7). In contrast, those who were not able to secure a categorical position, the average ABSITE percentile was 23.6 (SD = 25.3). There was a strong significant correlation between ABSITE percentile and securing a categorical position (P < 0.001). There was a significant association between citizenship and gaining a categorical position, with US citizens being significantly more likely to successfully gain a categorical position (P = 0.01; OR 3.32 (95% CI 1.28-8.56)). There was not a significant correlation between citizenship and ABSITE score. CONCLUSION: This study presents compelling evidence that ABSITE percentile score is positively associated with the probability of securing a categorical position for preliminary general surgery residents. It is therefore imperative that both preliminary residents and their programs place a high value on ABSITE performance to enhance successful career progression.

RESUMEN

Missing values (NA) often occur in cancer research, which may be due to reasons such as data protection, data loss, or missing follow-up data. Such incomplete patient information can have an impact on prediction models and other data analyses. Imputation methods are a tool for dealing with NA. Cancer data is often presented in an ordered categorical form, such as tumour grading and staging, which requires special methods. This work compares mode imputation, k nearest neighbour (knn) imputation, and, in the context of Multiple Imputation by Chained Equations (MICE), logistic regression model with proportional odds (mice_polr) and random forest (mice_rf) on a real-world prostate cancer dataset provided by the Cancer Registry of Rhineland-Palatinate in Germany. Our dataset contains relevant information for the risk classification of patients and the time between date of diagnosis and date of death. For the imputation comparison, we use Rubin's (1974) Missing Completely At Random (MCAR) mechanism to remove 10%, 20%, 30%, and 50% observations. The results are evaluated and ranked based on the accuracy per patient. Mice_rf performs significantly best for each percentage of NA, followed by knn, and mice_polr performs significantly worst. Furthermore, our findings indicate that the accuracy of imputation methods increases with a lower number of categories, a relatively even proportion of patients in the categories, or a majority of patients in a particular category.

Asunto(s)

RESUMEN

BACKGROUND: Urothelial Bladder Cancer (UBC) is a common cancer with a high risk of recurrence, which is influenced by the TNM classification, grading, age, and other factors. Recent studies demonstrate reliable and accurate recurrence prediction using Machine Learning (ML) algorithms and even outperform traditional approaches. However, most ML algorithms cannot process categorical input features, which must first be encoded into numerical values. Choosing the appropriate encoding strategy has a significant impact on the prediction quality. OBJECTIVE: We investigate the impact of encoding strategies for ordinal features in the prediction quality of ML algorithms. METHOD: We compare three different encoding strategies namely one-hot, ordinal, and entity embedding in predicting the 2-year recurrence in UBC patients using an artificial neural network. We use ordered categorical and numerical data of UBC patients provided by the Cancer Registry Rhineland-Palatinate. RESULTS: We show superior prediction quality using entity embedding encoding with 84.6% precision, an overall accuracy of 73.8%, and 68.9% AUC on testing data over 100 epochs after 30 runs compared to one-hot and ordinal encoding. CONCLUSION: We confirm the superiority of entity embedding encoding as it could provide a more detailed and accurate representation of ordinal features in numerical scales. This can lead to enhanced generalizability, resulting in significantly improved prediction quality.

Asunto(s)

RESUMEN

The complexity of the cancer problem domain presents challenges not only to the medical analysis systems tasked with its analysis, but also to the users of such systems. While it is desirable to assist users in operating these medical analysis systems, prior groundwork is required before this can be achieved, such as recognising patterns in the way users create certain analyses within these systems. In this paper, we use machine learning algorithms to analyse user behaviour patterns and attempt to predict the next user interaction within the CARESS medical analysis system. Since an appropriate pre-processing scheme is essential for the performance of these algorithms, we propose the usage of a Natural Language Processing (NLP)- inspired approach to preserve some semantic cohesion of the mostly categorical features of these user interactions. Furthermore, we propose to use a sliding window that contains information about the latest user interactions in combination with Latent Dirichlet Allocation (LDA) to extract a latent topic from these last interactions and use it as additional input to the machine learning models. We compare this pre-processing scheme with other approaches that utilise one-hot encoding and feature hashing. The results of our experiments show that the sliding window LDA scheme is a promising solution, that performs better for our use case than the other evaluated pre-processing schemes. Overall, our results provide an important piece for further research and development in the area of assisting users in operating analysis systems in complex problem domains.

Asunto(s)

RESUMEN

Acoustic information in speech changes continuously, yet listeners form discrete perceptual categories to ease the demands of perception. Being a more continuous/gradient as opposed to a more discrete/categorical listener may be further advantageous for understanding speech in noise by increasing perceptual flexibility and resolving ambiguity. The degree to which a listener's responses to a continuum of speech sounds are categorical versus continuous can be quantified using visual analog scaling (VAS) during speech labeling tasks. Here, we recorded event-related brain potentials (ERPs) to vowels along an acoustic-phonetic continuum (/u/ to /a/) while listeners categorized phonemes in both clean and noise conditions. Behavior was assessed using standard two alternative forced choice (2AFC) and VAS paradigms to evaluate categorization under task structures that promote discrete vs. continuous hearing, respectively. Behaviorally, identification curves were steeper under 2AFC vs. VAS categorization but were relatively immune to noise, suggesting robust access to abstract, phonetic categories even under signal degradation. Behavioral slopes were correlated with listeners' QuickSIN scores; shallower slopes corresponded with better speech in noise performance, suggesting a perceptual advantage to noise degraded speech comprehension conferred by a more gradient listening strategy. At the neural level, P2 amplitudes and latencies of the ERPs were modulated by task and noise; VAS responses were larger and showed greater noise-related latency delays than 2AFC responses. More gradient responders had smaller shifts in ERP latency with noise, suggesting their neural encoding of speech was more resilient to noise degradation. Interestingly, source-resolved ERPs showed that more gradient listening was also correlated with stronger neural responses in left superior temporal gyrus. Our results demonstrate that listening strategy modulates the categorical organization of speech and behavioral success, with more continuous/gradient listening being advantageous to sentential speech in noise perception.

Asunto(s)

RESUMEN

Highly pathogenic avian influenza (HPAI) is an important zoonotic disease. The study aims to identify farmer behaviour types to inform the design of behaviour change programmes for mitigating the transmission of HPAI. Therefore, the study utilised multivariate statistical analysis for gaining a better understanding of the relationships among farmers' 30 biosecurity behaviours, the risk of HPAI infection, and distinct features of commercial broiler farmers, which is different from using simple and few binary biosecurity measures. Convenience sampling was used to collect data from 303 Taiwan's farmers among which 40 farmers (13.2%) self-reported having had a HPAI outbreak in the study year while 16 farmers (5.3%) self-reported having had a HPAI outbreak in the past two years. Using categorical principal components analysis and a two-stage cluster analysis, four farmer clusters were identified with distinct features: 1)'Reserved' (4.6%) tended to choose 'No idea' for answering specific questions about HPAI; 2)'Secure' (76.3%) had a higher biosecurity status than the other farms; 3) 'Jeopardised' (16.8%) had a lower biosecurity status than the other farms; 4) 'No-response' (2.3%) tended to skip specific questions about HPAI. The biosecurity status of the 'Reserved' and 'No-response' clusters was undetermined, placing these farms at risk of HPAI infection. Compared to the 'Secure' cluster, the 'Jeopardised' cluster exhibited higher odds of self-reported HPAI in the study year (OR: 2.61, 95% CI: 1.22-5.58) and in the past two years (OR: 4.28, 95% CI: 1.39-13.19). Additionally, the 'Jeopardised' cluster showed increased odds of HPAI recurrence (OR: 4.01, 95% CI: 1.41-11.43). Our study demonstrates that inadequate biosecurity practices can elevate the occurrence or recurrence of HPAI outbreaks. The findings underscore the importance of distinguishing between these clusters to accurately assess the risk of HPAI infection across farms. Furthermore, understanding farmers' behaviours can inform the development of strategies aimed at behaviour change among farmers.

RESUMEN

Spatial locations can be encoded and maintained in working memory using different representations and strategies. Fine-grained representations provide detailed stimulus information, but are cognitively demanding and prone to inexactness. The uncertainty in fine-grained representations can be compensated by the use of coarse, but robust categorical representations. In this study, we employed an individual differences approach to identify brain activity correlates of the use of fine-grained and categorical representations in spatial working memory. We combined data from six functional magnetic resonance imaging studies, resulting in a sample of $155$ ($77$ women, $25 \pm 5$ years) healthy participants performing a spatial working memory task. Our results showed that individual differences in the use of spatial representations in working memory were associated with distinct patterns of brain activity. Higher precision of fine-grained representations was related to greater engagement of attentional and control brain systems throughout the task trial, and the stronger deactivation of the default network at the time of stimulus encoding. In contrast, the use of categorical representations was associated with lower default network activity during encoding and higher frontoparietal network activation during maintenance. These results may indicate a greater need for attentional resources and protection against interference for fine-grained compared with categorical representations.

Asunto(s)

RESUMEN

This study presents a novel approach for the optimization of genomic parental selection in breeding programs involving categorical and continuous-categorical multi-trait mixtures (CMs and CCMMs). Utilizing the Bayesian decision theory (BDT) and latent trait models within a multivariate normal distribution framework, we address the complexities of selecting new parental lines across ordinal and continuous traits for breeding. Our methodology enhances precision and flexibility in genetic selection, validated through extensive simulations. This unified approach presents significant potential for the advancement of genetic improvements in diverse breeding contexts, underscoring the importance of integrating both categorical and continuous traits in genomic selection frameworks.

Asunto(s)

RESUMEN

How does the brain simultaneously process signals that bring complementary information, like raw sensory signals and their transformed counterparts, without any disruptive interference? Contemporary research underscores the brain's adeptness in using decorrelated responses to reduce such interference. Both neurophysiological findings and artificial neural networks support the notion of orthogonal representation for signal differentiation and parallel processing. Yet, where, and how raw sensory signals are transformed into more abstract representations remains unclear. Using a temporal pattern discrimination task in trained monkeys, we revealed that the second somatosensory cortex (S2) efficiently segregates faithful and transformed neural responses into orthogonal subspaces. Importantly, S2 population encoding for transformed signals, but not for faithful ones, disappeared during a nondemanding version of this task, which suggests that signal transformation and their decoding from downstream areas are only active on-demand. A mechanistic computation model points to gain modulation as a possible biological mechanism for the observed context-dependent computation. Furthermore, individual neural activities that underlie the orthogonal population representations exhibited a continuum of responses, with no well-determined clusters. These findings advocate that the brain, while employing a continuum of heterogeneous neural responses, splits population signals into orthogonal subspaces in a context-dependent fashion to enhance robustness, performance, and improve coding efficiency.

Asunto(s)

RESUMEN

Background: Machine learning (ML) prediction models in healthcare and pharmacy-related research face challenges with encoding high-dimensional Healthcare Coding Systems (HCSs) such as ICD, ATC, and DRG codes, given the trade-off between reducing model dimensionality and minimizing information loss. Objectives: To investigate using Network Analysis modularity as a method to group HCSs to improve encoding in ML models. Methods: The MIMIC-III dataset was utilized to create a multimorbidity network in which ICD-9 codes are the nodes and the edges are the number of patients sharing the same ICD-9 code pairs. A modularity detection algorithm was applied using different resolution thresholds to generate 6 sets of modules. The impact of four grouping strategies on the performance of predicting 90-day Intensive Care Unit readmissions was assessed. The grouping strategies compared: 1) binary encoding of codes, 2) encoding codes grouped by network modules, 3) grouping codes to the highest level of ICD-9 hierarchy, and 4) grouping using the single-level Clinical Classification Software (CCS). The same methodology was also applied to encode DRG codes but limiting the comparison to a single modularity threshold to binary encoding.The performance was assessed using Logistic Regression, Support Vector Machine with a non-linear kernel, and Gradient Boosting Machines algorithms. Accuracy, Precision, Recall, AUC, and F1-score with 95% confidence intervals were reported. Results: Models utilized modularity encoding outperformed ungrouped codes binary encoding models. The accuracy improved across all algorithms ranging from 0.736 to 0.78 for the modularity encoding, to 0.727 to 0.779 for binary encoding. AUC, recall, and precision also improved across almost all algorithms. In comparison with other grouping approaches, modularity encoding generally showed slightly higher performance in AUC, ranging from 0.813 to 0.837, and precision, ranging from 0.752 to 0.782. Conclusions: Modularity encoding enhances the performance of ML models in pharmacy research by effectively reducing dimensionality and retaining necessary information. Across the three algorithms used, models utilizing modularity encoding showed superior or comparable performance to other encoding approaches. Modularity encoding introduces other advantages such as it can be used for both hierarchical and non-hierarchical HCSs, the approach is clinically relevant, and can enhance ML models' clinical interpretation. A Python package has been developed to facilitate the use of the approach for future research.

RESUMEN

We implement an analytic approach for ordinal measures and we use it to investigate the structure and the changes over time of self-worth in a sample of adolescents students in high school. We represent the variations in self-worth and its various sub-domains using entropy-based measures that capture the observed uncertainty. We then study the evolution of the entropy across four time points throughout a semester of high school. Our analytic approach yields information about the configuration of the various dimensions of the self together with time-related changes and associations among these dimensions. We represent the results using a network that depicts self-worth changes over time. This approach also identifies groups of adolescent students who show different patterns of associations, thus emphasizing the need to consider heterogeneity in the data.

RESUMEN

Categorical search involves looking for objects based on category information from long-term memory. Previous research has shown that search efficiency in categorical search is influenced by target/distractor similarity and category variability (i.e., heterogeneity). However, the interaction between these factors and their impact on different subprocesses of search remains unclear. This study examined the effects of target/distractor similarity and category variability on processes of categorical search. Using multidimensional scaling, we manipulated target/distractor similarity and measured category variability for target categories that participants searched for. Eye-tracking data were collected to examine attentional guidance and target verification. The results demonstrated that the effect of category variability on response times (RTs) was dependent on the level of target/distractor similarity. Specifically, when distractors were highly similar to target categories, there was a negative relation between RTs and variability, with low variability categories producing longer RTs than higher variability categories. Surprisingly, this trend was only present in the eye-tracking measures of target verification but not attentional guidance. Our results suggest that searchers more effectively guide attention to low-variability categories compared to high-variability categories, regardless of the degree of similarity between targets and distractors. However, low category variability interferes with target match decisions when distractors are highly similar to the category, thus the advantage that low category variability provides to searchers is not equal across processes of search.

RESUMEN

INTRODUCTION: International medical graduates (IMGs) make up a small but important percentage of the U.S. surgical workforce. Detailed and contemporary studies on IMGs matching into U.S. general surgery residency positions are lacking. Our objective was to study these trends over a 30-y period. METHODS: We utilized the National Resident Matching Program reports from 1994 to 2023 to analyze the trends of U.S. M.D. seniors, D.O. seniors, and U.S. citizen and non-U.S. citizen IMGs matching into first-year categorical and preliminary general surgery residency positions. The percent of positions filled were calculated and trended over time using linear regression, where ß coefficient estimated the percentage of annual change in matched positions, and the R2 coefficient measured the amount of variance explained (perfect regression R2 = 1.0). RESULTS: Over the last 30 y, IMG match percentages have increased for both categorical (ß = 0.218%, R2 = 0.49, P < 0.001) and preliminary (ß = 0.705%, R2 = 0.76, P < 0.001) general surgery positions, with a greater increase in preliminary positions (ß = 0.705%). The percentage of positions filled by M.D. U.S. seniors in categorical positions has steadily decreased over the 30-y period (ß = -0.625%, R2 = 0.79, P < 0.001), and this decrease has largely occurred with a concurrent greater increase in U.S. D.O. seniors match percentage rates (ß = 0.430%, R2 = 0.64, P < 0.001), rather than IMGs (ß = 0.218%). Allopathic M.D. U.S. seniors preliminary match percentages have steadily decreased at the steepest rate (ß = -0.927%, R2 = 0.80, P < 0.001). In categorical positions, non-U.S. citizen IMGs' match percentages (ß = 0.069%, R2 = 0.204, P = 0.012) increased at a slightly slower rate than U.S. citizen IMGs (ß = 0.149%, R2 = 0.607, P < 0.001). In preliminary positions, non-U.S. citizen IMGs' match percentages (ß = 0.33%, R2 = 0.478, P < 0.001) increased at a similar rate as U.S. citizen IMGs (ß = 0.375%, R2 = 0.823, P < 0.0.001). In the 2023 National Resident Matching Program match, U.S. citizen and non-U.S. citizen IMGs together made up 10.3% of the categorical and 44.5% of the preliminary general surgery positions that were filled. For categorical positions in 2023, there was no major difference between positions matched by U.S. citizen IMGs (4.62%) and non-U.S. citizen IMGs (5.72%); on the other hand, for preliminary positions in 2023, non-U.S. citizen IMGs (31.96%) filled 2.5× times the number of positions as U.S. citizen IMGs (12.54%). CONCLUSIONS: Over the last 30 y, U.S. allopathic M.D. seniors matching into categorical general surgery positions have steadily decreased, while both U.S. osteopathic D.O. seniors and IMGs matching have increased. These data have important implications for the future U.S. surgical workforce.

RESUMEN

Dating phylogenetic trees to obtain branch lengths in time unit is essential for many downstream applications but has remained challenging. Dating requires inferring substitution rates that can change across the tree. While we can assume to have information about a small subset of nodes from the fossil record or sampling times (for fast-evolving organisms), inferring the ages of the other nodes essentially requires extrapolation and interpolation. Assuming a distribution of branch rates, we can formulate dating as a constrained maximum likelihood (ML) estimation problem. While ML dating methods exist, their accuracy degrades in the face of model misspecification where the assumed parametric statistical distribution of branch rates vastly differs from the true distribution. Notably, most existing methods assume rigid, often unimodal, branch rate distributions. A second challenge is that the likelihood function involves an integral over the continuous domain of the rates and often leads to difficult non-convex optimization problems. To tackle these two challenges, we propose a new method called Molecular Dating using Categorical-models (MD-Cat). MD-Cat uses a categorical model of rates inspired by non-parametric statistics and can approximate a large family of models by discretizing the rate distribution into k categories. Under this model, we can use the Expectation- Maximization (EM) algorithm to co-estimate rate categories and branch lengths in time units. Our model has fewer assumptions about the true distribution of branch rates than parametric models such as Gamma or LogNormal distribution. Our results on two simulated and real datasets of Angiosperms and HIV and a wide selection of rate distributions show that MD-Cat is often more accurate than the alternatives, especially on datasets with exponential or multimodal rate distributions.

RESUMEN

Previous research has demonstrated that individuals from Western cultures exhibit categorical perception (CP) in their judgments of emotional faces. However, the extent to which this phenomenon characterises the judgments of facial expressions among East Asians remains relatively unexplored. Building upon recent findings showing that East Asians are more likely than Westerners to see a mixture of emotions in facial expressions of anger and disgust, the present research aimed to investigate whether East Asians also display CP for angry and disgusted faces. To address this question, participants from Canada and China were recruited to discriminate pairs of faces along the anger-disgust continuum. The results revealed the presence of CP in both cultural groups, as participants consistently exhibited higher accuracy and faster response latencies when discriminating between-category pairs of expressions compared to within-category pairs. Moreover, the magnitude of CP did not vary significantly across cultures. These findings provide novel evidence supporting the existence of CP for facial expressions in both East Asian and Western cultures, suggesting that CP is a perceptual phenomenon that transcends cultural boundaries. This research contributes to the growing literature on cross-cultural perceptions of facial expressions by deepening our understanding of how facial expressions are perceived categorically across cultures.

RESUMEN

The scope of this paper is a multivariate setting involving categorical variables. Following an external manipulation of one variable, the goal is to evaluate the causal effect on an outcome of interest. A typical scenario involves a system of variables representing lifestyle, physical and mental features, symptoms, and risk factors, with the outcome being the presence or absence of a disease. These variables are interconnected in complex ways, allowing the effect of an intervention to propagate through multiple paths. A distinctive feature of our approach is the estimation of causal effects while accounting for uncertainty in both the dependence structure, which we represent through a directed acyclic graph (DAG), and the DAG-model parameters. Specifically, we propose a Markov chain Monte Carlo algorithm that targets the joint posterior over DAGs and parameters, based on an efficient reversible-jump proposal scheme. We validate our method through extensive simulation studies and demonstrate that it outperforms current state-of-the-art procedures in terms of estimation accuracy. Finally, we apply our methodology to analyze a dataset on depression and anxiety in undergraduate students.

Asunto(s)

RESUMEN

BACKGROUND: The process of transferring patients from small rural primary care facilities to referral facilities impacts the quality of care and effectiveness of the referral healthcare system. The study aimed to develop and evaluate the psychometric properties of a scale measuring requirements for effective rural emergency transfer. METHODS: An exploratory sequential design was utilized to develop a scale designed to measure requirements for effective emergency transport. Phase one included a qualitative, interview study with 26 nursing transport providers. These transcripts were coded, and items developed for the proposed scale. Phase two included a content validity review by these 16 transport providers of the domains and items developed. Phase three included development and evaluation of psychometric properties of a scale designed to measure requirements for effective emergency transport. This scale was then tested initially with 84 items and later reduced to a final set of 58 items after completion by 302 transport nurses. The final scale demonstrated three factors (technology & tools; knowledge & skills; and organization). Each factor and the total score reported excellent scale reliability. RESULTS: The initial item pool consisted of 84 items, generated, and synthesized from an extensive literature review and the qualitative descriptive study exploring nurses' experiences in rural emergency patient transportation. A two-round modified Delphi method with experts generated a scale consisting of 58 items. A cross-sectional study design was used with 302 nurses in rural clinics and health in four rural health districts. A categorical principal components analysis identified three components explaining 63.35% of the total variance. The three factors, technology, tools, personal knowledge and skills, and organization, accounted for 27.32%, 18.15 and 17.88% of the total variance, respectively. The reliability of the three factors, as determined by the Categorical Principal Component Analysis (CATPCA)'s default calculation of the Cronbach Alpha, was 0.960, 0.946, and 0.956, respectively. The RET Cronbach alpha was 0.980. CONCLUSIONS: The study offers a three-factor scale to measure the effectiveness of emergency patient transport in rural facilities to better understand and improve care during emergency patient transport.

Asunto(s)

RESUMEN

INTRODUCTION: Little research has focused on assessing the mortality for fall height based on field-relevant categories like falls from greater than standing (FFGS), falls from standing (FFS), and falls from less than standing. METHODS: This retrospective observational study included patients evaluated for a fall incident at an urban Level I Trauma Center or included in Medical Examiner's log from January 1, 2015, to June 31, 2017. Descriptive statistics characterized the sample based on demographic variables such as age, race, sex, and insurance type, as well as injury characteristics like relative fall height, Glasgow Coma Scale (GCS), Injury Severity Score (ISS), traumatic brain injury, intensive care unit length of stay, and mortality. Bivariate analysis included Chi-square tests for categorical variables and Student t-tests for continuous variables. Subsequent multiple logistic regression modeled significant variables from bivariate analyses, including age, race, insurance status, fall height, ISS, and GCS. RESULTS: When adjusting for sex, age, race, insurance, ISS, and GCS, adults ≥65 who FFS had 1.93 times the odds of mortality than those who FFGS. However, those <65 who FFGS had 3.12 times the odds of mortality than those who FFS. Additionally, commercial insurance was not protective across age groups. CONCLUSIONS: The mortality for FFS may be higher than FFGS under certain circumstances, particularly among those ≥65 y. Therefore, prehospital collection should include accurate assessment of fall height and surface (i.e., water, concrete). Lastly, commercial insurance was likely a proxy for industrial falls, accounting for the surprising lack of protection against mortality.

Asunto(s)

RESUMEN

In psychiatry clinical trials, an instrument or questionnaire with rating scale is often used to access safety and efficacy of a test treatment under investigation. Statistical analysis based on the collected rating scale is then performed a determine whether there is an improvement in endpoint change from baseline mean scale. This approach needs on absolute change, however, may not actually reflect the performance of the test treatment under study because the change, which may be considered of clinically importance, may fall within the same category in terms of disease severity such as mild, moderate, and severe. In this case, it is suggested, in addition to absolute change approach, a categorical shift analysis be considered to determine whether the endpoint change from baseline has resulted in an improvement in categorical shift, in terms of disease severity shift e.g., from severe to mild or moderate. In this article, we explore the relationship between absolute change approach and categorical shift analysis based on rating scales for assessment of test treatment under study.

Asunto(s)

Asunto(s)