Median split, k-group split, and optimality in continuous populations

Interindividual variations in associative visual learning: Exploration, description, and partition of response characteristics

Relying on existing literature to identify suitable techniques for characterizing individual differences presents practical and methodological challenges. These challenges include the frequent absence of detailed descriptions of raw data, which hinders the assessment of analysis appropriateness, as well as the exclusion of data points deemed outliers, or the reliance on comparing only extreme groups by categorizing continuous variables into upper and lower quartiles. Despite the availability of algorithmic modeling in standard statistical software, investigations into individual differences predominantly focus on factor analysis and parametric tests. To address these limitations, this application-oriented study proposes a comprehensive approach that leverages behavioral responses through the use of signal detection theory and clustering techniques. Unlike conventional methods, signal detection theory considers both sensitivity and bias, offering insights into the intricate interplay between perceptual ability and decision-making processes. On the other hand, clustering techniques enable the identification and classification of distinct patterns within the dataset, allowing for the detection of singular behaviors that form the foundation of individual differences. In a broader framework, these combined approaches prove particularly advantageous when analyzing large and heterogeneous datasets provided by data archive platforms. By applying these techniques more widely, our understanding of the cognitive and behavioral processes underlying learning can be expedited and enhanced.

Examining the U-shaped relationship of sleep duration and systolic blood pressure with risk of cardiovascular events using a novel recursive gradient scanning model

Background

Observational studies have suggested U-shaped relationships between sleep duration and systolic blood pressure (SBP) with risks of many cardiovascular diseases (CVDs), but the cut-points that separate high-risk and low-risk groups have not been confirmed. We aimed to examine the U-shaped relationships between sleep duration, SBP, and risks of CVDs and confirm the optimal cut-points for sleep duration and SBP.

Methods

A retrospective analysis was conducted on NHANES 2007-2016 data, which included a nationally representative sample of participants. The maximum equal-odds ratio (OR) method was implemented to obtain optimal cut-points for each continuous independent variable. Then, a novel "recursive gradient scanning method" was introduced for discretizing multiple non-monotonic U-shaped independent variables. Finally, a multivariable logistic regression model was constructed to predict critical risk factors associated with CVDs after adjusting for potential confounders.

Results

A total of 26,691 participants (48.66% were male) were eligible for the current study with an average age of 49.43 ± 17.69 years. After adjusting for covariates, compared with an intermediate range of sleep duration (6.5-8.0 h per day) and SBP (95-120 mmHg), upper or lower values were associated with a higher risk of CVDs [adjusted OR (95% confidence interval) was 1.20 (1.04-1.40) for sleep duration and 1.17 (1.01-1.36) for SBP].

Conclusions

This study indicates U-shaped relationships between SBP, sleep duration, and risks of CVDs. Both short and long duration of sleep/higher and lower BP are predictors of cardiovascular outcomes. Estimated total sleep duration of 6.5-8.0 h per day/SBP of 95-120 mmHg is associated with lower risk of CVDs.

Multi-Omic Integration of Blood-Based Tumor-Associated Genomic and Lipidomic Profiles Using Machine Learning Models in Metastatic Prostate Cancer

PURPOSE

To determine prognostic and predictive clinical outcomes in metastatic hormone-sensitive prostate cancer (mHSPC) and metastatic castrate-resistant prostate cancer (mCRPC) on the basis of a combination of plasma-derived genomic alterations and lipid features in a longitudinal cohort of patients with advanced prostate cancer.

METHODS

A multifeature classifier was constructed to predict clinical outcomes using plasma-based genomic alterations detected in 120 genes and 772 lipidomic species as informative features in a cohort of 71 patients with mHSPC and 144 patients with mCRPC. Outcomes of interest were collected over 11 years of follow-up. These included in mHSPC state early failure of androgen-deprivation therapy (ADT) and exceptional responders to ADT; early death (poor prognosis) and long-term survivors in mCRPC state. The approach was to build binary classification models that identified discriminative candidates with optimal weights to predict outcomes. To achieve this, we built multi-omic feature-based classifiers using traditional machine learning (ML) methods, including logistic regression with sparse regularization, multi-kernel Gaussian process regression, and support vector machines.

RESULTS

The levels of specific ceramides (d18:1/14:0 and d18:1/17:0), and the presence of CHEK2 mutations, AR amplification, and RB1 deletion were identified as the most crucial factors associated with clinical outcomes. Using ML models, the optimal multi-omics feature combination determined resulted in AUC scores of 0.751 for predicting mHSPC survival and 0.638 for predicting ADT failure; and in mCRPC state, 0.687 for prognostication and 0.727 for exceptional survival. The models were observed to be superior than using a limited candidate number of features for developing multi-omic prognostic and predictive signatures.

CONCLUSION

Using a ML approach that incorporates multiple omic features improves the prediction accuracy for metastatic prostate cancer outcomes significantly. Validation of these models will be needed in independent data sets in future.

Establishment and Validation of a Predictive Model for Radiation-Associated Aspiration Pneumonia in Patients with Radiation-Induced Dysphagia after Nasopharyngeal Carcinoma

Introduction

Radiotherapy for patients with head and neck cancers raises their risk of aspiration pneumonia-related death. We aimed to develop and validate a model to predict radiation-associated aspiration pneumonia (RAP) among patients with dysphagia after radiotherapy for nasopharyngeal carcinoma (NPC).

Materials and Methods

A total of 453 dysphagic patients with NPC were retrospectively recruited from Sun Yat-Sen Memorial Hospital from January 2012 to January 2018. Patients were randomly divided into training cohort (n = 302) and internal validation cohort (n = 151) at a ratio of 2 : 1. The concordance index (C-index) and calibration curve were used to evaluate the accuracy and discriminative ability of this model. Moreover, decision curve analysis was performed to evaluate the net clinical benefit. The results were externally validated in 203 dysphagic patients from the First People's Hospital of Foshan.

Results

Derived from multivariable analysis of the training cohort, four independent factors were introduced to predict RAP, including Kubota water drinking test grades, the maximum radiation dose of lymph node gross tumor volume (Dmax of the GTVnd), neutrophil count, and erythrocyte sedimentation rate (ESR). The nomogram showed favorable calibration and discrimination regarding the training cohort, with a C-index of 0.749 (95% confidence interval (CI), 0.681 to 0.817), which was confirmed by the internal validation cohort (C-index 0.743; 95% CI, 0.669 to 0.818) and the external validation cohort (C-index 0.722; 95% CI, 0.606 to 0.838).

Conclusions

Our study established and validated a simple nomogram for RAP among patients with dysphagia after radiotherapy for NPC.

Correlation Between Vascular Geometry Changes and Long-Term Outcomes After Enterprise Stent Deployment for Intracranial Aneurysms Located on Small Arteries

BACKGROUND

Enterprise stents are widely used for intracranial aneurysms located on small arteries (<2.5 mm in diameter) and change the geometry of parent arteries. The purpose of this study was to investigate the correlation between vascular geometry changes and long-term outcomes.

METHODS

Between May 2013 and 2018, 1065 consecutive intracranial aneurysms were treated with Enterprise stents at our institution. After inclusion and exclusion criteria were applied, 377 aneurysms with >6 months of digital subtraction angiography follow-up were evaluated. The cohort comprised 101 aneurysms located on small parent arteries. After stent-assisted coiling, the vascular geometry parameters of small parent arteries were compared to explore their correlation with procedural complications, delayed stent migration, and recanalization.

RESULTS

The rate of delayed aneurysm occlusion in patients with initial efferent artery diameter (De) <2.5 mm was significantly higher than in patients with De >2.5 mm (62.2% vs. 40.2%; P = 0.032). At follow-up, vascular geometry parameters significantly increased (P < 0.001). In multivariate analyses, larger aneurysms and initial parent artery angle (α) <90° were independent predictors of procedural complications and discrepancy in vessel size (ΔD) >0.5 mm was an independent predictor of delayed stent migration. Larger aneurysms and follow-up angle change (ΔAngle) <30° were independent predictors for recanalization of aneurysms located on small arteries.

CONCLUSIONS

Enterprise stent-assisted coiling of intracranial aneurysms located on small arteries is safe and effective. Our study found that Enterprise deployment in small arteries had a low procedural complication rate and high stent tolerance. Vascular geometry changes play an important role in aneurysm recanalization.

Problematic Mobile Phone Use by Hong Kong Adolescents

Background

Recently there have been growing concerns about problematic mobile phone use by adolescent populations. This study aimed to address this concern through a study of severity and correlates of problematic mobile phone use with a sample of Hong Kong adolescents.

Methods

Data were collected from a sample of adolescents from three local secondary schools (ranging from high to low academic achievement levels) using a measuring scale (PCPU-Q, Yen et al., 2009) designated for Chinese adolescents. Participants were allocated into groups of “problematic users” and “non-problematic users” based on the number of occurrence of symptoms due to excessive and maladaptive use of mobile phone and possible functional impairments caused by problematic mobile phone use. A group of “at-risk users” was identified. A sample-based examination on distribution of these three groups of users was conducted via frequency counts and percentage calculation. A series of t-test were performed to make comparisons between “problematic” and “non-problematic” groups on selected personality and health related variable. Risk and protective factors were identified via correlational analysis and logistic regression analysis.

Results

Under a more stringent cut-off criterion of four or more reported symptoms (out of seven) plus one or more reported functional impairments (out of five), 22.9% of the adolescents participating in this study could be classified as problematic mobile phone users. However, a more lenient criterion (only 4 or more reported symptoms without consideration of functional impairment) reported a substantially more severe prevalence rate (29.3%). A new group of “at-risk” adolescents (6.4%) was identified with such a discrepancy of prevalence rate. Gender difference, some risk and protective factors were also identified for developing this technology-related problem.

Discussion and Conclusions

Adolescents who are vulnerable to suffer from this technology-related problem deserve more attention from helping professionals. Results of this study throw some insights on how to identify problematic mobile phone user applying a criterion-referenced approach. This study echoes a recent call for adopting a developmental perspective in understanding this problem and conducting research in this area. Anchored on present findings, effective interventions to tackle this rising problem among adolescents are suggested.

Survival prediction models for patients with anal carcinoma receiving definitive chemoradiation: A population-based study

The present study aimed to develop two nomograms in order to predict cancer-specific survival (CSS) and overall survival (OS) of patients with anal carcinoma receiving definitive chemoradiotherapy. Data from studies including patients with anal carcinoma, who were determined to be positive histologically and diagnosed between 2004 and 2010, were obtained from the Surveillance, Epidemiology, and End Results database. Significant prognostic factors for CSS and OS of patients were screened to develop nomograms through univariate and multivariate analyses. Nomograms were validated using internal and external data. The predictive abilities of the generated models were evaluated by concordance index (C-index) and calibration curves. Risk stratification was performed for patients with the same TNM stage. A total of 1,473 patients and six independent prognostic factors for CSS and OS, namely age, sex, ethnicity, marital status at diagnosis, T stage and N stage, were included in the nomogram calculations. Calibration curves demonstrated that nomogram prediction was in high accordance with actual observation. The C-indices of nomograms were greater than those of models based on the sixth edition of the American Joint Committee on Cancer TNM staging system for CSS prediction (training cohort, 0.72 vs. 0.70; validation cohort, 0.68 vs. 0.62) and OS (training cohort, 0.70 vs. 0.66; validation cohort, 0.68 vs. 0.62). Survival curves demonstrated significant survival differences among the different risk groups. Nomograms were more accurate than the conventional TNM staging system in prognosis prediction. In addition, survival performances of patients with the same TNM stage could be further distinguished by risk stratification, which provided individualized prediction for patients. These survival prediction methods may aid clinicians in patient counseling and in selecting more individualized therapeutic strategies.

A novel approach to determine two optimal cut-points of a continuous predictor with a U-shaped relationship to hazard ratio in survival data: simulation and application

BACKGROUND

In clinical and epidemiological researches, continuous predictors are often discretized into categorical variables for classification of patients. When the relationship between a continuous predictor and log relative hazards is U-shaped in survival data, there is a lack of a satisfying solution to find optimal cut-points to discretize the continuous predictor. In this study, we propose a novel approach named optimal equal-HR method to discretize a continuous variable that has a U-shaped relationship with log relative hazards in survival data.

METHODS

The main idea of the optimal equal-HR method is to find two optimal cut-points that have equal log relative hazard values and result in Cox models with minimum AIC value. An R package 'CutpointsOEHR' has been developed for easy implementation of the optimal equal-HR method. A Monte Carlo simulation study was carried out to investigate the performance of the optimal equal-HR method. In the simulation process, different censoring proportions, baseline hazard functions and asymmetry levels of U-shaped relationships were chosen. To compare the optimal equal-HR method with other common approaches, the predictive performance of Cox models with variables discretized by different cut-points was assessed.

RESULTS

Simulation results showed that in asymmetric U-shape scenarios the optimal equal-HR method had better performance than the median split method, the upper and lower quantiles method, and the minimum p-value method regarding discrimination ability and overall performance of Cox models. The optimal equal-HR method was applied to a real dataset of small cell lung cancer. The real data example demonstrated that the optimal equal-HR method could provide clinical meaningful cut-points and had good predictive performance in Cox models.

CONCLUSIONS

In general, the optimal equal-HR method is recommended to discretize a continuous predictor with right-censored outcomes if the predictor has an asymmetric U-shaped relationship with log relative hazards based on Cox regression models.

Development and validation of a nomogram for predicting survival in patients with resected non-small-cell lung cancer

PURPOSE

A nomogram is a useful and convenient tool for individualized cancer prognoses. We sought to develop a clinical nomogram for predicting survival of patients with resected non-small-cell lung cancer (NSCLC).

PATIENTS AND METHODS

On the basis of data from a multi-institutional registry of 6,111 patients with resected NSCLC in China, we identified and integrated significant prognostic factors for survival to build a nomogram. The model was subjected to bootstrap internal validation and to external validation with a separate cohort of 2,148 patients from the International Association for the Study of Lung Cancer (IASLC) database. The predictive accuracy and discriminative ability were measured by concordance index (C-index) and risk group stratification.

RESULTS

A total of 5,261 patients were included for analysis. Six independent prognostic factors were identified and entered into the nomogram. The calibration curves for probability of 1-, 3-, and 5-year overall survival (OS) showed optimal agreement between nomogram prediction and actual observation. The C-index of the nomogram was higher than that of the seventh edition American Joint Committee on Cancer TNM staging system for predicting OS (primary cohort, 0.71 v 0.68, respectively; P < .01; IASLC cohort, 0.67 v 0.64, respectively; P = .06). The stratification into different risk groups allowed significant distinction between survival curves within respective TNM categories.

CONCLUSION

We established and validated a novel nomogram that can provide individual prediction of OS for patients with resected NSCLC. This practical prognostic model may help clinicians in decision making and design of clinical studies.

A novel method to handle the effect of uneven sampling effort in biodiversity databases

How reliable are results on spatial distribution of biodiversity based on databases? Many studies have evidenced the uncertainty related to this kind of analysis due to sampling effort bias and the need for its quantification. Despite that a number of methods are available for that, little is known about their statistical limitations and discrimination capability, which could seriously constrain their use. We assess for the first time the discrimination capacity of two widely used methods and a proposed new one (FIDEGAM), all based on species accumulation curves, under different scenarios of sampling exhaustiveness using Receiver Operating Characteristic (ROC) analyses. Additionally, we examine to what extent the output of each method represents the sampling completeness in a simulated scenario where the true species richness is known. Finally, we apply FIDEGAM to a real situation and explore the spatial patterns of plant diversity in a National Park. FIDEGAM showed an excellent discrimination capability to distinguish between well and poorly sampled areas regardless of sampling exhaustiveness, whereas the other methods failed. Accordingly, FIDEGAM values were strongly correlated with the true percentage of species detected in a simulated scenario, whereas sampling completeness estimated with other methods showed no relationship due to null discrimination capability. Quantifying sampling effort is necessary to account for the uncertainty in biodiversity analyses, however, not all proposed methods are equally reliable. Our comparative analysis demonstrated that FIDEGAM was the most accurate discriminator method in all scenarios of sampling exhaustiveness, and therefore, it can be efficiently applied to most databases in order to enhance the reliability of biodiversity analyses.