Interval estimation in three-class receiver operating characteristic analysis: A fairly general approach based on the empirical likelihood

The empirical likelihood is a powerful nonparametric tool, that emulates its parametric counterpart-the parametric likelihood-preserving many of its large-sample properties. This article tackles the problem of assessing the discriminatory power of three-class diagnostic tests from an empirical likelihood perspective. In particular, we concentrate on interval estimation in a three-class receiver operating characteristic analysis, where a variety of inferential tasks could be of interest. We present novel theoretical results and tailored techniques studied to efficiently solve some of such tasks. Extensive simulation experiments are provided in a supporting role, with our novel proposals compared to existing competitors, when possible. It emerges that our new proposals are extremely flexible, being able to compete with contestants and appearing suited to accommodating several distributions, such, for example, mixtures, for target populations. We illustrate the application of the novel proposals with a real data example. The article ends with a discussion and a presentation of some directions for future research.

How to evaluate uncertainty estimates in machine learning for regression?

As neural networks become more popular, the need for accompanying uncertainty estimates increases. There are currently two main approaches to test the quality of these estimates. Most methods output a density. They can be compared by evaluating their loglikelihood on a test set. Other methods output a prediction interval directly. These methods are often tested by examining the fraction of test points that fall inside the corresponding prediction intervals. Intuitively, both approaches seem logical. However, we demonstrate through both theoretical arguments and simulations that both ways of evaluating the quality of uncertainty estimates have serious flaws. Firstly, both approaches cannot disentangle the separate components that jointly create the predictive uncertainty, making it difficult to evaluate the quality of the estimates of these components. Specifically, the quality of a confidence interval cannot reliably be tested by estimating the performance of a prediction interval. Secondly, the loglikelihood does not allow a comparison between methods that output a prediction interval directly and methods that output a density. A better loglikelihood also does not necessarily guarantee better prediction intervals, which is what the methods are often used for in practice. Moreover, the current approach to test prediction intervals directly has additional flaws. We show why testing a prediction or confidence interval on a single test set is fundamentally flawed. At best, marginal coverage is measured, implicitly averaging out overconfident and underconfident predictions. A much more desirable property is pointwise coverage, requiring the correct coverage for each prediction. We demonstrate through practical examples that these effects can result in favouring a method, based on the predictive uncertainty, that has undesirable behaviour of the confidence or prediction intervals. Finally, we propose a simulation-based testing approach that addresses these problems while still allowing easy comparison between different methods. This approach can be used for the development of new uncertainty quantification methods.

Multiple imputation of more than one environmental exposure with nondifferential measurement error

Measurement error is common in environmental epidemiologic studies, but methods for correcting measurement error in regression models with multiple environmental exposures as covariates have not been well investigated. We consider a multiple imputation approach, combining external or internal calibration samples that contain information on both true and error-prone exposures with the main study data of multiple exposures measured with error. We propose a constrained chained equations multiple imputation (CEMI) algorithm that places constraints on the imputation model parameters in the chained equations imputation based on the assumptions of strong nondifferential measurement error. We also extend the constrained CEMI method to accommodate nondetects in the error-prone exposures in the main study data. We estimate the variance of the regression coefficients using the bootstrap with two imputations of each bootstrapped sample. The constrained CEMI method is shown by simulations to outperform existing methods, namely the method that ignores measurement error, classical calibration, and regression prediction, yielding estimated regression coefficients with smaller bias and confidence intervals with coverage close to the nominal level. We apply the proposed method to the Neighborhood Asthma and Allergy Study to investigate the associations between the concentrations of multiple indoor allergens and the fractional exhaled nitric oxide level among asthmatic children in New York City. The constrained CEMI method can be implemented by imposing constraints on the imputation matrix using the mice and bootImpute packages in R.

From S-matrix theory to strings: Scattering data and the commitment to non-arbitrariness

The early history of string theory is marked by a shift from strong interaction physics to quantum gravity. The first string models and associated theoretical framework were formulated in the late 1960s and early 1970s in the context of the S-matrix program for the strong interactions. In the mid-1970s, the models were reinterpreted as a potential theory unifying the four fundamental forces. This paper provides a historical analysis of how string theory was developed out of S-matrix physics, aiming to clarify how modern string theory, as a theory detached from experimental data, grew out of an S-matrix program that was strongly dependent upon observable quantities. Surprisingly, the theoretical practice of physicists already turned away from experiment before string theory was recast as a potential unified quantum gravity theory. With the formulation of dual resonance models (the "hadronic string theory"), physicists were able to determine almost all of the models' parameters on the basis of theoretical reasoning. It was this commitment to "non-arbitrariness", i.e., a lack of free parameters in the theory, that initially drove string theorists away from experimental input, and not the practical inaccessibility of experimental data in the context of quantum gravity physics. This is an important observation when assessing the role of experimental data in string theory.

Climate change impacts on rainfall intensity-duration-frequency curves in local scale catchments

The increasing intensity and frequency of rainfall events, a critical aspect of climate change, pose significant challenges in the construction of intensity-duration-frequency (IDF) curves for climate projection. These curves are crucial for infrastructure development, but the non-stationarity of extreme rainfall raises concerns about their adequacy under future climate conditions. This research addresses these challenges by investigating the reasons behind the IPCC climate report's evidence about the validity that rainfall follows the Clausius-Clapeyron (CC) relationship, which suggests a 7% increase in precipitation per 1 °C increase in temperature. Our study provides guidelines for adjusting IDF curves in the future, considering both current and future climates. We calculate extreme precipitation changes and scaling factors for small urban catchments in Barranquilla, Colombia, a tropical region, using the bootstrapping method. This reveals the occurrence of a sub-CC relationship, suggesting that the generalized 7% figure may not be universally applicable. In contrast, our comparative analysis with Illinois, USA, an inland city in the north temperate zone, shows adherence to the CC relationship. This emphasizes the need for local parameter calculations rather than relying solely on the generalized 7% figure.

PARSEG: a computationally efficient approach for statistical validation of botanical seeds' images

Human recognition and automated image validation are the most widely used approaches to validate the output of binary segmentation methods but, as the number of pixels in an image easily exceeds several million, they become highly demanding from both practical and computational standpoint. We propose a method, called PARSEG, which stands for PArtitioning, Random Selection, Estimation, and Generalization; being the basic steps within this procedure. Suggested method enables us to perform statistical validation of binary images by selecting the minimum number of pixels from the original image to be used for validation without deteriorating the effectiveness of the validation procedure. It utilizes binary classifiers to accomplish image validation and selects the optimal sample of pixels according to a specific objective function. As a result, the computational complexity of the validation experiment is substantially reduced. The procedure's effectiveness is illustrated by considering images composed of approximately 13 million pixels from the field of seed recognition. PARSEG provides roughly the same precision of the validation process when extended to the entire image, but it utilizes only about 4% of the original number of pixels, thus reducing, by about 90%, the computing time required to validate a binary segmented image.

Development of a Nomogram to Predict the Risk for Acute Necrotizing Pancreatitis

Background/Aims

: Necrotizing pancreatitis (NP) presents a more severe clinical trajectory and increased mortality compared to edematous pancreatitis. Prompt identification of NP is vital for patient prognosis. A risk prediction model for NP among Chinese patients has been developed and validated to aid in early detection.

Methods

: A retrospective analysis was performed on 218 patients with acute pancreatitis (AP) to examine the association of various clinical variables with NP. The least absolute shrinkage and selection operator (LASSO) regression was utilized to refine variables and select predictors. Subsequently, a multivariate logistic regression was employed to construct a predictive nomogram. The model's accuracy was validated using bootstrap resampling (n=500) and its calibration assessed via a calibration curve. The model's clinical utility was evaluated through decision curve analysis.

Results

: Of the 28 potential predictors analyzed in 218 AP patients, the incidence of NP was 25.2%. LASSO regression identified 14 variables, with procalcitonin, triglyceride, white blood cell count at 48 hours post-admission, calcium at 48 hours post-admission, and hematocrit at 48 hours post-admission emerging as independent risk factors for NP. The resulting nomogram accurately predicted NP risk with an area under the curve of 0.822, sensitivity of 82.8%, and specificity of 76.4%. The bootstrap-validated area under the curve remained at 0.822 (95% confidence interval, 0.737 to 0.892). This model exhibited excellent calibration and demonstrated greater predictive efficacy and clinical utility for NP than APACHE II, Ranson, and BISAP.

Conclusions

: We have developed a prediction nomogram of NP that is of great value in guiding clinical decision.

Multisensory integration deficits in Schizophrenia and Autism evidenced in behaviour but not event related potentials

The process of integrating information from different sensory channels, known as multisensory integration (MSI) was assessed in two disorders, Autism Spectrum Disorder (ASD) and Schizophrenia (SCZ). 32 healthy controls (HC), 35 SCZ patients, and 23 ASD patients performed an audiovisual (AV) synchronous target detection task while reaction time (RT) and scalp recorded electrophysiological (EEG) activity were measured. MSI in the AV condition resulted in faster and less variable RTs compared to the unimodal conditions. Using our novel bootstrap method, MSI gain was observed in 78 % of HC, 26 % of ASD, and 48 % of SCZ patients. At the neural level, MSI in the AV condition resulted in larger amplitude of sensory evoked responses and cognitive P3 response compared to the corresponding unimodal conditions. These neural effects of MSI were not related to the behavioural MSI gain identified at the individual level and could not explain the deficits in behavioural MSI of patient groups. In conclusion, a robust MSI gain deficit in RT was observed in both patient groups that was not reflected in early perceptual and cognitive electro-cortical responses, suggesting that behavioural MSI deficits in ASD and SCZ may arise at late processing stages such as response selection.

Enrollment Forecast for Clinical Trials at the Planning Phase with Study-Level Historical Data

Given progressive developments and demands on clinical trials, accurate enrollment timeline forecasting is increasingly crucial for both strategic decision-making and trial execution excellence. Naïve approach assumes flat rates on enrollment using average of historical data, while traditional statistical approach applies simple Poisson-Gamma model using time-invariant rates for site activation and subject recruitment. Both of them are lack of non-trivial factors such as time and location. We propose a novel two-segment statistical approach based on Quasi-Poisson regression for subject accrual rate and Poisson process for subject enrollment and site activation. The input study-level data are publicly accessible and it can be integrated with historical study data from user's organization to prospectively predict enrollment timeline. The new framework is neat and accurate compared to preceding works. We validate the performance of our proposed enrollment model and compare the results with other frameworks on 7 curated studies.

A composite semiparametric homogeneity test for the distributions of multigroup interval-bounded longitudinal data

Motivated by comparing the distribution of longitudinal quality of life (QoL) data among different treatment groups from a cancer clinical trial, we propose a semiparametric test statistic for the homogeneity of the distributions of multigroup longitudinal measurements, which are bounded in a closed interval with excess observations taking the boundary values. Our procedure is based on a three-component mixed density ratio model and a composite empirical likelihood for the longitudinal data taking values inside the interval. A nonparametric bootstrap method is applied to calculate the p-value of the proposed test. Simulation studies are conducted to evaluate the proposed procedure, which show that the proposed test is effective in controlling type I errors and more powerful than the procedure which ignores the values on the boundaries. It is also robust to the model mispecification than the parametric test. The proposed procedure is also applied to compare the distributions of the scores of Physical Function subscale and Global Heath Status between the patients randomized to two treatment groups in a cancer clinical trial.

Dependence-Robust Confidence Intervals for Capture-Recapture Surveys

Capture-recapture (CRC) surveys are used to estimate the size of a population whose members cannot be enumerated directly. CRC surveys have been used to estimate the number of Coronavirus Disease 2019 (COVID-19) infections, people who use drugs, sex workers, conflict casualties, and trafficking victims. When k-capture samples are obtained, counts of unit captures in subsets of samples are represented naturally by a 2 k contingency table in which one element-the number of individuals appearing in none of the samples-remains unobserved. In the absence of additional assumptions, the population size is not identifiable (i.e., point identified). Stringent assumptions about the dependence between samples are often used to achieve point identification. However, real-world CRC surveys often use convenience samples in which the assumed dependence cannot be guaranteed, and population size estimates under these assumptions may lack empirical credibility. In this work, we apply the theory of partial identification to show that weak assumptions or qualitative knowledge about the nature of dependence between samples can be used to characterize a nontrivial confidence set for the true population size. We construct confidence sets under bounds on pairwise capture probabilities using two methods: test inversion bootstrap confidence intervals and profile likelihood confidence intervals. Simulation results demonstrate well-calibrated confidence sets for each method. In an extensive real-world study, we apply the new methodology to the problem of using heterogeneous survey data to estimate the number of people who inject drugs in Brussels, Belgium.

Central psychological symptoms from a network analysis of patients with anxiety, somatoform or personality disorders before psychotherapy

BACKGROUND

Cross-sectional network analysis examines the relationships between symptoms to explain how they constitute disorders. Up to now, research focuses mostly on depression, posttraumatic stress disorder, and rarely assesses larger networks of various symptoms measured with instruments independent of classifications. Studies on large groups of psychotherapy patients are also rare.

METHODS

Analyzing triangulated maximally filtered graph (TMFG) networks of 62 psychological symptoms reported by 4616 consecutive nonpsychotic adults in 1980-2015.

RESULTS

Case-dropping and nonparametric bootstrap proved the accuracy, stability and reliability of networks in patients' sex-, age-, and time of visit divided subgroups. Feeling that others are prejudiced against the patient was the most central symptom, followed by catastrophic fears, feeling inferior and underestimated. Sadness, panic, and sex-related complaints were less central than we expected. All analysed symptoms were connected, and we found only small sex-related differences between subsamples' networks. No differences were observed for time of visit and age of patients.

LIMITATION

Analyses were cross-sectional and retrospective, not allowing examination of directionality or causality. Further, data are at the between-person level; thus, it is unknown whether the network remains constant for any person over time. One self-report checklist and building binary network method may bias results. Our results indicate how symptoms co-occured before psychotherapy, not longitudinally. Our sample included public university hospital patients, all White-Europeans, predominantly females and university students.

CONCLUSIONS

Hostile projection, catastrophic fears, feeling inferior and underestimated were the most important psychological phenomena reported before psychotherapy. Exploring these symptoms would possibly lead to enhancement of treatments.

A comparison of the methods for detecting dyadic patterns in the actor-partner interdependence model

In the actor-partner interdependence model (APIM), various dyadic patterns between an actor and partner can be examined. One widely used approach is the parameter k method, which tests whether the ratio of the partner effect to the actor effect (p/a) is significantly different from pattern values such as -1 (contrast), 0 (actor-only or partner-only), and 1 (couple). Although using a phantom variable was a useful method for estimating the k ratio, it is no longer necessary due to the availability of statistical packages that allow for a direct estimation of the k ratio without the inclusion of the phantom variable. Moreover, it is possible to examine the patterns by testing new variables defined in different forms from the k or using the χ2 difference test. To date, no previous studies have evaluated and compared the various approaches for detecting the dyadic patterns in APIM. This study aims to assess and compare the performance of four different methods for detecting dyadic patterns: (1) phantom variable approach, (2) direct estimation of the parameter k, (3) new-variable approach, and (4) χ2 difference test. The first two methods frequently included multiple pattern values in there confidence interval. Furthermore, the phantom variable approach was prone to convergence issues. The other two alternatives performed better in detecting the dyadic patterns without convergence problems. Given the findings of the study, we suggest a novel procedure for examining dyadic patterns in APIM.

The importance of choosing a proper validation strategy in predictive models. A tutorial with real examples

Machine learning is the art of combining a set of measurement data and predictive variables to forecast future events. Every day, new model approaches (with high levels of sophistication) can be found in the literature. However, less importance is given to the crucial stage of validation. Validation is the assessment that the model reliably links the measurements and the predictive variables. Nevertheless, there are many ways in which a model can be validated and cross-validated reliably, but still, it may be a model that wrongly reflects the real nature of the data and cannot be used to predict external samples. This manuscript shows in a didactical manner how important the data structure is when a model is constructed and how easy it is to obtain models that look promising with wrong-designed cross-validation and external validation strategies. A comprehensive overview of the main validation strategies is shown, exemplified by three different scenarios, all of them focused on classification.

Empirical methods for the validation of time-to-event mathematical models taking into account uncertainty and variability: application to EGFR + lung adenocarcinoma

BACKGROUND

Over the past several decades, metrics have been defined to assess the quality of various types of models and to compare their performance depending on their capacity to explain the variance found in real-life data. However, available validation methods are mostly designed for statistical regressions rather than for mechanistic models. To our knowledge, in the latter case, there are no consensus standards, for instance for the validation of predictions against real-world data given the variability and uncertainty of the data. In this work, we focus on the prediction of time-to-event curves using as an application example a mechanistic model of non-small cell lung cancer. We designed four empirical methods to assess both model performance and reliability of predictions: two methods based on bootstrapped versions of parametric statistical tests: log-rank and combined weighted log-ranks (MaxCombo); and two methods based on bootstrapped prediction intervals, referred to here as raw coverage and the juncture metric. We also introduced the notion of observation time uncertainty to take into consideration the real life delay between the moment when an event happens, and the moment when it is observed and reported.

RESULTS

We highlight the advantages and disadvantages of these methods according to their application context. We have shown that the context of use of the model has an impact on the model validation process. Thanks to the use of several validation metrics we have highlighted the limit of the model to predict the evolution of the disease in the whole population of mutations at the same time, and that it was more efficient with specific predictions in the target mutation populations. The choice and use of a single metric could have led to an erroneous validation of the model and its context of use.

CONCLUSIONS

With this work, we stress the importance of making judicious choices for a metric, and how using a combination of metrics could be more relevant, with the objective of validating a given model and its predictions within a specific context of use. We also show how the reliability of the results depends both on the metric and on the statistical comparisons, and that the conditions of application and the type of available information need to be taken into account to choose the best validation strategy.

Glycosyltransferase-related prognostic and diagnostic biomarkers of uterine corpus endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) has a strong ability of invasion and metastasis, high recurrence rate, and poor survival. Glycosyltransferases are one of the most important enzymes that coordinate the glycosylation process, and abnormal modification of proteins by glycosyltransferases is closely related to the occurrence and development of cancer. However, there were fewer reports on glycosyltransferase related biomarkers in UCEC. In this paper, based on the UCEC transcriptome data published on The Cancer Genome Atlas (TCGA), we predicted the relationship between the expression of glycosyltransferase-related genes (GTs) and the diagnosis and prognosis of UCEC using bioinformatics methods. And validation of model genes by clinical samples. We used 4 methods: generalized linear model (GLM), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGB) to screen biomarkers with diagnostic significance, and the binary logistic regression was used to establish a diagnostic model for the 2-GTs (AUC = 0.979). And the diagnostic model was validated using a GEO external database (AUC = 0.978). Moreover, a prognostic model for the 6-GTs was developed using univariate, Lasso, and multivariate Cox regression analyses, and the model was made more stable by internal validation using the bootstrap. In addition, risk score is closely related to immune microenvironment (TME), immune infiltration, mutation, immunotherapy and chemotherapy. Overall, this study provides novel biomarkers for the diagnosis and prognosis of UCEC, and the models established by these biomarkers can also provide a good reference for individualized and precision medicine in UCEC.

Application of Hybrid ANN Techniques for Drought Forecasting in the Semi-Arid Region of India

The intensity and frequency of diverse hydro-meteorological disasters viz., extreme droughts, severe floods, and cyclones have increasing trends due to unsustainable management of land and water resources, coupled with increasing industrialization, urbanization and climate change. This study focuses on the forecasting of drought using selected Artificial Neural Network (ANN)-based models to enable decision-makers to improve regional water management plans and disaster mitigation/reduction plans. Four ANN models were developed in this study, viz., one conventional ANN model and three hybrid ANN models: (a) Wavelet based-ANN (WANN), (b) Bootstrap based-ANN (BANN), and (c) Wavelet-Bootstrap based-ANN (WBANN). The Standardized Precipitation Evapotranspiration Index (SPEI), the best drought index identified for the study area, was used as a variable for drought forecasting. Three drought indices, such as SPEI-3, SPEI-6 and SPEI-12 respectively representing "short-term", "intermediate-term", and "long-term" drought conditions, were forecasted for 1-month to 3-month lead times for six weather stations over the study area. Both statistical and graphical indicators were considered to assess the performance of the developed models. For the hybrid wavelet model, the performance was evaluated for different vanishing moments of Daubechies wavelets and decomposition levels. The best-performing bootstrap-based model was further used for analysing the uncertainty associated with different drought forecasts. Among the models developed for drought forecasting for 1 to 3 months, the performances of the WANN and WBANN models are superior to the simple ANN and BANN models for the SPEI-3, SPEI-6, and SPEI-12 up to the 3-month lead time. The performance of the WANN and WBANN models is the best for SPEI-12 (MAE = 0.091-0.347, NSE = 0.873-0.982) followed by SPEI-6 (MAE = 0.258-0.593; NSE = 0.487-0.848) and SPEI-3 (MAE = 0.332-0.787, NSE = 0.196-0.825) for all the stations up to 3-month lead time. This finding is supported by the WBANN analyze uncertainties as narrower band width for SPEI-12 (0.240-0.898) as compared to SPEI-6 (0.402-1.62) and SPEI-3 (0.474-2.304). Therefore, the WBANN model is recommended for the early warning of drought events as it facilitates the uncertainty analysis of drought forecasting results.

Bootstrap Statistics and Its Application in Disintegration and Dissolution Data Analysis

Disintegration time (DT) and rate of drug dissolution in different media are among the most widely studied crucial parameters for various types of drug products. In the ever-evolving landscape of generic formulation development, dissolution comparison of reference and test products is the major reliable in vitro method of establishing product similarity. This is one of the most widely accepted methods of proving pharma equivalency between two drug products. A well-studied match between the disintegration and dissolution profile of the test and reference formulations can ensure in vitro product similarity. Various statistical approaches have been employed to establish product performance similarity; among them, the similarity factor (f2) calculation based approach is the most widely accepted and explored method to date. However, the f2 statistics fail to predict the similarity of batches with unit-to-unit variability. Bootstrap statistical analysis of dissolution data between the test and reference products was introduced to overcome the problems associated with batches with unit variability. Bootstrap can also be applied to extract statistically significant results by treating a series of data from different batches, which can further help to understand the trend. The current review depicts different case study based approaches to show the applications of bootstrap statistics in disintegration and dissolution similarity evaluation for both conventional and additively manufactured solid dosage forms. It is concluded that bootstrap statistics can be a very promising and reliable data analytical tool for establishing in vitro product similarity for both conventional and additively manufactured formulations with a high level of intraunit variability.

A semiparametric multiply robust multiple imputation method for causal inference

Evaluating the impact of non-randomized treatment on various health outcomes is difficult in observational studies because of the presence of covariates that may affect both the treatment or exposure received and the outcome of interest. In the present study, we develop a semiparametric multiply robust multiple imputation method for estimating average treatment effects in such studies. Our method combines information from multiple propensity score models and outcome regression models, and is multiply robust in that it produces consistent estimators for the average causal effects if at least one of the models is correctly specified. Our proposed estimators show promising performances even with incorrect models. Compared with existing fully parametric approaches, our proposed method is more robust against model misspecifications. Compared with fully non-parametric approaches, our proposed method does not have the problem of curse of dimensionality and achieves dimension reduction by combining information from multiple models. In addition, it is less sensitive to the extreme propensity score estimates compared with inverse propensity score weighted estimators and augmented estimators. The asymptotic properties of our method are developed and the simulation study shows the advantages of our proposed method compared with some existing methods in terms of balancing efficiency, bias, and coverage probability. Rubin's variance estimation formula can be used for estimating the variance of our proposed estimators. Finally, we apply our method to 2009-2010 National Health Nutrition and Examination Survey (NHANES) to examine the effect of exposure to perfluoroalkyl acids (PFAs) on kidney function.

A bootstrapping dynamic two-stage SBM model: An application to assess industrial water use and health risk systems

Despite the great number of applications of bootstrapping data envelopment analysis (DEA) with a one-stage structure, there are limited attempts for approximating the distribution of the DEA estimator considering the two-stage structure across multiple periods. This research develops the dynamic two-stage non-radial DEA model based on smoothed bootstrap and subsampling bootstrap. Then, we run the proposed models on assessing the efficiency of China's industrial water use and health risk (IWUHR) systems and compare them with the bootstrapping results on standard radial network DEA. The results are as follows. (1) The proposed non-radial DEA model based on smoothed bootstrap can adjust original over-estimated and under-estimated values. (2) China's IWUHR system has good performance, and its HR stage performs better than the IWU stage for 30 provinces from 2011 to 2019. The poor performance of the IWU stage in Jiangxi and Gansu needs to be noticed. The provincial differences of the detailed bias-corrected efficiencies expand in the later period. (3) The rankings of IWU efficiency in the three regions are in agreement with that of HR efficiency: eastern, western, and central regions in this order. Particular attention should be paid to the downward trend of the bias-corrected IWUHR efficiency in the central region.

Leave-one-out cross-validation, penalization, and differential bias of some prediction model performance measures-a simulation study

BACKGROUND

The performance of models for binary outcomes can be described by measures such as the concordance statistic (c-statistic, area under the curve), the discrimination slope, or the Brier score. At internal validation, data resampling techniques, e.g., cross-validation, are frequently employed to correct for optimism in these model performance criteria. Especially with small samples or rare events, leave-one-out cross-validation is a popular choice.

METHODS

Using simulations and a real data example, we compared the effect of different resampling techniques on the estimation of c-statistics, discrimination slopes, and Brier scores for three estimators of logistic regression models, including the maximum likelihood and two maximum penalized likelihood estimators.

RESULTS

Our simulation study confirms earlier studies reporting that leave-one-out cross-validated c-statistics can be strongly biased towards zero. In addition, our study reveals that this bias is even more pronounced for model estimators shrinking estimated probabilities towards the observed event fraction, such as ridge regression. Leave-one-out cross-validation also provided pessimistic estimates of the discrimination slope but nearly unbiased estimates of the Brier score.

CONCLUSIONS

We recommend to use leave-pair-out cross-validation, fivefold cross-validation with repetitions, the enhanced or the .632+ bootstrap to estimate c-statistics, and leave-pair-out or fivefold cross-validation to estimate discrimination slopes.

Robust rank-based meta-analyses for two-sample designs with application to platelet counts of malaria infection data

In this paper, we propose robust meta-analysis procedures for individual studies that report a broad range of robust summary statistics for a two-sample problem. Summary statistics of individual studies could be presented in different forms including full data, medians of the two samples, the Hodges-Lehman and Wilcoxon estimates of the location shift parameters. Data synthesis is made under both fixed-effect and random-effect meta-analysis models. We systematically compare these robust meta-analysis procedures via simulation studies to meta-analysis procedure based on sample means and variances from individual studies under a wide range of error distributions. We show that the coverage probabilities of the robust meta-analysis confidence intervals are quite close to the nominal confidence level. We also show that mean square error (MSE) of the robust meta-analysis estimator is considerably smaller than that of the non-robust meta-analysis estimator under the contaminated normal, heavy tailed and skewed error distributions. The robust meta-analysis procedures are then applied to platelet count reduction for malaria infected patients in Ghana.

Propagating uncertainty about molecular evolution models and prior distributions to phylogenetic trees

Phylogenetic trees constructed from molecular sequence data rely on largely arbitrary assumptions about the substitution model, the distribution of substitution rates across sites, the version of the molecular clock, and, in the case of Bayesian inference, the prior distribution. Those assumptions affect results reported in the form of clade probabilities and error bars on divergence times and substitution rates. Overlooking the uncertainty in the assumptions leads to overly confident conclusions in the form of inflated clade probabilities and short confidence intervals or credible intervals. This paper demonstrates how to propagate that uncertainty by combining the models considered along with all of their assumptions, including their prior distributions. The combined models incorporate much more of the uncertainty than Bayesian model averages since the latter tend to settle on a single model due to the higher-level assumption that one of the models is true. Nucleotide sequence data illustrates the proposed model combination method.

Likelihood-based estimation and prediction for a measles outbreak in Samoa

Prediction of the progression of an infectious disease outbreak is important for planning and coordinating a response. Differential equations are often used to model an epidemic outbreak's behaviour but are challenging to parameterise. Furthermore, these models can suffer from misspecification, which biases predictions and parameter estimates. Stochastic models can help with misspecification but are even more expensive to simulate and perform inference with. Here, we develop an explicitly likelihood-based variation of the generalised profiling method as a tool for prediction and inference under model misspecification. Our approach allows us to carry out identifiability analysis and uncertainty quantification using profile likelihood-based methods without the need for marginalisation. We provide justification for this approach by introducing a new interpretation of the model approximation component as a stochastic constraint. This preserves the rationale for using profiling rather than integration to remove nuisance parameters while also providing a link back to stochastic models. We applied an initial version of this method during an outbreak of measles in Samoa in 2019-2020 and found that it achieved relatively fast, accurate predictions. Here we present the most recent version of our method and its application to this measles outbreak, along with additional validation.

Socioeconomic determinants of environmental efficiency: the case of the European Union

The study's main objective is to assess and evaluate the models of socioeconomic determinants of environmental efficiency in the European Union countries from 2010 to 2018. The two-step data envelopment analysis is implemented, using both constant and variable returns to scale assumption. Moreover, the results of the model of environmental efficiency determinants from four areas-tourism, circular economy, energy and resources use and quality of life-are presented. Based on our findings, it can be concluded that it is necessary to develop the concept of sustainable tourism because the enormous increase in foreign tourists harms environmental efficiency. It is also necessary to gradually transform economies into less energy-intensive towards knowledge-based economies. The positive impact of measures related to the pain of the circular economy was also demonstrated. In conclusion, we present several recommendations for EU policies concerning the current economic and energy situation.

Wildfire prediction using zero-inflated negative binomial mixed models: Application to Spain

Wildfires have changed in recent decades. The catastrophic wildfires make it necessary to have accurate predictive models on a country scale to organize firefighting resources. In Mediterranean countries, the number of wildfires is quite high but they are mainly concentrated around summer months. Because of seasonality, there are territories where the number of fires is zero in some months and is overdispersed in others. Zero-inflated negative binomial mixed models are adapted to this type of data because they can describe patterns that explain both number of fires and their non-occurrence and also provide useful prediction tools. In addition to model-based predictions, a parametric bootstrap method is applied for estimating mean squared errors and constructing prediction intervals. The statistical methodology and developed software are applied to model and to predict number of wildfires in Spain between 2002 and 2015 by provinces and months.

Empirical evaluation of internal validation methods for prediction in large-scale clinical data with rare-event outcomes: a case study in suicide risk prediction

BACKGROUND

There is increasing interest in clinical prediction models for rare outcomes such as suicide, psychiatric hospitalizations, and opioid overdose. Accurate model validation is needed to guide model selection and decisions about whether and how prediction models should be used. Split-sample estimation and validation of clinical prediction models, in which data are divided into training and testing sets, may reduce predictive accuracy and precision of validation. Using all data for estimation and validation increases sample size for both procedures, but validation must account for overfitting, or optimism. Our study compared split-sample and entire-sample methods for estimating and validating a suicide prediction model.

METHODS

We compared performance of random forest models estimated in a sample of 9,610,318 mental health visits ("entire-sample") and in a 50% subset ("split-sample") as evaluated in a prospective validation sample of 3,754,137 visits. We assessed optimism of three internal validation approaches: for the split-sample prediction model, validation in the held-out testing set and, for the entire-sample model, cross-validation and bootstrap optimism correction.

RESULTS

The split-sample and entire-sample prediction models showed similar prospective performance; the area under the curve, AUC, and 95% confidence interval was 0.81 (0.77-0.85) for both. Performance estimates evaluated in the testing set for the split-sample model (AUC = 0.85 [0.82-0.87]) and via cross-validation for the entire-sample model (AUC = 0.83 [0.81-0.85]) accurately reflected prospective performance. Validation of the entire-sample model with bootstrap optimism correction overestimated prospective performance (AUC = 0.88 [0.86-0.89]). Measures of classification accuracy, including sensitivity and positive predictive value at the 99^th, 95^th, 90^th, and 75^th percentiles of the risk score distribution, indicated similar conclusions: bootstrap optimism correction overestimated classification accuracy in the prospective validation set.

CONCLUSIONS

While previous literature demonstrated the validity of bootstrap optimism correction for parametric models in small samples, this approach did not accurately validate performance of a rare-event prediction model estimated with random forests in a large clinical dataset. Cross-validation of prediction models estimated with all available data provides accurate independent validation while maximizing sample size.

Assessing the Most Vulnerable Subgroup to Type II Diabetes Associated with Statin Usage: Evidence from Electronic Health Record Data

There have been increased concerns that the use of statins, one of the most commonly prescribed drugs for treating coronary artery disease, is potentially associated with the increased risk of new-onset Type II diabetes (T2D). Nevertheless, to date, there is no robust evidence supporting as to whether and what kind of populations are indeed vulnerable for developing T2D after taking statins. In this case study, leveraging the biobank and electronic health record data in the Partner Health System, we introduce a new data analysis pipeline and a novel statistical methodology that address existing limitations by (i) designing a rigorous causal framework that systematically examines the causal effects of statin usage on T2D risk in observational data, (ii) uncovering which patient subgroup is most vulnerable for developing T2D after taking statins, and (iii) assessing the replicability and statistical significance of the most vulnerable subgroup via a bootstrap calibration procedure. Our proposed approach delivers asymptotically sharp confidence intervals and debiased estimate for the treatment effect of the most vulnerable subgroup in the presence of high-dimensional covariates. With our proposed approach, we find that females with high T2D genetic risk are at the highest risk of developing T2D due to statin usage.

Confidence intervals in molecular dating by maximum likelihood

Molecular dating has been widely used to infer the times of past evolutionary events using molecular sequences. This paper describes three bootstrap methods to infer confidence intervals under a penalized likelihood framework. The basic idea is to use data pseudoreplicates to infer uncertainty in the branch lengths of a phylogeny reconstructed with molecular sequences. The three specific bootstrap methods are nonparametric (direct tree bootstrapping), semiparametric (rate smoothing), and parametric (Poisson simulation). Our extensive simulation study showed that the three methods perform generally well under a simple strict clock model of molecular evolution; however, the results were less positive with data simulated using an uncorrelated or a correlated relaxed clock model. Several factors impacted, possibly in interaction, the performance of the confidence intervals. Increasing the number of calibration points had a positive effect, as well as increasing the sequence length or the number of sequences although both latter effects depended on the model of evolution. A case study is presented with a molecular phylogeny of the Felidae (Mammalia: Carnivora). A comparison was made with a Bayesian analysis: the results were very close in terms of confidence intervals and there was no marked tendency for an approach to produce younger or older bounds compared to the other.

Linearity Characterization and Uncertainty Quantification of Spectroradiometers via Maximum Likelihood and the Non-parametric Bootstrap

A technique for characterizing and correcting the linearity of radiometric instruments is known by the names the "flux-addition method" and the "combinatorial technique". In this paper, we develop a rigorous uncertainty quantification method for use with this technique and illustrate its use with both synthetic data and experimental data from a "beam conjoiner" instrument. We present a probabilistic model that relates the instrument readout to a set of unknown fluxes via a set of polynomial coefficients. Maximum likelihood estimates (MLEs) of the unknown fluxes and polynomial coefficients are recommended, while a non-parametric bootstrap algorithm enables uncertainty quantification including standard errors and confidence intervals. The synthetic data represent plausible outputs of a radiometric instrument and enable testing and validation of the method. The MLEs for these data are found to be approximately unbiased, and confidence intervals derived from the bootstrap replicates are found to be consistent with their target coverage of 95 % . For the polynomial coefficients, the observed coverages range from 91 % to 99 % . The experimental data set illustrates how a complete calibration with uncertainties can be achieved using the method plus one well-known flux level. The uncertainty contribution attributable to estimation of the instrument's nonlinear response is less than 0.025 % over most of its range.

AN OMNIBUS TEST FOR DETECTION OF SUBGROUP TREATMENT EFFECTS VIA DATA PARTITIONING

Late-stage clinical trials have been conducted primarily to establish the efficacy of a new treatment in an intended population. A corollary of population heterogeneity in clinical trials is that a treatment might be effective for one or more subgroups, rather than for the whole population of interest. As an example, the phase III clinical trial of panitumumab in metastatic colorectal cancer patients failed to demonstrate its efficacy in the overall population, but a subgroup associated with tumor KRAS status was found to be promising (Peeters et al. (Am. J. Clin. Oncol. 28 (2010) 4706-4713)). As we search for such subgroups via data partitioning based on a large number of biomarkers, we need to guard against inflated type I error rates due to multiple testing. Commonly-used multiplicity adjustments tend to lose power for the detection of subgroup treatment effects. We develop an effective omnibus test to detect the existence of, at least, one subgroup treatment effect, allowing a large number of possible subgroups to be considered and possibly censored outcomes. Applied to the panitumumab trial data, the proposed test would confirm a significant subgroup treatment effect. Empirical studies also show that the proposed test is applicable to a variety of outcome variables and maintains robust statistical power.

Dynamics diagnosis of the COVID-19 deaths using the Pearson diagram

The pandemic COVID-19 brings with it the need for studies and tools to help those in charge make decisions. Working with classical time series methods such as ARIMA and SARIMA has shown promising results in the first studies of COVID-19. We advance in this branch by proposing a risk factor map induced by the well-known Pearson diagram based on multivariate kurtosis and skewness measures to analyze the dynamics of deaths from COVID-19. In particular, we combine bootstrap for time series with SARIMA modeling in a new paradigm to construct a map on which one can analyze the dynamics of a set of time series. The proposed map allows a risk analysis of multiple countries in the four different periods of the pandemic COVID-19 in 55 countries. Our empirical evidence suggests a direct relationship between the multivariate skewness and kurtosis. We observe that the multivariate kurtosis increase leads to the rise of the multivariate skewness. Our findings reveal that the countries with high risk from the behavior of the number of deaths tend to have pronounced skewness and kurtosis values.

Robust Inference for Partially Observed Functional Response Data

Irregular functional data in which densely sampled curves are observed over different ranges pose a challenge for modeling and inference, and sensitivity to outlier curves is a concern in applications. Motivated by applications in quantitative ultrasound signal analysis, this paper investigates a class of robust M-estimators for partially observed functional data including functional location and quantile estimators. Consistency of the estimators is established under general conditions on the partial observation process. Under smoothness conditions on the class of M-estimators, asymptotic Gaussian process approximations are established and used for large sample inference. The large sample approximations justify a bootstrap approximation for robust inferences about the functional response process. The performance is demonstrated in simulations and in the analysis of irregular functional data from quantitative ultrasound analysis.

A test for comparing conditional ROC curves with multidimensional covariates

The comparison of Receiver Operating Characteristic (ROC) curves is frequently used in the literature to compare the discriminatory capability of different classification procedures based on diagnostic variables. The performance of these variables can be sometimes influenced by the presence of other covariates, and thus they should be taken into account when making the comparison. A new non-parametric test is proposed here for testing the equality of two or more dependent ROC curves conditioned to the value of a multidimensional covariate. Projections are used for transforming the problem into a one-dimensional approach easier to handle. Simulations are carried out to study the practical performance of the new methodology. The procedure is then used to analyse a real data set of patients with Pleural Effusion to compare the diagnostic capability of different markers.

A flexible test for early-stage studies with multiple endpoints

This paper builds on the recently proposed prediction test for muliple endpoints. The prediction test combines information across multiple endpoints while accounting for the correlation between them. The test performs well with small samples relative to the number of endpoints of interest and is flexible in the hypotheses across the individual endpoints that can be combined. The prediction test addresses a global hypothesis that is of particular interest in early-stage studies and can be used as justification for continuing on to a larger trial. However, the prediction test has several limitations which we seek to address. First, the prediction test is overly conservative when both the effect sizes across all endpoints and the number of endpoints are small. By using a parametric bootstrap to estimate the null distribution, we show that the test achieves the nominal error rate in this situation and increases the power of the test. Second, we provide a framework to allow for predictions of a difference on one or more endpoints. Finally, we extend the test with a composite null hypothesis that allows for different null hypothesized predictive abilities across the endpoints which can be especially useful if the study contains both familiar and novel endpoints. We use an example from a physical activity trial to illustrate these extensions.

An equivalence test between features lists, based on the Sorensen-Dice index and the joint frequencies of GO term enrichment

Background

In integrative bioinformatic analyses, it is of great interest to stablish the equivalence between gene or (more in general) feature lists, up to a given level and in terms of their annotations in the Gene Ontology. The aim of this article is to present an equivalence test based on the proportion of GO terms which are declared as enriched in both lists simultaneously.

Results

On the basis of these data, the dissimilarity between gene lists is measured by means of the Sorensen–Dice index. We present two flavours of the same test: One of them based on the asymptotic normality of the test statistic and the other based on the bootstrap method.

Conclusions

The accuracy of these tests is studied by means of simulation and their possible interest is illustrated by using them over two real datasets: A collection of gene lists related to cancer and a collection of gene lists related to kidney rejection after transplantation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04739-2.

Statistical Guideline #7 Adjust Type 1 Error in Multiple Testing

This is one in a series of statistical guidelines designed to highlight common statistical considerations in behavioral medicine research. The goal is to briefly discuss appropriate ways to analyze and present data in the International Journal of Behavioral Medicine (IJBM). Collectively, the series will culminate in a set of basic statistical guidelines to be adopted by IJBM and integrated into the journal's official instructions for authors, and to serve as an independent resource. If you have ideas for a future topic, please email the Statistical Editor, Ren Liu at rliu45@ucmerced.edu.

The generalized sigmoidal quantile function

In this note we introduce a new smooth nonparametric quantile function estimator based on a newly defined generalized expectile function and termed the sigmoidal quantile function estimator. We also introduce a hybrid quantile function estimator, which combines the optimal properties of the classic kernel quantile function estimator with our new generalized sigmoidal quantile function estimator. The generalized sigmoidal quantile function can estimate quantiles beyond the range of the data, which is important for certain applications given smaller sample sizes. This property of extrapolation is illustrated in order to improve standard bootstrap smoothing resampling methods.

Data envelopment analysis efficiency in the public sector using provider and customer opinion: An application to the Spanish health system

Measuring the relative efficiency of a finite fixed set of service-producing units (hospitals, state services, libraries, banks,...) is an important purpose of Data Envelopment Analysis (DEA). We illustrate an innovative way to measure this efficiency using stochastic indexes of the quality from these services. The indexes obtained from the opinion-satisfaction of the customers are estimators, from the statistical view point, of the quality of the service received (outputs); while, the quality of the offered service is estimated with opinion-satisfaction indexes of service providers (inputs). The estimation of these indicators is only possible by asking a customer and provider sample, in each service, through surveys. The technical efficiency score, obtained using the classic DEA models and estimated quality indicators, is an estimator of the unknown population efficiency that would be obtained if in each one of the services, interviews from all their customers and all their providers were available. With the object of achieving the best precision in the estimate, we propose results to determine the sample size of customers and providers needed so that with their answers can achieve a fixed accuracy in the estimation of the population efficiency of these service-producing units through the use of a novel one bootstrap confidence interval. Using this bootstrap methodology and quality opinion indexes obtained from two surveys, one of doctors and another of patients, we analyze the efficiency in the health care system of Spain.

Nomogram to Predict Symptomatic Intracranial Hemorrhage after Intravenous Thrombolysis in Acute Ischemic Stroke in an Asian Population

BACKGROUND

Symptomatic intracranial hemorrhage (sICH) is a serious hemorrhagic complication after intravenous thrombolysis (IVT) in acute ischemic stroke (AIS) patients. Most existing predictive scoring systems were derived from Western countries.

OBJECTIVE

To develop a nomogram to predict the possibility of sICH after IVT in an Asian population.

METHODS

This retrospective cohort study included AIS patients treated with recombinant tissue plasminogen activator (rt-PA) in a tertiary hospital in Shenzhen, China, from January 2014 to December 2020. The end point was sICH within 36 hours of IVT treatment. Multivariable logistic regression was used to identify risk factors of sICH, and a predictive nomogram was developed. Area under the curve of receiver operating characteristic curves (AUC), calibration curve, and decision curve analyses were performed. The nomogram was validated by bootstrap resampling.

RESULTS

Data on a total of 462 patients were collected, of whom 20 patients (4.3%) developed sICH. In the multivariate logistic regression model, the National Institute of Health stroke scale scores (NIHSS) (odds ratio [OR], 1.14; 95% confidence interval [CI], 1.06-1.23, P < 0.001), onset to treatment time (OTT) (OR, 1.02; 95% CI, 1.01-1.03, P < 0.001), neutrophil to lymphocyte ratio (NLR) (OR, 1.22; 95% CI, 1.09-1.35, P < 0.001), and cardioembolism (OR, 3.74; 95% CI, 1.23-11.39, P = 0.020) were independent predictors for sICH and were used to construct a nomogram. Our nomogram exhibited favorable discrimination ability [AUC, 0.878; specificity, 87.35%; and sensitivity, 73.81%]. Bootstrapping for 500 repetitions was performed to further validate the nomogram. The AUC of the bootstrap model was 0.877 (95% CI: 0.823-0.922). The calibration curve exhibited good fit and calibration. The decision curve revealed good positive net benefits and clinical effects.

CONCLUSION

The nomogram consisted of the predictors NIHSS, OTT, NLR, and cardioembolism could be used as an auxiliary tool to predict the individual risk of sICH in Chinese AIS patients after IVT. Further external verification among more diverse patient populations is needed to demonstrate the accuracy of the model's predictions.

Key factors influencing multidrug-resistant tuberculosis in patients under anti-tuberculosis treatment in two centres in Burundi: a mixed effect modelling study

Background

Despite the World Health Organization efforts to expand access to the tuberculosis treatment, multidrug resistant tuberculosis (MDR-TB) remains a major threat. MDR-TB represents a challenge for clinicians and staff operating in national tuberculosis (TB) programmes/centres. In sub-Saharan African countries including Burundi, MDR-TB coexists with high burden of other communicable and non-communicable diseases, creating a complex public health situation which is difficult to address. Tackling this will require targeted public health intervention based on evidence which well defines the at-risk population. In this study, using data from two referral anti-tuberculosis in Burundi, we model the key factors associated with MDR-TB in Burundi.

Methods

A case-control study was conducted from 1^stAugust 2019 to 15^th January 2020 in Kibumbu Sanatorium and Bujumbura anti-tuberculosis centres for cases and controls respectively. In all, 180 TB patients were selected, comprising of 60 cases and 120 controls using incidence density selection method. The associated factors were carried out by mixed effect logistic regression. Model performance was assessed by the Area under Curve (AUC). Model was internally validated via bootstrapping with 2000 replications. All analysis were done using R Statistical 3.5.0.

Results

MDR-TB was more identified among patients who lived in rural areas (51.3%), in patients’ residence (69.2%) and among those with a household size of six or more family members (59.5%).

Most of the MDR-TB cases had already been under TB treatment (86.4%), had previous contact with an MDR-TR case (85.0%), consumed tobacco (55.5%) and were diabetic (66.6 %). HIV prevalence was 32.3 % in controls and 67.7 % among cases. After modelling using mixed effects, Residence of patients (aOR= 1.31, 95%C: 1.12-1.80), living in houses with more than 6 family members (aOR= 4.15, 95% C: 3.06-5.39), previous close contact with MDR-TB (aOR= 6.03, 95% C: 4.01-8.12), history of TB treatment (aOR= 2.16, 95% C: 1.06-3.42), tobacco consumption (aOR = 3.17 ,95% C: 2.06-5.45) and underlying diabetes’ ( aOR= 4.09,95% CI = 2.01-16.79) were significantly associated with MDR-TB. With 2000 stratified bootstrap replicates, the model had an excellent predictive performance, accurately predicting 88.15% (95% C: 82.06%-92.8%) of all observations. The coexistence of risk factors to the same patients increases the risk of MDR-TB occurrence. TB patients with no any risk factors had 17.6% of risk to become MDR-TB. That probability was respectively three times and five times higher among diabetic and close contact MDR-TB patients.

Conclusion

The relatively high TB’s prevalence and MDR-TB occurrence in Burundi raises a cause for concern especially in this context where there exist an equally high burden of chronic diseases including malnutrition. Targeting interventions based on these identified risk factors will allow judicious channel of resources and effective public health planning.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-12233-2.

Propagating clade and model uncertainty to confidence intervals of divergence times and branch lengths

Confidence intervals of divergence times and branch lengths do not reflect uncertainty about their clades or about the prior distributions and other model assumptions on which they are based. Uncertainty about the clade may be propagated to a confidence interval by multiplying its confidence level by the bootstrap proportion of its clade or by another probability that the clade is correct. (If the confidence level is 95% and the bootstrap proportion is 90%, then the uncertainty-adjusted confidence level is (0.95)(0.90) = 86%.) Uncertainty about the model can be propagated to the confidence interval by reporting the union of the confidence intervals from all the plausible models. Unless there is no overlap between the confidence intervals, that results in an uncertainty-adjusted interval that has as its lower and upper limits the most extreme limits of the models. The proposed methods of uncertainty quantification may be used together.

Estimators and confidence intervals of f2 using bootstrap methodology for the comparison of dissolution profiles

BACKGROUND AND OBJECTIVES

The most widely used method to compare dissolution profiles is the similarity factor f₂. When this method is not applicable, the confidence interval of f₂ using bootstrap methodology has been recommended instead. As neither details of the estimator nor the types of confidence intervals are described in the guidelines, the suitability of five estimators and fourteen types of confidence intervals were investigated in this study by simulation.

METHODS

One million individual dissolution profiles were simulated for the reference and test populations with predefined target population f₂ values, where random samples of different sizes were drawn without replacement. From each pair of random samples, five f₂ estimators were calculated, and fourteen types of confidence intervals were obtained using 5000 bootstrap samples. The whole process was repeated 10000 times and the percentage of the similarity conclusions was measured. In addition, the uncertainty associated with the current practice of using f^₂ point estimate alone for the statistical inference was evaluated.

RESULTS

When combined with different types of confidence intervals, the estimated f₂ (f^₂), the bias-corrected f₂ (f^_2,bc), and the variance- and bias-corrected f₂ (f^_2,vcbc) are not suitable estimators due to higher-than-acceptable type I errors. The estimator f^_2,exp, calculated based on the mathematical expectation of f^₂, and f^_2,vcexp, the variance-corrected f^_2,exp, showed acceptable type I errors when combined with any of the ten percentile intervals. However, they have the drawback of low power, which might be addressed by increasing the sample size. To properly control the type I error, samples with at least 12 units should be used.

CONCLUSION

The best combinations of estimator and type of confidence interval are f^_2,exp and f^_2,vcexp combined with any of the ten types of percentile intervals. When the sample f₂ value is close to 50, the use of the confidence interval of f₂ is recommended even when the variability of the dissolution profiles is low and the prerequisites defined in the regulatory guidelines for using the conventional f₂ method are fulfilled in order to control the type I error rate.

A note on median regression for complex surveys

There is a great need for statistical methods for analyzing skewed responses in complex sample surveys. Quantile regression is a logical option in addressing this problem but is often accompanied by incorrect variance estimation. We show how the variance can be estimated correctly by including the survey design in the variance estimation process. In a simulation study, we illustrate that the variance of the median regression estimator has a very small relative bias with appropriate coverage probability. The motivation for our work stems from the National Health and Nutrition Examination Survey where we demonstrate the impact of our results on iodine deficiency in females compared with males adjusting for other covariates.

Development and validation of a prediction model for deep vein thrombosis in older non-mild acute pancreatitis patients

BACKGROUND

Deep vein thrombosis (DVT) may cause pulmonary embolus, leading to late deaths. The systemic inflammatory and hypercoagulable state of moderate and severe acute pancreatitis (non-mild acute pancreatitis, NMAP) patients may contribute to the development of venous thromboembolism. Accurate prediction of DVT is conducive to clinical decisions.

AIM

To develop and validate a potential new prediction nomogram model for the occurrence of DVT in NMAP.

METHODS

NMAP patient admission between 2013.1.1 and 2018.12.31 at the West China Hospital of Sichuan University was collected. A total of 220 patients formed the training set for nomogram development, and a validation set was constructed using bootstrapping with 100 resamplings. Univariate and multivariate logistic regression analyses were used to estimate independent risk factors associated with DVT. The independent risk factors were included in the nomogram. The accuracy and utility of the nomogram were evaluated by calibration curve and decision curve analysis, respectively.

RESULTS

A total of 220 NMAP patients over 60 years old were enrolled for this analysis. DVT was detected in 80 (36.4%) patients. The final nomogram included age, sex, surgery times, D-dimer, neutrophils, any organ failure, blood culture, and classification. This model achieved good concordance indexes of 0.827 (95%CI: 0.769-0.885) and 0.803 (95%CI: 0.743-0.860) in the training and validation sets, respectively.

CONCLUSION

We developed and validated a prediction nomogram model for DVT in older patients with NMAP. This may help guide doctors in making sound decisions regarding the administration of DVT prophylaxis.

Inference on the overlap coefficient: The binormal approach and alternatives

The overlap coefficient (OVL) measures the similarity between two distributions through the overlapping area of their distribution functions. Given its intuitive description and ease of visual representation by the straightforward depiction of the amount of overlap between the two corresponding histograms based on samples of measurements from each one of the two distributions, the development of accurate methods for confidence interval construction can be useful for applied researchers. The overlap coefficient has received scant attention in the literature since it lacks readily available software for its implementation, while inferential procedures that can cover the whole range of distributional scenarios for the two underlying distributions are missing. Such methods, both parametric and non-parametric are developed in this article, while R-code is provided for their implementation. Parametric approaches based on the binormal model show better performance and are appropriate for use in a wide range of distributional scenarios. Methods are assessed through a large simulation study and are illustrated using a dataset from a study on human immunodeficiency virus-related cognitive function assessment.

The executive function of bilingual and monolingual children: A technical efficiency approach

This paper introduces a novel approach to evaluate performance in the executive functioning skills of bilingual and monolingual children. This approach targets method- and analysis-specific issues in the field, which has reached an impasse (Antoniou et al., 2021). This study moves beyond the traditional approach towards bilingualism by using an array of executive functioning tasks and frontier methodologies, which allow us to jointly consider multiple tasks and metrics in a new measure; technical efficiency (TE). We use a data envelopment analysis technique to estimate TE for a sample of 32 Greek-English bilingual and 38 Greek monolingual children. In a second stage, we compare the TE of the groups using an ANCOVA, a bootstrap regression, and a k-means nearest-neighbour technique, while controlling for a range of background variables. Results show that bilinguals have superior TE compared to their monolingual counterparts, being around 6.5% more efficient. Robustness tests reveal that TE yields similar results to the more complex conventional MANCOVA analyses, while utilising information in a more efficient way. By using the TE approach on a relevant existing dataset, we further highlight TE's advantages compared to conventional analyses; not only does TE use a single measure, instead of two principal components, but it also allows more group observations as it accounts for differences between the groups by construction.

Clustering assessment in weighted networks

We provide a systematic approach to validate the results of clustering methods on weighted networks, in particular for the cases where the existence of a community structure is unknown. Our validation of clustering comprises a set of criteria for assessing their significance and stability. To test for cluster significance, we introduce a set of community scoring functions adapted to weighted networks, and systematically compare their values to those of a suitable null model. For this we propose a switching model to produce randomized graphs with weighted edges while maintaining the degree distribution constant. To test for cluster stability, we introduce a non parametric bootstrap method combined with similarity metrics derived from information theory and combinatorics. In order to assess the effectiveness of our clustering quality evaluation methods, we test them on synthetically generated weighted networks with a ground truth community structure of varying strength based on the stochastic block model construction. When applying the proposed methods to these synthetic ground truth networks' clusters, as well as to other weighted networks with known community structure, these correctly identify the best performing algorithms, which suggests their adequacy for cases where the clustering structure is not known. We test our clustering validation methods on a varied collection of well known clustering algorithms applied to the synthetically generated networks and to several real world weighted networks. All our clustering validation methods are implemented in R, and will be released in the upcoming package clustAnalytics.

A Generalized Linear modeling approach to bootstrapping multi-frame PET image data

PET imaging is an important diagnostic tool for management of patients with cancer and other diseases. Medical decisions based on quantitative PET information could potentially benefit from the availability of tools for evaluation of associated uncertainties. Raw PET data can be viewed as a sample from an inhomogeneous Poisson process so there is the possibility to directly apply bootstrapping to raw projection-domain list-mode data. Unfortunately this is computationally impractical, particularly if data reconstruction is iterative or the acquisition protocol is dynamic. We develop a flexible statistical linear model analysis to be used with multi-frame PET image data to create valid bootstrap samples. The technique is illustrated using data from dynamic PET studies with fluoro-deoxyglucose (FDG) and fluoro-thymidine (FLT) in brain and breast cancer patients. As is often the case with dynamic PET studies, data have been archived without raw list-mode information. Using the bootstrapping technique maps of kinetic parameters and associated uncertainties are obtained. The quantitative performance of the approach is assessed by simulation. The proposed image-domain bootstrap is found to substantially match the projection-domain alternative. Analysis of results points to a close relation between relative uncertainty in voxel-level kinetic parameters and local reconstruction error. This is consistent with statistical theory.