Aberrant global and local dynamic properties in schizophrenia with instantaneous phase method based on Hilbert transform.

BACKGROUND: Emerging functional imaging studies suggest that schizophrenia is associated with aberrant spatiotemporal interaction which may result in aberrant global and local dynamic properties. METHODS: We investigated the dynamic functional connectivity (FC) by using instantaneous phase method based on Hilbert transform to detect abnormal spatiotemporal interaction in schizophrenia. Based on resting-state functional magnetic resonance imaging, two independent datasets were included, with 114 subjects from COBRE [51 schizophrenia patients (SZ) and 63 healthy controls (HCs)] and 96 from OpenfMRI (36 SZ and 60 HCs). Phase differences and instantaneous coupling matrices were firstly calculated at all time points by extracting instantaneous parameters. Global [global synchrony and intertemporal closeness (ITC)] and local dynamic features [strength of FC (sFC) and variability of FC (vFC)] were compared between two groups. Support vector machine (SVM) was used to estimate the ability to discriminate two groups by using all aberrant features. RESULTS: We found SZ had lower global synchrony and ITC than HCs on both datasets. Furthermore, SZ had a significant decrease in sFC but an increase in vFC, which were mainly located at prefrontal cortex, anterior cingulate cortex, temporal cortex and visual cortex or temporal cortex and hippocampus, forming significant dynamic subnetworks. SVM analysis revealed a high degree of balanced accuracy (85.75%) on the basis of all aberrant dynamic features. CONCLUSIONS: SZ has worse overall spatiotemporal stability and extensive FC subnetwork lesions compared to HCs, which to some extent elucidates the pathophysiological mechanism of schizophrenia, providing insight into time-variation properties of patients with other mental illnesses.

Dirichlet composition distribution for compositional data with zero components: An application to fluorescence in situ hybridization (FISH) detection of chromosome.

Zeros in compositional data are very common and can be classified into rounded and essential zeros. The rounded zero refers to a small proportion or below detection limit value, while the essential zero refers to the complete absence of the component in the composition. In this article, we propose a new framework for analyzing compositional data with zero entries by introducing a stochastic representation. In particular, a new distribution, namely the Dirichlet composition distribution, is developed to accommodate the possible essential-zero feature in compositional data. We derive its distributional properties (e.g., its moments). The calculation of maximum likelihood estimates via the Expectation-Maximization (EM) algorithm will be proposed. The regression model based on the new Dirichlet composition distribution will be considered. Simulation studies are conducted to evaluate the performance of the proposed methodologies. Finally, our method is employed to analyze a dataset of fluorescence in situ hybridization (FISH) for chromosome detection.

Fast QLB algorithm and hypothesis tests in logistic model for ophthalmologic bilateral correlated data.

In ophthalmologic or otolaryngologic studies, bilateral correlated data often arise when observations involving paired organs (e.g., eyes, ears) are measured from each subject. Based on Donner's model , in this paper, we focus on investigating the relationship between the disease probability and covariates (such as ages, weights, gender, and so on) via the logistic regression for the analysis of bilateral correlated data. We first propose a new minorization-maximization (MM) algorithm and a fast quadratic lower bound (QLB) algorithm to calculate the maximum likelihood estimates of the vector of regression coefficients, and then develop three large-sample tests (i.e., the likelihood ratio test, Wald test, and score test) to test if covariates have a significant impact on the disease probability. Simulation studies are conducted to evaluate the performance of the proposed fast QLB algorithm and three testing methods. A real ophthalmologic data set in Iran is used to illustrate the proposed methods.

Poisson item count techniques with noncompliance.

The Poisson item count technique (PICT) is a survey method that was recently developed to elicit respondents' truthful answers to sensitive questions. It simplifies the well-known item count technique (ICT) by replacing a list of independent innocuous questions in known proportions with a single innocuous counting question. However, ICT and PICT both rely on the strong "no design effect assumption" (ie, respondents give the same answers to the innocuous items regardless of the absence or presence of the sensitive item in the list) and "no liar" (ie, all respondents give truthful answers) assumptions. To address the problem of self-protective behavior and provide more reliable analyses, we introduced a noncompliance parameter into the existing PICT. Based on the survey design of PICT, we considered more practical model assumptions and developed the corresponding statistical inferences. Simulation studies were conducted to evaluate the performance of our method. Finally, a real example of automobile insurance fraud was used to demonstrate our method.

Confidence intervals for proportion ratios of stratified correlated bilateral data.

In stratified bilateral studies, responses from two paired body parts are correlated. Confidence intervals (CIs), which reveal various features of the data, should take the correlations into account. In this article, five CI methods (sample-size weighted naïve Maximum likelihood estimation (MLE)-based Wald-type CI, complete MLE-based Wald-type CI, profile likelihood CI, MLE-based score CI and pooled MLE-based Wald-type CI) are derived for proportion ratios under the assumption of equal correlation coefficient within each stratum. Monte Carlo simulation shows that the complete MLE-based Wald-type CI approach generally produces the shortest mean interval width and satisfactory empirical coverage probability with close form solution; while the profile likelihood CI and the MLE-based score CI provide preferred ratio of non coverage probability and are more symmetric. Two real examples are used to demonstrate the performance of the proposed methods.

Efficient statistical inference for a parallel study with missing data by using an exact method.

In a parallel group study comparing a new treatment with a standard of care, missing data often occur for various reasons. When the outcome is binary, the data from such studies can be summarized into a 2 × 3 contingency table, with the missing observations in the last column. When the missingness is neither related to the outcome of interest nor related to other outcomes from the study but it is covariate dependent with the sole covariate being treatment, this type of missing data mechanism is considered as missing at random. In 2016, Tian et al.  proposed three statistics to test the hypothesis that the response rate is equivalent for a parallel group study with missing data. The asymptotic limiting distributions of these test statistics were used for statistical inference. However, asymptotic approaches for testing proportions generally do not have satisfactory performance with regard to type I error rate control for a clinical trial with the sample size from small to medium. For this reason, we consider an exact approach based on maximization to provide valid and efficient statistical inference for a parallel group study with missing data. Exact approaches can guarantee the type I error rate and they are computationally feasible in this setting. We conduct extensive numerical studies to compare the performance of the exact approach based on the three statistics for a one-sided hypothesis testing problem. We conclude that the exact approach based on the likelihood ratio statistic is more powerful than the exact approach based on the other two statistics. Two real clinical trial data sets are used to illustrate the application of the proposed exact approach.

A new multivariate zero-adjusted Poisson model with applications to biomedicine.

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, 1989) cannot be used to fit multivariate count data with excess zero-vectors; (ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases; (iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods.

Testing homogeneity of proportion ratios for stratified correlated bilateral data in two-arm randomized clinical trials.

Stratified data analysis is an important research topic in many biomedical studies and clinical trials. In this article, we develop five test statistics for testing the homogeneity of proportion ratios for stratified correlated bilateral binary data based on an equal correlation model assumption. Bootstrap procedures based on these test statistics are also considered. To evaluate the performance of these statistics and procedures, we conduct Monte Carlo simulations to study their empirical sizes and powers under various scenarios. Our results suggest that the procedure based on score statistic performs well generally and is highly recommended. When the sample size is large, procedures based on the commonly used weighted least square estimate and logarithmic transformation with Mantel-Haenszel estimate are recommended as they do not involve any computation of maximum likelihood estimates requiring iterative algorithms. We also derive approximate sample size formulas based on the recommended test procedures. Finally, we apply the proposed methods to analyze a multi-center randomized clinical trial for scleroderma patients.

Confidence-interval construction for rate ratio in matched-pair studies with incomplete data.

Matched-pair design is often used in clinical trials to increase the efficiency of establishing equivalence between two treatments with binary outcomes. In this article, we consider such a design based on rate ratio in the presence of incomplete data. The rate ratio is one of the most frequently used indices in comparing efficiency of two treatments in clinical trials. In this article, we propose 10 confidence-interval estimators for the rate ratio in incomplete matched-pair designs. A hybrid method that recovers variance estimates required for the rate ratio from the confidence limits for single proportions is proposed. It is noteworthy that confidence intervals based on this hybrid method have closed-form solution. The performance of the proposed confidence intervals is evaluated with respect to their exact coverage probability, expected confidence interval width, and distal and mesial noncoverage probability. The results show that the hybrid Agresti-Coull confidence interval based on Fieller's theorem performs satisfactorily for small to moderate sample sizes. Two real examples from clinical trials are used to illustrate the proposed confidence intervals.

A nitrous oxide/oxygen fixed mixture to reduce pain induced by the hypodermic injection: study protocol for a randomized, controlled trial.

BACKGROUND: Patients with hematological malignancies received multiple hypodermic injections of recombinant human granulocyte colony-stimulating factor. Procedural pain is one of the most common iatrogenic causes of pain in patients with hematological malignancies. It is also identified as the most commonly occurring problem in clinical care in the Department of Hematology and Oncology at Shenzhen University General Hospital. However, providing immediate relief from pain induced by hypodermic injection of recombinant human granulocyte colony-stimulating factor remains a major challenge. This trial aims to evaluate the safety and analgesic efficacy of a fixed nitrous oxide/oxygen mixture for patients with hematological malignancies and experiencing procedural pain caused by hypodermic injection of recombinant human granulocyte colony-stimulating factor in the department. METHODS: The nitrous oxide/oxygen study is a single-center, randomized, double-blind, placebo-controlled trial involving patients with hematological malignancies who require hypodermic injections of recombinant human granulocyte colony-stimulating factor for treatment. This trial was conducted in the Hematology and Oncology Department of Shenzhen University General Hospital. A total of 54 eligible patients were randomly allocated to either the fixed nitrous oxide/oxygen mixture group (n = 36) or the oxygen group (n = 18). Neither the investigators nor the patients known about the randomization list and the nature of the gas mixture in each cylinder. Outcomes were monitored at the baseline (T0), immediately after hypodermic injection of recombinant human granulocyte colony-stimulating factor (T1), and 5 min after hypodermic injection of recombinant human granulocyte colony-stimulating factor (T2) for each group. The primary outcome measure was the score in the numerical rating scale corresponding to the highest level of pain experienced during hypodermic injection of recombinant human granulocyte colony-stimulating factor. Secondary outcomes included the fear of pain, anxiety score, four physiological parameters, adverse effects, total time of gas administration, satisfaction from both patients and nurses, and the acceptance of the patients. DISCUSSION: This study focused on the safety and analgesic efficacy during hypodermic injection of recombinant human granulocyte colony-stimulating factor procedure. Data on the feasibility and safety of nitrous oxide/oxygen therapy was provided if proven beneficial to patients with hematological malignancies during hypodermic injection of recombinant human granulocyte colony-stimulating factor and widely administered to patients with procedural pain in the department. TRIAL REGISTRATION: Chinese Clinical Trial Register, ChiCTR2200061507. Registered on June 27, 2022. http://www.chictr.org.cn/edit.aspx?pid=170573&htm=4.

A multivariate zero-inflated binomial model for the analysis of correlated proportional data.

In this paper, a new multivariate zero-inflated binomial (MZIB) distribution is proposed to analyse the correlated proportional data with excessive zeros. The distributional properties of purposed model are studied. The Fisher scoring algorithm and EM algorithm are given for the computation of estimates of parameters in the proposed MZIB model with/without covariates. The score tests and the likelihood ratio tests are derived for assessing both the zero-inflation and the equality of multiple binomial probabilities in correlated proportional data. A limited simulation study is performed to evaluate the performance of derived EM algorithms for the estimation of parameters in the model with/without covariates and to compare the nominal levels and powers of both score tests and likelihood ratio tests. The whitefly data is used to illustrate the proposed methodologies.

A new multivariate t distribution with variant tail weights and its application in robust regression analysis.

In this paper, we propose a new kind of multivariate t distribution by allowing different degrees of freedom for each univariate component. Compared with the classical multivariate t distribution, it is more flexible in the model specification that can be used to deal with the variant amounts of tail weights on marginals in multivariate data modeling. In particular, it could include components following the multivariate normal distribution, and it contains the product of independent t-distributions as a special case. Subsequently, it is extended to the regression model as the joint distribution of the error terms. Important distributional properties are explored and useful statistical methods are developed. The flexibility of the specified structure in better capturing the characteristic of data is exemplified by both simulation studies and real data analyses.

Simultaneous confidence interval construction for many-to-one comparisons of proportion differences based on correlated paired data.

In some medical researches such as ophthalmological, orthopaedic and otolaryngologic studies, it is often of interest to compare multiple groups with a control using data collected from paired organs of patients. The major difficulty in performing the data analysis is to adjust the multiplicity between the comparison of multiple groups, and the correlation within the same patient's paired organs. In this article, we construct asymptotic simultaneous confidence intervals (SCIs) for many-to-one comparisons of proportion differences adjusting for multiplicity and the correlation. The coverage probabilities and widths of the proposed CIs are evaluated by Monte Carlo simulation studies. The methods are illustrated by a real data example.

[Research of coupling effects among various water quality components].

Based on aquatic optics Monte Carlo hyperspectral simulation, the interactions between spectral characteristics of chlorophyll a, total suspended matter (TSM) and colored dissolved organic matter (CDOM) were discussed. The result shows that the nonlinear and spectrally varying interactions between different water components are extremely highly asymmetric. The existing of chlorophyll a and CDOM has little effects on the characteristic wavebands selection and information retrieval of TSM, while the effects of TSM on chlorophyll a are obvious. With the stepwise additions of TSM, the response of chlorophyll a to its concentration becomes weak. When the concentration of TSM increases to a certain degree, the spectral response of chlorophyll a concentration will disappear. Even at the sensitivity waveband of chlorophyll a such as 670 nm, when the TSM is in high concentration, the spectral reflectance will not change with chlorophyll a concentration, which lead to difficulty to extract the chlorophyll a concentration in turbid water dominated by suspended matter. The existing of CDOM causes the blue and green band ratio algorithm to fail when the chlorophyll a is in middle to high concentration. The spectral effects on CDOM of the water body dominated by TSM are more obvious than that dominated by chlorophyll a. There are strong inhibition effects of TSM on the CDOM spectral properties in the short bands. The research results can provide theoretical basis for characteristic waveband selection, the application scope of water component concentration inversion algorithm and the waveband setting for case 2 water remote sensing.

Design and sample size for evaluating combinations of drugs of linear and loglinear dose-response curves.

The study of drug combinations has become important in drug development due to its potential for efficacy at lower, less toxic doses and the need to move new therapies rapidly into clinical trials. The goal is to identify which combinations are additive, synergistic, or antagonistic. Although there exists statistical framework for finding doses and sample sizes needed to detect departure from additivity, e.g., the power maximized F-test, different classes of drugs of different does-response shapes require different derivation for calculating sample size and finding doses. Motivated by two anticancer combination studies that we are involved with, this article proposes dose-finding and sample size method for detecting departures from additivity of two drugs with linear and log-linear single dose-response curves. The first study involves combination of two drugs, where one single drug dose-response curve is linear and the other is log-linear. The second study involves combinations of drugs whose single drug dose-response curves are linear. The experiment had been planned with the common fixed ratio design before we were consulted, but the resulting data missed the synergistic combinations. However, the experiment based on the proposed design was able to identify the synergistic combinations as anticipated. Thus we shall summarize the analysis of the data collected according to the proposed design and discuss why the commonly used fixed ratio method failed and the implications of the proposed method for other combination studies.

Non-iterative sampling-based Bayesian methods for identifying changepoints in the sequence of cases of haemolytic uraemic syndrome.

Diarrhoea-associated Haemolytic uraemic syndrome (HUS) is a disease that affects the kidneys and other organs. Motivated by the annual number of cases of HUS collected in Birmingham and Newcastle of England, respectively, from 1970 to 1989, we consider Bayesian changepoint analysis with specific attention to Poisson changepoint models. For changepoint models with unknown number of changepoints, we propose a new non-iterative Bayesian sampling approach (called exact IBF sampling), which completely avoids the problem of convergence and slow convergence associated with iterative Markov chain Monte Carlo (MCMC) methods. The idea is to first utilize the sampling inverse Bayes formula (IBF) to derive the conditional distribution of the latent data given the observed data, and then to draw iid samples from the complete-data posterior distribution. For the purpose of selecting the appropriate model (or determining the number of changepoints), we develop two alternative formulae to exactly calculate marginal likelihood (or Bayes factor) by using the exact IBF output and the point-wise IBF, respectively. The HUS data are re-analyzed using the proposed methods. Simulations are implemented to validate the performance of the proposed methods.

Common risk difference test and interval estimation of risk difference for stratified bilateral correlated data.

Bilateral correlated data are often encountered in medical researches such as ophthalmologic (or otolaryngologic) studies, in which each unit contributes information from paired organs to the data analysis, and the measurements from such paired organs are generally highly correlated. Various statistical methods have been developed to tackle intra-class correlation on bilateral correlated data analysis. In practice, it is very important to adjust the effect of confounder on statistical inferences, since either ignoring the intra-class correlation or confounding effect may lead to biased results. In this article, we propose three approaches for testing common risk difference for stratified bilateral correlated data under the assumption of equal correlation. Five confidence intervals of common difference of two proportions are derived. The performance of the proposed test methods and confidence interval estimations is evaluated by Monte Carlo simulations. The simulation results show that the score test statistic outperforms other statistics in the sense that the former has robust type I error rates with high powers. The score confidence interval induced from the score test statistic performs satisfactorily in terms of coverage probabilities with reasonable interval widths. A real data set from an otolaryngologic study is used to illustrate the proposed methodologies.

Multifunctional Thermal Management Materials with Excellent Heat Dissipation and Generation Capability for Future Electronics.

Thermal management materials (TMMs) used in electronic devices are crucial for future electronics and technologies such as flexible electronics and artificial intelligence (AI) technologies. As future electronics will work in a more complicated circumstance, the overheating and overcooling problems can exist in the same electronics while the common TMMs cannot meet the demand of thermal management for future electronics. In this work, nacre-mimetic graphene-based films with super flexibility and durability (in over 10,000 tensile cycles), excellent capability to dissipate excess heat (20.84 W/(m·K) at only 16-22 µm thickness), and outstanding heating performance to generate urgent heat for electronics under extremely cold conditions are fabricated by a facile solution casting method, and the fabricated composites are proved to be superior multifunctional TMMs for the thermal management in electronic chips. In addition, the application of the paper-like films with high in-plane thermal conductivity to a flexible heat spreader and film heater is demonstrated by simulation using a finite volume method, which shows the high importance of the in-plane thermal conductivity in thermal management of electronics.

Efficient methods for estimating constrained parameters with applications to lasso logistic regression.

Fitting logistic regression models is challenging when their parameters are restricted. In this article, we first develop a quadratic lower-bound (QLB) algorithm for optimization with box or linear inequality constraints and derive the fastest QLB algorithm corresponding to the smallest global majorization matrix. The proposed QLB algorithm is particularly suited to problems to which EM-type algorithms are not applicable (e.g., logistic, multinomial logistic, and Cox's proportional hazards models) while it retains the same EM ascent property and thus assures the monotonic convergence. Secondly, we generalize the QLB algorithm to penalized problems in which the penalty functions may not be totally differentiable. The proposed method thus provides an alternative algorithm for estimation in lasso logistic regression, where the convergence of the existing lasso algorithm is not generally ensured. Finally, by relaxing the ascent requirement, convergence speed can be further accelerated. We introduce a pseudo-Newton method that retains the simplicity of the QLB algorithm and the fast convergence of the Newton method. Theoretical justification and numerical examples show that the pseudo-Newton method is up to 71 (in terms of CPU time) or 107 (in terms of number of iterations) times faster than the fastest QLB algorithm and thus makes bootstrap variance estimation feasible. Simulations and comparisons are performed and three real examples (Down syndrome data, kyphosis data, and colon microarray data) are analyzed to illustrate the proposed methods.

Valid statistical inference methods for a case-control study with missing data.

The main objective of this paper is to derive the valid sampling distribution of the observed counts in a case-control study with missing data under the assumption of missing at random by employing the conditional sampling method and the mechanism augmentation method. The proposed sampling distribution, called the case-control sampling distribution, can be used to calculate the standard errors of the maximum likelihood estimates of parameters via the Fisher information matrix and to generate independent samples for constructing small-sample bootstrap confidence intervals. Theoretical comparisons of the new case-control sampling distribution with two existing sampling distributions exhibit a large difference. Simulations are conducted to investigate the influence of the three different sampling distributions on statistical inferences. One finding is that the conclusion by the Wald test for testing independency under the two existing sampling distributions could be completely different (even contradictory) from the Wald test for testing the equality of the success probabilities in control/case groups under the proposed distribution. A real cervical cancer data set is used to illustrate the proposed statistical methods.