Sequential covariate-adjusted randomization via hierarchically minimizing Mahalanobis distance and marginal imbalance.

In comparative studies, covariate balance and sequential allocation schemes have attracted growing academic interest. Although many theoretically justified adaptive randomization methods achieve the covariate balance, they often allocate patients in pairs or groups. To better meet the practical requirements where the clinicians cannot wait for other participants to assign the current patient for some economic or ethical reasons, we propose a method that randomizes patients individually and sequentially. The proposed method conceptually separates the covariate imbalance, measured by the newly proposed modified Mahalanobis distance, and the marginal imbalance, that is the sample size difference between the 2 groups, and it minimizes them with an explicit priority order. Compared with the existing sequential randomization methods, the proposed method achieves the best possible covariate balance while maintaining the marginal balance directly, offering us more control of the randomization process. We demonstrate the superior performance of the proposed method through a wide range of simulation studies and real data analysis, and also establish theoretical guarantees for the proposed method in terms of both the convergence of the imbalance measure and the subsequent treatment effect estimation.

Network and covariate adjusted response-adaptive design for binary response.

Randomization is a distinguishing feature of clinical trials for unbiased assessment of treatment efficacy. With a growing demand for more flexible and efficient randomization schemes and motivated by the idea of adaptive design, in this article we propose the network and covariate adjusted response-adaptive (NCARA) design that can concurrently manage three challenges: (1) maximizing benefits of a trial by assigning more patients to the superior treatment group randomly; (2) balancing social network ties across treatment arms to eliminate potential network interference; and (3) ensuring balance of important covariates, such as age, gender, and other potential confounders. We conduct simulation with different network structures and a variety of parameter settings. It is observed that the NCARA design outperforms four alternative randomization designs in solving the above-mentioned problems and has comparable power and type I error for detecting true difference between treatment groups. In addition, we conduct real data analysis to implement the new design in two clinical trials. Compared to equal randomization (the original design utilized in the trials), the NCARA design slightly increases power, largely increases the percentage of patients assigned to the better-performing group, and significantly improves network and covariate balances. It is also noted that the advantages of the NCARA design are augmented when the sample size is small and the level of network interference is high. In summary, the proposed NCARA design assists researchers in conducting clinical trials with high-quality and high-efficiency.

Lq-based robust analytics on ultrahigh and high dimensional data.

Ultrahigh and high dimensional data are common in regression analysis for various fields, such as omics data, finance, and biological engineering. In addition to the problem of dimension, the data might also be contaminated. There are two main types of contamination: outliers and model misspecification. We develop an unique method that takes into account the ultrahigh or high dimensional issues and both types of contamination. In this article, we propose a framework for feature screening and selection based on the minimum Lq-likelihood estimation (MLqE), which accounts for the model misspecification contamination issue and has also been shown to be robust to outliers. In numerical analysis, we explore the robustness of this framework under different outliers and model misspecification scenarios. To examine the performance of this framework, we conduct real data analysis using the skin cutaneous melanoma data. When comparing with traditional screening and feature selection methods, the proposed method shows superiority in both variable identification effectiveness and parameter estimation accuracy.

The effect of post-abortion care (PAC) on anxiety in women with spontaneous abortion based on MicroRNA-21 expression, cortisol level, and Fordyce happiness pattern.

Abortion is one of the most common complications in pregnancy, and the cause of its occurrence in many cases remains unknown. The high prevalence and consequences of anxiety in women with spontaneous abortion could highlight the importance and role of post-abortion care (PAC). Detection and identification of biomarkers related to abortion and anxiety can effectively diagnose and prevent complications. Among the known biomarkers, microRNAs and the cortisol level have high potential. Therefore, the present study evaluated the effect of post-abortion care (PAC) on anxiety in women with spontaneous abortion based on MicroRNA-21 expression, cortisol level, and Fordyce happiness pattern. In this randomized clinical trial, 72 women with spontaneous abortion were studied and randomly divided into two groups of intervention (n = 36) and control (n = 36). Data were collected through a demographic questionnaire and HADS. To assess PAC, the intervention group was consulted in 8 sessions of 60 minutes in the first 72 hours after abortion. Meetings were held twice a week for four weeks. Both groups were followed up immediately after and one month after the intervention. To evaluate biological factors, 4ml of blood sample was obtained from the subjects. Blood cortisol levels were measured by the Cortisol Competitive Human ELISA Kit (Thermo-Fisher, USA), and microRNA-21 evaluation was performed by Real-time PCR technique. Data were analyzed using SPSS16 software. Results showed that before the intervention, there was no significant difference in the mean score of anxiety between the control and intervention groups (P> 0.05); But at the time immediately and one month after the intervention, there was a significant difference in the mean score of anxiety (p <0.001). The results of biological factors evaluation showed that in the intervention group, serum cortisol levels and microRNA-21 expression decreased significantly (p <0.05). In general, PAC based on the happiness pattern can control the anxiety of women with spontaneous abortion. Therefore, it is recommended as an effective and non-invasive intervention in preventing women's psychological problems after spontaneous abortion.

Robust group variable screening based on maximum Lq-likelihood estimation.

Variable screening plays an important role in ultra-high-dimensional data analysis. Most of the previous analyses have focused on individual predictor screening using marginal correlation or other rank-based techniques. When predictors can be naturally grouped, the structure information should be incorporated while applying variable screening. This study presents a group screening procedure that is based on maximum Lq-likelihood estimation, which is being increasingly used for robust estimation. The proposed method is robust against data contamination, including a heavy-tailed distribution of the response and a mixture of observations from different distributions. The sure screening property is rigorously established. Simulations demonstrate the competitive performance of the proposed method, especially in terms of its robustness against data contamination. Two real data analyses are presented to further illustrate its performance.

Model confidence bounds for variable selection.

In this article, we introduce the concept of model confidence bounds (MCB) for variable selection in the context of nested models. Similarly to the endpoints in the familiar confidence interval for parameter estimation, the MCB identifies two nested models (upper and lower confidence bound models) containing the true model at a given level of confidence. Instead of trusting a single selected model obtained from a given model selection method, the MCB proposes a group of nested models as candidates and the MCB's width and composition enable the practitioner to assess the overall model selection uncertainty. A new graphical tool-the model uncertainty curve (MUC)-is introduced to visualize the variability of model selection and to compare different model selection procedures. The MCB methodology is implemented by a fast bootstrap algorithm that is shown to yield the correct asymptotic coverage under rather general conditions. Our Monte Carlo simulations and real data examples confirm the validity and illustrate the advantages of the proposed method.

Penalized integrative semiparametric interaction analysis for multiple genetic datasets.

In this article, we consider a semiparametric additive partially linear interaction model for the integrative analysis of multiple genetic datasets. The goals are to identify important genetic predictors and gene-gene interactions and to estimate the nonparametric functions that describe the environmental effects at the same time. To find the similarities and differences of the genetic effects across different datasets, we impose a group structure on the regression coefficients matrix under the homogeneity assumption, ie, models for different datasets share the same sparsity structure, but the coefficients may differ across datasets. We develop an iterative approach to estimate the parameters of main effects, interactions and nonparametric functions, where a reparametrization of interaction parameters is implemented to meet the strong hierarchy assumption. We demonstrate the advantages of the proposed method in identification, estimation, and prediction in a series of numerical studies. We also apply the proposed method to the Skin Cutaneous Melanoma data and the lung cancer data from the Cancer Genome Atlas.

Assisted gene expression-based clustering with AWNCut.

In the research on complex diseases, gene expression (GE) data have been extensively used for clustering samples. The clusters so generated can serve as the basis for disease subtype identification, risk stratification, and many other purposes. With the small sample sizes of genetic profiling studies and noisy nature of GE data, clustering analysis results are often unsatisfactory. In the most recent studies, a prominent trend is to conduct multidimensional profiling, which collects data on GEs and their regulators (copy number alterations, microRNAs, methylation, etc.) on the same subjects. With the regulation relationships, regulators contain important information on the properties of GEs. We develop a novel assisted clustering method, which effectively uses regulator information to improve clustering analysis using GE data. To account for the fact that not all GEs are informative, we propose a weighted strategy, where the weights are determined data-dependently and can discriminate informative GEs from noises. The proposed method is built on the NCut technique and effectively realized using a simulated annealing algorithm. Simulations demonstrate that it can well outperform multiple direct competitors. In the analysis of TCGA cutaneous melanoma and lung adenocarcinoma data, biologically sensible findings different from the alternatives are made.

Extraperitoneal versus transperitoneal cesarean section: a retrospective study.

OBJECTIVES: To evaluate extraperitoneal cesarean section as a routine elective surgery. METHODS: In this retrospective study, 461 primiparas were divided into the extraperitoneal and transperitoneal cesarean section groups according to the operation type in a random, but non-blinded, manner. The outcome measures were intraoperative blood loss, operation duration, postoperative gas passage time, postoperative pain, postoperative complications, and neonatal indicators. RESULTS: The operation duration of the extraperitoneal cesarean section group was significantly lower than that of the transperitoneal cesarean section group (P < 0.05). Compared to the transperitoneal cesarean section group, the extraperitoneal cesarean section group had neonates with higher birth weights and fewer neonatal transfers (P < 0.05). There was no difference in other maternal surgical or neonatal complications between the two groups. CONCLUSION: While extraperitoneal cesarean section can be safely performed as a routine procedure in the surgical delivery of primiparas, it must be performed by well-trained surgeons. In view of its advantages, it is worth being promoted in senior general hospitals as a routine choice.Abbreviations: CS: Cesarean section; ECS: Extraperitoneal; TCS: Transperitoneal; VAS: Visual analogue scale.

Syndrome evaluation in traditional Chinese medicine using second-order latent variable model.

The syndrome is one of the most important concepts and ingredients in the theoretical and clinical research of traditional Chinese medicine (TCM). TCM doctors believe that all diseases are caused by an imbalance in the patient's body, which is called syndrome. All the therapies and formulas in TCM are decided according to the patients' syndrome situation. To quantitatively evaluate the level of syndrome, many statistical methodologies have been discussed in recent years. In this article, we introduce a second-order latent variable model to evaluate the level of patients' syndrome with many clinical symptoms. An objective evaluation score can be easily derived by the proposed model, with a high speed of convergence and without joint-distribution assumption. We illustrate the application of this model by an analysis of premenstrual disorder syndrome of liver-qi invasion syndrome evaluation research.

Peptoniphilus indolicus infection in a pregnant woman: a case report.

BACKGROUND: Peptoniphilus indolicus belongs is a gram-positive anaerobic coccus (GPAC), which can cause bacterial vaginitis. However, only a few studies have reported severe infection of P. indolicus. This study presented the first case of severe infection of P. indolicus during pregnancy. It aimed to help to fill the gap in the literature, find out the factors that accelerate infection and discuss the significance of the GPAC test. CASE PRESENTATION: A 35-year-old woman was admitted due to unbearable abdominal pain with dilation of the cervical opening at 22+ weeks of gestation. A blood test revealed electrolyte disturbance and hypoproteinemia. A day before admission, the patient developed pain in the lower abdomen accompanied by yellow-green vaginal discharge. Two hours after admission, the patient suddenly presented with hyperpyrexia and chills. Timely and adequate antibiotic and cooling treatments were administered. After 14 h, the patient again developed chills that lasted for approximately 20 min, accompanied by uterine contractions and membrane rupture. After 3 h, she had a miscarriage and rapidly developed septic shock. She was transferred to the intensive care unit for further infection control, shock correction, and circulatory stabilization. The cultures of blood, secretion specimen, and amniotic fluid indicated P. indolicus infection using a matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, an advanced tool for bacterial species identification. CONCLUSIONS: P. indolicus is an opportunistic pathogen in pregnant women. Poor physical conditions and pregnancy may accelerate disease progression and lead to severe inflammation.

Testing for treatment effect in covariate-adaptive randomized trials with generalized linear models and omitted covariates.

Concerns have been expressed over the validity of statistical inference under covariate-adaptive randomization despite the extensive use in clinical trials. In the literature, the inferential properties under covariate-adaptive randomization have been mainly studied for continuous responses; in particular, it is well known that the usual two-sample t-test for treatment effect is typically conservative. This phenomenon of invalid tests has also been found for generalized linear models without adjusting for the covariates and are sometimes more worrisome due to inflated Type I error. The purpose of this study is to examine the unadjusted test for treatment effect under generalized linear models and covariate-adaptive randomization. For a large class of covariate-adaptive randomization methods, we obtain the asymptotic distribution of the test statistic under the null hypothesis and derive the conditions under which the test is conservative, valid, or anti-conservative. Several commonly used generalized linear models, such as logistic regression and Poisson regression, are discussed in detail. An adjustment method is also proposed to achieve a valid size based on the asymptotic results. Numerical studies confirm the theoretical findings and demonstrate the effectiveness of the proposed adjustment method.

Machine Learning Classification of Verified Head Impact Exposure Strengthens Associations with Brain Changes.

Cumulative exposure to head impacts during contact sports can elicit potentially deleterious brain white matter alterations in young athletes. Head impact exposure is commonly quantified using wearable sensors; however, these sensors tend to overestimate the number of true head impacts that occur and may obfuscate potential relationships with longitudinal brain changes. The purpose of this study was to examine whether data-driven filtering of head impact exposure using machine learning classification could produce more accurate quantification of exposure and whether this would reveal more pronounced relationships with longitudinal brain changes. Season-long head impact exposure was recorded for 22 female high school soccer athletes and filtered using three methods-threshold-based, heuristic filtering, and machine learning (ML) classification. The accuracy of each method was determined using simultaneous video recording of a subset of the sensor-recorded impacts, which was used to confirm which sensor-recorded impacts corresponded with true head impacts and the ability of each method to detect the true impacts. Each filtered dataset was then associated with the athletes' pre- and post-season MRI brain scans to reveal longitudinal white matter changes. The threshold-based, heuristic, and ML approaches achieved 22.0% accuracy, 44.6%, and 83.5% accuracy, respectively. ML classification also revealed significant longitudinal brain white matter changes, with negative relationships observed between head impact exposure and reductions in mean and axial diffusivity and a positive relationship observed between exposure and fractional anisotropy (all p < 0.05).

Integrative Interaction Analysis using Threshold Gradient Directed Regularization.

For many complex business and industry problems, high-dimensional data collection and modeling have been conducted. It has been shown that interactions may have important implications beyond main effects. The number of unknown parameters in an interaction analysis can be larger or much larger than the sample size. As such, results generated from analyzing a single dataset are often unsatisfactory. Integrative analysis, which jointly analyzes the raw data from multiple independent studies, has been conducted in a series of recent studies and shown to outperform single-dataset analysis, meta-analysis, and other multi-datasets analyses. In this study, our goal is to conduct integrative analysis in interaction analysis. For regularized estimation and selection of important interactions (and main effects), we apply a Threshold Gradient Directed Regularization (TGDR) approach. Advancing from the exiting studies, the TGDR approach is modified to respect the "main effects, interactions" hierarchy. The proposed approach has an intuitive formulation and is computationally simple and broadly applicable. Simulations and the analyses of financial early warning system data and news-APP recommendation behavior data demonstrate its satisfactory practical performance.

Penalized multiple inflated values selection method with application to SAFER data.

Expanding on the zero-inflated Poisson model, the multiple-inflated Poisson model is applied to analyze count data with multiple inflated values. The existing studies on the multiple-inflated Poisson model determined the inflated values by inspecting the histogram of count response and fitting the model with different combinations of inflated values, which leads to relatively complicated computations and may overlook some real inflated points. We address a two-stage inflated values selection method, which takes all values of count response as potential inflated values and adopts the adaptive lasso regularization on the mixing proportion of those values. Numerical studies demonstrate the excellent performance both on inflated values selection and parameters estimation. Moreover, a specially designed simulation, based on the structure of data from a randomized clinical trial of an HIV sexual risk education intervention, performs well and ensures our method could be generalized to the real situation. An empirical analysis of a clinical trial dataset is used to elucidate the multiple-inflated Poisson model.

Variable selection in strong hierarchical semiparametric models for longitudinal data.

In this paper, we consider the variable selection problem in semiparametric additive partially linear models for longitudinal data. Our goal is to identify relevant main effects and corresponding interactions associated with the response variable. Meanwhile, we enforce the strong hierarchical restriction on the model, that is, an interaction can be included in the model only if both the associated main effects are included. Based on B-splines basis approximation for the nonparametric components, we propose an iterative estimation procedure for the model by penalizing the likelihood with a partial group minimax concave penalty (MCP), and use BIC to select the tuning parameter. To further improve the estimation efficiency, we specify the working covariance matrix by maximum likelihood estimation. Simulation studies indicate that the proposed method tends to consistently select the true model and works efficiently in estimation and prediction with finite samples, especially when the true model obeys the strong hierarchy. Finally, the China Stock Market data are fitted with the proposed model to illustrate its effectiveness.