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Semiparametric probit models have recently attracted some attention for regression analysis of failure time data partly due to the popularity of the normal distribution and its special features. In this paper, we discuss the fitting of such models to informative current status data, which often occur in many areas such as medical studies and whose analysis has also recently attracted a lot of attention. For inference, a sieve maximum likelihood approach is developed and the methodology is further generalized to a class of generalized semiparametric probit models. A simulation study is conducted to assess the finite sample properties of the presented approach and indicates that it works well in practical situations. An application that motivated this study is provided., (© 2019 John Wiley & Sons, Ltd.)

This paper discusses regression analysis of current status data, a type of failure time data where each study subject is observed only once, in the presence of dependent censoring. Furthermore, there may exist a cured subgroup, meaning that a proportion of study subjects are not susceptible to the failure event of interest. For the problem, we develop a sieve maximum likelihood estimation approach with the use of latent variables and Bernstein polynomials. For the determination of the proposed estimators, an EM algorithm is developed and the asymptotic properties of the estimators are established. Extensive simulation studies are conducted and indicate that the proposed method works well for practical situations. A motivating application from a tumorigenicity experiment is also provided.

This paper discusses regression analysis of current status failure time data arising from the additive hazards model in the presence of informative censoring. Many methods have been developed for regression analysis of current status data under various regression models if the censoring is noninformative, and also there exists a large literature on parametric analysis of informative current status data in the context of tumorgenicity experiments. In this paper, a semiparametric maximum likelihood estimation procedure is presented and in the method, the copula model is employed to describe the relationship between the failure time of interest and the censoring time. Furthermore, I-splines are used to approximate the nonparametric functions involved and the asymptotic consistency and normality of the proposed estimators are established. A simulation study is conducted and indicates that the proposed approach works well for practical situations. An illustrative example is also provided.

Group testing is an effective way to reduce the time and cost associated with conducting large-scale screening for infectious diseases. Benefits are realized through testing pools formed by combining specimens, such as blood or urine, from different individuals. In some studies, individuals are assessed only once and a time-to-event endpoint is recorded, for example, the time until infection. Combining group testing with this type of endpoint results in group-tested current status data (Petito & Jewell, 2016). To analyse these complex data, we propose methods that estimate a proportional hazard regression model based on test outcomes from measuring the pools. A sieve maximum likelihood estimation approach is developed that approximates the cumulative baseline hazard function with a piecewise constant function. To identify the sieve estimator, a computationally efficient expectation-maximization algorithm is derived by using data augmentation. Asymptotic properties of both the parametric and nonparametric components of the sieve estimator are then established by applying modern empirical process theory. Numerical results from simulation studies show that our proposed method performs nominally and has advantages over the corresponding estimation method based on individual testing results. We illustrate our work by analysing a chlamydia dataset collected by the State Hygienic Laboratory at the University of Iowa. [ABSTRACT FROM AUTHOR]

Current status data are encountered in a wide range of applications, including tumorigenic experiments and demographic studies. In this case, each subject has one observation, and the only information obtained is whether the event of interest happened at the moment of observation. In addition to censoring, truncating is also very common in practice. This paper examines the regression analysis of current status data with informative censoring times, considering the presence of left truncation. In addition, we propose an inference approach based on sieve maximum likelihood estimation (SMLE). A copula-based approach is used to describe the relationship between the failure time of interest and the censoring time. The spline function is employed to approximate the unknown nonparametric function. We have established the asymptotic properties of the proposed estimator. Simulation studies suggest that the developed procedure works well in practice. We also applied the developed method to a real dataset derived from an AIDS cohort research. [ABSTRACT FROM AUTHOR]

In this paper, we discuss regression analysis of bivariate interval-censored failure time data that often occur in biomedical and epidemiological studies. To solve this problem, we propose a kind of general and flexible copula-based semiparametric partly linear additive hazards models that can allow for both time-dependent covariates and possible nonlinear effects. For inference, a sieve maximum likelihood estimation approach based on Bernstein polynomials is proposed to estimate the baseline hazard functions and nonlinear covariate effects. The resulting estimators of regression parameters are shown to be consistent, asymptotically efficient and normal. A simulation study is conducted to assess the finite-sample performance of this method and the results show that it is effective in practice. Moreover, an illustration is provided. [ABSTRACT FROM AUTHOR]

The incubation period is a key characteristic of an infectious disease. In the outbreak of a novel infectious disease, accurate evaluation of the incubation period distribution is critical for designing effective prevention and control measures. Estimation of the incubation period distribution based on limited information from retrospective inspection of infected cases is highly challenging due to censoring and truncation. In this paper, we consider a semiparametric regression model for the incubation period and propose a sieve maximum likelihood approach for estimation based on the symptom onset time, travel history, and basic demographics of reported cases. The approach properly accounts for the pandemic growth and selection bias in data collection. We also develop an efficient computation method and establish the asymptotic properties of the proposed estimators. We demonstrate the feasibility and advantages of the proposed methods through extensive simulation studies and provide an application to a dataset on the outbreak of COVID-19. [ABSTRACT FROM AUTHOR]

Objective: To determine if preoperative albumin-alkaline phosphatase ratio (AAPR) is predictive of clinical outcomes in patients with degenerative lumbar diseases undergoing lumbar fusion. Method: 326 patients undergoing posterior lumbar decompression and fusion were retrospectively analyzed. The cumulative grade was calculated by summing the Pfirrmann grades of all lumbar discs. Grouping was based on the 50th percentile of cumulative grade. The relationship between AAPR, intervertebral disc degeneration (IDD) severity, and fusion rate was explored using correlation analyses and logistic regression models. Meanwhile, the ROC curve evaluated the discrimination ability of AAPR in predicting severe degeneration and non-fusion. Results: High AAPR levels were significantly negatively correlated with severe degeneration and non-fusion rate. A multivariate binary logistic analysis revealed that high preoperative AAPR was an independent predictor of severe degeneration and postoperative non-fusion (OR: 0.114; 95% CI: 0.027–0.482; p = 0.003; OR: 0.003; 95% CI: 0.0003–0.022; p < 0.001). The models showed excellent discrimination and calibration. The areas under the curve (AUC) of severe degeneration and non-fusion identified by AAPR were 0.635 and 0.643. Conclusion: The AAPR can help predict the severity of disc degeneration and the likelihood of non-fusion. [ABSTRACT FROM AUTHOR]

This article discusses regression analysis of multivariate doubly censored data with a wide class of flexible semiparametric transformation frailty models. The proposed models include many commonly used regression models as special cases such as the proportional hazards and proportional odds frailty models. For inference, we propose a nonparametric maximum likelihood estimation method and develop a new expectation–maximization algorithm for its implementation. The proposed estimators of the finite-dimensional parameters are shown to be consistent, asymptotically normal and semiparametrically efficient. We also conduct a simulation study to assess the finite sample performance of the developed estimation method, and the proposed methodology is applied to a set of real data arising from an AIDS study. [ABSTRACT FROM AUTHOR]

A novel condition monitoring method based on the adaptive multivariate control charts and the supervisory control and data acquisition (SCADA) system is developed. Two types of control charts are adopted: one is the adaptive exponential weighted moving average (AEWMA) control chart for abnormal state detection, and the other is the multivariate exponential weighted moving average (MEWMA) control chart for anomaly location determination. Optimization procedures for these control charts are implemented to achieve minimum out-of-control average running length. Multivariate regression analysis is utilized to obtain the normal condition prediction model of wind turbine with fault-free SCADA data. After comparing the regression accuracy of several popular algorithms in the MRA, the random forest is adopted for feature selection and regression prediction. Various tests on the wind turbine with normal and abnormal states are conducted. The performance and robustness of various control charts are compared comprehensively. Compared with conventional control charts, the AEWMA control chart is more sensitive to the abnormal state and thus has a more effective anomaly identification ability and better robustness. It is shown that the MEWMA control chart combined with the out-of-limit number index can effectively locate and identify the abnormal component. [ABSTRACT FROM AUTHOR]

Misclassified current status data occur when each subject under study is observed only once and the failure status at the observation time is determined by a diagnostic test with imperfect sensitivity and specificity. In this article, we provide a methodology for the analysis of such data under a wide class of flexible semiparametric transformation models. For inference, a nonparametric maximum likelihood estimation procedure is proposed along with the development of an EM algorithm. Furthermore, we show that the resulting estimators of regression parameters are consistent, asymptotically normal and semiparametrically efficient. A simulation study and a real data application demonstrate that the proposed approach performs well in practice and has substantial superiority over the naive method that ignores the misclassification. [ABSTRACT FROM AUTHOR]

Failure time data occur in many areas and also in various forms and in particular, many authors have discussed regression analysis of failure time data in the presence of interval censoring, a cured subgroup or mismeasured covariates. However, it does not seem to exist an established procedure that can deal with all three issues together. Corresponding to this, we propose a sieve maximum likelihood estimation procedure that takes into account all three issues with the use of the SIMEX algorithm. The asymptotic properties of the proposed estimators are established, and an extensive simulation study is also conducted and suggests that the proposed method works well for practical situations. [ABSTRACT FROM AUTHOR]

Regression analysis of interval-censored failure time data with noninformative censoring has been widely investigated and many methods have been proposed. Sometimes the mechanism behind the interval censoring may be informative and several approaches have also been developed for this latter situation. However, all of these existing methods are for single models and it is well known that in many situations, one may prefer more flexible models. Corresponding to this, the linear transformation model is considered and a maximum likelihood estimation method is established. In the proposed method, the association between the failure time of interest and the censoring time is modelled by the copula model, and the involved nonparametric functions are approximated by spline functions. The large sample properties of the proposed estimators are derived. Numerical results show that the proposed method performs well in practical application. Besides, a real data example is presented for the illustration. [ABSTRACT FROM AUTHOR]

Many methods have been developed in the literature for regression analysis of current status data with noninformative censoring and also some approaches have been proposed for semiparametric regression analysis of current status data with informative censoring. However, the existing approaches for the latter situation are mainly on specific models such as the proportional hazards model and the additive hazard model. Corresponding to this, in this paper, we consider a general class of semiparametric linear transformation models and develop a sieve maximum likelihood estimation approach for the inference. In the method, the copula model is employed to describe the informative censoring or relationship between the failure time of interest and the censoring time, and Bernstein polynomials are used to approximate the nonparametric functions involved. The asymptotic consistency and normality of the proposed estimators are established, and an extensive simulation study is conducted and indicates that the proposed approach works well for practical situations. In addition, an illustrative example is provided. [ABSTRACT FROM AUTHOR]

Abstract: Doubly censored failure time data occur in many areas including demographical studies, epidemiology studies, medical studies and tumorigenicity experiments, and correspondingly some inference procedures have been developed in the literature (Biometrika, 91, 2004, 277; Comput. Statist. Data Anal., 57, 2013, 41; J. Comput. Graph. Statist., 13, 2004, 123). In this paper, we discuss regression analysis of such data under a class of flexible semiparametric transformation models, which includes some commonly used models for doubly censored data as special cases. For inference, the non‐parametric maximum likelihood estimation will be developed and in particular, we will present a novel expectation–maximization algorithm with the use of subject‐specific independent Poisson variables. In addition, the asymptotic properties of the proposed estimators are established and an extensive simulation study suggests that the proposed methodology works well for practical situations. The method is applied to an AIDS study. [ABSTRACT FROM AUTHOR]

This article discusses the regression analysis of bivariate current status or case I interval-censored failure time data under the marginal proportional hazards model. Several estimation procedures have been proposed for this problem, but each method either applies only to limited situations or no theoretical justification has been provided. Using Bernstein polynomials and the copula model we develop a sieve maximum likelihood estimation approach that applies to more general situations. In particular this method leaves the underlying copula model completely unspecified and can be easily implemented. The proposed estimators are shown to be strongly consistent and the asymptotic normality and efficiency of the regression parameter estimator are established. Simulation studies are conducted to assess the performance of the proposed method. An illustrative example is also provided. The Canadian Journal of Statistics 45: 410-424; 2017 © 2017 Statistical Society of Canada [ABSTRACT FROM AUTHOR]

Interval-censored failure time data arise in a number of fields and many authors have discussed various issues related to their analysis. However, most of the existing methods are for univariate data and there exists only limited research on bivariate data, especially on regression analysis of bivariate interval-censored data. We present a class of semiparametric transformation models for the problem and for inference, a sieve maximum likelihood approach is developed. The model provides a great flexibility, in particular including the commonly used proportional hazards model as a special case, and in the approach, Bernstein polynomials are employed. The strong consistency and asymptotic normality of the resulting estimators of regression parameters are established and furthermore, the estimators are shown to be asymptotically efficient. Extensive simulation studies are conducted and indicate that the proposed method works well for practical situations. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

This article develops an adjusted empirical likelihood (EL) method for the additive hazards model. The adjusted EL ratio is shown to have a central chi-squared limiting distribution under the null hypothesis. We also evaluate its asymptotic distribution as a non central chi-squared distribution under the local alternatives of ordern− 1/2, deriving the expression for the asymptotic power function. Simulation studies and a real example are conducted to evaluate the finite sample performance of the proposed method. Compared with the normal approximation-based method, the proposed method tends to have more larger empirical power and smaller confidence regions with comparable coverage probabilities. [ABSTRACT FROM PUBLISHER]

A paddle method for measurement of the disintegrating time of orally disintegrating tablets with rizatriptan benzoate as a model drug was evaluated. The paddle method employed a dissolution test assembly with tablets immersed in disintegrating medium through a fastened sinker. Paddle stirring rate, opening of the sinker sieve, and tablet crushing strength influenced disintegrating time greatly. A logarithmic relationship was observed between disintegrating time and paddle revolution speed, while disintegrating time values and tablet crushing strength were fitted to a linear equation. The paddle method values with limited deviation were in good correlation with in vivo results. The paddle method was employed to optimize the disintegrating time of rizatriptan benzoate orally disintegrating tablets using a factorial design. The best-fit quadratic equation with a regression coefficient of 0.996 was highly predictive, which was indicative that the paddle method was precise and applicable in formulation optimization. [ABSTRACT FROM AUTHOR]

Lacustrine shale oil resources are abundant and play important roles in maintaining a sustainable economy. The uncertainty in the movable oil content of organic-rich shales with strong heterogeneity is the greatest risk in their exploration. Various methods exist for movable oil content evaluation, but they present many challenges, especially insufficient accuracy, complicated test procedures, high costs, and limited application. In this study, an extensive review of the theories, techniques, and methods of movable oil content evaluation and their merits and demerits for lacustrine shales were conducted. Then, taking the lacustrine shale of the Paleogene Shahejie Formation (50.5–33 Ma) of the Dongpu Depression, Bohai Bay Basin, as an example, the multi-step Rock-Eval pyrolysis method combined with light hydrocarbon calibration was utilized to evaluate the movable oil content Subsequently, key factors controlling movable oil content were investigated, including organic matter richness, type, and thermal maturity, quartz content, clay mineral content, pore volume, and specific surface area. Both the systematic cluster analysis results and regression standardization coefficients indicate that the total organic carbon content, pore volume, and specific surface area are the main controls on movable oil content. Finally, a quantitative estimation model for movable oil content was established and validated using a statistical product and service solutions modeling. This study improves the understanding of shale oil enrichment laws and provides a novel and practical quantitative model for movable oil content estimation in lacustrine organic-rich shales across the world. [ABSTRACT FROM AUTHOR]

Studies have shown that the renin-angiotensin system (RAS) might play an essential role in intervertebral disc degeneration (IDD). The study aimed to investigate the relationship between serum angiotensin-converting enzyme (ACE) concentration and IDD and its predictive value for severe disc degeneration. 245 patients who came to our hospital for low back pain were recruited, and blood samples were collected for routine examination. Descriptive data and demographic parameters were collected. The cumulative grade 1 was calculated by summing up the Pfirrmann grade of all lumbar discs. ACE concentration grouping was determined via tertile split. Correlation analysis and multivariable linear regression analysis were performed to determine the relationship between ACE and IDD. The receiver's degree of disc degeneration (ROC) curve determined the ACE's predictive value. Results indicated that there was no significant difference in demographic parameters among groups. Correlation analysis and multivariate linear analysis showed that ACE was an independent risk factor for IDD. The cumulative grade 1 increased significantly with the increase in ACE concentration, which was consistent with the correlation analysis. Average Pfirrmann grade < 4 indicates mild to moderate degeneration, and grade ≥ 4 indicates severe degeneration in terms of an individual disc. From L1/2 to L5/S1, the mean plasma ACE concentration was significantly higher in the severe degeneration group than in the mild to moderate degeneration group. According to the ROC curve, the cut-off value of ACE levels was 22.5. patients with ACE > 22.5 had severe degeneration. The sensitivity and specificity were 0.762 and 0.521, respectively. • Local tissue renin-angiotensin system (tRAS) has been shown to exist in intervertebral disc tissue. • Tissue renin-angiotensin system (tRAS) was excessively activated in the human degenerated disc tissue. • The tissue renin-angiotensin system (tRAS) is composed of renin, angiotensin-converting enzyme (ACE), and angiotensinogen. • The serum ACE can be used to predict the disc degeneration severity, deepening its clinical significance. [ABSTRACT FROM AUTHOR]