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Objective: Since 2016, the Chinese government has been piloting a public long-term care insurance (LTCI) scheme. This study examined whether the LTCI scheme reduced the use of informal care and how this has varied across income groups. Method: We used data from the 2011, 2014, and 2018 waves of Chinese Longitudinal Healthy Longevity Survey, focusing on community-dwelling older adults aged 65 years and older. We used staggered difference-in-differences analyses with propensity score matching to examine the effects of the policy. Results: The LTCI scheme reduced the probability and intensity of informal care use by 5.7% (p <.05) and 17.4% (p <.05), respectively. The policy impact was limited to older people in the middle-income group, reducing the probability and intensity of informal care use by 15.6% (p <.001) and 43.1% (p <.05), respectively. We did not find a statistically significant policy effect for older adults with high or low incomes. Conclusions: The LTCI scheme had different effects on reducing the informal care burden for family caregivers by income level. We suggest that the scheme should entitle people with low incomes to a preferential co-payment rate, thereby enhancing their access to formal care. [ABSTRACT FROM AUTHOR]

Studies on detailed types of health changes and the associations between the types and income inequality are inadequate. This study analyses the global distribution of the compression and expansion of morbidity in 194 countries and territories between 1990 and 2016, and investigates the role of income inequality in the distribution. This study shows that all seven types of health changes coexist, despite being distributed unevenly. The relative expansion of morbidity with increased or constant life expectancy (Type 6, 54.48%) is the most popular type, followed by the relative compression of morbidity with increased or constant life expectancy (Type 3, 30.71%). Income distribution within a society matters for health changes. Societies with greater income inequality tend to have Type 6, a worse scenario of health changes. Measures to reduce income inequality or mitigate its adverse effects will contribute to the relative compression of morbidity with increased or constant life expectancy. [ABSTRACT FROM AUTHOR]

Endoplasmic reticulum stress (ERS) is involved in the pathological process of vascular dementia (VD). GJ-4 is extracted from Gardenia jasminoides J. Ellis and has been reported to have protective roles in ischemia-related brain damage. However, the role of GJ-4 in ERS has not been elucidated. We established a VD rat model through bilateral common carotid arteries occlusion (2-VO). The rats were intragastrically administrated with GJ-4 (10, 25, and 50 mg/kg) and nimodipine (10 mg/kg). Data from a Morris water maze test showed that GJ-4 could significantly alleviate learning and memory deficits in VD rats. Nissl and cleaved caspase-3 staining revealed that GJ-4 can inhibit apoptosis and thus exert a protective role in the brain of 2-VO rats. Western blot results suggested that GJ-4 significantly reduced ERS-related protein expression and inhibited apoptosis through suppression of the PERK/eIF2 α /ATF4/CHOP signaling pathway. For in vitro studies, the oxygen-glucose deprivation (OGD) SH-SY5Y model was employed. Western blot and Hoechst 33342/PI double staining were utilized to explore the effects of crocetin, the main active metabolite of GJ-4. Like GJ-4 in vivo, crocetin in vitro also decreased ERS-related protein expression and inhibited the activation of the PERK/eIF2 α /ATF4/CHOP signaling pathway. Thus, crocetin exerted similar protective roles on OGD challenged SH-SY5Y cells in vitro. In summary, GJ-4 and crocetin reduce the ERS in the brain of VD rats and SY5Y cells subjected to OGD and inhibit neuronal apoptosis through suppression of the PERK/eIF2 α /ATF4/CHOP pathway, suggesting that GJ-4 may be useful for the treatment of VD. [ABSTRACT FROM AUTHOR]

The public long‐term care insurance (LTCI) scheme has been implemented in a few countries. Although the hypotheses of crowding‐out, crowding‐in and specialisation can facilitate our understanding of the relationship between LTCI and informal care use, existing studies may suffer from reverse causality. Employing a quasi‐experimental design, this study examined the policy effect of LTCI on informal care use among community‐dwelling older adults in China. Based on the data from three waves of the Chinese Longitudinal Healthy Longevity Survey, a staggered difference‐in‐differences (DID) with propensity score matching (PSM) approach was used to analyse the impact of LTCI on probability and hours of informal care use. The results showed that, for disabled older adults, LTCI reduced 43.3% of the probability and 82.4% of the weekly hours of informal care. LTCI also exhibited a spillover effect among nondisabled older adults through reducing the probability and weekly hours of informal care by 5.2% and 12.2%, respectively. Therefore, we argue that policymakers can consider rolling out the scheme for the entire country. Meanwhile, measures are needed to avoid a sharp decrease in informal care provision. [ABSTRACT FROM AUTHOR]

Long-term care insurance (LTCI) is one of the important institutional responses to the growing care needs of the ageing population. Although previous studies have evaluated the impacts of LTCI on health care utilization and expenditure in developed countries, whether such impacts exist in developing countries is unknown. The Chinese government has initiated policy experimentation on LTCI to cope with the growing and unmet need for aged care. Employing a quasi-experiment design, this study aims to examine the policy treatment effect of LTCI on health care utilization and out-of-pocket health expenditure (OOP) in China. The Propensity Score Matching with difference-in-difference approach was used to analyse the data obtained from four waves of China Health and Retirement Longitudinal Study. Our findings indicate that, in the aspect of health care utilization, the introduction of LTCI significantly reduced the number of outpatient visits by 0.322 times (P < 0.05), the number of hospitalizations by 0.158 times (P < 0.01) and the length of inpatient stay during last year by 1.441 days (P < 0.01). In the aspect of OOP, we found that LTCI significantly reduced the inpatient OOP during last year by 533.47 yuan (P < 0.01), but it did not exhibit an impact on the outpatient OOP during last year. LTCI also had a significantly negative impact on the total OOP by 512.56 yuan. These results are stable in the robustness tests. Considering the evident policy treatment effect of LTCI on health care utilization and OOP, the expansion of LTCI could help reduce the needs for health care services and contain the increases in OOP in China. [ABSTRACT FROM AUTHOR]

In an infectious disease cohort study, individuals who have been infected with a pathogen are often recruited for follow up. The period between infection and the onset of symptomatic disease, referred to as the incubation period, is of interest because of its importance on disease surveillance and control. However, the incubation period is often difficult to ascertain due to the uncertainty associated with asymptomatic infection onset time. An additional complication is that the observed infected subjects are likely to have longer incubation periods due to the prevalent sampling. In this article, we demonstrate how to estimate the distribution of the incubation period with the uncertain infection onset, subject to left-truncation and right-censoring. We employ a family of sufficiently general parametric models, the generalized odds-rate class of regression models, for the underlying incubation period and its correlation with covariates. In simulation studies, we assess the finite sample performance of the model fitting and hazard function estimation. The proposed method is illustrated on data from the HIV/AIDS study on injection drug users admitted to a detoxification program in Badalona, Spain. [ABSTRACT FROM AUTHOR]

Background: Poor outcomes for patients with ovarian cancer relate to dormant, drug-resistant cancer cells that survive after primary surgery and chemotherapy. Ovarian cancer (OvCa) cells persist in poorly vascularized scars on the peritoneal surface and depend on autophagy to survive nutrient deprivation. The authors have sought drugs that target autophagic cancer cells selectively to eliminate residual disease.Methods: By using unbiased small-interfering RNA (siRNA) screens, the authors observed that knockdown of anaplastic lymphoma kinase (ALK) reduced the survival of autophagic OvCa cells. Small-molecule ALK inhibitors were evaluated for their selective toxicity against autophagic OvCa cell lines and xenografts. Autophagy was induced by reexpression of GTP-binding protein Di-Ras3 (DIRAS3) or serum starvation and was evaluated with Western blot analysis, fluorescence imaging, and transmission electron microscopy. Signaling pathways required for crizotinib-induced apoptosis of autophagic cells were explored with flow cytometric analysis, Western blot analysis, short-hairpin RNA knockdown of autophagic proteins, and small-molecule inhibitors of STAT3 and BCL-2.Results: Induction of autophagy by reexpression of DIRAS3 or serum starvation in multiple OvCa cell lines significantly reduced the 50% inhibitory concentration of crizotinib and other ALK inhibitors. In 2 human OvCa xenograft models, the DIRAS3-expressing tumors treated with crizotinib had significantly decreased tumor burden and long-term survival in 67% to 79% of mice. Crizotinib treatment of autophagic cancer cells further enhanced autophagy and induced autophagy-mediated apoptosis by decreasing phosphorylated STAT3 and BCL-2 signaling.Conclusions: Crizotinib may eliminate dormant, autophagic, drug-resistant OvCa cells that remain after conventional cytoreductive surgery and combination chemotherapy. A clinical trial of ALK inhibitors as maintenance therapy after second-look operations should be seriously considered. [ABSTRACT FROM AUTHOR]

Two-phase sampling designs, including nested case-control and case-cohort designs, are frequently utilized in large cohort studies involving expensive biomarkers. To analyze data from two-phase designs with a binary outcome, parametric models such as logistic regression are often adopted. However, when the model assumptions are not valid, parametric models may lead to biased estimation and risk evaluation. In this paper, we propose a robust semiparametric regression model for binary outcomes and an easy-to-implement computational procedure that combines the pool-adjacent violators algorithm with inverse probability weighting. The asymptotic properties are established, including consistency and the convergence rate. Simulation studies show that the proposed method performs well and is more robust than logistic regression methods. We demonstrate the application of the proposed method to real data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. [ABSTRACT FROM AUTHOR]

A common phenomenon in cancer syndromes is for an individual to have multiple primary cancers (MPC) at different sites during his/her lifetime. Patients with Li-Fraumeni syndrome (LFS), a rare pediatric cancer syndrome mainly caused by germline TP53 mutations, are known to have a higher probability of developing a second primary cancer than those with other cancer syndromes. In this context, it is desirable to model the development of MPC to enable better clinical management of LFS. Here, we propose a Bayesian recurrent event model based on a non-homogeneous Poisson process in order to obtain penetrance estimates for MPC related to LFS. We employed a familywise likelihood that facilitates using genetic information inherited through the family pedigree and properly adjusted for the ascertainment bias that was inevitable in studies of rare diseases by using an inverse probability weighting scheme. We applied the proposed method to data on LFS, using a family cohort collected through pediatric sarcoma patients at MD Anderson Cancer Center from 1944 to 1982. Both internal and external validation studies showed that the proposed model provides reliable penetrance estimates for MPC in LFS, which, to the best of our knowledge, have not been reported in the LFS literature. [ABSTRACT FROM AUTHOR]

Much progress has been made in the area of adaptive designs for clinical trials. However, little has been done regarding adaptive designs to identify optimal treatment strategies in animal studies. Motivated by an animal study of a novel strategy for treating strokes, we propose a Bayesian multi-stage cost-effectiveness design to simultaneously identify the optimal dose and determine the therapeutic treatment window for administrating the experimental agent. We consider a non-monotonic pattern for the dose-schedule-efficacy relationship and develop an adaptive shrinkage algorithm to assign more cohorts to admissible strategies. We conduct simulation studies to evaluate the performance of the proposed design by comparing it with two standard designs. These simulation studies show that the proposed design yields a significantly higher probability of selecting the optimal strategy, while it is generally more efficient and practical in terms of resource usage. [ABSTRACT FROM AUTHOR]

Evaluating and understanding the risk and safety of using medications for autoimmune disease in a woman during her pregnancy will help both clinicians and pregnant women to make better treatment decisions. However, utilizing spontaneous abortion (SAB) data collected in observational studies of pregnancy to derive valid inference poses two major challenges. First, the data from the observational cohort are not random samples of the target population due to the sampling mechanism. Pregnant women with early SAB are more likely to be excluded from the cohort, and there may be substantial differences between the observed SAB time and those in the target population. Second, the observed data are heterogeneous and contain a "cured" proportion. In this article, we consider semiparametric models to simultaneously estimate the probability of being cured and the distribution of time to SAB for the uncured subgroup. To derive the maximum likelihood estimators, we appropriately adjust the sampling bias in the likelihood function and develop an expectation-maximization algorithm to overcome the computational challenge. We apply the empirical process theory to prove the consistency and asymptotic normality of the estimators. We examine the finite sample performance of the proposed estimators in simulation studies and illustrate the proposed method through an application to SAB data from pregnant women. [ABSTRACT FROM AUTHOR]

Interstitial pneumonia (IP) is a lethal complication in lymphoma patients undergoing chemotherapy. A total of 2212 consecutive patients diagnosed with lymphoma between 2009 and 2014 were enrolled in the present study. IP was defined as diffuse pulmonary interstitial infiltrate found on computed tomography scans. IP was observed in 106 patients. Of these, 23 patients were excluded from the study. Finally, 83 patients with IP were included in this study. The incidence of IP was 3.9% (7/287) in Hodgkin lymphoma and 2.4% (76/1925) in non-Hodgkin lymphoma (P = 0.210). The median number of chemotherapy cycles before IP was 3. The median time from the cessation of chemotherapy to IP was 17 days. Eighty-two (98.8%) patients recovered after the treatment with glucocorticoids. Sixty-six (79.5%) patients had a delay in chemotherapy, and 14 (16.9%) patients had premature termination of chemotherapy. Sixty-nine patients were re-treated with chemotherapy after remission from IP, of which 22 (31.9%) experienced IP recurrence. The incidence of IP recurrence was significantly higher in patients re-treated with a similar regimen than in those re-treated with an alternative regimen (65.4 vs. 11.6%, P < 0.001). In a multivariate Cox regression analysis, B symptoms and a history of drug allergies were identified as risk factors for IP. In conclusion, IP is a life-threatening complication in lymphoma patients. Glucocorticoid therapy with continuous monitoring of chest radiographic changes may be a favourable strategy for treating IP. However, IP may recur, especially in patients re-treated with a similar chemotherapy regimen. [ABSTRACT FROM AUTHOR]

Background: Because of the current epidemic of human papillomavirus (HPV)-related oropharyngeal cancer (OPC), a screening strategy is urgently needed. The presence of serum antibodies to HPV-16 early (E) antigens is associated with an increased risk for OPC. The purpose of this study was to evaluate the diagnostic accuracy of antibodies to a panel of HPV-16 E antigens in screening for OPC.Methods: This case-control study included 378 patients with OPC, 153 patients with nonoropharyngeal head and neck cancer (non-OPC), and 782 healthy control subjects. The tumor HPV status was determined with p16 immunohistochemistry and HPV in situ hybridization. HPV-16 E antibody levels in serum were identified with an enzyme-linked immunosorbent assay. A trained binary logistic regression model based on the combination of all E antigens was predefined and applied to the data set. The sensitivity and specificity of the assay for distinguishing HPV-related OPC from controls were calculated. Logistic regression analysis was used to calculate odds ratios with 95% confidence intervals for the association of head and neck cancer with the antibody status.Results: Of the 378 patients with OPC, 348 had p16-positive OPC. HPV-16 E antibody levels were significantly higher among patients with p16-positive OPC but not among patients with non-OPC or among controls. Serology showed high sensitivity and specificity for HPV-related OPC (binary classifier: 83% sensitivity and 99% specificity for p16-positive OPC).Conclusions: A trained binary classification algorithm that incorporates information about multiple E antibodies has high sensitivity and specificity and may be advantageous for risk stratification in future screening trials. Cancer 2017;123:4886-94. © 2017 American Cancer Society. [ABSTRACT FROM AUTHOR]

In comparative effectiveness studies of multicomponent, sequential interventions like blood product transfusion (plasma, platelets, red blood cells) for trauma and critical care patients, the timing and dynamics of treatment relative to the fragility of a patient's condition is often overlooked and underappreciated. While many hospitals have established massive transfusion protocols to ensure that physiologically optimal combinations of blood products are rapidly available, the period of time required to achieve a specified massive transfusion standard (e.g. a 1:1 or 1:2 ratio of plasma or platelets:red blood cells) has been ignored. To account for the time-varying characteristics of transfusions, we use semiparametric rate models for multivariate recurrent events to estimate blood product ratios. We use latent variables to account for multiple sources of informative censoring (early surgical or endovascular hemorrhage control procedures or death). The major advantage is that the distributions of latent variables and the dependence structure between the multivariate recurrent events and informative censoring need not be specified. Thus, our approach is robust to complex model assumptions. We establish asymptotic properties and evaluate finite sample performance through simulations, and apply the method to data from the PRospective Observational Multicenter Major Trauma Transfusion study. [ABSTRACT FROM AUTHOR]

Publication bias occurs when the published research results are systematically unrepresentative of the population of studies that have been conducted, and is a potential threat to meaningful meta-analysis. The Copas selection model provides a flexible framework for correcting estimates and offers considerable insight into the publication bias. However, maximizing the observed likelihood under the Copas selection model is challenging because the observed data contain very little information on the latent variable. In this article, we study a Copas-like selection model and propose an expectation-maximization (EM) algorithm for estimation based on the full likelihood. Empirical simulation studies show that the EM algorithm and its associated inferential procedure performs well and avoids the non-convergence problem when maximizing the observed likelihood. [ABSTRACT FROM AUTHOR]

Cancer studies frequently yield multiple event times that correspond to landmarks in disease progression, including non-terminal events (i.e., cancer recurrence) and an informative terminal event (i.e., cancer-related death). Hence, we often observe semi-competing risks data. Work on such data has focused on scenarios in which the cause of the terminal event is known. However, in some circumstances, the information on cause for patients who experience the terminal event is missing; consequently, we are not able to differentiate an informative terminal event from a non-informative terminal event. In this article, we propose a method to handle missing data regarding the cause of an informative terminal event when analyzing the semi-competing risks data. We first consider the nonparametric estimation of the survival function for the terminal event time given missing cause-of-failure data via the expectation-maximization algorithm. We then develop an estimation method for semi-competing risks data with missing cause of the terminal event, under a pre-specified semiparametric copula model. We conduct simulation studies to investigate the performance of the proposed method. We illustrate our methodology using data from a study of early-stage breast cancer. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this article, we consider methods for assessing covariate effects on survival outcome in the target population when data are collected under prevalent sampling. We investigate a flexible semiparametric density ratio model without the constraints of the constant disease incidence rate and discrete covariates as required in Shen and others 2012. For inference, we introduce two likelihood approaches with distinct computational algorithms. We first develop a full likelihood approach to obtain the most efficient estimators by an iterative algorithm. Under the density ratio model, we exploit the invariance property of uncensored failure times from the prevalent cohort and also propose a computationally convenient estimation procedure that uses a conditional pairwise likelihood. The empirical performance and efficiency of the two approaches are evaluated through simulation studies. The proposed methods are applied to the Surveillance, Epidemiology, and End Results Medicare linked data for women diagnosed with stage IV breast cancer. [ABSTRACT FROM AUTHOR]

For many forms of cancer, patients will receive the initial regimen of treatments, then experience cancer progression and eventually die of the disease. Understanding the disease process in patients with cancer is essential in clinical, epidemiological and translational research. One challenge in analyzing such data is that death dependently censors cancer progression (e.g., recurrence), whereas progression does not censor death. We deal with the informative censoring by first selecting a suitable copula model through an exploratory diagnostic approach and then developing an inference procedure to simultaneously estimate the marginal survival function of cancer relapse and an association parameter in the copula model. We show that the proposed estimators possess consistency and weak convergence. We use simulation studies to evaluate the finite sample performance of the proposed method, and illustrate it through an application to data from a study of early stage breast cancer. [ABSTRACT FROM AUTHOR]

There is no clear classification rule to rapidly identify trauma patients who are severely hemorrhaging and may need substantial blood transfusions. Massive transfusion (MT), defined as the transfusion of at least 10 units of red blood cells within 24 h of hospital admission, has served as a conventional surrogate that has been used to develop early predictive algorithms and establish criteria for ordering an MT protocol from the blood bank. However, the conventional MT rule is a poor proxy, because it is likely to misclassify many severely hemorrhaging trauma patients as they could die before receiving the 10th red blood cells transfusion. In this article, we propose to use a latent class model to obtain a more accurate and complete metric in the presence of early death. Our new approach incorporates baseline patient information from the time of hospital admission, by combining respective models for survival time and usage of blood products transfused within the framework of latent class analysis. To account for statistical challenges, caused by induced dependent censoring inherent in 24-h sums of transfusions, we propose to estimate an improved standard via a pseudo-likelihood function using an expectation-maximization algorithm with the inverse weighting principle. We evaluated the performance of our new standard in simulation studies and compared with the conventional MT definition using actual patient data from the Prospective Observational Multicenter Major Trauma Transfusion study. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes a new estimation procedure for the survival time distribution with left-truncated and right-censored data, where the distribution of the truncation time is known up to a finite-dimensional parameter vector. The paper expands on the Vardis multiplicative censoring model (Vardi, 1989. Multiplicative censoring, renewal processes, deconvolution and decreasing density: non-parametric estimation. Biometrika 76: , 751-761), establishes the connection between the likelihood under a generalized multiplicative censoring model and that for left-truncated and right-censored survival time data, and derives an Expectation-Maximization algorithm for model estimation. A formal test for checking the truncation time distribution is constructed based on the semiparametric likelihood ratio test statistic. In particular, testing the stationarity assumption that the underlying truncation time is uniformly distributed is performed by embedding the null uniform truncation time distribution in a smooth alternative (Neyman, 1937. Smooth test for goodness of fit. Skandinavisk Aktuarietidskrift 20: , 150-199). Asymptotic properties of the proposed estimator are established. Simulations are performed to evaluate the finite-sample performance of the proposed methods. The methods and theories are illustrated by analyzing the Canadian Study of Health and Aging and the Channing House data, where the stationarity assumption with respect to disease incidence holds for the former but not the latter. [ABSTRACT FROM AUTHOR]

In longitudinal data analyses, the observation times are often assumed to be independent of the outcomes. In applications in which this assumption is violated, the standard inferential approach of using the generalized estimating equations may lead to biased inference. Current methods require the correct specification of either the observation time process or the repeated measure process with a correct covariance structure. In this article, we construct a novel pairwise likelihood method for longitudinal data that allows for dependence between observation times and outcomes. This method investigates the marginal covariate effects on the repeated measure process, while leaving the probability structure of the observation time process unspecified. The novelty of this method is that it yields consistent estimator of the marginal covariate effects without specification of the observation time process or the covariance structure of the repeated measures process. Large sample properties of the regression coefficient estimates and a pairwise likelihood ratio test procedure are established. Simulation studies demonstrate that the proposed method performs well in finite samples. An analysis of weight loss data from a web-based program is presented to illustrate the proposed method. [ABSTRACT FROM AUTHOR]