Smoothed quantile residual life regression analysis with application to the Korea HIV/AIDS cohort study
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SUBMITTER: Kim S
PROVIDER: S-EPMC10873972 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:A time-specific log-linear regression method on quantile residual lifetime is proposed. Under the proposed regression model, any quantile of a time-to-event distribution among survivors beyond a certain time point is associated with selected covariates under right censoring. Consistency and asymptotic normality of the regression estimator are established. An asymptotic test statistic is proposed to evaluate the covariate effects on the quantile residual lifetimes at a specific time point. Evaluation of the test statistic does not require estimation of the variance-covariance matrix of the regression estimators, which involves the probability density function of the survival distribution with censoring. Simulation studies are performed to assess finite sample properties of the regression parameter estimator and test statistic. The new regression method is applied to a breast cancer data set with long-term follow-up to estimate the patients' median residual lifetimes, adjusting for important prognostic factors.
| S-EPMC3050018 | biostudies-literature
Project description:Censored quantile regression models, which offer great flexibility in assessing covariate effects on event times, have attracted considerable research interest. In this study, we consider flexible estimation and inference procedures for competing risks quantile regression, which not only provides meaningful interpretations by using cumulative incidence quantiles but also extends the conventional accelerated failure time model by relaxing some of the stringent model assumptions, such as global linearity and unconditional independence. Current method for censored quantile regressions often involves the minimization of the L1 -type convex function or solving the nonsmoothed estimating equations. This approach could lead to multiple roots in practical settings, particularly with multiple covariates. Moreover, variance estimation involves an unknown error distribution and most methods rely on computationally intensive resampling techniques such as bootstrapping. We consider the induced smoothing procedure for censored quantile regressions to the competing risks setting. The proposed procedure permits the fast and accurate computation of quantile regression parameter estimates and standard variances by using conventional numerical methods such as the Newton-Raphson algorithm. Numerical studies show that the proposed estimators perform well and the resulting inference is reliable in practical settings. The method is finally applied to data from a soft tissue sarcoma study.
| S-EPMC6156950 | biostudies-literature
Project description:Quantile regression is a powerful tool for learning the relationship between a response variable and a multivariate predictor while exploring heterogeneous effects. This paper focuses on statistical inference for quantile regression in the "increasing dimension" regime. We provide a comprehensive analysis of a convolution smoothed approach that achieves adequate approximation to computation and inference for quantile regression. This method, which we refer to as conquer, turns the non-differentiable check function into a twice-differentiable, convex and locally strongly convex surrogate, which admits fast and scalable gradient-based algorithms to perform optimization, and multiplier bootstrap for statistical inference. Theoretically, we establish explicit non-asymptotic bounds on estimation and Bahadur-Kiefer linearization errors, from which we show that the asymptotic normality of the conquer estimator holds under a weaker requirement on dimensionality than needed for conventional quantile regression. The validity of multiplier bootstrap is also provided. Numerical studies confirm conquer as a practical and reliable approach to large-scale inference for quantile regression. Software implementing the methodology is available in the R package conquer.
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Project description:OBJECTIVES:To manage evidence-based diseases, it is important to identify the characteristics of patients in each country. METHODS:The Korea HIV/AIDS Cohort Study seeks to identify the epidemiological characteristics of 1,442 Korean individuals with human immunodeficiency virus (HIV) infection (12% of Korean individuals with HIV infection in 2017) who visited 21 university hospitals nationwide. The descriptive statistics were presented using the Korea HIV/AIDS cohort data (2006-2016). RESULTS:Men accounted for 93.3% of the total number of respondents, and approximately 55.8% of respondents reported having an acute infection symptom. According to the transmission route, infection caused by sexual contact accounted for 94.4%, of which 60.4% were caused by sexual contact with the same sex or both males and females. Participants repeatedly answered the survey to decrease depression and anxiety scores. Of the total participants, 89.1% received antiretroviral therapy (ART). In the initial ART, 95.3% of patients were treated based on the recommendation. The median CD4 T-cell count at the time of diagnosis was 229.5 and improved to 331 after the initial ART. Of the patients, 16.6% and 9.4% had tuberculosis and syphilis, respectively, and 26.7% had pneumocystis pneumonia. In the medical history, sexually transmitted infectious diseases showed the highest prevalence, followed by endocrine diseases. The main reasons for termination were loss to follow-up (29.9%) and withdrawal of consent (18.7%). CONCLUSIONS:Early diagnosis and ART should be performed at an appropriate time to prevent the development of new infection.
| S-EPMC6815876 | biostudies-literature
Project description:The number of persons infected by HIV/AIDS has consistently increased in Korea since the first case of HIV/AIDS infection in 1985 and reached 15,208 by 2016. About 1,100 new patients with HIV/ AIDS infections have emerged every year since 2013. In Korea, the Korea HIV/AIDS Cohort Study was established for the evidenced-based prevention, treatment, and effective management of patients infected with human immunodeficiency virus (HIV) in December 2006. This study monitored 1,438 patients, who accounted for about 10% of all patients with HIV/AIDS in Korea, for 10 years with the following aims: (1) to develop an administrative system for the establishment of a HIV/AIDS cohort-based study; (2) to standardize methodologies and the case report forms; and (3) to standardize multi-cohort data and develop a data cleaning method. This study aims to monitor at least 1,000 patients (excluding those for whom investigation had been completed) per year (estimated number of patients who can be monitored by January 2018: 939). By December 2016, the sex distribution was 93.3% for men, and 6.7% for women (gender ratio, 13.9:1.0), and 98.9% of all participants were Korean. More than 50.0% of the participants were confirmed as HIV positive after 2006. This study reports competitive, long-term research that aimed to develop policies for the prevention of chronic infectious diseases for patients with HIV. The data collected over the last decade will be used to develop indices for HIV treatment and health promotion.
| S-EPMC6178365 | biostudies-literature
Project description:Hypertension is one of the crucial risk factors for morbidity and mortality around the world, and South Africa has a significant unmet need for hypertension care. This study aims to establish the potential risk factors of hypertension amongst adults in South Africa attributable to high systolic and diastolic blood pressure over time by fitting panel quantile regression models. Data obtained from the South African National Income Dynamics Study (NIDS) Household Surveys carried out from 2008 to 2018 (Wave 1 to Wave 5) was employed to develop both the fixed effects and random effects panel quantile regression models. Age, BMI, gender (males), race, exercises, cigarette consumption, and employment status were significantly associated with either one of the BP measures across all the upper quantiles or at the 75th quantile only. Suggesting that these risk factors have contributed to the exacerbation of uncontrolled hypertension prevalence over time in South Africa.
| S-EPMC9141596 | biostudies-literature
Project description:ObjectivesThe aim of effective data quality control and management is to minimize the impact of errors on study results by identifying and correcting them. This study presents the results of a data quality control system for the Korea HIV/AIDS Cohort Study that took into account the characteristics of the data.MethodsThe HIV/AIDS Cohort Study in Korea conducts repeated measurements every 6 months using an electronic survey administered to voluntarily consenting participants and collects data from 21 hospitals. In total, 5,795 sets of data from 1,442 participants were collected from the first investigation in 2006 to 2016. The data refining results of 2015 and 2019 were converted into the data refining rate and compared.ResultsThe quality control system involved 3 steps at different points in the process, and each step contributed to data quality management and results. By improving data quality control in the pre-phase and the data collection phase, the estimated error value in 2019 was 1,803, reflecting a 53.9% reduction from 2015. Due to improvements in the stage after data collection, the data refining rate was 92.7% in 2019, a 24.21%p increase from 2015.ConclusionsDespite this quality management strategy, errors may still exist at each stage. Logically possible errors for the post-review refining of downloaded data should be actively identified with appropriate consideration of the purpose and epidemiological characteristics of the study data. To improve data quality and reliability, data management strategies should be systematically implemented.
| S-EPMC7871148 | biostudies-literature
Project description:BACKGROUND:Despite declines in mortality and morbidity rates of patients with human immunodeficiency virus (HIV) infection as the result of highly active antiretroviral therapy, liver diseases due to chronic hepatitis B virus (HBV) and hepatitis C virus (HCV) infections are a leading cause of death among HIV-infected patients. However, HIV and HBV or HCV coinfection is still poorly documented, and more information is needed to better understand the characteristics of HIV-infected patients in Korea. MATERIALS AND METHODS:A cross-sectional study was performed to investigate clinical characteristics and prevalence of HBV and HCV infection in HIV patients enrolled in the Korea HIV/acquired immune deficiency syndrome (AIDS) cohort study from 17 institutions between December 2006 and July 2013. RESULTS:Among the 1,218 HIV-infected participants, 541 were included in this study. The prevalence of HBV-HIV and HCV-HIV coinfection was 5.0% (27/541) and 1.7% (9/541), respectively. There was no patient who was positive for both HBs antigen and HCV antibody. In multivariate logistic regression analysis, HBV unvaccinated status was a significant risk factor for HBV-HIV coinfection (odds ratio = 4.95, 95% confidence interval = 1.43-17.13). CONCLUSIONS:HBV and HCV infection was more common in HIV-infected persons enrolled in the Korean HIV/AIDS cohort, than in the general population in Korea.
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Project description:Human intelligence is usually measured by well-established psychometric tests through a series of problem solving. The recorded cognitive scores are continuous but usually heavy-tailed with potential outliers and violating the normality assumption. Meanwhile, magnetic resonance imaging (MRI) provides an unparalleled opportunity to study brain structures and cognitive ability. Motivated by association studies between MRI images and human intelligence, we propose a tensor quantile regression model, which is a general and robust alternative to the commonly used scalar-on-image linear regression. Moreover, we take into account rich spatial information of brain structures, incorporating low-rankness and piece-wise smoothness of imaging coefficients into a regularized regression framework. We formulate the optimization problem as a sequence of penalized quantile regressions with a generalized Lasso penalty based on tensor decomposition, and develop a computationally efficient alternating direction method of multipliers algorithm (ADMM) to estimate the model components. Extensive numerical studies are conducted to examine the empirical performance of the proposed method and its competitors. Finally, we apply the proposed method to a large-scale important dataset: the Human Connectome Project. We find that the tensor quantile regression can serve as a prognostic tool to assess future risk of cognitive impairment progression. More importantly, with the proposed method, we are able to identify the most activated brain subregions associated with quantiles of human intelligence. The prefrontal and anterior cingulate cortex are found to be mostly associated with lower and upper quantile of fluid intelligence. The insular cortex associated with median of fluid intelligence is a rarely reported region.
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Project description:Mass spectrometry proteomics, characterized by spiky, spatially heterogeneous functional data, can be used to identify potential cancer biomarkers. Existing mass spectrometry analyses utilize mean regression to detect spectral regions that are differentially expressed across groups. However, given the inter-patient heterogeneity that is a key hallmark of cancer, many biomarkers are only present at aberrant levels for a subset of, not all, cancer samples. Differences in these biomarkers can easily be missed by mean regression, but might be more easily detected by quantile-based approaches. Thus, we propose a unified Bayesian framework to perform quantile regression on functional responses. Our approach utilizes an asymmetric Laplace working likelihood, represents the functional coefficients with basis representations which enable borrowing of strength from nearby locations, and places a global-local shrinkage prior on the basis coefficients to achieve adaptive regularization. Different types of basis transform and continuous shrinkage priors can be used in our framework. A scalable Gibbs sampler is developed to generate posterior samples that can be used to perform Bayesian estimation and inference while accounting for multiple testing. Our framework performs quantile regression and coefficient regularization in a unified manner, allowing them to inform each other and leading to improvement in performance over competing methods as demonstrated by simulation studies. We also introduce an adjustment procedure to the model to improve its frequentist properties of posterior inference. We apply our model to identify proteomic biomarkers of pancreatic cancer that are differentially expressed for a subset of cancer patients compared to the normal controls, which were missed by previous mean-regression based approaches. Supplementary materials for this article are available online.
| S-EPMC10655915 | biostudies-literature