Association of long-term exposure to PM2.5 and survival following ischemic heart disease.

BACKGROUND: Numerous studies have suggested that long-term exposure to particulate matter ≤2.5 µm (PM2.5) may cause cardiovascular morbidity and mortality. However, susceptibility among those with a history of ischemic heart disease is less clearly understood. We aimed to evaluate whether long-term PM2.5 exposure is related to mortality among patients with ischemic heart disease. METHODS: We followed up 306,418 patients hospitalized with ischemic heart disease in seven major cities in South Korea between 2008 and 2016 using the National Health Insurance Database. We linked the modeled PM2.5 data corresponding to each patient's administrative districts and estimated hazard ratios (HRs) of cause-specific mortality associated with the long-term exposure to PM2.5 in time-varying Cox proportional hazard models after adjusting for individual- and area-level characteristics. We also estimated HRs by sex, age group (65-74 vs. ≥75 years), and household income. RESULTS: Of the patients with ischemic heart disease, mean age at the discharge was 76.8 years, and 105,913 died during a mean follow-up duration of 21.4 months. The HR of all-cause mortality was 1.10 [95% confidence intervals (CI): 1.07, 1.14] per 10 µg/m3 increase in a 12-month moving average PM2.5. The HRs of cardiovascular, stroke, and ischemic heart disease were 1.17 (95% CI: 1.11, 1.24), 1.17 (95% CI: 1.06, 1.30), and 1.25 (95% CI: 1.15, 1.35), respectively. The subgroup analyses showed that participants aged 65-74 years were more susceptible to adverse effects of PM2.5 exposure. We did not observe any differences in the risk by sex and household income. CONCLUSION: Mortality from all-cause and cardiovascular disease following hospitalization due to ischemic heart disease was higher among individuals with greater PM2.5 exposure in seven major cities in South Korea. The result supports the association of long-term exposure to air pollution with poor prognosis among patients with ischemic heart disease.

Weighted least-squares regression with competing risks data.

The semiparametric accelerated failure time (AFT) model linearly relates the logarithm of the failure time to a set of covariates, while leaving the error distribution unspecified. This model has been widely investigated in survival literature due to its simple interpretation and relationship with linear models. However, there has been much less focus on developing AFT-type linear regression methods for analyzing competing risks data, in which patients can potentially experience one of multiple failure causes. In this article, we propose a simple least-squares (LS) linear regression model for a cause-specific subdistribution function, where the conventional LS equation is modified to account for data incompleteness under competing risks. The proposed estimators are shown to be consistent and asymptotically normal with consistent estimation of the variance-covariance matrix. We further extend the proposed methodology to risk prediction and analysis under clustered competing risks scenario. Simulation studies suggest that the proposed method provides rapid and valid statistical inferences and predictions. Application of our method to two oncology datasets demonstrate its utility in routine clinical data analysis.

Doubly-robust methods for differences in restricted mean lifetimes using pseudo-observations.

In clinical studies or trials comparing survival times between two treatment groups, the restricted mean lifetime (RML), defined as the expectation of the survival from time 0 to a prespecified time-point, is often the quantity of interest that is readily interpretable to clinicians without any modeling restrictions. It is well known that if the treatments are not randomized (as in observational studies), covariate adjustment is necessary to account for treatment imbalances due to confounding factors. In this article, we propose a simple doubly-robust pseudo-value approach to effectively estimate the difference in the RML between two groups (akin to a metric for estimating average causal effects), while accounting for confounders. The proposed method combines two general approaches: (a) group-specific regression models for the time-to-event and covariate information, and (b) inverse probability of treatment assignment weights, where the RMLs are replaced by the corresponding pseudo-observations for survival outcomes, thereby mitigating the estimation complexities in presence of censoring. The proposed estimator is double-robust, in the sense that it is consistent if at least one of the two working models remains correct. In addition, we explore the potential of available machine learning algorithms in causal inference to reduce possible bias of the causal estimates in presence of a complex association between the survival outcome and covariates. We conduct extensive simulation studies to assess the finite-sample performance of the pseudo-value causal effect estimators. Furthermore, we illustrate our methodology via application to a dataset from a breast cancer cohort study. The proposed method is implementable using the R package drRML, available in GitHub.

Sleep duration and mortality in Korean adults: a population-based prospective cohort study.

BACKGROUND: Increasing evidence suggests that sleep duration is associated with risks of various diseases including type 2 diabetes, cardiovascular disease (CVD), and certain types of cancer. However, the relationship with mortality is not clear, particularly in non-European populations. In this study, we investigated the association between sleep duration and mortality in a population-based prospective cohort of Korean adults. METHODS: This analysis included 34,264 participants (14,704 men and 19,560 women) of the Korea National Health and Nutrition Examination Survey (KNHANES) 2007-2013 who agreed to mortality follow-up through December 31, 2016. Sleep duration was self-reported at baseline and was categorized into four groups: ≤4, 5-6, 7-8, and ≥ 9 h/day. Cox proportional hazards models were performed to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the associations with mortality (all-cause as well as CVD- and cancer-specific), adjusting for potential confounders. RESULTS: During up to 9.5 years of follow-up, we identified a total of 1028 deaths. We observed the lowest mortality at 5-6 h/day sleep. Compared with 7-8 h/day of sleep, short (≤4 h/day) and long (≥9 h/day) sleep were associated with a 1.05-fold (95% CI = 0.79-1.39) and 1.47-fold (95% CI = 1.15-1.87) higher all-cause mortality, respectively. After additional adjustment for self-rated health, the positive association with short sleep disappeared (HR = 0.99, 95% CI = 0.75-1.32) and the association with long sleep was slightly attenuated (HR = 1.38, 95% CI = 1.08-1.76). Long sleep was also nonsignificantly positively associated with both cancer-mortality (HR = 1.30, 95% CI = 0.86-1.98) and CVD-mortality (HR = 1.27, 95% CI = 0.73-2.21). There was no statistically significant evidence for nonlinearity in the relationships between sleep duration and mortality (all-cause as well as CVD- and cancer-specific). Effect modification by age, sex, education, and occupation were not statistically significant. CONCLUSIONS: Our findings suggest that long sleep duration is associated with an increased all-cause mortality in Korean adults.

Statistical analysis of clustered mixed recurrent-event data with application to a cancer survivor study.

In long-term follow-up studies on recurrent events, the observation patterns may not be consistent over time. During some observation periods, subjects may be monitored continuously so that each event occurence time is known. While during the other observation periods, subjects may be monitored discretely so that only the number of events in each period is known. This results in mixed recurrent-event and panel-count data. In these data, there is dependence among within-subject events. Furthermore, if the data are collected from multiple centers, then there is another level of dependence among within-center subjects. Literature exists for clustered recurrent-event data, but not for clustered mixed recurrent-event and panel-count data. Ignoring the cluster effect may lead to less efficient analysis. In this paper, we present a marginal modeling approach to take into account the cluster effect and provide asymptotic distributions of the resulting regression parameters. Our simulation study demonstrates that this approach works well for practical situations. It was applied to a study comparing the hospitalization rates between childhood cancer survivors and healthy controls, with data collected from 26 medical institutions across North America during more than 20 years of follow-up.

Semiparametric accelerated failure time cure rate mixture models with competing risks.

Modern medical treatments have substantially improved survival rates for many chronic diseases and have generated considerable interest in developing cure fraction models for survival data with a non-ignorable cured proportion. Statistical analysis of such data may be further complicated by competing risks that involve multiple types of endpoints. Regression analysis of competing risks is typically undertaken via a proportional hazards model adapted on cause-specific hazard or subdistribution hazard. In this article, we propose an alternative approach that treats competing events as distinct outcomes in a mixture. We consider semiparametric accelerated failure time models for the cause-conditional survival function that are combined through a multinomial logistic model within the cure-mixture modeling framework. The cure-mixture approach to competing risks provides a means to determine the overall effect of a treatment and insights into how this treatment modifies the components of the mixture in the presence of a cure fraction. The regression and nonparametric parameters are estimated by a nonparametric kernel-based maximum likelihood estimation method. Variance estimation is achieved through resampling methods for the kernel-smoothed likelihood function. Simulation studies show that the procedures work well in practical settings. Application to a sarcoma study demonstrates the use of the proposed method for competing risk data with a cure fraction.

Smoothed quantile regression analysis of competing risks.

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.

Safety and efficacy of coil embolization of the septal perforator for septal ablation in patients with hypertrophic obstructive cardiomyopathy.

OBJECTIVE: The objective of this study was to evaluate safety, efficacy, and durability of coil embolization of the major septal perforator of the left anterior descending coronary artery in patients with hypertrophic obstructive cardiomyopathy (HOCM). BACKGROUND: The long-term effect of coil embolization therapy in HOCM patients is not well defined. METHODS: We evaluated 24 symptomatic HOCM patients in a single center who underwent coil embolization of the septal perforator artery(ies). RESULTS: Twenty-four patients on optimal medical therapy presented with NYHA functional class III (75%) or IV (25%) underwent the procedure. The procedure was successful in 22 patients, with significant reduction in left ventricular outflow tract (LVOT) gradient. The functional class significantly improved to class I (54.2%) or II (41.7%) (P < = 0.01). The LVOT gradient was significantly lower during follow up echocardiography (21.3 ± 19 vs. 81.3 ± 41 mm Hg; P < = 0.01). Interventricular septal thickness decreased over time (16.3 ± 3 vs. 18.5 ± 2 mm, P< = 0.01). The procedure was aborted in one of the patients after the third coil prolapsed from the septal perforator in to the left anterior descending artery. The coil was effectively snared out. Three patients required additional coil placement in the second major septal perforator. New permanent pacemaker placement was required in one patient. However, three patients underwent ICD implantation at follow up due to ventricular arrhythmias. CONCLUSIONS: The results of this study suggest that the use of coil embolization for septal ablation is safe, effective, and durable in patients with symptomatic HOCM. © 2016 Wiley Periodicals, Inc.

A two-stage approach for dynamic prediction of time-to-event distributions.

Dynamic prediction uses longitudinal biomarkers for real-time prediction of an individual patient's prognosis. This is critical for patients with an incurable disease such as cancer. Biomarker trajectories are usually not linear, nor even monotone, and vary greatly across individuals. Therefore, it is difficult to fit them with parametric models. With this consideration, we propose an approach for dynamic prediction that does not need to model the biomarker trajectories. Instead, as a trade-off, we assume that the biomarker effects on the risk of disease recurrence are smooth functions over time. This approach turns out to be computationally easier. Simulation studies show that the proposed approach achieves stable estimation of biomarker effects over time, has good predictive performance, and is robust against model misspecification. It is a good compromise between two major approaches, namely, (i) joint modeling of longitudinal and survival data and (ii) landmark analysis. The proposed method is applied to patients with chronic myeloid leukemia. At any time following their treatment with tyrosine kinase inhibitors, longitudinally measured BCR-ABL gene expression levels are used to predict the risk of disease progression. Copyright © 2016 John Wiley & Sons, Ltd.

A joint latent class analysis for adjusting survival bias with application to a trauma transfusion study.

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.