Introduction: Lung cancer (LC) is the most common type of cancer and causes of death among males. This study aims to estimate the survival rate of lung cancer patients by employing the benefits of ...Bayesian modeling in determining factors affecting the survival of lung cancer in Kerman province, Iran.
Methods: We conducted a historical cohort study of 195 patients with lung cancer from 2016 to 2018. In this study, we used linear dependent Dirichlet process (LDDP), and employed some results of the previous study as informative prior for better estimation.
Results: Of the 195 patients, 160 died. The mean age of patients at the time of diagnosis was 62.43±12.55. The median survival time of patients was 10.4 months. Men accounted for 75.9% of the total patients. One, two, and three-year survival rate was 44.5%, 22.9%, and 16.4%, respectively. The multivariable model results showed that treatments were significant. Other variables had no significant effect.
Conclusion: Our study highlights the importance of prompt diagnosis and appropriate treatment in improving the survival rate of lung cancer patients. We found that patients who received at least one usual lung cancer treatment, such as chemotherapy, radiation therapy, or surgery, had higher survival rates compared to those who did not receive any treatment. While our study has some limitations, such as its retrospective design, our use of Bayesian modeling techniques allowed us to effectively incorporate prior information from previous studies to improve estimation accuracy.
Estimating the FDR significance threshold in genome-wide association studies remains a major challenge in distinguishing true positive hypotheses from false positive and negative errors. Several ...comparative methods for multiple testing comparison have been developed to determine the significance threshold; however, these methods may be overly conservative and lead to an increase in false negative results. The local FDR approach is suitable for testing many associations simultaneously based on the empirical Bayes perspective. In the local FDR, the maximum likelihood estimator is sensitive to bias when the GWAS model contains two or more explanatory variables as genetic parameters simultaneously. The main criticism of local FDR is that it focuses only locally on the effects of single nucleotide polymorphism (SNP) in tails of distribution, whereas the signal associations are distributed across the whole genome. The advantage of the Bayesian perspective is that knowledge of prior distribution comes from other genetic parameters included in the GWAS model, such as linkage disequilibrium (LD) analysis, minor allele frequency (MAF) and call rate of significant associations. We also proposed Bayesian survival FDR to solve the multi-collinearity and large-scale problems, respectively, in grain yield (GY) vector in bread wheat with large-scale SNP information. The objective of this study was to obtain a short list of SNPs that are reliably associated with GY under low and high levels of nitrogen (N) in the population. The five top significant SNPs were compared with different Bayesian models. Based on the time to events in the Bayesian survival analysis, the differentiation between minor and major alleles within the association panel can be identified.
Readmission following discharge from an initial hospitalization is a key marker of quality of healthcare in the United States. For the most part, readmission has been studied among patients with ..."acute" health conditions, such as pneumonia and heart failure, with analyses based on a logistic-Normal generalized linear mixed model. Naïve application of this model to the study of readmission among patients with "advanced" health conditions such as pancreatic cancer, however, is problematic because it ignores death as a competing risk. A more appropriate analysis is to imbed such a study within the semicompeting risks framework. To our knowledge, however, no comprehensive statistical methods have been developed for cluster-correlated semicompeting risks data. To resolve this gap in the literature we propose a novel hierarchical modeling framework for the analysis of cluster-correlated semicompeting risks data that permits parametric or nonparametric specifications for a range of components giving analysts substantial flexibility as they consider their own analyses. Estimation and inference is performed within the Bayesian paradigm since it facilitates the straightforward characterization of (posterior) uncertainty for all model parameters, including hospital-specific random effects. Model comparison and choice is performed via the deviance information criterion and the log-pseudo marginal likelihood statistic, both of which are based on a partially marginalized likelihood. An efficient computational scheme, based on the Metropolis-Hastings-Green algorithm, is developed and had been implemented in the R package SemiCompRisks. A comprehensive simulation study shows that the proposed framework performs very well in a range of data scenarios, and outperforms competitor analysis strategies. The proposed framework is motivated by and illustrated with an ongoing study of the risk of readmission among Medicare beneficiaries diagnosed with pancreatic cancer. Using data on n = 5298 patients at J=112 hospitals in the six New England states between 2000-2009, key scientific questions we consider include the role of patient-level risk factors on the risk of readmission and the extent of variation in risk across hospitals not explained by differences in patient case-mix. Supplementary materials for this article are available online.
We propose a class of cure rate models motivated by analysis of colon cancer and triple-negative breast cancer survival data. This class is indexed by an adaptive activation parameter and a function. ...We establish that the class is stochastically ordered in the activation parameter and also establish two identifiability results for this class. The first- and last-activation models are members of this class whereas many cure rate models proposed in the literature are also part of this class. We illustrate that while first- and last-activation models may perform poorly under model misspecifications, the proposed model with adaptive activation provides appropriate inference in these cases. We apply the proposed approach to assess treatment-sex interaction on cure rate in a colon cancer study and to assess role of tumor heterogeneity and ethnic disparity in breast cancer.
Many Arctic shorebird populations are declining, and quantifying adult survival and the effects of anthropogenic factors is a crucial step toward a better understanding of population dynamics. We ...used a recently developed, spatially explicit Cormack–Jolly–Seber model in a Bayesian framework to obtain broad-scale estimates of true annual survival rates for 6 species of shorebirds at 9 breeding sites across the North American Arctic in 2010–2014. We tested for effects of environmental and ecological variables, study site, nest fate, and sex on annual survival rates of each species in the spatially explicit framework, which allowed us to distinguish between effects of variables on site fidelity versus true survival. Our spatially explicit analysis produced estimates of true survival rates that were substantially higher than previously published estimates of apparent survival for most species, ranging from S = 0.72 to 0.98 across 5 species. However, survival was lower for the arcticola subspecies of Dunlin (Calidris alpina arcticola; S = 0.54), our only study taxon that migrates through the East Asian–Australasian Flyway. Like other species that use that flyway, arcticola Dunlin could be experiencing unsustainably low survival rates as a result of loss of migratory stopover habitat. Survival rates of our study species were not affected by timing of snowmelt or summer temperature, and only 2 species showed minor variation among study sites. Furthermore, although previous reproductive success, predator abundance, and the availability of alternative prey each affected survival of one species, no factors broadly affected survival across species. Overall, our findings of few effects of environmental or ecological variables suggest that annual survival rates of adult shorebirds are generally robust to conditions at Arctic breeding sites. Instead, conditions at migratory stopovers or overwintering sites might be driving adult survival rates and should be the focus of future studies.
In the USA, the Centers for Medicare and Medicaid Services use 30-day readmission, following hospitalization, as a proxy outcome to monitor quality of care. These efforts generally focus on treatable ...health conditions, such as pneumonia and heart failure. Expanding quality-of-care systems to monitor conditions for which treatment options are limited or non-existent, such as pancreatic cancer, is challenging because of the non-trivial force of mortality; 30-day mortality for pancreatic cancer is approximately 30%. In the statistical literature, data that arise when the observation of the time to some non-terminal event is subject to some terminal event are referred to as 'semicompeting risks data'. Given such data, scientific interest may lie in at least one of three areas: estimation or inference for regression parameters, characterization of dependence between the two events and prediction given a covariate profile. Existing statistical methods focus almost exclusively on the first of these; methods are sparse or non-existent, however, when interest lies with understanding dependence and performing prediction. We propose a Bayesian semiparametric regression framework for analysing semicompeting risks data that permits the simultaneous investigation of all three of the aforementioned scientific goals. Characterization of the induced posterior and posterior predictive distributions is achieved via an efficient Metropolis–Hastings–Green algorithm, which has been implemented in an R package. The framework proposed is applied to data on 16051 individuals who were diagnosed with pancreatic cancer between 2005 and 2008, obtained from Medicare part A. We found that increased risk for readmission is associated with a high comorbidity index, a long hospital stay at initial hospitalization, non-white race, being male and discharge to home care.
Hospital readmissions present an increasingly important challenge for health‐care organizations. Readmissions are expensive and often unnecessary, putting patients at risk and costing $15 billion ...annually in the United States alone. Currently, 17% of Medicare patients are readmitted to a hospital within 30 days of initial discharge with readmissions typically being more expensive than the original visit to the hospital. Recent legislation penalizes organizations with a high readmission rate. The medical literature conjectures that many readmissions can be avoided or mitigated by post‐discharge monitoring. To develop a good monitoring plan it is critical to anticipate the timing of a potential readmission and to effectively monitor the patient for readmission causing conditions based on that knowledge. This research develops new methods to empirically generate an individualized estimate of the time to readmission density function and then uses this density to optimize a post‐discharge monitoring schedule and staffing plan to support monitoring needs. Our approach integrates classical prediction models with machine learning and transfer learning to develop an empirical density that is personalized to each patient. We then transform an intractable monitoring plan optimization with stochastic discharges and health state evolution based on delay‐time models into a weakly coupled network flow model with tractable subproblems after applying a new pruning method that leverages the problem structure. Using this multi‐methodologic approach on two large inpatient datasets, we show that optimal readmission prediction and monitoring plans can identify and mitigate 40–70% of readmissions before they generate an emergency readmission.
In Bayesian semi-parametric analyses of time-to-event data, non-parametric process priors are adopted for the baseline hazard function or the cumulative baseline hazard function for a given finite ...partition of the time axis. However, it would be controversial to suggest a general guideline to construct an optimal time partition. While a great deal of research has been done to relax the assumption of the fixed split times for other non-parametric processes, to our knowledge, no methods have been developed for a gamma process prior, which is one of the most widely used in Bayesian survival analysis. In this paper, we propose a new Bayesian framework for proportional hazards models where the cumulative baseline hazard function is modelled a priori by a gamma process. A key feature of the proposed framework is that the number and position of interval cutpoints are treated as random and estimated based on their posterior distributions.
Statistical analyses that investigate risk factors for Alzheimer's disease (AD) are often subject to a number of challenges. Some of these challenges arise due to practical considerations regarding ...data collection such that the observation of AD events is subject to complex censoring including left-truncation and either interval or right-censoring. Additional challenges arise due to the fact that study participants under investigation are often subject to competing forces, most notably death, that may not be independent of AD. Towards resolving the latter, researchers may choose to embed the study of AD within the "semi-competing risks" framework for which the recent statistical literature has seen a number of advances including for the so-called illness-death model. To the best of our knowledge, however, the semi-competing risks literature has not fully considered analyses in contexts with complex censoring, as in studies of AD. This is particularly the case when interest lies with the accelerated failure time (AFT) model, an alternative to the traditional multiplicative Cox model that places emphasis away from the hazard function. In this article, we outline a new Bayesian framework for estimation/inference of an AFT illness-death model for semi-competing risks data subject to complex censoring. An efficient computational algorithm that gives researchers the flexibility to adopt either a fully parametric or a semi-parametric model specification is developed and implemented. The proposed methods are motivated by and illustrated with an analysis of data from the Adult Changes in Thought study, an on-going community-based prospective study of incident AD in western Washington State.
The need to provide accurate predictions in the evolution of the COVID-19 epidemic has motivated the development of different epidemiological models. These models require a careful calibration of ...their parameters to capture the dynamics of the phenomena and the uncertainty in the data. This work analyzes different parameters related to the personal evolution of COVID-19 (i.e., time of recovery, length of stay in hospital and delay in hospitalization). A Bayesian Survival Analysis is performed considering the age factor and period of the epidemic as fixed predictors to understand how these features influence the evolution of the epidemic. These results can be easily included in the epidemiological SIR model to make prediction results more stable.
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