A Composite Model via Proportional Intensity Function and Additive Hazard Function

2014 ◽  
Vol 631-632 ◽  
pp. 3-6
Author(s):  
Huan Bin Liu ◽  
Tong Yin ◽  
Cheng Wang

This paper mainly discusses a composite model which the end time is an additive hazard function and the recurrent event process is a proportional intensity function, the covariate is time-independent, and censoring is dependent on recurrent events process and end times. Based on the likelihood method, Delta method, U-statistic method and the idea of general estimation equation, the estimation of unknown parameters and unknown functions in this composite model is proposed.

2014 ◽  
Vol 631-632 ◽  
pp. 27-30
Author(s):  
Huan Bin Liu ◽  
Tong Yin ◽  
Cong Jun Rao

Recurrent events data refers to the observation of individuals, which contains the recurrent event time of interest. This paper mainly discusses a joint model when the end time is a multiplicable hazard function and the recurrent event process is a multiplicable intensity function. Based on the likelihood method, Delta method, U-statistic method and the idea of general estimation equation, the estimation of unknown parameters and unknown functions in the model is provided. It provides a new method of parameter estimation for the statistic analysis of recurrent events data.


2014 ◽  
Vol 631-632 ◽  
pp. 90-93
Author(s):  
Huan Bin Liu ◽  
Tong Yin ◽  
Cheng Wang

Recurrent events data refers to the observation of individuals, which contains the recurrent event time of interest. In this paper, we focus on the statistical analysis of recurrent event, and present a composite model based on the proportional intensity function and additive hazard function, and then prove the consistency and asymptotic properties of the estimation under certain regularity conditions.


2014 ◽  
Vol 631-632 ◽  
pp. 86-89
Author(s):  
Huan Bin Liu ◽  
Tong Yin ◽  
Cong Jun Rao

With the development of biology, medical statistics and economy, the study of recurrent event data has made great progress. Many important statistical models of failed time and recurrence process are established. In this paper, we study a joint model based on multiplicable hazard function and proportional hazard function. Considering that the covariate is time-independent, and censoring is dependent on recurrent event processes and end times, we prove the consistency and asymptotic properties of the estimation under certain regularity conditions for this model.


2020 ◽  
Vol 91 (4) ◽  
pp. 352-357
Author(s):  
Jessica Tedford ◽  
Valerie Skaggs ◽  
Ann Norris ◽  
Farhad Sahiar ◽  
Charles Mathers

INTRODUCTION: Atrial fibrillation (AF) is one of the most common cardiac arrhythmias in the general population and is considered disqualifying aeromedically. This study is a unique examination of significant outcomes in aviators with previous history of both AF and stroke.METHODS: Pilots examined by the FAA between 2002 and 2012 who had had AF at some point during his or her medical history were reviewed, and those with an initial stroke or transient ischemic attack (TIA) during that time period were included in this study. All records were individually reviewed to determine stroke and AF history, medical certification history, and recurrent events. Variables collected included medical and behavior history, stroke type, gender, BMI, medication use, and any cardiovascular or neurological outcomes of interest. Major recurrent events included stroke, TIA, cerebrovascular accident, death, or other major events. These factors were used to calculate CHA2DS2-VASc scores.RESULTS: Of the 141 pilots selected for the study, 17.7% experienced a recurrent event. At 6 mo, the recurrent event rate was 5.0%; at 1 yr, 5.8%; at 3 yr 6.9%; and at 5 yr the recurrent event rate was 17.3%. No statistical difference between CHA2DS2-VASc scores was found as it pertained to number of recurrent events.DISCUSSION: We found no significant factors predicting risk of recurrent event and lower recurrence rates in pilots than the general population. This suggests CHA2DS2-VASc scores are not appropriate risk stratification tools in an aviation population and more research is necessary to determine risk of recurrent events in aviators with atrial fibrillation.Tedford J, Skaggs V, Norris A, Sahiar F, Mathers C. Recurrent stroke risk in pilots with atrial fibrillation. Aerosp Med Hum Perform. 2020; 91(4):352–357.


Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 726
Author(s):  
Lamya A. Baharith ◽  
Wedad H. Aljuhani

This article presents a new method for generating distributions. This method combines two techniques—the transformed—transformer and alpha power transformation approaches—allowing for tremendous flexibility in the resulting distributions. The new approach is applied to introduce the alpha power Weibull—exponential distribution. The density of this distribution can take asymmetric and near-symmetric shapes. Various asymmetric shapes, such as decreasing, increasing, L-shaped, near-symmetrical, and right-skewed shapes, are observed for the related failure rate function, making it more tractable for many modeling applications. Some significant mathematical features of the suggested distribution are determined. Estimates of the unknown parameters of the proposed distribution are obtained using the maximum likelihood method. Furthermore, some numerical studies were carried out, in order to evaluate the estimation performance. Three practical datasets are considered to analyze the usefulness and flexibility of the introduced distribution. The proposed alpha power Weibull–exponential distribution can outperform other well-known distributions, showing its great adaptability in the context of real data analysis.


2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Moumita Chatterjee ◽  
Sugata Sen Roy

AbstractIn this article, we model alternately occurring recurrent events and study the effects of covariates on each of the survival times. This is done through the accelerated failure time models, where we use lagged event times to capture the dependence over both the cycles and the two events. However, since the errors of the two regression models are likely to be correlated, we assume a bivariate error distribution. Since most event time distributions do not readily extend to bivariate forms, we take recourse to copula functions to build up the bivariate distributions from the marginals. The model parameters are then estimated using the maximum likelihood method and the properties of the estimators studied. A data on respiratory disease is used to illustrate the technique. A simulation study is also conducted to check for consistency.


2020 ◽  
Vol 70 (4) ◽  
pp. 953-978
Author(s):  
Mustafa Ç. Korkmaz ◽  
G. G. Hamedani

AbstractThis paper proposes a new extended Lindley distribution, which has a more flexible density and hazard rate shapes than the Lindley and Power Lindley distributions, based on the mixture distribution structure in order to model with new distribution characteristics real data phenomena. Its some distributional properties such as the shapes, moments, quantile function, Bonferonni and Lorenz curves, mean deviations and order statistics have been obtained. Characterizations based on two truncated moments, conditional expectation as well as in terms of the hazard function are presented. Different estimation procedures have been employed to estimate the unknown parameters and their performances are compared via Monte Carlo simulations. The flexibility and importance of the proposed model are illustrated by two real data sets.


Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
LAURA EVENSEN ◽  
Nan Liu ◽  
Yijun Wang ◽  
Bernadette Boden-Albala

Objective: To describe the relationship between sleep problems, measured by the Medical Outcomes Sleep scale (MOS) at baseline, in ischemic stroke and TIA (IS/TIA) patients and the likelihood of having a recurrent event, leading to vascular death. Background: Among IS/TIA patients, there is increased risk for recurrent vascular events, including stroke, MI and vascular death. While history of stroke is a major predictor of recurrent events, there may be unidentified factors in play. Sleep quality may predict recurrent vascular events, but little is known about the relationship between sleep and recurrent events in IS/TIA patients. Methods: The Stroke Warning Information and Faster Treatment (SWIFT) Study is an NINDS SPOTRIAS funded randomized trial to study the effect of culturally appropriate, interactive education on stroke knowledge and time to arrival after IS/TIA. Sleep problems and recurrent event information were collected among consentable IS/TIA patients. Cox proportional hazards models were used to describe relationships between sleep and recurrent vascular events in IS/TIA patients. The MOS, a 12 item sleep assessment, measures 6 dimensions of sleep: initiation, maintenance, quantity, adequacy, somnolence and respiratory impairment. Results: Over 5 years, the SWIFT study cohort of 1198 [77% IS; 23% TIA] patients were prospectively enrolled. This cohort was 50% female; 50% Hispanic, 31% White and 18% Black, with a mean NIHSS of 3.2 [SD ±3.8]. 750 subjects completed the MOS scale at baseline. In a multivariate analysis, after adjusting for demographics and vascular risk factors: gender, age, race ethnicity, NIHSS, stroke history, qualifying event type, hypertension, diabetes, smoking and family stroke history, longer sleep initiation is associated with combined outcome of IS/TIA, MI and vascular death [p=0.1, HR=1.09]. Significant predictors of vascular death included: trouble falling asleep (initiation) [p=0.05, HR=1.15]; not ‘getting enough sleep to feel rested’ and not ‘getting the amount of sleep you need’ (adequacy) [p=0.06, HR=1.18 and p=0.03, HR=1.18, respectively]; shortness of breath or headache upon waking (respiratory impairment) [p=0.003, HR=1.33]; restless sleep [p=0.07, HR=1.15] and waking at night with trouble resuming sleep [p=0.004, HR=1.23] (maintenance); daytime drowsiness [p=0.05, HR=1.18] and trouble staying awake [p=0.01, HR=1.25] (somnolence); and taking naps (quantity) [p=0.03, HR=1.22]. Conclusions: Sleep problems represent diverse, modifiable risk factors for secondary vascular events, particularly vascular death. Exploring sleep dimensions may yield crucial information for reduction of secondary vascular events in IS/TIA patients. Further investigation is needed to fully understand the effects of sleep on secondary vascular event incidence.


2015 ◽  
Vol 26 (4) ◽  
pp. 1969-1981 ◽  
Author(s):  
Jing Ning ◽  
Mohammad H Rahbar ◽  
Sangbum Choi ◽  
Jin Piao ◽  
Chuan Hong ◽  
...  

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.


Author(s):  
Zhen Chen ◽  
Tangbin Xia ◽  
Ershun Pan

In this paper, a segmental hidden Markov model (SHMM) with continuous observations, is developed to tackle the problem of remaining useful life (RUL) estimation. The proposed approach has the advantage of predicting the RUL and detecting the degradation states simultaneously. As the observation space is discretized into N segments corresponding to N hidden states, the explicit relationship between actual degradation paths and the hidden states can be depicted. The continuous observations are fitted by Gaussian, Gamma and Lognormal distribution, respectively. To select a more suitable distribution, model validation metrics are employed for evaluating the goodness-of-fit of the available models to the observed data. The unknown parameters of the SHMM can be estimated by the maximum likelihood method with the complete data. Then a recursive method is used for RUL estimation. Finally, an illustrate case is analyzed to demonstrate the accuracy and efficiency of the proposed method. The result also suggests that SHMM with observation probability distribution which is closer to the real data behavior may be more suitable for the prediction of RUL.


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