A general additive-multiplicative rates model for recurrent event data

In this article, we propose a general additive-multiplicative rates model for recurrent event data. The proposed model includes the additive rates and multiplicative rates models as special cases. For the inference on the model parameters, estimating equation approaches are developed, and asymptotic properties of the proposed estimators are established through modern empirical process theory. In addition, an illustration with multiple-infection data from a clinic study on chronic granulomatous disease is provided.

An additive-multiplicative rates model for multivariate recurrent events with event categories missing at random

Multivariate recurrent event data arises when study subjects may experience more than one type of recurrent events. In some situations, however, although event times are always observed, event categories may be partially missing. In this paper, an additive-multiplicative rates model is proposed for the analysis of multivariate recurrent event data when event categories are missing at random. A weighted estimating equations approach is developed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. In addition, a model-checking technique is presented to assess the adequacy of the model. Simulation studies are conducted to evaluate the finite sample behavior of the proposed estimators, and an application to a platelet transfusion reaction study is provided.

A class of transformation rate models for recurrent event data

Recurrent event data frequently occur in longitudinal studies, and it is often of interest to estimate the effects of covariates on the recurrent event rate. This paper considers a class of semiparametric transformation rate models for recurrent event data, which uses an additive AMen model as its covariate dependent baseline. The new models are flexible in that they allow for both additive and multiplicative covariate effects, and some covariate effects are allowed to be nonparametric and time-varying. An estimating procedure is proposed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. Simulation studies and a real data analysis demonstrate that the proposed method performs well and is appropriate for practical use.

Semiparametric Additive Intensity Model with Frailty for Recurrent Events

The seminal Cox＇s proportional intensity model with multiplicative frailty is a popular approach to analyzing the frequently encountered recurrent event data in scientific studies. In the case of violating the proportional intensity assumption, the additive intensity model is a useful alternative. Both the additive and proportional intensity models provide two principal frameworks for studying the association between the risk factors and the disease recurrences. However, methodology devel- opment on the additive intensity model with frailty is lacking, although would be valuable. In this paper, we propose an additive intensity model with additive frailty to formulate the effects of possibly time-dependent covariates on recurrent events as well as to evaluate the intra-class dependence within recurrent events which is captured by the frailty variable. The asymptotic properties for both the regression parameters and the association parameters in frailty distribution are established. Fhrthermore, we also investigate the large-sample properties of the estimator for the cumulative baseline intensity function.

VARIABLE SELECTION FOR RECURRENT EVENT DATA WITH INFORMATIVE CENSORING

Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first propose the estimation methods to select the significant variables, and then prove the asymptotic behavior of the proposed estimator. Furthermore, the authors discuss the computing algorithm to assess the proposed estimator via the linear function approximation and generalized cross validation method for determination of the tuning parameters. Finally, the finite sample estimation for the asymptotical covariance matrix is also proposed.

Joint Analysis of Recurrent Event Data with a Dependent Terminal Event

Recurrent event data frequently occur in many longitudinal studies, and the observation on recurrent events could be stopped by a terminal event such as death. This paper considers joint modeling and analysis of recurrent event and terminal event data through a common subject-specific frailty, in which the proportional intensity model is used for modeling the recurrent event process and the additive hazards model is used for modeling the terminal event time. Estimating equation approaches are developed for parameter estimation and asymptotic properties of the resulting estimators are established. In addition, some procedures are presented for model checking. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a heart failure study is provided.

Partially Time-varying Coefficient Linear Rate Model for the Recurrent Event Data

In [13], Schaubel et al. proposed a semiparametric partially linear rate model for the statistical analysis of recurrent event data. But they only considered the model with time-independent covariate effects. In this paper, rate function of the recurrent event is modeled by a semipaxametric partially linear function which can include the time-varying effects. We propose the method of generalized estimating equations to make inferences about both the time-varying effects and time-independent effects. The large sample properties are established, while extensive simulation studies are carried out to examine the proposed procedures. At last, we apply the procedures to the well-known bladder cancer study.

Recurrent Takotsubo cardiomyopathy triggered by emotionally stressful events: A case report

BACKGROUND Takotsubo cardiomyopathy(TCM)is characterized by reversible left ventricular dysfunction triggered by emotional or physical stress.Only 1%-2%of patients with acute coronary syndrome are diagnosed with TCM.Although obstructive coronary artery disease is frequently considered to be the cause of chest pain,TCM should be considered in some clinical settings.In this case,clinicians did not make a timely and accurate diagnosis for TCM due to a lack of knowledge until the third hospitalization with a left ventriculogram.CASE SUMMARY A 55-year-old postmenopausal woman had intermittent chest pain following emotionally stressful events three times in the past 3 years.Cardiac troponin levels increased after each instance of symptom onset.A transthoracic echocardiogram showed reversible left ventricular dysfunction.The patient underwent three coronary angiograms without evidence of coronary artery disease.A left ventriculogram was first performed at the third hospitalization and revealed apical akinesia with ballooning of the apical region and consistent hypercontractile basal segments.The diagnosis of TCM was confirmed.The patient was treated with an angiotensin-converting-enzyme inhibitor(perindopril)and aβ-blocker(metoprolol).No complications occurred during the patient’s hospitalization.The patient was told to avoid stressful events.During the 9-mo follow-up visit,the patient was asymptomatic with an ejection fraction of 55%.CONCLUSION Clinicians should be conscious of the possibility of TCM,especially in postmenopausal women presenting with clinical manifestations similar to acute coronary syndrome without coronary occlusion.

Adaptive Conditional Hazard Regression Modeling of Multiple Event Times

Recurrent event time data and more general multiple event time data are commonly analyzed using extensions of Cox regression, or proportional hazards regression, as used with single event time data. These methods treat covariates, either time-invariant or time-varying, as having multiplicative effects while general dependence on time is left un-estimated. An adaptive approach is formulated for analyzing multiple event time data. Conditional hazard rates are modeled in terms of dependence on both time and covariates using fractional polynomials restricted so that the conditional hazard rates are positive-valued and so that excess time probability functions (generalizing survival functions for single event times) are decreasing. Maximum likelihood is used to estimate parameters adjusting for right censored event times. Likelihood cross-validation (LCV) scores are used to compare models. Adaptive searches through alternate conditional hazard rate models are controlled by LCV scores combined with tolerance parameters. These searches identify effective models for the underlying multiple event time data. Conditional hazard regression is demonstrated using data on times between tumor recurrence for bladder cancer patients. Analyses of theory-based models for these data using extensions of Cox regression provide conflicting results on effects to treatment group and the initial number of tumors. On the other hand, fractional polynomial analyses of these theory-based models provide consistent results identifying significant effects to treatment group and initial number of tumors using both model-based and robust empirical tests. Adaptive analyses further identify distinct moderation by group of the effect of tumor order and an additive effect to group after controlling for nonlinear effects to initial number of tumors and tumor order. Results of example analyses indicate that adaptive conditional hazard rate modeling can generate useful insights into multiple event time data.

Adaptive Conditional Hazard Regression Modeling of Multiple Event Times

Recurrent event time data and more general multiple event time data are commonly analyzed using extensions of Cox regression, or proportional hazards regression, as used with single event time data. These methods treat covariates, either time-invariant or time-varying, as having multiplicative effects while general dependence on time is left un-estimated. An adaptive approach is formulated for analyzing multiple event time data. Conditional hazard rates are modeled in terms of dependence on both time and covariates using fractional polynomials restricted so that the conditional hazard rates are positive-valued and so that excess time probability functions (generalizing survival functions for single event times) are decreasing. Maximum likelihood is used to estimate parameters adjusting for right censored event times. Likelihood cross-validation (LCV) scores are used to compare models. Adaptive searches through alternate conditional hazard rate models are controlled by LCV scores combined with tolerance parameters. These searches identify effective models for the underlying multiple event time data. Conditional hazard regression is demonstrated using data on times between tumor recurrence for bladder cancer patients. Analyses of theory-based models for these data using extensions of Cox regression provide conflicting results on effects to treatment group and the initial number of tumors. On the other hand, fractional polynomial analyses of these theory-based models provide consistent results identifying significant effects to treatment group and initial number of tumors using both model-based and robust empirical tests. Adaptive analyses further identify distinct moderation by group of the effect of tumor order and an additive effect to group after controlling for nonlinear effects to initial number of tumors and tumor order. Results of example analyses indicate that adaptive conditional hazard rate modeling can generate useful insights into multiple event time data.

A Semiparametric Additive-multiplicative Rates Model for the Weighted Composite Endpoint of Recurrent and Terminal Events

Recurrent event data are commonly encountered in many scientific fields,including biomedical studies,clinical trials and epidemiological surveys,and many statistical methods have been proposed for their analysis.In this paper,we consider to use a weighted composite endpoint of recurrent and terminal events,which is weighted by the severity of each event,to assess the overall effects of covariates on the two types of events.A flexible additive-multiplicative model incorporating both multiplicative and additive effects on the rate function is proposed to analyze such weighted composite event process,and more importantly,the dependence structure among the recurrent and terminal events is left unspecified.For the estimation,we construct the unbiased estimating equations by virtue of the inverse probability weighting technique,and the resulting estimators are shown to be consistent and asymptotically normal under some mild regularity conditions.We evaluate the finite-sample performance of the proposed method via simulation studies and apply the proposed method to a set of real data arising from a bladder cancer study.

The Additive-multiplicative Hazards Model for Multiple Type of Recurrent Gap Times

Recurrent event gap times data frequently arise in biomedical studies and often more than one type of event is of interest. To evaluate the effects of covariates on the marginal recurrent event hazards functions, there exist two types of hazards models： the multiplicative hazards model and the additive hazards model. In the paper, we propose a more flexible additive-multiplicative hazards model for multiple type of recurrent gap times data, wherein some covariates are assumed to be additive while others are multiplicative. An estimating equation approach is presented to estimate the regression parameters. We establish asymptotic properties of the proposed estimators.

Efficacy and safety of aspirin antiplatelet therapy within 48 h of symptom onset in patients with acute stroke

BACKGROUND Aspirin is a widely used antiplatelet agent that reduces the risk of recurrent ischemic stroke and other vascular events.However,the optimal timing and dose of aspirin initiation after an acute stroke remain controversial.AIM To evaluate the efficacy and safety of aspirin antiplatelet therapy within 48 h of symptom onset in patients with acute stroke.METHODS We conducted a randomized,open-label,controlled trial in 60 patients with acute ischemic or hemorrhagic stroke who were admitted to our hospital within 24 h of symptom onset.Patients were randomly assigned to receive either aspirin 300 mg daily or no aspirin within 48 h of stroke onset.The primary outcome was the occurrence of recurrent stroke,myocardial infarction,or vascular death within 90 d.The secondary outcomes were functional outcomes at 90 d measured using the modified Rankin Scale(mRS),incidence of bleeding complications,and mortality rate.RESULTS The mean age of the patients was 67.8 years and 55%of them were male.The median time from stroke onset to randomization was 12 h.The baseline characteristics were well balanced between the two groups.The primary outcome occurred in 6.7%of patients in the aspirin group and 16.7%of patients in the no aspirin group(relative risk=0.40,95%confidence interval:0.12-1.31,P=0.13).The mRS score at 90 d was significantly lower in the aspirin group than in the no aspirin group(median,2 vs 3,respectively;P=0.04).The incidence of bleeding complications was similar between the groups(6.7%vs 6.7%,P=1.00).The mortality rates were also comparable between the two groups(10%vs 13.3%,P=0.69).CONCLUSION Aspirin use is associated with favorable functional outcomes but does not significantly reduce the risk of recurrent vascular events.Its acceptable safety profile is comparable to that of no aspirin.Further studies with larger sample sizes and longer follow-up periods are needed to confirm these findings.

White Matter Lesions Predict Recurrent Vascular Events in Patients with Transient Ischemic Attacks

Background： White matter lesions （WMLs） are common findings in brain magnetic resonance imaging （MRI） and are strongly associated with stroke incidence, recurrence, and prognosis. However, the relationship between WMLs and transient ischemic attacks （TIAs） is not well established. This study aimed to determine the clinical significance of WMLs in patients with TIA. Methods： A total of 181 consecutive inpatients with first-ever TIA were enrolled. Brain MRls within 2 days of symptom onset were used to measure WML volumes. Recurrent vascular events within 1 year of TIA onset were assessed. The relationship between WMLs and recurrent risk of vascular events was determined by a multivariate logistic regression. Results： WMLs were identified in 104 patients （57.5%）. Age and ratio of hypertension were significantly different between patients with and without WMLs. The incidence of vascular events in patients with WMLs significantly increased in comparison to those without WMLs （21.15% vs. 5.19%, 95% confidence interval [CI]： 1.18-[ 5.20, P = 0.027） after controlling for cont／~unders. Furthermore, distributions of WML loads were found to be different between patients who developed vascular events and those who did not. WML volumes were demonstrated to be correlated with recurrent risks, and the fourth quartile of WML volumes led to an 8.5-fold elevation of recurrent risk of vascular events compared with the first quartile （95% CI： 1.52-47.65, P = 0.015） alier adjusting for hyperlipidemia. Conclusion： WMLs occur frequently in patients with T1A and are associated wiila the high risk of recurrent vascular events, suggesting a predictive neuroimaging marker for TIA outcomes.

Cholecystectomy reduces subsequent cholangiocarcinoma risk in choledocholithiasis patients undergoing endoscopic intervention

BACKGROUND Cholangiocarcinoma is a disease with a high mortality rate.Our previous study revealed that cholelithiasis patients who undergo endoscopic sphincterotomy(ES)/endoscopic papillary balloon dilatation are at a higher risk for subsequent cholangiocarcinoma than cholelithiasis patients who undergo cholecystectomy.AIM To clarify the relationship between recurrent biliary events and subsequent cholangiocarcinoma risk in choledocholithiasis patients.METHODS From one million random cases in the Taiwan National Health Insurance Research Database 2004–2011,we selected symptomatic choledocholithiasis patients older than 18 years who were admitted from January 2005 to December 2009(study group).Cases for a control group were defined as individuals who had never been diagnosed with cholelithiasis,matched by sex and age in a 1:3 ratio.The study group was further divided into ES/endoscopic papillary balloon dilatation,both ES/endoscopic papillary balloon dilatation and cholecystectomy,and no intervention groups.RESULTS We included 2096 choledocholithiasis patients without previous intervention or cholangiocarcinoma.A total of 12(2.35%),11(0.74%),and 1(1.00%)subsequent cholangiocarcinoma cases were diagnosed among 511 ES/endoscopic papillary balloon dilatation patients,1485 patients with no intervention,and 100 ES/endoscopic papillary balloon dilatation and cholecystectomy patients,respectively.The incidence rates of recurrent biliary event were 527.79/1000 person-years and 286.69/1000 person-years in the subsequent cholangiocarcinoma and no cholangiocarcinoma group,showing a high correlation between subsequent cholangiocarcinoma risk and recurrent biliary events.CONCLUSION Choledocholithiasis patients who undergo further cholecystectomy after ES/endoscopic papillary balloon dilatation have decreased subsequent cholangiocarcinoma risk due to reduced recurrent biliary events.

Liver Fibrosis Scoring Systems as Novel Tools for Predicting Recurrent Cardiovascular Events in Patients with a Prior Cardiovascular Event

Objective:Regarding the secondary prevention of cardiovascular disease(CVD),there is great interest in preventing recurrent cardiovascular events(RCVEs).The prognostic importance of liver fibrosis scores(LFSs)has previously been reported in variousCVDs.We hypothesized that LFSs might also be useful predictors for RCVEs in patients with prior cardiovascular events(CVEs).Herein,we aimed to evaluate the associations of LFSs with RCVEs in a large,real-world cohort of coronary artery disease(CAD)patients with a prior CVE.Methods:In this multicenter prospective study,6527 consecutive patients with angiography-diagnosed CAD who had experienced a prior CVE(acute coronary syndrome,stroke,percutaneous coronary intervention,or coronary artery bypass grafting)were enrolled.LFSs were computed according to the published formulas:non-alcoholic fatty liver disease fibrosis score(NFS)includes age,body mass index(BMI),impaired fasting glycemia or diabetes mellitus(DM),aspartate aminotransferase(AST)/alanine aminotransferase(ALT)ratio,platelets,and albumin;fibrosis-4(FIB-4)includes age,AST,ALT,and platelets;Forns score includes age,gamma-glutamyltransferase(GGT),and platelets;BARD includes BMI,AST/ALT ratio,and DM;GGT/platelet ratio includes GGT and platelets;AST/ALT ratio includes AST and ALT;and AST/platelet ratio index includes AST and platelets.The originally reported cutoffs were used for the categorization of low-,intermediate-,and high-score subgroups.All patients were followed up for the occurrence of RCVEs(comprising cardiovascular death,non-fatal myocardial infarction,and stroke).Cox and Poisson regression analyses were used to assess the relationship of baseline LFSs with the risk of RCVE.Results:During a mean follow-up of(54.67±18.80)months,532(8.2%)RCVEs were recorded.Intermediate and high NFS,FIB-4,Forns,and BARD scores were independently associated with an increased risk of RCVE(hazard ratios ranging from 1.42 to 1.75 for intermediate scores and 1.35 to 2.52 for high scores).In the subgroup analyses of sex,age,BMI,DM,and hypertension status,the increased risk of RCVEs with high LFSs(NFS,FIB-4,Forns,and BARD)was maintained across the different subgroups(all P<0.05).Conclusion:This study showed that LFSs are indeed independently associated with RCVEs,suggesting that LFSs may be used as novel tools for risk stratification in CAD patients with a prior CVE.

An Additive Hazards Model for Clustered Recurrent Gap Times

In this article, clustered recurrent gap time is investigated. A marginal additive haz- ards model is proposed without specifying the association of the individuals within the same cluster. The relationship among the gap times for the same individual is also left unspecified. An estimating equation-based inference procedure is developed for the model parameters, and the asymptotic proper- ties of the resulting estimators are established. In addition, a lack-of-fit test is presented to assess the adequacy of the model. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a clinic study on chronic granulomatous disease （CGD） is illustrated.

The Proportional Hazards Model for Multiple Type Recurrent Gap Times

Recurrent events data and gap times between recurrent events are frequently encountered in many clinical and observational studies,and often more than one type of recurrent events is of interest.In this paper,we consider a proportional hazards model for multiple type recurrent gap times data to assess the effect of covaxiates on the censored event processes of interest.An estimating equation approach is used to obtain the estimators of regression coefficients and baseline cumulative hazard functions.We examine asymptotic properties of the proposed estimators.Finite sample properties of these estimators are demonstrated by simulations.

Additive Rate Model With Auxiliary Covariates

In this paper, we consider an inference method for recurrent event data in which the primary exposure covariate is assessed only in a validation set, while as an auxiliary covariate for the main exposure is available for the full cohort. Additive rate model is considered. The existing estimating equations in the absence of primary exposure are corrected by taking use of the validation data and auxiliary information, which yield consistent and asymptotically normal estimators of the regression parameters. The estimated baseline mean process is shown to converge weakly to a zero-mean Gaussian process. Extensive simulations are conducted to evaluate finite sample performance.

The gap in research on polyploidization between plants and vertebrates:model systems and strategic challenges

Polyploidization via whole-genome duplications （WGD） is a common phenomenon in organisms. However, investigations into this phenomenon differ greatly between plants and animals. Recent research on polyploid plants illustrates the immediate changes that follow WGDs and the mechanisms behind in both genetic and epigenetic consequences. Unfortunately, equivalent questions remain to be explored in animals. Enlightened by botanical research, the study of polyploidization in vertebrates involves the identification of model animals and the establishment of strategies. Here we review and compare the research on plants and vertebrates while considering intrageneric or intraspecific variation in genome size. Suitable research methods on recently established poly- ploidy systems could provide important clues for under- standing what happens after WGDs in vertebrates. The approach yields insights into survival and the rarity of polyploidization in vertebrates. The species of Carassius and the allopolyploid system of goldfish × common carp hybridization appear to be suitable models for unraveling the evolution and adaptation of polyploid vertebrates.