scholarly journals Median Analysis of Repeated Measures Associated with Recurrent Events in Presence of Terminal Event

2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Rajeshwari Sundaram ◽  
Ling Ma ◽  
Subhashis Ghoshal
Keyword(s):  
Repeated Measures ◽  
Recurrent Events ◽  
Point Processes ◽  
Real Data ◽  
Recurrent Event ◽  
Recurrent Event Data ◽  
Finite Sample ◽  
Positive Jump ◽  
The Mean ◽  
Mean Function

AbstractRecurrent events are often encountered in medical follow up studies. In addition, such recurrences have other quantities associated with them that are of considerable interest, for instance medical costs of the repeated hospitalizations and tumor size in cancer recurrences. These processes can be viewed as point processes, i.e. processes with arbitrary positive jump at each recurrence. An analysis of the mean function for such point processes have been proposed in the literature. However, such point processes are often skewed, leading to median as a more appropriate measure than the mean. Furthermore, the analysis of recurrent event data is often complicated by the presence of death. We propose a semiparametric model for assessing the effect of covariates on the quantiles of the point processes. We investigate both the finite sample as well as the large sample properties of the proposed estimators. We conclude with a real data analysis of the medical cost associated with the treatment of ovarian cancer.

scholarly journals Semiparametric Regression Estimation for Recurrent Event Data with Errors in Covariates under Informative Censoring

2016 ◽  
Vol 12 (2) ◽  
Author(s):  
Hsiang Yu ◽  
Yu-Jen Cheng ◽  
Ching-Yun Wang
Keyword(s):  
Measurement Error ◽  
Recurrent Events ◽  
Semiparametric Regression ◽  
Informative Censoring ◽  
Recurrent Event ◽  
Occurrence Rate ◽  
Event Data ◽  
Recurrent Event Data ◽  
Finite Sample ◽  
Cancer Trial

AbstractRecurrent event data arise frequently in many longitudinal follow-up studies. Hence, evaluating covariate effects on the rates of occurrence of such events is commonly of interest. Examples include repeated hospitalizations, recurrent infections of HIV, and tumor recurrences. In this article, we consider semiparametric regression methods for the occurrence rate function of recurrent events when the covariates may be measured with errors. In contrast to the existing works, in our case the conventional assumption of independent censoring is violated since the recurrent event process is interrupted by some correlated events, which is called informative drop-out. Further, some covariates may be measured with errors. To accommodate for both informative censoring and measurement error, the occurrence of recurrent events is modelled through an unspecified frailty distribution and accompanied with a classical measurement error model. We propose two corrected approaches based on different ideas, and we show that they are numerically identical when estimating the regression parameters. The asymptotic properties of the proposed estimators are established, and the finite sample performance is examined via simulations. The proposed methods are applied to the Nutritional Prevention of Cancer trial for assessing the effect of the plasma selenium treatment on the recurrence of squamous cell carcinoma.

scholarly journals METODE MULTIPLE IMPUTATION UNTUK MENGATASI KOVARIAT TAK LENGKAP PADA DATA KEJADIAN BERULANG

2019 ◽  
Vol 2 (2) ◽  
pp. 47
Author(s):  
Rianti Siswi Utami ◽  
Danardono Danardono
Keyword(s):  
Multiple Imputation ◽  
Recurrent Events ◽  
Mean Squared Error ◽  
Estimation Method ◽  
Real Data ◽  
Recurrent Event ◽  
Missing Observations ◽  
Recurrent Event Data ◽  
Event Times ◽  
The Right

Multiple imputation is one of estimation method used to impute missing observations. This method imputes missing observation several times then it is more possible to get the right estimate than just one time imputation. In this research, the method will be applied to estimate missing observations in covariates of recurrent event data. Some multiple imputation methods will be considered including combination of the event indicator, the event  times,   the logarithm of event times, and the cumulative baseline hazard. To compare these methods, Monte Carlo simulation will be used based on relative bias and Mean Squared Error (MSE). The recurrent events will be modelled using Cox proportional hazard model. Furthermore, real data application will be presented.

scholarly journals Estimating the ratio of multivariate recurrent event rates with application to a blood transfusion study

2015 ◽  
Vol 26 (4) ◽  
pp. 1969-1981 ◽  
Author(s):  
Jing Ning ◽  
Mohammad H Rahbar ◽  
Sangbum Choi ◽  
Jin Piao ◽  
Chuan Hong ◽  
...  
Keyword(s):  
Blood Cells ◽  
Latent Variables ◽  
Recurrent Events ◽  
Blood Product ◽  
Massive Transfusion ◽  
Informative Censoring ◽  
Recurrent Event ◽  
Dependence Structure ◽  
Blood Product Transfusion ◽  
Finite Sample

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.

Semiparametric methods for regression analysis of panel count data and mixed panel count data

2017 ◽  
Author(s):  
◽  
Guanglei Yu
Keyword(s):  
Regression Analysis ◽  
Simulation Study ◽  
Count Data ◽  
Recurrent Events ◽  
Recurrent Event ◽  
Mixed Data ◽  
Panel Count Data ◽  
Event Data ◽  
Recurrent Event Data ◽  
Recurrent Event Process

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Recurrent event data and panel count data are two common types of data that have been studied extensively in event history studies in literature. By recurrent event data, we mean that subjects are observed continuously in the follow-up study and thus occurrence times of recurrent events of interest are available. For panel count data, subjects are monitored periodically at discrete observation times and thus only numbers of recurrent events between two subsequent observations are recorded. In addition, one may face mixed panel count data in practice, which are the mixture of recurrent event data and panel count data. They arise when each study subject may be observed continuously during the whole study period, continuously over some study periods and at some time points otherwise, or only at some discrete time points. That is, these mixed data provide complete or incomplete information on the recurrent event process over different time periods for different subjects. It is well-known that in panel count data, the observation process may carry information on the underlying recurrent event process and the censoring may also be dependent in practice. Under such circumstance, the first part of this dissertation will discuss regression analysis of panel count data with informative observations and drop-outs. For the problem, a general means model is presented that can allow both additive and multiplicative effects of covariates on the underlying recurrent event process. In addition, the proportional rates model and the accelerated failure time model are employed to describe the covariate effects on the observation process and the dropout or follow-up process, respectively. For estimation of regression parameters, some estimating equation-based procedures are developed and the asymptotic properties of the proposed estimators are established. In addition, a resampling approach is proposed for the estimation of the covariance matrix of the proposed estimator and a model checking procedure is also provided. The results from an extensive simulation study indicate that the proposed methodology works well for practical situations and it is applied to a motivated set of real data from the Childhood Cancer Survivor Study (CCSS) given in Section 1.1.2.2. In the second part of this dissertation, we will consider regression analysis of mixed panel count data. One major problem in the statistical inference on the mixed data is to combine these two different types of data structures. Since panel count data can be viewed as interval-censored recurrent event data with exact occurrence times of events of interest unobserved or missing, they may be augmented by filling in those missing data by imputation. Then the mixed data can be converted to recurrent event data on which the existing statistical inference method can be easily implemented. Motivated by this, a multiple imputation-based estimation approach is proposed. A simulation study is conducted to study the finite-sample properties of the proposed methodology and it shows that the proposed method is more efficient than the existing method. Also, an illustrative example from the CCSS is provided. The third part of this dissertation still considers regression analysis of mixed panel count data but in the presence of a dependent terminal event, which precludes further occurrence of either recurrent events of interest or observations. For this problem, we present a marginal modeling approach which acknowledges the fact that there will be no more recurrent events after the terminal event and leaves the correlation structure unspecified. To estimate the parameters of interest, an estimating equation-based procedure is developed and the inverse probability of survival weighting technique is used. Asymptotic properties of proposed estimators are also established and finite-sample properties are assessed in a simulation study. We again apply this proposed methodology to the CCSS. In the last part of this dissertation, we will discuss some work directions of the future research.

scholarly journals Causal inference for recurrent event data using pseudo-observations

Biostatistics ◽  
2020 ◽  
Author(s):  
Chien-Lin Su ◽  
Robert W Platt ◽  
Jean-François Plante
Keyword(s):  
Goodness Of Fit ◽  
Variance Estimation ◽  
Recurrent Event ◽  
Event Data ◽  
Robust Estimators ◽  
Recurrent Event Data ◽  
Finite Sample ◽  
Data Set ◽  
Asymptotically Normal ◽  
Doubly Robust

Summary Recurrent event data are commonly encountered in observational studies where each subject may experience a particular event repeatedly over time. In this article, we aim to compare cumulative rate functions (CRFs) of two groups when treatment assignment may depend on the unbalanced distribution of confounders. Several estimators based on pseudo-observations are proposed to adjust for the confounding effects, namely inverse probability of treatment weighting estimator, regression model-based estimators, and doubly robust estimators. The proposed marginal regression estimator and doubly robust estimators based on pseudo-observations are shown to be consistent and asymptotically normal. A bootstrap approach is proposed for the variance estimation of the proposed estimators. Model diagnostic plots of residuals are presented to assess the goodness-of-fit for the proposed regression models. A family of adjusted two-sample pseudo-score tests is proposed to compare two CRFs. Simulation studies are conducted to assess finite sample performance of the proposed method. The proposed technique is demonstrated through an application to a hospital readmission data set.

Additive-Accelerated Hazard Ratio Model for Multiple Type Recurrent Events Gap Time

2012 ◽  
Vol 263-266 ◽  
pp. 131-134
Author(s):  
Huan Bin Liu
Keyword(s):  
Recurrent Events ◽  
Clinical Medicine ◽  
Estimating Equation ◽  
Hazard Ratio ◽  
Recurrent Event ◽  
Estimation Methods ◽  
Ratio Model ◽  
Recurrent Event Data ◽  
Multiple Type Recurrent Events ◽  
Multiple Type

Multiple type recurrent event data is often observed in clinical medicine, epidemiology, biostatistics, and other related applied research fields. This paper presents an additive-accelerated hazard ratio model for multiple type recurrent events data, and gives the estimation methods of unknown parameter and non-parameter function based on the idea of estimating equation. In addition, it provides the expression of unknown regression coefficient as well as the benchmark hazard ratio function.

scholarly journals Bayesian analysis of multi-type recurrent events and dependent termination with nonparametric covariate functions

2015 ◽  
Vol 26 (6) ◽  
pp. 2869-2884 ◽  
Author(s):  
Li-An Lin ◽  
Sheng Luo ◽  
Bingshu E Chen ◽  
Barry R Davis
Keyword(s):  
Recurrent Events ◽  
Method Development ◽  
Frailty Model ◽  
Recurrent Event ◽  
Lipid Lowering ◽  
Event Data ◽  
Recurrent Event Data ◽  
B Splines ◽  
Terminal Time ◽  
Event Times

Multi-type recurrent event data occur frequently in longitudinal studies. Dependent termination may occur when the terminal time is correlated to recurrent event times. In this article, we simultaneously model the multi-type recurrent events and a dependent terminal event, both with nonparametric covariate functions modeled by B-splines. We develop a Bayesian multivariate frailty model to account for the correlation among the dependent termination and various types of recurrent events. Extensive simulation results suggest that misspecifying nonparametric covariate functions may introduce bias in parameter estimation. This method development has been motivated by and applied to the lipid-lowering trial component of the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial.

scholarly journals Mixture cure models with time-varying and multilevel frailties for recurrent event data

2019 ◽  
Vol 29 (5) ◽  
pp. 1368-1385 ◽  
Author(s):  
Richard Tawiah ◽  
Geoffrey J McLachlan ◽  
Shu Kay Ng
Keyword(s):  
Recurrent Events ◽  
General Setting ◽  
Efficient Estimation ◽  
Recurrent Event ◽  
Time Varying ◽  
Event Data ◽  
Recurrent Event Data ◽  
Mixture Cure Model ◽  
Generalised Linear Mixed Model ◽  
Gap Times

Many medical studies yield data on recurrent clinical events from populations which consist of a proportion of cured patients in the presence of those who experience the event at several times (uncured). A frailty mixture cure model has recently been postulated for such data, with an assumption that the random subject effect (frailty) of each uncured patient is constant across successive gap times between recurrent events. We propose two new models in a more general setting, assuming a multivariate time-varying frailty with an AR(1) correlation structure for each uncured patient and addressing multilevel recurrent event data originated from multi-institutional (multi-centre) clinical trials, using extra random effect terms to adjust for institution effect and treatment-by-institution interaction. To solve the difficulties in parameter estimation due to these highly complex correlation structures, we develop an efficient estimation procedure via an EM-type algorithm based on residual maximum likelihood (REML) through the generalised linear mixed model (GLMM) methodology. Simulation studies are presented to assess the performances of the models. Data sets from a colorectal cancer study and rhDNase multi-institutional clinical trial were analyzed to exemplify the proposed models. The results demonstrate a large positive AR(1) correlation among frailties across successive gap times, indicating a constant frailty may not be realistic in some situations. Comparisons of findings with existing frailty models are discussed.

Dynamical systems defined on point processes

1998 ◽  
Vol 35 (03) ◽  
pp. 581-588
Author(s):  
Laurence A. Baxter
Keyword(s):  
Stochastic Process ◽  
Poisson Process ◽  
Point Processes ◽  
Linear Boltzmann Equation ◽  
Semidynamical System ◽  
The Mean ◽  
Mean Function ◽  
Necessary And Sufficient ◽  
The Relationship

This paper introduces a new stochastic process in which the iterates of a dynamical system evolving in discrete time coincide with the events of a Poisson process. The autocovariance function of the stochastic process is studied and a necessary and sufficient condition for it to vanish is deduced. It is shown that the mean function of this process comprises a continuous-time semidynamical system if the underlying dynamical map is linear. The flow of probability density functions generated by the stochastic process is analysed in detail, and the relationship between the flow and the solutions of the linear Boltzmann equation is investigated. It is shown that the flow is a semigroup if and only if the point process defining the stochastic process is Poisson, thereby providing a new characterization of the Poisson process.

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