scholarly journals An Introspective Overview of the Dynamics of Recurrent Events Data Analysis

2021 ◽  
pp. 144-162
Author(s):  
Anthony Joe Turkson ◽  
Timothy Simpson ◽  
John Awuah Addor
Keyword(s):  
Recurrent Events ◽  
Research Question ◽  
Frailty Model ◽  
Recurrent Event ◽  
Outcome Variable ◽  
Recurrence Time ◽  
Recurrent Event Data ◽  
Data Set ◽  
Baseline Hazard ◽  
Event Models

A recurrent event remains the outcome variable of interest in many biometric studies. Recurrent events can be explained as events of defined interest that can occur to same person more than once during the study period. This study presents an overview of different pertinent recurrent models for analyzing recurrent events. Aims: To introduce, compare, evaluate and discuss pros and cons of four models in analyzing recurrent events, so as to validate previous findings in respect of the superiority or appropriateness of these models. Study Design:  A comparative studies based on simulation of recurrent event models applied to a tertiary data on cancer studies.  Methodology: Codes in R were implemented for simulating four recurrent event models, namely; The Andersen and Gill model; Prentice, Williams and Peterson models; Wei, Lin and Weissferd; and Cox frailty model. Finally, these models were applied to analyze the first forty subjects from a study of Bladder Cancer Tumors. The data set contained the first four repetitions of the tumor for each patient, and each recurrence time was recorded from the entry time of the patient into the study. An isolated risk interval is defined by each time to an event or censoring. Results: The choice and usage of any of the models lead to different conclusions, but the choice depends on: risk intervals; baseline hazard; risk set; and correlation adjustment or simplistically, type of data and research question. The PWP-GT model could be used if the research question is focused on whether treatment was effective for the  event since the previous event happened. However, if the research question is designed to find out whether treatment was effective for the  event since the start of treatment, then we could use the PWP- TT. The AG model will be adequate if a common baseline hazard could be assumed, but the model lacks the details and versatility of the event-specific models. The WLW model is very suitable for data with diverse events for the same person, which underscores a potentially different baseline hazard for each type. Conclusion: PWP-GT has proven to be the most useful model for analyzing recurrent event data.

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 Dynamic prediction of recurrent events data by landmarking with application to a follow-up study of patients after kidney transplant

2016 ◽  
Vol 27 (3) ◽  
pp. 832-845 ◽  
Author(s):  
JZ Musoro ◽  
GH Struijk ◽  
RB Geskus ◽  
IJM ten Berge ◽  
AH Zwinderman
Keyword(s):  
Recurrent Events ◽  
Proportional Hazards Model ◽  
Repeated Measurements ◽  
Cox Proportional Hazards Model ◽  
Recurrent Event ◽  
Data Sets ◽  
Event Data ◽  
Recurrent Event Data ◽  
Dynamic Prediction ◽  
Data Set

This paper extends dynamic prediction by landmarking to recurrent event data. The motivating data comprised post-kidney transplantation records of repeated infections and repeated measurements of multiple markers. At each landmark time point ts, a Cox proportional hazards model with a frailty term was fitted using data of individuals who were at risk at landmark s. This model included the time-updated marker values at ts as time-fixed covariates. Based on a stacked data set that merged all landmark data sets, we considered supermodels that allow parameters to depend on the landmarks in a smooth fashion. We described and evaluated four ways to parameterize the supermodels for recurrent event data. With both the study data and simulated data sets, we compared supermodels that were fitted on stacked data sets that consisted of either overlapping or non-overlapping landmark periods. We observed that for recurrent event data, the supermodels may yield biased estimates when overlapping landmark periods are used for stacking. Using the best supermodel amongst the ones considered, we dynamically estimated the probability to remain infection free between ts and a prediction horizon thor, conditional on the information available at ts.

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.

scholarly journals Left-censored recurrent event analysis in epidemiological studies: a proposal for when the number of previous episodes is unknown

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Gilma Hernández-Herrera ◽  
David Moriña ◽  
Albert Navarro
Keyword(s):  
At Risk ◽  
Recurrent Events ◽  
Counting Process ◽  
Frailty Model ◽  
R Package ◽  
Epidemiological Studies ◽  
Recurrent Event ◽  
Event Analysis ◽  
Gap Time

Abstract Background When dealing with recurrent events in observational studies it is common to include subjects who became at risk before follow-up. This phenomenon is known as left censoring, and simply ignoring these prior episodes can lead to biased and inefficient estimates. We aimed to propose a statistical method that performs well in this setting. Methods Our proposal was based on the use of models with specific baseline hazards. In this, the number of prior episodes were imputed when unknown and stratified according to whether the subject had been at risk of presenting the event before t = 0. A frailty term was also used. Two formulations were used for this “Specific Hazard Frailty Model Imputed” based on the “counting process” and “gap time.” Performance was then examined in different scenarios through a comprehensive simulation study. Results The proposed method performed well even when the percentage of subjects at risk before follow-up was very high. Biases were often below 10% and coverages were around 95%, being somewhat conservative. The gap time approach performed better with constant baseline hazards, whereas the counting process performed better with non-constant baseline hazards. Conclusions The use of common baseline methods is not advised when knowledge of prior episodes experienced by a participant is lacking. The approach in this study performed acceptably in most scenarios in which it was evaluated and should be considered an alternative in this context. It has been made freely available to interested researchers as R package miRecSurv.

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.

General joint frailty model for recurrent event data with a dependent terminal event: Application to follicular lymphoma data

2012 ◽  
Vol 31 (11-12) ◽  
pp. 1162-1176 ◽  
Author(s):  
Yassin Mazroui ◽  
Simone Mathoulin-Pelissier ◽  
Pierre Soubeyran ◽  
Virginie Rondeau
Keyword(s):  
Follicular Lymphoma ◽  
Frailty Model ◽  
Recurrent Event ◽  
Terminal Event ◽  
Event Data ◽  
Recurrent Event Data

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.

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.

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