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

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.

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Management and Outcome of Venous Thrombosis in Patients with Myeloproliferative Neoplasms: Data from the Israeli MPN Working Group

Acta Haematologica ◽

10.1159/000511426 ◽

2020 ◽

pp. 1-8

Author(s):

Odit Gutwein ◽

Noa Lavi ◽

Merav Barzilai ◽

Adi Shacham-Abulafia ◽

Avi Leader ◽

...

Keyword(s):

Venous Thrombosis ◽

Recurrent Events ◽

Myeloproliferative Neoplasms ◽

Antiplatelet Agents ◽

Recurrent Event ◽

Factors Associated ◽

High Incidence ◽

To Receive

The BCR-ABL-negative myeloproliferative neoplasms (MPN) are associated with high incidence of venous thrombosis and a significant rate of recurrent events, but there is no consensus regarding their management. In this retrospective study, we analyzed 96 patients with MPN-related venous thrombosis. The index venous thrombosis occurred at a median age of 58 years (IQR 37–71), with 58% of the events involving unusual sites. Patients who were on antiplatelet agents at the time of index thrombosis tended to be older than patients who were not receiving antiplatelets at the time of index thrombosis. The majority of index thromboses occurring after the diagnosis of MPN had uncontrolled blood counts at the time of event and were not receiving antithrombotic agents. Following the thrombotic episode, 75% of patients received long-term anticoagulation. At a median follow-up of 3.4 years, the recurrence rate was 14%. Thrombophilia was significantly more prevalent among patients with recurrent thrombosis compared to patients without recurrence (<i>p</i> < 0.01). Patients who developed a recurrent event early were more likely to have thrombophilia (either inherited or antiphospholipid antibodies), and controlled blood counts, and were likely to receive anticoagulation at the time of recurrence compared to patients with later recurrences. Thrombophilia may contribute to venous thrombosis recurrence, especially early after the index venous thrombosis. Suboptimal anticoagulation and blood count control are factors associated with late venous thrombosis recurrence.

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Relationship between classic vascular risk factors and cumulative recurrent cardiovascular event burden in patients with clinically manifest vascular disease: results from the UCC-SMART prospective cohort study

BMJ Open ◽

10.1136/bmjopen-2020-038881 ◽

2021 ◽

Vol 11 (3) ◽

pp. e038881

Author(s):

Tamar Irene de Vries ◽

Jan Westerink ◽

Michiel L Bots ◽

Folkert W Asselbergs ◽

Yvo M Smulders ◽

...

Keyword(s):

Risk Factors ◽

Blood Pressure ◽

Cohort Study ◽

Cardiovascular Event ◽

Prospective Cohort ◽

Cardiovascular Events ◽

Recurrent Events ◽

Hdl Cholesterol ◽

Recurrent Event

ObjectiveThe aim of the current study was to assess the relationship between classic cardiovascular risk factors and risk of not only the first recurrent atherosclerotic cardiovascular event, but also the total number of non-fatal and fatal cardiovascular events in patients with recently clinically manifest cardiovascular disease (CVD).DesignProspective cohort study.SettingTertiary care centre.Participants7239 patients with a recent first manifestation of CVD from the prospective UCC-SMART (Utrecht Cardiovascular Cohort - Second Manifestations of ARTerial disease) cohort study.Outcome measuresTotal cardiovascular events, including myocardial infarction, stroke, vascular interventions, major limb events and cardiovascular mortality.ResultsDuring a median follow-up of 8.9 years, 1412 patients had one recurrent cardiovascular event, while 1290 patients had two or more recurrent events, with a total of 5457 cardiovascular events during follow-up. The HRs for the first recurrent event and cumulative event burden using Prentice-Williams-Peterson models, respectively, were 1.36 (95% CI 1.25 to 1.48) and 1.26 (95% CI 1.17 to 1.35) for smoking, 1.14 (95% CI 1.11 to 1.18) and 1.09 (95% CI 1.06 to 1.12) for non-high-density lipoprotein (HDL) cholesterol, and 1.05 (95% CI 1.03 to 1.07) and 1.04 (95% CI 1.03 to 1.06) for systolic blood pressure per 10 mm Hg.ConclusionsIn a cohort of patients with established CVD, systolic blood pressure, non-HDL cholesterol and current smoking are important risk factors for not only the first, but also subsequent recurrent events during follow-up. Recurrent event analysis captures the full cumulative burden of CVD in patients.

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

Statistical Methods in Medical Research ◽

10.1177/0962280215613378 ◽

2015 ◽

Vol 26 (6) ◽

pp. 2869-2884 ◽

Cited By ~ 6

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.

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F11 is associated with recurrent VTE in women

Thrombosis and Haemostasis ◽

10.1160/th15-06-0459 ◽

2016 ◽

Vol 115 (02) ◽

pp. 406-414 ◽

Cited By ~ 4

Author(s):

Maria Bruzelius ◽

Maria Ljungqvist ◽

Matteo Bottai ◽

Annica Bergendal ◽

Rona J. Strawbridge ◽

...

Keyword(s):

Recurrence Rate ◽

Genetic Variants ◽

Recurrent Events ◽

Recurrent Event ◽

Genetic Associations ◽

Risk Alleles ◽

Common Genetic Variants ◽

Recurrent Vte ◽

First Time

SummaryGenetic associations for the reoccurrence of venous thromboembolism (VTE) are not well described. Our aim was to investigate if common genetic variants, previously found to contribute to the prediction of first time thrombosis in women, were associated with risk of recurrence. The Thromboembolism Hormone Study (TEHS) is a Swedish nationwide case-control study (2002–2009). A cohort of 1,010 women with first time VTE was followed up until a recurrent event, death or November 2011. The genetic variants in F5 rs6025, F2 rs1799963, ABO rs514659, FGG rs2066865, F11 rs2289252, PROC rs1799810 and KNG1 rs710446 were assessed together with clinical variables. Recurrence rate was calculated as the number of events over the accumulated patient-time. Cumulative recurrence was calculated by Kaplan-Meier curve. Cox proportional-hazard model was used to estimate hazard ratios (HR) and 95 % confidence intervals (95 % CI) between groups. A total of 101 recurrent events occurred during a mean follow-up time of five years. The overall recurrence rate was 20 per 1,000 person-years (95 % CI; 16-24). The recurrence rate was highest in women with unprovoked first event and obesity. Carriers of the risk alleles of F5 rs6025 (HR=1.7 (95 % CI; 1.1–2.6)) and F11 rs2289252 (HR=1.8 (95 % CI; 1.1–3.0)) had significantly higher rates of recurrence compared to non-carriers. The cumulative recurrence was 2.5-fold larger in carriers of both F5 rs6025 and F11 rs2289252 than in non-carriers at five years follow-up. In conclusion, F5 rs6025 and F11 rs2289252 contributed to the risk of recurrent VTE and the combination is of potential clinical relevance for risk prediction.Supplementary Material to this article is available online at www.thrombosis-online.com.

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Time-dependent prognostic score matching for recurrent event analysis to evaluate a treatment assigned during follow-up

Biometrics ◽

10.1111/biom.12361 ◽

2015 ◽

Vol 71 (4) ◽

pp. 950-959 ◽

Cited By ~ 3

Author(s):

Abigail R. Smith ◽

Douglas E. Schaubel

Keyword(s):

Prognostic Score ◽

Recurrent Event ◽

Time Dependent ◽

Event Analysis

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Performance of recurrent event models on defect proneness data

Annals of Operations Research ◽

10.1007/s10479-020-03884-2 ◽

2020 ◽

Author(s):

M. K. Lintu ◽

Asha Kamath

Keyword(s):

Clinical Trials ◽

Survival Analysis ◽

Quality Assessment ◽

Recurrent Events ◽

Epidemiological Studies ◽

Recurrent Event ◽

Repeated Events ◽

Module Size ◽

Event Models ◽

The Impact

AbstractThe repeated occurrence of the same event in a process is commonly observed in many domains. Such events are referred to as recurrent events. The time to occurrence of these repeated events varies from unit to unit with a possibility of events not occurring among some of the units. Invariably such data are dealt with using some of the techniques in survival analysis called recurrent event models, which are commonly encountered in epidemiological studies and clinical trials. However, it applies to other domains in science and technology. We illustrate the usefulness of recurrent event models in the context of defect proneness analysis in quality assessment of software. Some of the models in practice are introduced on data collected to study the impact of module size on defect proneness in the Mozilla product. Module size plays a significant role in defect proneness and each defect fix makes the class more susceptible to further defects. The risk estimates obtained from the different models vary owing to the differences in the properties of the models as well as the assumptions underlying it.

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Additive-Multiplicative Hazards Model with Recurrent Event Data

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.461.637 ◽

2012 ◽

Vol 461 ◽

pp. 637-641

Author(s):

Huan Bin Liu ◽

Ying Ye

Keyword(s):

Recurrent Events ◽

Recurrent Event ◽

Event Data ◽

Recurrent Event Data ◽

Time Data ◽

Hazards Model ◽

Gap Time

In this paper, the additive-multiplicative hazards model with gap time data of recurrent events is studied. Based on this model, two cases are discussed, i.e., the observed data are gap time of recurrent event, and the observed data are a group recurrent events recurrence one time, and the parametric and nonparametric estimations are given. Then the efficiency of estimators for these two cases is compared.

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Additive rates model for recurrent event data with intermittently observed time-dependent covariates

Statistical Methods in Medical Research ◽

10.1177/09622802211027593 ◽

2021 ◽

Vol 30 (10) ◽

pp. 2239-2255

Author(s):

Tianmeng Lyu ◽

Xianghua Luo ◽

Chiung-Yu Huang ◽

Yifei Sun

Keyword(s):

Recurrent Events ◽

Epidemiologic Study ◽

Recurrent Event ◽

Time Dependent ◽

Occurrence Rate ◽

Absolute Difference ◽

Event Data ◽

Recurrent Event Data ◽

Time Dependent Covariates

Various regression methods have been proposed for analyzing recurrent event data. Among them, the semiparametric additive rates model is particularly appealing because the regression coefficients quantify the absolute difference in the occurrence rate of the recurrent events between different groups. Estimation of the additive rates model requires the values of time-dependent covariates being observed throughout the entire follow-up period. In practice, however, the time-dependent covariates are usually only measured at intermittent follow-up visits. In this paper, we propose to kernel smooth functions involving time-dependent covariates across subjects in the estimating function, as opposed to imputing individual covariate trajectories. Simulation studies show that the proposed method outperforms simple imputation methods. The proposed method is illustrated with data from an epidemiologic study of the effect of streptococcal infections on recurrent pharyngitis episodes.

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An Introspective Overview of the Dynamics of Recurrent Events Data Analysis

Asian Journal of Probability and Statistics ◽

10.9734/ajpas/2021/v15i430371 ◽

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.

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