Estimating time-varying effects for overdispersed recurrent events data with treatment switching

Chen, Qingxia; Zeng, Donglin; Ibrahim, Joseph G.; Akacha, Mouna; Schmidli, Heinz

doi:10.1093/biomet/ass091

Cited by 12 publications

(17 citation statements)

References 43 publications

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“…, whose work showed consistency, efficiency, and convergence to a Gaussian process in the presence of missing genotypes. Similar arguments (for example ) can be applied to establish asymptotic properties of estimators considered here.…”

Section: Methodsmentioning

confidence: 94%

Estimation of genetic risk function with covariates in the presence of missing genotypes

et al. 2017

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In genetic epidemiological studies, family history data are collected on relatives of study participants and used to estimate the age-specific risk of disease for individuals who carry a causal mutation. However, a family member’s genotype data may not be collected due to the high cost of in-person interview to obtain blood sample or death of a relative. Previously, efficient nonparametric genotype-specific risk estimation in censored mixture data has been proposed without considering covariates. With multiple predictive risk factors available, risk estimation requires a multivariate model to account for additional covariates that may affect disease risk simultaneously. Therefore, it is important to consider the role of covariates in the genotype-specific distribution estimation using family history data. We propose an estimation method that permits more precise risk prediction by controlling for individual characteristics and incorporating interaction effects with missing genotypes in relatives, and thus gene-gene interactions and gene-environment interactions can be handled within the framework of a single model. We examine performance of the proposed methods by simulations and apply them to estimate the age-specific cumulative risk of Parkinson’s disease (PD) in carriers of LRRK2 G2019S mutation using first-degree relatives who are at genetic risk for PD. The utility of estimated carrier risk is demonstrated through designing a future clinical trial under various assumptions. Such sample size estimation is seen in the Huntington’s disease literature using the length of abnormal expansion of a CAG repeat in the HTT gene, but is less common in the PD literature.

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Section: Methodsmentioning

confidence: 94%

Estimation of genetic risk function with covariates in the presence of missing genotypes

et al. 2017

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“…The model has wide applications, for example, the adaptive treatment randomization problem studied by Qi et al (), the treatment switching problem studied by Chen et al () and the nonlinear interactions between dose intensity and other covariates studied by Yin et al (). Our previous work studied the version of model that specified the right‐most term parametrically, γ T ( U i ( t ); θ ) X 3 i ( t ), where γ ( u , θ ) is a p 3 ‐dimensional vector of possibly nonlinear parametric functions defined on the range

scriptU

of U i (·).…”

Section: Statistical Modelsmentioning

confidence: 99%

Analysis of generalized semiparametric mixed varying‐coefficients models for longitudinal data

Sun

Heng

et al. 2019

Can J Statistics

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The generalized semiparametric mixed varying‐coefficient effects model for longitudinal data can accommodate a variety of link functions and flexibly model different types of covariate effects, including time‐constant, time‐varying and covariate‐varying effects. The time‐varying effects are unspecified functions of time and the covariate‐varying effects are nonparametric functions of a possibly time‐dependent exposure variable. A semiparametric estimation procedure is developed that uses local linear smoothing and profile weighted least squares, which requires smoothing in the two different and yet connected domains of time and the time‐dependent exposure variable. The asymptotic properties of the estimators of both nonparametric and parametric effects are investigated. In addition, hypothesis testing procedures are developed to examine the covariate effects. The finite‐sample properties of the proposed estimators and testing procedures are examined through simulations, indicating satisfactory performances. The proposed methods are applied to analyze the AIDS Clinical Trial Group 244 clinical trial to investigate the effects of antiretroviral treatment switching in HIV‐infected patients before and after developing the T215Y antiretroviral drug resistance mutation. The Canadian Journal of Statistics 47: 352–373; 2019 © 2019 Statistical Society of Canada

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“…6 Treatment can be initiated after the beginning of follow-up, which occurs frequently in studies without randomization. While some existing recurrent event methods can incorporate time-dependent covariates, 7 these traditional methods often do not give interpretations that satisfy the research question of interest. In the settings often of interest, treatment initiation depends on internal processes such as disease progression or the event history itself, violating the assumption of most time-dependent recurrent event methods that time-dependent covariates be external.…”

Section: Introductionmentioning

confidence: 99%

Estimating the effect of a rare time‐dependent treatment on the recurrent event rate

Smith

Zhu

Goodrich

et al. 2018

Statistics in Medicine

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In many observational studies, the objective is to estimate the effect of treatment or state-change on the recurrent event rate. If treatment is assigned after the start of follow-up, traditional methods (eg, adjustment for baseline-only covariates or fully conditional adjustment for time-dependent covariates) may give biased results. We propose a two-stage modeling approach using the method of sequential stratification to accurately estimate the effect of a time-dependent treatment on the recurrent event rate. At the first stage, we estimate the pretreatment recurrent event trajectory using a proportional rates model censored at the time of treatment. Prognostic scores are estimated from the linear predictor of this model and used to match treated patients to as yet untreated controls based on prognostic score at the time of treatment for the index patient. The final model is stratified on matched sets and compares the posttreatment recurrent event rate to the recurrent event rate of the matched controls. We demonstrate through simulation that bias due to dependent censoring is negligible, provided the treatment frequency is low, and we investigate a threshold at which correction for dependent censoring is needed. The method is applied to liver transplant (LT), where we estimate the effect of development of post-LT End Stage Renal Disease (ESRD) on rate of days hospitalized.

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Estimating time-varying effects for overdispersed recurrent events data with treatment switching

Cited by 12 publications

References 43 publications

Estimation of genetic risk function with covariates in the presence of missing genotypes

Estimation of genetic risk function with covariates in the presence of missing genotypes

Analysis of generalized semiparametric mixed varying‐coefficients models for longitudinal data

Estimating the effect of a rare time‐dependent treatment on the recurrent event rate

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