Marginal analysis of current status data with informative cluster size using a class of semiparametric transformation cure models

Lam, Kwok Fai; Lee, Chun Yin; Wong, Kin Yau; Bandyopadhyay, Dipankar

doi:10.1002/sim.8910

Cited by 3 publications

(6 citation statements)

References 46 publications

(51 reference statements)

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“…Note that the estimate of HbA1c, though negative, was not significant for the usual GEE model. Findings from the Lam et al 32 study, which analyzed the traditional current status PD responses (not multistate) recently from the GAAD data using a class of semiparametric transformation cure models, also revealed that HbA1c is a significant predictor associated with healthy teeth. With the exception of gender (though, not significant), the estimated effects from both the GEE and CWGEE models were similar in sign, but with varying magnitudes.…”

Section: Application: Gaad Datamentioning

confidence: 93%

Pseudo-value regression of clustered multistate current status data with informative cluster sizes

Anyaso-Samuel

Bandyopadhyay

Datta

2023

Stat Methods Med Res

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Multistate current status data presents a more severe form of censoring due to the single observation of study participants transitioning through a sequence of well-defined disease states at random inspection times. Moreover, these data may be clustered within specified groups, and informativeness of the cluster sizes may arise due to the existing latent relationship between the transition outcomes and the cluster sizes. Failure to adjust for this informativeness may lead to a biased inference. Motivated by a clinical study of periodontal disease, we propose an extension of the pseudo-value approach to estimate covariate effects on the state occupation probabilities for these clustered multistate current status data with informative cluster or intra-cluster group sizes. In our approach, the proposed pseudo-value technique initially computes marginal estimators of the state occupation probabilities utilizing nonparametric regression. Next, the estimating equations based on the corresponding pseudo-values are reweighted by functions of the cluster sizes to adjust for informativeness. We perform a variety of simulation studies to study the properties of our pseudo-value regression based on the nonparametric marginal estimators under different scenarios of informativeness. For illustration, the method is applied to the motivating periodontal disease dataset, which encapsulates the complex data-generation mechanism.

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Section: Application: Gaad Datamentioning

confidence: 93%

Pseudo-value regression of clustered multistate current status data with informative cluster sizes

Anyaso-Samuel

Bandyopadhyay

Datta

2023

Stat Methods Med Res

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“…This approach assumes the correct marginal distribution for 𝑇 1 , … , 𝑇 𝑁 but does not model the association among subjects within the same cluster. Similar to Zhang and Sun (2010a), Zhao et al (2018), and Lam et al (2021, it maximizes a cluster-weighted pseudo loglikelihood function, where the contribution of each subject to the log-likelihood is weighted by the size of its cluster. Details of the estimation and computation methods are described in Section S4 of the Supporting information.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…(2018), and Lam et al. (2021), it maximizes a cluster‐weighted pseudo log‐likelihood function, where the contribution of each subject to the log‐likelihood is weighted by the size of its cluster. Details of the estimation and computation methods are described in Section S4 of the Supporting information.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…For interval-censored data, the finite-sample performance of the two methods are found to be similar and satisfactory based on the Cox model with a paramet-ric or nonparametric cumulative baseline hazard function (Zhang & Sun, 2010a. Zhao et al (2018) proposed to use within-cluster resampling to estimate the parameters based on a linear transformation model, while Lam et al (2021) proposed a class of semiparametric transformation cure survival models for clustered current-status data based on the cluster-weighted generalized estimating equations approach. In the marginal approach, the association among the cluster size and the response variables within a cluster is not characterized.…”

Section: Introductionmentioning

confidence: 99%

“…(2018) proposed to use within‐cluster resampling to estimate the parameters based on a linear transformation model, while Lam et al. (2021) proposed a class of semiparametric transformation cure survival models for clustered current‐status data based on the cluster‐weighted generalized estimating equations approach. In the marginal approach, the association among the cluster size and the response variables within a cluster is not characterized.…”

Section: Introductionmentioning

confidence: 99%

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A semiparametric joint model for cluster size and subunit‐specific interval‐censored outcomes

et al. 2022

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Clustered data frequently arise in biomedical studies, where observations, or subunits, measured within a cluster are associated. The cluster size is said to be informative, if the outcome variable is associated with the number of subunits in a cluster. In most existing work, the informative cluster size issue is handled by marginal approaches based on within‐cluster resampling, or cluster‐weighted generalized estimating equations. Although these approaches yield consistent estimation of the marginal models, they do not allow estimation of within‐cluster associations and are generally inefficient. In this paper, we propose a semiparametric joint model for clustered interval‐censored event time data with informative cluster size. We use a random effect to account for the association among event times of the same cluster as well as the association between event times and the cluster size. For estimation, we propose a sieve maximum likelihood approach and devise a computationally‐efficient expectation‐maximization algorithm for implementation. The estimators are shown to be strongly consistent, with the Euclidean components being asymptotically normal and achieving semiparametric efficiency. Extensive simulation studies are conducted to evaluate the finite‐sample performance, efficiency and robustness of the proposed method. We also illustrate our method via application to a motivating periodontal disease dataset.

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Partly linear single-index cure models with a nonparametric incidence link function

Lee,

Wong,

Bandyopadhyay

2024

Stat Methods Med Res

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In cancer studies, it is commonplace that a fraction of patients participating in the study are cured, such that not all of them will experience a recurrence, or death due to cancer. Also, it is plausible that some covariates, such as the treatment assigned to the patients or demographic characteristics, could affect both the patients’ survival rates and cure/incidence rates. A common approach to accommodate these features in survival analysis is to consider a mixture cure survival model with the incidence rate modeled by a logistic regression model and latency part modeled by the Cox proportional hazards model. These modeling assumptions, though typical, restrict the structure of covariate effects on both the incidence and latency components. As a plausible recourse to attain flexibility, we study a class of semiparametric mixture cure models in this article, which incorporates two single-index functions for modeling the two regression components. A hybrid nonparametric maximum likelihood estimation method is proposed, where the cumulative baseline hazard function for uncured subjects is estimated nonparametrically, and the two single-index functions are estimated via Bernstein polynomials. Parameter estimation is carried out via a curated expectation-maximization algorithm. We also conducted a large-scale simulation study to assess the finite-sample performance of the estimator. The proposed methodology is illustrated via application to two cancer datasets.

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Marginal analysis of current status data with informative cluster size using a class of semiparametric transformation cure models

Cited by 3 publications

References 46 publications

Pseudo-value regression of clustered multistate current status data with informative cluster sizes

Pseudo-value regression of clustered multistate current status data with informative cluster sizes

A semiparametric joint model for cluster size and subunit‐specific interval‐censored outcomes

Partly linear single-index cure models with a nonparametric incidence link function

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