Nonparametric inference for panel count data with competing risks

Sreedevi, E. P.; Sankaran, P. G.

doi:10.1080/02664763.2020.1795816

Cited by 2 publications

(1 citation statement)

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“…Define a counting process N j (t) = {N j (t); t ≥ 0} where N j (t) denote the number of recurrences of the event due to cause j upto time t. Now, E(N j (t)) = Λ j (t) for j = 1, 2, .., k denote the expected number of cumulative events due to cause (mode) j upto time t. The function Λ j (t) is the mean function of the counting process N j (t) and can be termed as the cause specific mean functions (Sreedevi and Sankaran, 2020). Assume that, corresponding to each subject we observe a d × 1 vector of covariates denoted by Z.…”

Section: The Proportional Mean Modelmentioning

confidence: 99%

Proportional mean model for panel count data with multiple modes of recurrence

Sreedevi¹,

Sankaran²

2021

Preprint

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Panel count data is common when the study subjects are exposed to recurrent events, observed only at discrete time points. In this article, we consider the regression analysis of panel count data with multiple modes of recurrence. We propose a proportional mean model to estimate the effect of covariates on the underlying counting process due to different modes of recurrence. The simultaneous estimation of baseline cumulative mean functions and regression parameters of (k > 1) recurrence modes are studied in detail. Asymptotic properties of the proposed estimators are also established. A Monte Carlo simulation study is carried out to validate the finite sample behaviour of the proposed estimators. The methods are applied to a real data arising from skin cancer chemoprevention trial.

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Section: The Proportional Mean Modelmentioning

confidence: 99%