Browsing by Subject "Missing Data"

Now showing 1 - 2 of 2
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    Nonparametric inference for Markov processes with missing absorbing state
    (Statistica Sinica, 2019) Bakoyannis, Giorgos; Zhang, Ying; Yiannoutsos, Constantin T.
    This study examines nonparametric estimations of a transition proba- bility matrix of a nonhomogeneous Markov process with a nite state space and a partially observed absorbing state. We impose a missing-at-random assumption and propose a computationally e cient nonparametric maximum pseudolikelihood estimator (NPMPLE). The estimator depends on a parametric model that is used to estimate the probability of each absorbing state for the missing observations based, potentially, on auxiliary data. For the latter model, we propose a formal goodness- of- t test based on a residual process. Using modern empirical process theory, we show that the estimator is uniformly consistent and converges weakly to a tight mean-zero Gaussian random eld. We also provide a methodology for constructing simultaneous con dence bands. Simulation studies show that the NPMPLE works well with small sample sizes and that it is robust against some degree of misspec- i cation of the parametric model for the missing absorbing states. The method is illustrated using HIV data from sub-Saharan Africa to estimate the transition probabilities of death and disengagement from HIV care.
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    Semiparametric regression on cumulative incidence function with interval-censored competing risks data and missing event types
    (Biostatistics, 2021) Park, Jun; Bakoyannis, Giorgos; Zhang, Ying; Yiannoutsos, Constantin T.
    Competing risk data are frequently interval-censored, that is, the exact event time is not observed but only known to lie between two examination time points such as clinic visits. In addition to interval censoring, another common complication is that the event type is missing for some study participants. In this article, we propose an augmented inverse probability weighted sieve maximum likelihood estimator for the analysis of interval-censored competing risk data in the presence of missing event types. The estimator imposes weaker than usual missing at random assumptions by allowing for the inclusion of auxiliary variables that are potentially associated with the probability of missingness. The proposed estimator is shown to be doubly robust, in the sense that it is consistent even if either the model for the probability of missingness or the model for the probability of the event type is misspecified. Extensive Monte Carlo simulation studies show good performance of the proposed method even under a large amount of missing event types. The method is illustrated using data from an HIV cohort study in sub-Saharan Africa, where a significant portion of events types is missing. The proposed method can be readily implemented using the new function ciregic_aipw in the R package intccr.