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Browsing by Author "Park, Jun"
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Item Retention in care and viral suppression in the PMTCT continuum at a large referral facility in western Kenya(Springer, 2022) Humphrey, John M.; Songok, Julia; Ofner, Susan; Musick, Beverly; Alera, Marsha; Kipchumba, Bett; McHenry, Megan S.; Carlucci, James G.; Park, Jun; Mwangi, Winfred; Yiannoutsos, Constantin; Bakoyannis, Giorgos; Wools-Kaloustian, Kara; Medicine, School of MedicineMedical records of pregnant and postpartum women living with HIV and their infants attending a large referral facility in Kenya from 2015 to 2019 were analyzed to identify characteristics associated with retention in care and viral suppression. Women were stratified based on the timing of HIV care enrollment: known HIV-positive (KHP; enrolled pre-pregnancy) and newly HIV-positive (NHP; enrolled during pregnancy). Associations with retention at 18 months postpartum and viral suppression (< 1000 copies/mL) were determined. Among 856 women (20% NHP), retention was 83% for KHPs and 53% for NHPs. Viral suppression was 88% for KHPs and 93% for NHPs, but 19% of women were missing viral load results. In a competing risk model, viral suppression increased by 18% for each additional year of age but was not associated with other factors. Overall, 1.9% of 698 infants with ≥ 1 HIV test result were HIV-positive. Tailored interventions are needed to promote retention and viral load testing, particularly for NHPs, in the PMTCT continuum.Item Semiparametric Competing Risks Regression Under Interval Censoring Using the R Package intccr(Elsevier, 2019-05) Park, Jun; Bakoyannis, Giorgos; Yiannoutsos, Constantin T.; Biostatistics, School of Public HealthBackground and objective: Competing risk data are frequently interval-censored in real-world applications, that is, the exact event time is not precisely observed but is only known to lie between two time points such as clinic visits. This type of data requires special handling because the actual event times are unknown. To deal with this problem we have developed an easy-to-use open-source statistical software. Methods: An approach to perform semiparametric regression analysis of the cumulative incidence function with interval-censored competing risks data is the sieve maximum likelihood method based on B-splines. An important feature of this approach is that it does not impose restrictive parametric assumptions. Also, this methodology provides semiparametrically efficient estimates. Implementation of this methodology can be easily performed using our new R package intccr. Results: The R package intccr performs semiparametric regression analysis of the cumulative incidence function based on interval-censored competing risks data. It supports a large class of models including the proportional odds and the Fine-Gray proportional subdistribution hazards model as special cases. It also provides the estimated cumulative incidence functions for a particular combination of covariate values. The package also provides some data management functionality to handle data sets which are in a long format involving multiple lines of data per subject. Conclusions: The R package intccr provides a convenient and flexible software for the analysis of the cumulative incidence function based on interval-censored competing risks data.Item 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.Item Semiparametric regression on cumulative incidence function with interval-censored competing risks data and missing event types(Oxford University Press, 2022) Park, Jun; Bakoyannis, Giorgos; Zhang, Ying; Yiannoutsos, Constantin T.; Biostatistics, School of Public HealthCompeting 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.Item Semiparametric Regression Under Left-Truncated and Interval-Censored Competing Risks Data and Missing Cause of Failure(2020-04) Park, Jun; Bakoyannis, Giorgos; Yiannoutsos, Constantin T.; Zhang, Ying; Gao, Sujuan; Song, YiqingObservational studies and clinical trials with time-to-event data frequently involve multiple event types, known as competing risks. The cumulative incidence function (CIF) is a particularly useful parameter as it explicitly quantifies clinical prognosis. Common issues in competing risks data analysis on the CIF include interval censoring, missing event types, and left truncation. Interval censoring occurs when the event time is not observed but is only known to lie between two observation times, such as clinic visits. Left truncation, also known as delayed entry, is the phenomenon where certain participants enter the study after the onset of disease under study. These individuals with an event prior to their potential study entry time are not included in the analysis and this can induce selection bias. In order to address unmet needs in appropriate methods and software for competing risks data analysis, this thesis focuses the following development of application and methods. First, we develop a convenient and exible tool, the R package intccr, that performs semiparametric regression analysis on the CIF for interval-censored competing risks data. Second, we adopt the augmented inverse probability weighting method to deal with both interval censoring and missing event types. We show that the resulting estimates are consistent and double robust. We illustrate this method using data from the East-African International Epidemiology Databases to Evaluate AIDS (IeDEA EA) where a significant portion of the event types is missing. Last, we develop an estimation method for semiparametric analysis on the CIF for competing risks data subject to both interval censoring and left truncation. This method is applied to the Indianapolis-Ibadan Dementia Project to identify prognostic factors of dementia in elder adults. Overall, the methods developed here are incorporated in the R package intccr.