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22 results on '"Palipana, Anushka"'

1. A joint model for (un)bounded longitudinal markers, competing risks, and recurrent events using patient registry data

Author

Afonso, Pedro Miranda, Rizopoulos, Dimitris, Palipana, Anushka K., Gecili, Emrah, Brokamp, Cole, Clancy, John P., Szczesniak, Rhonda D., and Andrinopoulou, Eleni-Rosalina

Subjects
Statistics - Methodology
Abstract

Joint models for longitudinal and survival data have become a popular framework for studying the association between repeatedly measured biomarkers and clinical events. Nevertheless, addressing complex survival data structures, especially handling both recurrent and competing event times within a single model, remains a challenge. This causes important information to be disregarded. Moreover, existing frameworks rely on a Gaussian distribution for continuous markers, which may be unsuitable for bounded biomarkers, resulting in biased estimates of associations. To address these limitations, we propose a Bayesian shared-parameter joint model that simultaneously accommodates multiple (possibly bounded) longitudinal markers, a recurrent event process, and competing risks. We use the beta distribution to model responses bounded within any interval (a,b) without sacrificing the interpretability of the association. The model offers various forms of association, discontinuous risk intervals, and both gap and calendar timescales. A simulation study shows that it outperforms simpler joint models. We utilize the US Cystic Fibrosis Foundation Patient Registry to study the associations between changes in lung function and body mass index, and the risk of recurrent pulmonary exacerbations, while accounting for the competing risks of death and lung transplantation. Our efficient implementation allows fast fitting of the model despite its complexity and the large sample size from this patient registry. Our comprehensive approach provides new insights into cystic fibrosis disease progression by quantifying the relationship between the most important clinical markers and events more precisely than has been possible before. The model implementation is available in the R package JMbayes2.

Published
2024

9. Effects of a Digital Intervention to Improve DASH and Blood Pressure Among US Adults.

Author

Miller, Hailey N., Askew, Sandy, Berger, Miriam B., Kay, Melissa C., Palipana, Anushka, Trefney, Elizabeth, Blackman Carr, Loneke T., Barnes, Cherie, Tyson, Crystal C., Svetkey, Laura P., Shaw, Ryan, Steinberg, Dori M., Yang, Qing, and Bennett, Gary G.

Abstract

BACKGROUND: Dietary Approaches to Stop Hypertension (DASH) is a recommended first-line treatment for adults with hypertension, yet adherence to DASH is low. To evaluate the efficacy of a digital health intervention (DHI), compared with attention control, on changes in DASH adherence and blood pressure among adults with hypertension. METHODS: Nourish was a 12-month, parallel, 2-arm, randomized controlled trial of a virtually delivered DHI. Participants had a previous diagnosis of hypertension. The primary outcome was a 6-month change in DASH adherence. The secondary outcome was a change in blood pressure. We used linear mixed models to compare 6 and 12-month changes in DASH adherence, systolic blood pressure, and diastolic blood pressure. RESULTS: Nourish randomized 301 adults who averaged 54.4 (SD, 13.4) years and predominately identified as female (65%), White (53%), or Black (31%). Adjusted mean baseline DASH score was 2.30 (95% CI, 2.03–2.58). The adjusted mean baseline systolic blood pressure and diastolic blood pressure were 123.2 (95% CI, 119.5–126.9) and 77.1 (95% CI, 74.6–79.6) mm Hg. DASH score change was not significantly different between arms at 6 months (M diff, 0.02 [95% CI, −0.37 to 0.40]). Yet, DHI participants had significantly greater 12-month changes in DASH score, relative to control (M diff, 0.62 [95% CI, 0.16–1.08]). Between-group differences in 6-month changes were insignificant for systolic blood pressure and marginally significant for diastolic blood pressure, despite the DHI group showing significant blood pressure reductions from baseline. CONCLUSIONS: A DHI led to modest improvements in DASH and blood pressure among adults with hypertension but did not outperform the attention control. Further research is needed to understand the utility of DHIs to promote DASH and identify intervention components that support long-term behavior change. [ABSTRACT FROM AUTHOR]

Published
2025
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10. A case study of inclusion of rural populations in research: Implications for science and health equity.

Author

Noonan, Devon, Lam, Wendy K. K., Goodrich, James, Sullivan, Sydney, Bentley‐Edwards, Keisha, Koeberl, Dwight, Palipana, Anushka, and McClernon, F. Joseph

Subjects
MANAGEMENT information systems, INFORMATION resources management, RACE, RACIAL differences, RESEARCH management, ACADEMIC medical centers, RURAL population
Abstract

Prior research highlights that rural populations have been historically underrepresented/excluded from clinical research. The primary objective of this study was to describe the inclusion of rural populations within our research enterprise using Clinical Research Management System demographic information at a large academic medical center in the Southeast. This was a cross‐sectional study using participant demographic information for all protocols entered into our Clinical Research Management System between May 2018 and March 2021. Descriptive statistics were used to analyze the representation of rural and non‐rural participants and demographic breakdown by age, sex, race, and ethnicity for our entire enterprise and at the state level. We also compared Material Community Deprivation Index levels between urban and rural participants. Results indicated that 19% of the research population was classified as rural and 81% as non‐rural for our entire sample, and 17.5% rural and 82.5% urban for our state‐level sample. There were significant differences in race, sex, and age between rural and non‐rural participants and Material Community Deprivation Indices between rural and non‐rural participants. Lessons learned and recommendations for increasing the inclusion of rural populations in research are discussed. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Efficacy of BETTER transitional care intervention for diverse patients with traumatic brain injury and their families: Study protocol of a randomized controlled trial

Author

Oyesanya, Tolu O., primary, Ibemere, Stephanie O., additional, You, HyunBin, additional, Emerson, Maralis Mercado, additional, Pan, Wei, additional, Palipana, Anushka, additional, Kandel, Melissa, additional, Ingram, Darius, additional, Soto, Mayra, additional, Pioppo, Anne, additional, Albert, Brittany, additional, Walker-Atwater, Tamia, additional, Hawes, Jodi, additional, Komisarow, Jordan, additional, Ramos, Katherine, additional, Byom, Lindsey, additional, Gonzalez-Guarda, Rosa, additional, Van Houtven, Courtney H., additional, Agarwal, Suresh, additional, and Prvu Bettger, Janet, additional

Published
2024
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12. A joint model for (un)bounded longitudinal markers, competing risks, and recurrent events using patient registry data

Author

Miranda Afonso, Pedro, Rizopoulos, Dimitris, Palipana, Anushka K., Gecili, Emrah, Brokamp, Cole, Clancy, John P., Szczesniak, Rhonda D., Andrinopoulou, Elrozy, Miranda Afonso, Pedro, Rizopoulos, Dimitris, Palipana, Anushka K., Gecili, Emrah, Brokamp, Cole, Clancy, John P., Szczesniak, Rhonda D., and Andrinopoulou, Elrozy

Abstract

Joint models for longitudinal and survival data have become a popular framework for studying the association between repeatedly measured biomarkers and clinical events. Nevertheless, addressing complex survival data structures, especially handling both recurrent and competing event times within a single model, remains a challenge. This causes important information to be disregarded. Moreover, existing frameworks rely on a Gaussian distribution for continuous markers, which may be unsuitable for bounded biomarkers, resulting in biased estimates of associations. To address these limitations, we propose a Bayesian shared-parameter joint model that simultaneously accommodates multiple (possibly bounded) longitudinal markers, a recurrent event process, and competing risks. We use the beta distribution to model responses bounded within any interval (a,b) without sacrificing the interpretability of the association. The model offers various forms of association, discontinuous risk intervals, and both gap and calendar timescales. A simulation study shows that it outperforms simpler joint models. We utilize the US Cystic Fibrosis Foundation Patient Registry to study the associations between changes in lung function and body mass index, and the risk of recurrent pulmonary exacerbations, while accounting for the competing risks of death and lung transplantation. Our efficient implementation allows fast fitting of the model despite its complexity and the large sample size from this patient registry. Our comprehensive approach provides new insights into cystic fibrosis disease progression by quantifying the relationship between the most important clinical markers and events more precisely than has been possible before. The model implementation is available in the R package JMbayes2.

Published
2024

13. Predicting lung function decline in cystic fibrosis:the impact of initiating ivacaftor therapy

Author

Zhou, Grace C, Wang, Ziyun, Palipana, Anushka K, Andrinopoulou, Eleni-Rosalina, Miranda Afonso, Pedro, McPhail, Gary L, Siracusa, Christopher M, Gecili, Emrah, Szczesniak, Rhonda D, Zhou, Grace C, Wang, Ziyun, Palipana, Anushka K, Andrinopoulou, Eleni-Rosalina, Miranda Afonso, Pedro, McPhail, Gary L, Siracusa, Christopher M, Gecili, Emrah, and Szczesniak, Rhonda D

Abstract

BACKGROUND: Modulator therapies that seek to correct the underlying defect in cystic fibrosis (CF) have revolutionized the clinical landscape. Given the heterogeneous nature of lung disease progression in the post-modulator era, there is a need to develop prediction models that are robust to modulator uptake.METHODS: We conducted a retrospective longitudinal cohort study of the CF Foundation Patient Registry (N = 867 patients carrying the G551D mutation who were treated with ivacaftor from 2003 to 2018). The primary outcome was lung function (percent predicted forced expiratory volume in 1 s or FEV1pp). To characterize the association between ivacaftor initiation and lung function, we developed a dynamic prediction model through covariate selection of demographic and clinical characteristics. The ability of the selected model to predict a decline in lung function, clinically known as an FEV1-indicated exacerbation signal (FIES), was evaluated both at the population level and individual level.RESULTS: Based on the final model, the estimated improvement in FEV1pp after ivacaftor initiation was 4.89% predicted (95% confidence interval [CI]: 3.90 to 5.89). The rate of decline was reduced with ivacaftor initiation by 0.14% predicted/year (95% CI: 0.01 to 0.27). More frequent outpatient visits prior to study entry and being male corresponded to a higher overall FEV1pp. Pancreatic insufficiency, older age at study entry, a history of more frequent pulmonary exacerbations, lung infections, CF-related diabetes, and use of Medicaid insurance corresponded to lower FEV1pp. The model had excellent predictive accuracy for FIES events with an area under the receiver operating characteristic curve of 0.83 (95% CI: 0.83 to 0.84) for the independent testing cohort and 0.90 (95% CI: 0.89 to 0.90) for 6-month forecasting with the masked cohort. The root-mean-square errors of the FEV1pp predictions for these cohorts were 7.31% and 6.78% predicted, respectively, with sta

Published
2024

15. Efficiently analyzing large patient registries with Bayesian joint models for longitudinal and time-to-event data

Author

Miranda Afonso, Pedro, Rizopoulos, Dimitris, Palipana, Anushka K., Zhou, Grace C., Brokamp, Cole, Szczesniak, Rhonda D., Andrinopoulou, Elrozy, Miranda Afonso, Pedro, Rizopoulos, Dimitris, Palipana, Anushka K., Zhou, Grace C., Brokamp, Cole, Szczesniak, Rhonda D., and Andrinopoulou, Elrozy

Abstract

The joint modeling of longitudinal and time-to-event outcomes has become a popular tool in follow-up studies. However, fitting Bayesian joint models to large datasets, such as patient registries, can require extended computing times. To speed up sampling, we divided a patient registry dataset into subsamples, analyzed them in parallel, and combined the resulting Markov chain Monte Carlo draws into a consensus distribution. We used a simulation study to investigate how different consensus strategies perform with joint models. In particular, we compared grouping all draws together with using equal- and precision-weighted averages. We considered scenarios reflecting different sample sizes, numbers of data splits, and processor characteristics. Parallelization of the sampling process substantially decreased the time required to run the model. We found that the weighted-average consensus distributions for large sample sizes were nearly identical to the target posterior distribution. The proposed algorithm has been made available in an R package for joint models, JMbayes2. This work was motivated by the clinical interest in investigating the association between ppFEV1, a commonly measured marker of lung function, and the risk of lung transplant or death, using data from the US Cystic Fibrosis Foundation Patient Registry (35,153 individuals with 372,366 years of cumulative follow-up). Splitting the registry into five subsamples resulted in an 85% decrease in computing time, from 9.22 to 1.39 hours. Splitting the data and finding a consensus distribution by precision-weighted averaging proved to be a computationally efficient and robust approach to handling large datasets under the joint modeling framework.

Published
2023

16. Built environment factors predictive of early rapid lung function decline in cystic fibrosis

Author

Gecili, Emrah, primary, Brokamp, Cole, additional, Rasnick, Erika, additional, Afonso, Pedro M., additional, Andrinopoulou, Eleni‐Rosalina, additional, Dexheimer, Judith W., additional, Clancy, John P., additional, Keogh, Ruth H., additional, Ni, Yizhao, additional, Palipana, Anushka, additional, Pestian, Teresa, additional, Vancil, Andrew, additional, Zhou, Grace C., additional, Su, Weiji, additional, Siracusa, Christopher, additional, Ryan, Patrick, additional, and Szczesniak, Rhonda D., additional

Published
2023
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17. Seasonality, mediation and comparison (SMAC) methods to identify influences on lung function decline

Author

Gecili, Emrah, Palipana, Anushka, Brokamp, Cole, Huang, Rui, Andrinopoulou, Eleni Rosalina, Pestian, Teresa, Rasnick, Erika, Keogh, Ruth H., Ni, Yizhao, Clancy, John P., Ryan, Patrick, Szczesniak, Rhonda D., Gecili, Emrah, Palipana, Anushka, Brokamp, Cole, Huang, Rui, Andrinopoulou, Eleni Rosalina, Pestian, Teresa, Rasnick, Erika, Keogh, Ruth H., Ni, Yizhao, Clancy, John P., Ryan, Patrick, and Szczesniak, Rhonda D.

Abstract

This study develops a comprehensive method to assess seasonal influences on a longitudinal marker and compare estimates between cohorts. The method extends existing approaches by (i) combining a sine-cosine model of seasonality with a specialized covariance function for modeling longitudinal correlation; (ii) performing mediation analysis on a seasonality model. An example dataset and R code are provided. The bundle of methods is referred to as seasonality, mediation and comparison (SMAC). The case study described utilizes lung function as the marker observed on a cystic fibrosis cohort but SMAC can be used to evaluate other markers and in other disease contexts. Key aspects of customization are as follows. • This study introduces a novel seasonality model to fit trajectories of lung function decline and demonstrates how to compare this model to a conventional model in this context. • Steps required for mediation analyses in the seasonality model are shown. • The necessary calculations to compare seasonality models between cohorts, based on estimation coefficients, are derived in the study.

Published
2021

19. Seasonality, mediation and comparison (SMAC) methods to identify influences on lung function decline

Author

Gecili, Emrah, primary, Palipana, Anushka, additional, Brokamp, Cole, additional, Huang, Rui, additional, Andrinopoulou, Eleni-Rosalina, additional, Pestian, Teresa, additional, Rasnick, Erika, additional, Keogh, Ruth H., additional, Ni, Yizhao, additional, Clancy, John P., additional, Ryan, Patrick, additional, and Szczesniak, Rhonda D., additional

Published
2021
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20. Univariate and Multivariate Joint Models with Flexible Covariance Structures for Dynamic Prediction of Longitudinal and Time-to-event Data.

Author

Palipana, Anushka

Subjects
Statistics, Target functions, Medical Monitoring, Real-time prediction, Univariate Joint Models, Multivariate Joint Models, Integrated Fractional Brownian Motion
Abstract

Joint modeling of noisily measured biomarkers alongside time-to-event outcomes, such as those used for evaluating disease progression over time and dynamic prediction of survival, has revolutionized statistical science. It is also becoming increasingly clear with the advent of non-stationary Gaussian processes applied to medical monitoring studies that typical random effects within a longitudinal sub-model do not properly reflect complex fluctuations in biological processes. In this body of work, we propose a novel, flexible five-component longitudinal sub-model for univariate and multivariate joint modeling frameworks. The most noteworthy development is our introduction in these modeling frameworks of scaled integrated fractional Brownian Brownian motion (IFBM), a more generalized version of the integrated Brownian motion stochastic process that has been demonstrated to accurately represent biological processes observed with uncertainty. While integrated Brownian motion has been investigated as a method for developing target functions capable of properly predicting threshold changes in disease progression markers, no applications have used joint longitudinal-survival modeling or assessed the utility of IFBM for such purposes. As the event sub-model, the Cox proportional hazards model is used. For Bayesian posterior calculation and inference, we employ Markov chain Monte Carlo (MCMC) techniques. Using data from national patient registries, we use this novel method to assess lung function trajectories and mortality in two distinct rare lung diseases-lymphangioleiomyomatosis and cystic fibrosis. We investigate clinically important target functions with the goal of predicting rapid lung function decline in each disease, including environmental exposures and community characteristics in the application of cystic fibrosis. Each application includes a comparison of IFBM, with literature-based integrated Ornstein-Uhlenbeck (IOU), and random intercepts and slopes modeling.Numerous longitudinal outcomes are collected in order to conduct a thorough investigation of the multidimensional impairment caused by a disease. When a patient's lung function rapidly deteriorates, it alerts health care professionals to monitor the patient's illness development and make informed and timely medical decisions, such as preparing the patient for lung transplantation. Cystic fibrosis is a multisystem condition. As a result, we present a multivariate joint model that employs a flexible covariance structure to effectively capture the highly variable nature of these longitudinal processes while modeling lung function, and growth simultaneously. This model is expected to improve the prediction accuracy of survival probability when compared to a univariate joint model that employs random-slopes random-intercepts model to depict the longitudinal process. Along with developing a dynamic prediction framework for predicting the future outcome trajectories and predictive probabilities of CF patients using clinically relevant target functions, we also developed a method for calculating dynamic event-free probabilities using multivariate longitudinal data. Our suggested model is assessed using simulations and is applied to the US Cystic Fibrosis Foundation–Patient Registry (CFF-PR) data set.

Published
2022

21. Effects of a Digital Intervention to Improve DASH and Blood Pressure Among US Adults.

Author

Miller H, Askew S, Kay MC, Palipana A, Trefney E, Blackman Carr LT, Barnes CN, Tyson C, Svetkey L, Shaw RJ, Steinberg DM, Yang Q, Bennett GG, and Berger MB

Abstract

Background: Dietary Approaches to Stop Hypertension (DASH) is a recommended first-line treatment for adults with hypertension, yet adherence to DASH is low., Purpose: To evaluate the efficacy of a digital health intervention (DHI), compared with attention control, on changes in DASH adherence and blood pressure among adults with hypertension., Methods: Nourish was a 12-month, parallel, 2-arm, randomized controlled trial of a virtually delivered DHI. Participants had a previous diagnosis of hypertension. The primary outcome was a 6-month change in DASH adherence. The secondary outcome was a change in blood pressure. We used linear mixed models to compare 6 and 12-month changes in DASH adherence, systolic blood pressure, and diastolic blood pressure., Results: Nourish randomized 301 adults who averaged 54.4 (SD, 13.4) years and predominately identified as female (65%), White (53%), or Black (31%). Adjusted mean baseline DASH score was 2.30 (95% CI, 2.03-2.58). The adjusted mean baseline systolic blood pressure and diastolic blood pressure were 123.2 (95% CI, 119.5-126.9) and 77.1 (95% CI, 74.6-79.6) mm Hg. DASH score change was not significantly different between arms at 6 months ( M diff , 0.02 [95% CI, -0.37 to 0.40]). Yet, DHI participants had significantly greater 12-month changes in DASH score, relative to control ( M diff , 0.62 [95% CI, 0.16-1.08]). Between-group differences in 6-month changes were insignificant for systolic blood pressure and marginally significant for diastolic blood pressure, despite the DHI group showing significant blood pressure reductions from baseline., Conclusions: A DHI led to modest improvements in DASH and blood pressure among adults with hypertension but did not outperform the attention control. Further research is needed to understand the utility of DHIs to promote DASH and identify intervention components that support long-term behavior change.

Published
2024
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22. A study protocol for risk stratification in children with concussion (RSiCC): Theoretical framework, design, and methods.

Author

Reuter-Rice K, Fitterer AN, Duquette P, Yang Q, Palipana AK, Laskowitz D, Garrett ME, Fletcher M, Smith J, Makor L, Grant G, Ramsey K, Bloom OJ, and Ashley-Koch AE

Subjects
Humans, Child, Adolescent, Male, Female, Biomarkers, Risk Assessment, Brain Concussion, Post-Concussion Syndrome diagnosis
Abstract

Research shows that one in five children will experience a concussion by age 16. Compared to adults, children experience longer and more severe postconcussive symptoms (PCS), with severity and duration varying considerably among children and complicating management of these patients. Persistent PCS can result in increased school absenteeism, social isolation, and psychological distress. Although early PCS diagnosis and access to evidence-based interventions are strongly linked to positive health and academic outcomes, symptom severity and duration are not fully explained by acute post-injury symptoms. Prior research has focused on the role of neuroinflammation in mediating PCS and associated fatigue; however relationship between inflammatory biomarkers and PCS severity, has not examined longitudinally. To identify which children are at high risk for persistent PCS and poor health, academic, and social outcomes, research tracking PCS trajectories and describing school-based impacts across the entire first year postinjury is critically needed. This study will 1) define novel PCS trajectory typologies in a racially/ethnically diverse population of 500 children with concussion (11-17 years, near equal distribution by sex), 2) identify associations between these typologies and patterns of inflammatory biomarkers and genetic variants, 3) develop a risk stratification model to identify children at risk for persistent PCS; and 4) gain unique insights and describe PCS impact, including fatigue, on longer-term academic and social outcomes. We will be the first to use NIH's symptom science model and patient-reported outcomes to explore the patterns of fatigue and other physical, cognitive, psychological, emotional and academic responses to concussion in children over a full year. Our model will enable clinicians and educators to identify children most at risk for poor long-term health, social, and academic outcomes after concussion. This work is critical to meeting our long-term goal of developing personalized concussion symptom-management strategies to improve outcomes and reduce disparities in the health and quality of life of children., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Reuter-Rice et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Published
2024
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