Browsing by Subject "longitudinal data"

Now showing 1 - 6 of 6
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    Developing Automated Computer Algorithms to Track Periodontal Disease Change from Longitudinal Electronic Dental Records
    (MDPI, 2023-03-08) Patel, Jay S.; Kumar, Krishna; Zai, Ahad; Shin, Daniel; Willis, Lisa; Thyvalikakath, Thankam P.
    Objective: To develop two automated computer algorithms to extract information from clinical notes, and to generate three cohorts of patients (disease improvement, disease progression, and no disease change) to track periodontal disease (PD) change over time using longitudinal electronic dental records (EDR). Methods: We conducted a retrospective study of 28,908 patients who received a comprehensive oral evaluation between 1 January 2009, and 31 December 2014, at Indiana University School of Dentistry (IUSD) clinics. We utilized various Python libraries, such as Pandas, TensorFlow, and PyTorch, and a natural language tool kit to develop and test computer algorithms. We tested the performance through a manual review process by generating a confusion matrix. We calculated precision, recall, sensitivity, specificity, and accuracy to evaluate the performances of the algorithms. Finally, we evaluated the density of longitudinal EDR data for the following follow-up times: (1) None; (2) Up to 5 years; (3) > 5 and ≤ 10 years; and (4) >10 and ≤ 15 years. Results: Thirty-four percent (n = 9954) of the study cohort had up to five years of follow-up visits, with an average of 2.78 visits with periodontal charting information. For clinician-documented diagnoses from clinical notes, 42% of patients (n = 5562) had at least two PD diagnoses to determine their disease change. In this cohort, with clinician-documented diagnoses, 72% percent of patients (n = 3919) did not have a disease status change between their first and last visits, 669 (13%) patients’ disease status progressed, and 589 (11%) patients’ disease improved. Conclusions: This study demonstrated the feasibility of utilizing longitudinal EDR data to track disease changes over 15 years during the observation study period. We provided detailed steps and computer algorithms to clean and preprocess the EDR data and generated three cohorts of patients. This information can now be utilized for studying clinical courses using artificial intelligence and machine learning methods.
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    Joint models for longitudinal and survival data
    (2014-07-11) Yang, Lili; Gao, Sujuan; Yu, Menggang; Tu, Wanzhu; Callahan, Christopher M.; Zollinger, Terrell
    Epidemiologic and clinical studies routinely collect longitudinal measures of multiple outcomes. These longitudinal outcomes can be used to establish the temporal order of relevant biological processes and their association with the onset of clinical symptoms. In the first part of this thesis, we proposed to use bivariate change point models for two longitudinal outcomes with a focus on estimating the correlation between the two change points. We adopted a Bayesian approach for parameter estimation and inference. In the second part, we considered the situation when time-to-event outcome is also collected along with multiple longitudinal biomarkers measured until the occurrence of the event or censoring. Joint models for longitudinal and time-to-event data can be used to estimate the association between the characteristics of the longitudinal measures over time and survival time. We developed a maximum-likelihood method to joint model multiple longitudinal biomarkers and a time-to-event outcome. In addition, we focused on predicting conditional survival probabilities and evaluating the predictive accuracy of multiple longitudinal biomarkers in the joint modeling framework. We assessed the performance of the proposed methods in simulation studies and applied the new methods to data sets from two cohort studies.
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    A Longitudinal Study of Pediatricians Early in their Careers: PLACES
    (AAP, 2015-08) Frintner, Mary Pat; Cull, William L.; Byrne, Bobbi J.; Freed, Gary L.; Katakam, Shesha K.; Leslie, Laurel K.; Miller, Ashley A.; Starmer, Amy Jost; Olson, Lynn M.; Department of Pediatrics, IU School of Medicine
    The American Academy of Pediatrics (AAP) launched the Pediatrician Life and Career Experience Study (PLACES), a longitudinal study that tracks the personal and professional experiences of early career pediatricians, in 2012. We used a multipronged approach to develop the study methodology and survey domains and items, including review of existing literature and qualitative research with the target population. We chose to include 2 cohorts of US pediatricians on the basis of residency graduation dates, including 1 group who were several years out of residency (2002–2004 Residency Graduates Cohort) and a second group who recently graduated from residency at study launch (2009–2011 Residency Graduates Cohort). Recruitment into PLACES was a 2-stage process: (1) random sample recruitment from the target population and completion of an initial intake survey and (2) completion of the first Annual Survey by pediatricians who responded positively to stage 1. Overall, 41.2% of pediatricians randomly selected to participate in PLACES indicated positive interest in the study by completing intake surveys; of this group, 1804 (93.7%) completed the first Annual Survey and were considered enrolled in PLACES. Participants were more likely to be female, AAP members, and graduates of US medical schools compared with the target sample; weights were calculated to adjust for these differences. We will survey PLACES pediatricians 2 times per year. PLACES data will allow the AAP to examine career and life choices and transitions experienced by early-career pediatricians.
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    Modeling diurnal hormone profiles by hierarchical state space models.
    (Wiley, 2015-10-30) Liu, Ziyue; Guo, Wensheng; Department of Biostatistics, Richard M. Fairbanks School of Public Health
    Adrenocorticotropic hormone (ACTH) diurnal patterns contain both smooth circadian rhythms and pulsatile activities. How to evaluate and compare them between different groups is a challenging statistical task. In particular, we are interested in testing 1) whether the smooth ACTH circadian rhythms in chronic fatigue syndrome and fibromyalgia patients differ from those in healthy controls, and 2) whether the patterns of pulsatile activities are different. In this paper, a hierarchical state space model is proposed to extract these signals from noisy observations. The smooth circadian rhythms shared by a group of subjects are modeled by periodic smoothing splines. The subject level pulsatile activities are modeled by autoregressive processes. A functional random effect is adopted at the pair level to account for the matched pair design. Parameters are estimated by maximizing the marginal likelihood. Signals are extracted as posterior means. Computationally efficient Kalman filter algorithms are adopted for implementation. Application of the proposed model reveals that the smooth circadian rhythms are similar in the two groups but the pulsatile activities in patients are weaker than those in the healthy controls.
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    Stochastic functional estimates in longitudinal models with interval‐censored anchoring events
    (Wiley, 2020-09) Chu, Chenghao; Zhang, Ying; Tu, Wanzhu; Biostatistics, School of Public Health
    Timelines of longitudinal studies are often anchored by specific events. In the absence of the fully observed anchoring event times, the study timeline becomes undefined, and the traditional longitudinal analysis loses its temporal reference. In this paper, we considered an analytical situation where the anchoring events are interval censored. We demonstrated that by expressing the regression parameter estimators as stochastic functionals of a plug‐in estimate of the unknown anchoring event time distribution, the standard longitudinal models could be extended to accommodate the situation of less well‐defined timelines. We showed that for a broad class of longitudinal models, the functional parameter estimates are consistent and asymptotically normally distributed with a 𝑛⎯⎯√ convergence rate under mild regularity conditions. Applying the developed theory to linear mixed‐effects models, we further proposed a hybrid computational procedure that combines the strengths of the Fisher's scoring method and the expectation‐expectation (EM) algorithm for model parameter estimation. We conducted a simulation study to validate the asymptotic properties and to assess the finite sample performance of the proposed method. A real data example was used to illustrate the proposed method. The method fills in a gap in the existing longitudinal analysis methodology for data with less well‐defined timelines.
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    Vitamin D Status during Pregnancy and the Risk of Gestational Diabetes Mellitus: A Longitudinal Study in a Multiracial Cohort
    (Wiley, 2019) Xia, Jin; Song, Yiqing; Rawal, Shristi; Wu, Jing; Hinkle, Stefanie N.; Tsai, Michael Y.; Zhang, Cuilin; Epidemiology, School of Public Health
    Aims Emerging evidence suggests that maternal vitamin D status may be associated with gestational diabetes (GDM). However, the temporal relation remains unclear due to the lack of longitudinal data on vitamin D over pregnancy. We aimed to prospectively and longitudinally investigate vitamin D status during early to mid‐pregnancy in relation to GDM risk. Methods In a nested case‐control study of 107 GDM cases and 214 controls within the Fetal Growth Studies‐Singleton Cohort, plasma levels of 25‐hydroxyvitamin D2 and D3 (25(OH)D) and vitamin D binding protein were measured at gestational weeks 10‐14, 15‐26, 23‐31, and 33‐39; we further calculated total, free, and bioavailable 25(OH)D. Conditional logistic regression models and linear mixed‐effects models were used. Results We observed a threshold effect for the relation of vitamin D biomarkers with GDM risk. Vitamin D deficiency (<50 nmol/L) at 10‐14 gestational weeks was associated with a 2.82‐fold increased risk for GDM [odds ratio (OR) =2.82, 95% confidence interval (CI): 1.15‐6.93]. Women with persistent vitamin D deficiency at 10‐14 and 15‐26 weeks of gestation had a 4.46‐fold elevated risk for GDM compared to women persistently non‐deficient (OR=4.46, 95% CI: 1.15‐17.3). Conclusions Maternal vitamin D deficiency as early as the first trimester of pregnancy was associated with an elevated risk of GDM. The association was stronger for women who were persistently deficient through the 2nd trimester. Assessment of vitamin D status in early pregnancy may be clinically important and valuable for improving risk stratification and developing effective interventions for the primary prevention of GDM.