Publications by authors named "Peihua Qiu"

34 Publications

Non-parametric treatment time-lag effect estimation.

Stat Methods Med Res 2022 Jan 16;31(1):62-75. Epub 2021 Nov 16.

Department of Biostatistics, University of Florida, Gainesville, FL, USA.

In general, the change point problem considers inference of a change in distribution for a set of time-ordered observations. This has applications in a large variety of fields, and can also apply to survival data. In survival analysis, most existing methods compare two treatment groups for the entirety of the study period. Some treatments may take a length of time to show effects in subjects. This has been called the time-lag effect in the literature, and in cases where time-lag effect is considerable, such methods may not be appropriate to detect significant differences between two groups. In this paper, we propose a novel non-parametric approach for estimating the point of treatment time-lag effect by using an empirical divergence measure. Theoretical properties of the estimator are studied. The results from the simulated data and the applications to real data examples support our proposed method.
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http://dx.doi.org/10.1177/09622802211032693DOI Listing
January 2022

Edge-Preserving Denoising of Image Sequences.

Authors:
Fan Yi Peihua Qiu

Entropy (Basel) 2021 Oct 12;23(10). Epub 2021 Oct 12.

Department of Biostatistics, University of Florida, Gainesville, FL 32603, USA.

To monitor the Earth's surface, the satellite of the NASA Landsat program provides us image sequences of any region on the Earth constantly over time. These image sequences give us a unique resource to study the Earth's surface, changes of the Earth resource over time, and their implications in agriculture, geology, forestry, and more. Besides natural sciences, image sequences are also commonly used in functional magnetic resonance imaging (fMRI) of medical studies for understanding the functioning of brains and other organs. In practice, observed images almost always contain noise and other contaminations. For a reliable subsequent image analysis, it is important to remove such contaminations in advance. This paper focuses on image sequence denoising, which has not been well-discussed in the literature yet. To this end, an edge-preserving image denoising procedure is suggested. The suggested method is based on a jump-preserving local smoothing procedure, in which the bandwidths are chosen such that the possible spatio-temporal correlations in the observed image intensities are accommodated properly. Both theoretical arguments and numerical studies show that this method works well in the various cases considered.
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http://dx.doi.org/10.3390/e23101332DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8535002PMC
October 2021

Effective disease surveillance by using covariate information.

Authors:
Peihua Qiu Kai Yang

Stat Med 2021 11 30;40(26):5725-5745. Epub 2021 Jul 30.

Department of Biostatistics, University of Florida, Gainesville, Florida, USA.

Effective surveillance of infectious diseases, cancers, and other deadly diseases is critically important for public health and safety of our society. Incidence data of such diseases are often collected spatially from different clinics and hospitals through a regional, national or global disease reporting system. In such a system, new batches of data keep being collected over time, and a decision needs to be made immediately after new data are collected regarding whether there is a disease outbreak at the current time point. This is the disease surveillance problem that will be focused in this article. There are some existing methods for solving this problem, most of which use the disease incidence data only. In practice, however, disease incidence is often associated with some covariates, including the air temperature, humidity, and other weather or environmental conditions. In this article, we develop a new methodology for disease surveillance which can make use of helpful covariate information to improve its effectiveness. A novelty of this new method is behind the property that only those covariate information that is associated with a true disease outbreak can help trigger a signal. The new method can accommodate seasonality, spatio-temporal data correlation, and nonparametric data distribution. These features make it feasible to use in many real applications.
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http://dx.doi.org/10.1002/sim.9150DOI Listing
November 2021

Joint modeling of multivariate nonparametric longitudinal data and survival data: A local smoothing approach.

Authors:
Lu You Peihua Qiu

Stat Med 2021 Dec 25;40(29):6689-6706. Epub 2021 Sep 25.

Department of Biostatistics, University of Florida, Tampa, Florida, USA.

In many clinical studies, evaluating the association between longitudinal and survival outcomes is of primary concern. For analyzing data from such studies, joint modeling of longitudinal and survival data becomes an appealing approach. In some applications, there are multiple longitudinal outcomes whose longitudinal pattern is difficult to describe by a parametric form. For such applications, existing research on joint modeling is limited. In this article, we develop a novel joint modeling method to fill the gap. In the new method, a local polynomial mixed-effects model is used for describing the nonparametric longitudinal pattern of the multiple longitudinal outcomes. Two model estimation procedures, that is, the local EM algorithm and the local penalized quasi-likelihood estimation, are explored. Practical guidelines for choosing tuning parameters and for variable selection are provided. The new method is justified by some theoretical arguments and numerical studies.
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http://dx.doi.org/10.1002/sim.9206DOI Listing
December 2021

Ratings of Perceived Exertion During Walking: Predicting Major Mobility Disability and Effect of Structured Physical Activity in Mobility-Limited Older Adults.

J Gerontol A Biol Sci Med Sci 2021 09;76(10):e264-e271

Department of Aging and Geriatric Research, University of Florida, Gainesville, USA.

Background: This study evaluated the association between ratings of perceived exertion (RPE) of walking and major mobility disability (MMD), as well as their transitions in response to a physical activity (PA) compared to a health education (HE) program.

Methods: Older adults (n = 1633) who were at risk for mobility impairment were randomized to structured PA or HE programs. During a 400 m walk, participants rated exertion as "light" or "hard." An MMD event was defined as the inability to walk 400 m. MMD events and RPE values were assessed every 6 months for an average of 2.6 years.

Results: Participants rating their exertion as "hard" had a nearly threefold higher risk of MMD compared with those rating their exertion as "light" (HR: 2.61, 95% CI: 2.19-3.11). The association was held after adjusting for disease conditions, depression, cognitive function, and walking speed (HR: 2.24, 95% CI: 1.87-2.69). The PA group was 25% more likely to transition from "light" to "hard" RPE than the HE group (HR: 1.25, 95% CI: 1.05-1.49). Additionally, the PA group was 27% (HR: 0.73, 95% CI: 0.55 - 0.97) less likely to transition from a "hard" RPE to inability to walk 400 m and was more likely to recover their ability to walk 400 m by transitioning to a "hard" RPE (HR: 2.10, 95% CI: 1.39-3.17) than the HE group.

Conclusions: Older adults rating "hard" effort during a standardized walk test were at increased risk of subsequent MMD. A structured PA program enabled walking recovery, but was more likely to increase transition from "light" to "hard" effort, which may reflect the greater capacity to perform the test.
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http://dx.doi.org/10.1093/gerona/glab036DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8436976PMC
September 2021

Innovations in Geroscience to enhance mobility in older adults.

Exp Gerontol 2020 12 22;142:111123. Epub 2020 Oct 22.

University of Florida, Department of Psychology, 945 Center Drive, Gainesville, FL 32611, United States. Electronic address:

Aging is the primary risk factor for functional decline; thus, understanding and preventing disability among older adults has emerged as an important public health challenge of the 21st century. The science of gerontology - or geroscience - has the practical purpose of "adding life to the years." The overall goal of geroscience is to increase healthspan, which refers to extending the portion of the lifespan in which the individual experiences enjoyment, satisfaction, and wellness. An important facet of this goal is preserving mobility, defined as the ability to move independently. Despite this clear purpose, this has proven to be a challenging endeavor as mobility and function in later life are influenced by a complex interaction of factors across multiple domains. Moreover, findings over the past decade have highlighted the complexity of walking and how targeting multiple systems, including the brain and sensory organs, as well as the environment in which a person lives, can have a dramatic effect on an older person's mobility and function. For these reasons, behavioral interventions that incorporate complex walking tasks and other activities of daily living appear to be especially helpful for improving mobility function. Other pharmaceutical interventions, such as oxytocin, and complementary and alternative interventions, such as massage therapy, may enhance physical function both through direct effects on biological mechanisms related to mobility, as well as indirectly through modulation of cognitive and socioemotional processes. Thus, the purpose of the present review is to describe evolving interventional approaches to enhance mobility and maintain healthspan in the growing population of older adults in the United States and countries throughout the world. Such interventions are likely to be greatly assisted by technological advances and the widespread adoption of virtual communications during and after the COVID-19 era.
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http://dx.doi.org/10.1016/j.exger.2020.111123DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7581361PMC
December 2020

Disparities in Pancreatic Ductal Adenocarcinoma-The Significance of Hispanic Ethnicity, Subgroup Analysis, and Treatment Facility on Clinical Outcomes.

Cancer Med 2020 06 13;9(12):4069-4082. Epub 2020 Apr 13.

Department of Surgery, University of Florida College of Medicine, Gainesville, FL, USA.

Background: Disparities exist among patients with pancreatic ductal adenocarcinoma (PDAC). Non-White race is regarded as a negative predictor of expected treatment and overall survival. Data suggest that Academic Research Programs (ARP) provide better outcomes for minorities, but ethnic/minority outcomes are underreported. We hypothesize that outcomes among racially/ethnically diverse PDAC patients may be influenced by treatment facility.

Methods: The National Cancer Database was used to identify 170,327 patients diagnosed with PDAC between 2004 and 2015. Cox proportional-hazard regression was used to compare survival between race/ethnic groups across facilities.

Results: In unadjusted models, compared to non-Hispanic Whites (NHW), non-Hispanic Blacks (NHB) had the worst overall survival (HR = 1.05, 95%CI: 1.03-1.06, P < .001) and Hispanics had the best overall survival (HR = 0.92, 95%CI: 0.90-0.94, P < .001). After controlling for socioeconomic and clinical covariates, NHB (HR = 0.95, 95%CI: 0.93-0.96, P < .001) had better overall survival compared to NHW, and Hispanics continued to have the best comparative outcomes (HR = 0.84, 95%CI: 0.82-0.86, P < .001). Among Hispanics, Dominicans and South/Central Americans lived the longest, at 10.25 and 9.82 months, respectively. The improved survival in Hispanics was most pronounced at ARP (HR = 0.80, 95%CI: 0.77-0.84, P < .001) and Integrated Network Cancer Programs (HR = 0.78, 95%CI: 0.73-0.84, P < .001). NHB had improved survival over NHW at Comprehensive Community Care Programs (HR = 0.96, 95%CI: 0.93-0.98, P = .002) and ARP (HR = 0.96, 95%CI: 0.94-0.98, P = .001), which was influenced by income, education, and surgical resection.

Conclusion: Survival was improved at ARP for all populations. Hispanics had the best comparative overall survival. NHB had improved overall survival at higher volume centers, but this was dependent upon income, education, and surgical resection.
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http://dx.doi.org/10.1002/cam4.3042DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7300394PMC
June 2020

Optimizing the Treatment of CRPS With Ketamine.

Clin J Pain 2020 07;36(7):516-523

Department of Neurology, Drexel University College of Medicine, Philadelphia, PA.

Objective: This study aimed to develop a method that objectively measures the clinical benefits of ketamine infusions to treat complex regional pain syndrome (CRPS), thus making it possible, for the first time, to determine the optimal dosing of ketamine and duration of treatment to treat CRPS.

Materials And Methods: All patients were diagnosed with hyperalgesia associated with CRPS. Patients underwent an outpatient, 4-day, escalating dose ketamine infusion. Hyperalgesia was measured using pain thresholds. Clinical outcome was determined without knowledge of the patient's pain thresholds throughout treatment.

Results: We found a correlation between pain thresholds and the intensity of pain reported by the patient at various sites of the body. We found that clinical outcomes correlated with improvement in pain thresholds. There was a plateau in pain thresholds between days 3 and 4 for the lower extremities. There was no plateau in pain thresholds observed for the upper extremities.

Discussion: Our findings suggest that 4 days of treatment are sufficient for the treatment of CRPS of the lower extremities. For the upper extremities, >4 days may be required. Our study is the first to utilize quantitative sensory testing to direct the treatment of a chronic pain disorder.
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http://dx.doi.org/10.1097/AJP.0000000000000831DOI Listing
July 2020

Early detection of high disease activity in juvenile idiopathic arthritis by sequential monitoring of patients' health-related quality of life scores.

Biom J 2020 09 11;62(5):1343-1356. Epub 2020 Mar 11.

Department of Biostatistics, University of Florida, Gainesville, FL, USA.

Juvenile idiopathic arthritis (JIA) is a chronic disease. During its "high disease activity (HDA)" stage, JIA can cause severe pain, and thus could seriously affect patients' physical and psychological health. Early detection of the HDA stage of JIA can reduce the damage of the disease by treating it at an early stage and alleviating the painful experience of the patients. So far, no effective cure of JIA has been found, and one major goal of disease management is to improve patients' quality of life. To this end, patients' health-related quality of life (HRQOL) scores are routinely collected over time from JIA patients. In this paper, we demonstrate that a new statistical methodology called dynamic screening system (DySS) is effective for early detection of the HDA stage of JIA. By this approach, a patient's HRQOL scores are monitored sequentially, and a signal is given by DySS once the longitudinal pattern of the scores is found to be significantly different from the pattern of patients with low disease activity. Dimension reduction of the observed HRQOL scores and the corresponding impact on the performance of DySS are also discussed.
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http://dx.doi.org/10.1002/bimj.201900127DOI Listing
September 2020

Association of Long-Term Dynamics in Circulating Testosterone with Serum PSA in Prostate Cancer-Free Men with Initial-PSA < 4 ng/mL.

Horm Cancer 2019 12 16;10(4-6):168-176. Epub 2019 Oct 16.

Department of Epidemiology, University of Florida, Gainesville, FL, USA.

We previously reported that an accelerated decline in circulating testosterone level is associated with a higher risk of prostate cancer (PCa). This study is to examine whether testosterone change rate is related to serum prostate-specific antigen (PSA) concentration among PCa-free men. Longitudinal data were derived from electronic medical records at a tertiary hospital in the Southeastern USA. PCa-free men with initial-PSA < 4 ng/mL and ≥ 2 testosterone measurements were included (n = 632). Three PSA measures (peak, the most recent, and average PSA) during the study period (from first testosterone measurement to the most recent hospital visit) were examined using multivariable-adjusted geometric means and were compared across quintiles of testosterone change rate (ng/dL/month) and current testosterone level (cross-sectional). Mean (standard deviation, SD) age at baseline was 59.3 (10.5) years; mean study period was 93.0 (55.3) months. After adjusting for covariates including baseline testosterone, the three PSA measures all significantly increased across quintile of testosterone change rate from increase to decline (peak PSA: quint 1 = 1.09, quint 5 = 1.41; the most recent PSA: quint 1 = 0.85, quint 5 = 1.00; average PSA: quint 1 = 0.89, quint 5 = 1.02; all P < 0.001). But current testosterone level was not associated with PSA levels. Stratified analyses indicated men with higher adiposity (body mass index > 24.1 kg/m) or lower baseline testosterone (≤ 296 ng/dL) were more sensitive to testosterone change in regard to PSA. Among PCa-free men, accelerated testosterone decline might correlate with higher serum PSA concentration. It will help to elucidate the mechanisms relating aging-accompanying testosterone dynamics to prostate carcinogenesis.
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http://dx.doi.org/10.1007/s12672-019-00369-yDOI Listing
December 2019

Week-to-week predictors of weight loss and regain.

Health Psychol 2019 Dec 30;38(12):1150-1158. Epub 2019 Sep 30.

Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University.

Objectives: Despite increased interest in the development of individually tailored weight management programs, little is known about what factors proximally predict weight change.

Method: The current study investigated proximal (week-to-week) predictors of weight loss and regain in 74 adults during a 3-month, Internet-based behavioral weight loss program followed by a 9-month "maintenance" period (during which no additional intervention was provided). Participants were asked to self-weigh daily using scales that transmitted weight via the cellular network and to answer a brief questionnaire each week querying mood, behaviors, and cognitions hypothesized to be associated with weight loss and regain.

Results: Longitudinal multilevel models demonstrated that weight loss during initial intervention was proximally predicted by (a) greater frequency of self-monitoring weight and caloric intake, consistency between eating choices and weight loss goals, and importance of "staying on track" with these goals and (b) less negative mood, boredom with weight control efforts, hunger, and temptation to eat foods "not on plan" (ps < .05). Greater weight regain after intervention was also proximally predicted by these factors (with effects in the opposite direction) and additionally by less physical activity, less positive mood, more stress, greater temptation to skip planned physical activity, and higher ratings of the amount of effort required to stay on track (ps < .05).

Conclusions: Results confirmed the importance of self-monitoring for weight loss and maintenance and identified other key week-to-week predictors of weight change. Results also supported efforts to develop intervention approaches specifically focused on weight loss maintenance. Future research should investigate whether using identified predictors to tailor intervention content and timing can improve weight outcomes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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http://dx.doi.org/10.1037/hea0000798DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6861630PMC
December 2019

Nonparametric estimation of the spatio-temporal covariance structure.

Authors:
Kai Yang Peihua Qiu

Stat Med 2019 10 11;38(23):4555-4565. Epub 2019 Jul 11.

Department of Biostatistics, University of Florida, Gainesville, Florida.

Spatio-temporal modeling is an active research problem with broad applications. In this problem, proper description and estimation of the data covariance structure plays an important role. In the literature, most available methods assume that the data covariance is stationary and follows a specific parametric form. In practice, however, such assumptions are hardly valid or difficult to verify. In this paper, we propose a new and flexible method for estimating the underlying covariance structure. Our proposed method does not require the covariance to be stationary or follow a parametric form. It can accommodate nonparametric space-time-varying mean structure of the observed data. Under some mild regularity conditions, it is shown that our estimated covariance structure converges to the true covariance structure. The proposed method is also justified numerically by a simulation study and an application to a hand, foot, and mouth disease data.
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http://dx.doi.org/10.1002/sim.8315DOI Listing
October 2019

Errors-in-variables jump regression using local clustering.

Stat Med 2019 08 22;38(19):3642-3655. Epub 2019 May 22.

Department of Biostatistics, University of Florida, Gainesville, Florida.

Errors-in-variables (EIV) regression is widely used in econometric models. The statistical analysis becomes challenging when the regression function is discontinuous and the distribution of measurement error is unknown. In the literature, most existing jump regression methods either assume that there is no measurement error involved or require that jumps are explicitly detected before the regression function can be estimated. In some applications, however, the ultimate goal is to estimate the regression function and to preserve the jumps in the process of estimation. In this paper, we are concerned with reconstructing jump regression curve from data that involve measurement error. We propose a direct jump-preserving method that does not explicitly detect jumps. The challenge of restoring jump structure masked by measurement error is handled by local clustering. Theoretical analysis shows that the proposed curve estimator is statistically consistent. A numerical comparison with an existing jump regression method highlights its jump-preserving property. Finally, we demonstrate our method by an application to a health tax policy study in Australia.
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http://dx.doi.org/10.1002/sim.8205DOI Listing
August 2019

Objective Preoperative Parameters Predict Difficult Pelvic Dissections and Clinical Outcomes.

J Surg Res 2018 12 30;232:15-25. Epub 2018 Jun 30.

Department of Surgery, University of Florida, Gainesville, Florida.

Background: Objective criteria to predict difficult pelvic dissection with prognostic significance are lacking. Previous studies have focused on predicting intraoperative conversion and not evaluated factors specific to pelvic surgery. We aimed to develop an objective, prognostic, preoperative assessment to predict difficult pelvic dissections and clinical outcomes. Such a model is much needed, may facilitate objective comparisons between rectal cancer centers, or may serve as a stratification variable in clinical trials.

Materials And Methods: Patients who underwent low anterior resection or abdominoperineal resection for rectal cancer within 10 cm of the anal verge (2009-2014) were retrospectively analyzed. Procedures were categorized into "routine" or "difficult" based on predefined criteria. All patients underwent 14 measurements on preoperative imaging. Outcomes were compared between the two groups. Stepwise multivariate logistic regression was used to develop the prediction model, which was validated in an independent data set.

Results: Of the 280 patients analyzed, 80 fulfilled the inclusion criteria. Baseline characteristics were similar except for more males having a "difficult" pelvis. "Difficult" patients were significantly more likely to have a narrower pelvis, smaller pelvic volumes, a longer pelvis, more curved sacrum, and more acute anorectal angle. Difficult cases correlated significantly with higher blood loss, hospital costs, longer operative time, and length of stay. A practical model to predict difficult pelvic dissections was created and included male gender, previous radiation, and length from promontory to pelvic floor > 130 mm. Model validation was performed in 40 patients from an independent data set.

Conclusions: An objective, validated model that predicts a difficult pelvic dissection and associated worse clinical outcome is possible.
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http://dx.doi.org/10.1016/j.jss.2018.05.042DOI Listing
December 2018

Readmission After Elective Ileostomy in Colorectal Surgery Is Predictable.

JSLS 2018 Jul-Sep;22(3)

Departments of Surgery.

Background And Objectives: Patients who undergo colorectal surgery have high postoperative morbidity, with ileostomates being the most disadvantaged. Recent studies assessing readmission risk factors do not provide a specific prediction model and, if so, do not focus on patients who have had colorectal surgery; thus, the results of these studies have limited applicability to our specialized practice. We wanted to develop a prediction model for readmission within 30 days of discharge after ileostomy creation.

Methods: Patients who underwent elective ileostomy creation from 2013 to 2016 at the University of Florida were included in this retrospective study. Factors significantly associated with readmission within 30 days after discharge were identified by comparing a cohort that was readmitted within 30 days with one that was not. A practical, predictive model that stratified a patient's risk of readmission after the index procedure was developed.

Results: A total of 86 iliostomates were included; of those, 22 (26%) were readmitted within 30 days. Factors significantly associated with readmission included preoperative steroid use, history of diabetes, history of depression, lack of a hospital social worker or postoperative ostomy education, and the presence of complications after the index procedure. A model predicting readmission within 30 days of discharge that comprised the first 4 factors was developed, with a sensitivity of 73% and a specificity of 77%.

Conclusion: Prediction of readmission in patients who undergo ileostomy creation is possible, suggesting interventions addressing predictive factors that may help decrease the readmission rate. Prospective validation of the model in a larger cohort is needed.
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http://dx.doi.org/10.4293/JSLS.2018.00008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158969PMC
December 2018

Spatiotemporal incidence rate data analysis by nonparametric regression.

Authors:
Kai Yang Peihua Qiu

Stat Med 2018 06 21;37(13):2094-2107. Epub 2018 Feb 21.

Department of Biostatistics, University of Florida, Gainesville, FL 32611, U.S.A.

To monitor the incidence rates of cancers, AIDS, cardiovascular diseases, and other chronic or infectious diseases, some global, national, and regional reporting systems have been built to collect/provide population-based data about the disease incidence. Such databases usually report daily, monthly, or yearly disease incidence numbers at the city, county, state, or country level, and the disease incidence numbers collected at different places and different times are often correlated, with the ones closer in place or time being more correlated. The correlation reflects the impact of various confounding risk factors, such as weather, demographic factors, lifestyles, and other cultural and environmental factors. Because such impact is complicated and challenging to describe, the spatiotemporal (ST) correlation in the observed disease incidence data has complicated ST structure as well. Furthermore, the ST correlation is hidden in the observed data and cannot be observed directly. In the literature, there has been some discussion about ST data modeling. But, the existing methods either impose various restrictive assumptions on the ST correlation that are hard to justify, or ignore partially or entirely the ST correlation. This paper aims to develop a flexible and effective method for ST disease incidence data modeling, using nonparametric local smoothing methods. This method can properly accommodate the ST data correlation. Theoretical justifications and numerical studies show that it works well in practice.
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http://dx.doi.org/10.1002/sim.7622DOI Listing
June 2018

Characterizing the Pattern of Weight Loss and Regain in Adults Enrolled in a 12-Week Internet-Based Weight Management Program.

Obesity (Silver Spring) 2018 Feb 13;26(2):318-323. Epub 2017 Dec 13.

Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University, Providence, Rhode Island, USA.

Objective: Although the trajectory of weight change during and/or after behavioral weight management interventions is believed to include a period of weight loss followed by maintenance and later regain, the sparse data produced by existing study designs (conducting assessments at 3- to 6-month intervals) have limited investigation into the precise pattern.

Methods: Seventy-five adults were asked to self-weigh daily via "smart" scales during a 12-week, Internet-based weight loss program and for an additional 9 months with no further intervention. Longitudinal change-point mixed-effect models were used to characterize overall weight change patterns and identify when individuals moved from weight loss to maintenance/regain.

Results: Analyses suggested a three-phase model. During the first phase, participants lost weight at a (mean ± SE) rate of -0.46 ± 0.04 kg/wk; after 77.66 ± 3.96 days, they transitioned to regain (0.07 ± 0.02 kg/wk). The next transition occurred at 222.55 ± 7.23 days, after which the rate of regain decreased slightly (0.06 ± 0.02 kg/wk). Exploratory analyses identified baseline/demographic factors predicting the timing of transition points and slope of weight change within phases.

Conclusions: In contrast to the hypothesized trajectory, results demonstrated that participants transitioned immediately from weight loss to regain (with no "maintenance" period) and later to a slower rate of regain. Future studies should investigate whether extended-care programs change or merely delay this pattern.
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http://dx.doi.org/10.1002/oby.22083DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5783775PMC
February 2018

Prognostic Value of Clinical vs Pathologic Stage in Rectal Cancer Patients Receiving Neoadjuvant Therapy.

J Natl Cancer Inst 2018 05;110(5):460-466

Department of Surgery, University of Florida College of Medicine, Gainesville, FL.

Background: Neoadjuvant chemoradiation is currently standard of care in stage II-III rectal cancer, resulting in tumor downstaging for patients with treatment-responsive disease. However, the prognosis of the downstaged patient remains controversial. This work critically analyzes the relative contribution of pre- and post-therapy staging to the anticipated survival of downstaged patients.

Methods: The National Cancer Database (NCDB) was queried for patients with rectal cancer treated with transabdominal resection between 2004 and 2014. Stage II-III patients downstaged with neoadjuvant radiation were compared with stage I patients treated with definitive resection alone. Patients with positive surgical margins were excluded. Overall survival was evaluated using both Kaplan-Meier analyses and Cox proportional hazards models. All statistical tests were two-sided.

Results: A total of 44 320 patients were eligible for analysis. Survival was equivalent for patients presenting with cT1N0 disease undergoing resection (mean survival = 113.0 months, 95% confidence interval [CI] = 110.8 to 115.3 months) compared with those downstaged to pT1N0 from both cT3N0 (mean survival = 114.9 months, 95% CI = 110.4 to 119.3 months, P = .12) and cT3N1 disease (mean survival = 115.4 months, 95% CI = 110.1 to 120.7 months, P = .22). Survival statistically significantly improved in patients downstaged to pT2N0 from cT3N0 disease (mean survival = 109.0 months, 95% CI = 106.7 to 111.2 months, P < .001) and cT3N1 (mean survival = 112.8 months, 95% CI = 110.0 to 115.7 months, P < .001), compared with cT2N0 patients undergoing resection alone (mean survival = 100.0 months, 95% CI = 97.5 to 102.5 months). Multiple survival analysis confirmed that final pathologic stage dictated long-term outcomes in patients undergoing neoadjuvant radiation (hazard ratio [HR] of pT2 = 1.24, 95% CI = 1.10 to 1.41; HR of pT3 = 1.81, 95% CI = 1.61 to 2.05; HR of pT4 = 2.72, 95% CI = 2.28 to 3.25, all P ≤ .001 vs pT1; HR of pN1 = 1.50, 95% CI = 1.41 to 1.59; HR of pN2 = 2.17, 95% CI = 2.00 to 2.35, both P < .001 vs pN0); while clinical stage at presentation had little to no predictive value (HR of cT2 = 0.81, 95% CI = 0.69 to 0.95, P = .008; HR of cT3 = 0.83, 95% CI = 0.72 to 0.96, P = .009; HR of cT4 = 1.02, 95% CI = 0.85 to 1.21, P = .87 vs cT1; HR of cN1 = 0.96, 95% CI = 0.91 to 1.02, P = .19; HR of cN2 = 0.96, 95% CI = 0.86 to 1.08, P = .48 vs cN0).

Conclusions: Survival in patients with rectal cancer undergoing neoadjuvant radiation is driven by post-therapy pathologic stage, regardless of pretherapy clinical stage. These data will further inform prognostic discussions with patients.
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http://dx.doi.org/10.1093/jnci/djx228DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6279292PMC
May 2018

Oral Human Papillomavirus Infection: Differences in Prevalence Between Sexes and Concordance With Genital Human Papillomavirus Infection, NHANES 2011 to 2014.

Ann Intern Med 2017 Nov 17;167(10):714-724. Epub 2017 Oct 17.

From University of Florida, Gainesville, Florida; Baylor College of Medicine and The University of Texas Health Science Center at Houston School of Public Health, Houston, Texas; Harvard Medical School, Boston, Massachusetts; and Weill Cornell Medicine, New York, New York.

Background: The burden of human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) is disproportionately high among men, yet empirical evidence regarding the difference in prevalence of oral HPV infection between men and women is limited. Concordance of oral and genital HPV infection among men is unknown.

Objective: To determine the prevalence of oral HPV infection, as well as the concordance of oral and genital HPV infection, among U.S. men and women.

Design: Nationally representative survey.

Setting: Civilian noninstitutionalized population.

Participants: Adults aged 18 to 69 years from NHANES (National Health and Nutrition Examination Survey), 2011 to 2014.

Measurements: Oral rinse, penile swab, and vaginal swab specimens were evaluated by polymerase chain reaction followed by type-specific hybridization.

Results: The overall prevalence of oral HPV infection was 11.5% (95% CI, 9.8% to 13.1%) in men and 3.2% (CI, 2.7% to 3.8%) in women (equating to 11 million men and 3.2 million women nationwide). High-risk oral HPV infection was more prevalent among men (7.3% [CI, 6.0% to 8.6%]) than women (1.4% [CI, 1.0% to 1.8%]). Oral HPV 16 was 6 times more common in men (1.8% [CI, 1.3% to 2.2%]) than women (0.3% [CI, 0.1% to 0.5%]) (1.7 million men vs. 0.27 million women). Among men and women who reported having same-sex partners, the prevalence of high-risk HPV infection was 12.7% (CI, 7.0% to 18.4%) and 3.6% (CI, 1.4% to 5.9%), respectively. Among men who reported having 2 or more same-sex oral sex partners, the prevalence of high-risk HPV infection was 22.2% (CI, 9.6% to 34.8%). Oral HPV prevalence among men with concurrent genital HPV infection was 4-fold greater (19.3%) than among those without it (4.4%). Men had 5.4% (CI, 5.1% to 5.8%) greater predicted probability of high-risk oral HPV infection than women. The predicted probability of high-risk oral HPV infection was greatest among black participants, those who smoked more than 20 cigarettes daily, current marijuana users, and those who reported 16 or more lifetime vaginal or oral sex partners.

Limitation: Sexual behaviors were self-reported.

Conclusion: Oral HPV infection is common among U.S. men. This study's findings provide several policy implications to guide future OPSCC prevention efforts to combat this disease.

Primary Funding Source: National Cancer Institute.
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http://dx.doi.org/10.7326/M17-1363DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6203692PMC
November 2017

High Generic Drug Prices and Market Competition: A Retrospective Cohort Study.

Ann Intern Med 2017 Aug 4;167(3):145-151. Epub 2017 Jul 4.

From University of Florida, Gainesville, Florida; Brigham and Women's Hospital, Boston, Massachusetts; and University of Utah, Salt Lake City, Utah.

Background: Prices for some generic drugs have increased in recent years, adversely affecting patients who rely on them.

Objective: To determine the association between market competition levels and the change in generic drug prices in the United States.

Design: Retrospective cohort study.

Setting: Prescription claims from commercial health plans between 2008 and 2013.

Measurements: The 5.5 years of data were divided into 11 study periods of 6 months each. The Herfindahl-Hirschman Index (HHI)-calculated by summing the squares of individual manufacturers' market shares, with higher values indicating a less competitive market-and average drug prices were estimated for the generic drugs in each period. The HHI value estimated in the baseline period (first half of 2008) was modeled as a fixed covariate. Models estimated price changes over time by level of competition, adjusting for drug shortages, market size, and dosage forms.

Results: From 1.08 billion prescription claims, a cohort of 1120 generic drugs was identified. After adjustment, drugs with quadropoly (HHI value of 2500, indicating relatively high levels of competition), duopoly (HHI value of 5000), near-monopoly (HHI value of 8000), and monopoly (HHI value of 10 000) levels of baseline competition were associated with price changes of -31.7% (95% CI, -34.4% to -28.9%), -11.8% (CI, -18.6% to -4.4%), 20.1% (CI, 5.5% to 36.6%), and 47.4% (CI, 25.4% to 73.2%), respectively, over the study period.

Limitation: Study findings may not be generalizable to drugs that became generic after 2008.

Conclusion: Market competition levels were associated with a change in generic drug prices. Such measurements may be helpful in identifying older prescription drugs at higher risk for price change in the future.

Primary Funding Source: None.
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http://dx.doi.org/10.7326/M16-1432DOI Listing
August 2017

Evaluation of the treatment time-lag effect for survival data.

Lifetime Data Anal 2018 04 28;24(2):310-327. Epub 2017 Jan 28.

Department of Biostatistics, University of Florida, Gainesville, FL, 32611, USA.

Medical treatments often take a period of time to reveal their impact on subjects, which is the so-called time-lag effect in the literature. In the survival data analysis literature, most existing methods compare two treatments in the entire study period. In cases when there is a substantial time-lag effect, these methods would not be effective in detecting the difference between the two treatments, because the similarity between the treatments during the time-lag period would diminish their effectiveness. In this paper, we develop a novel modeling approach for estimating the time-lag period and for comparing the two treatments properly after the time-lag effect is accommodated. Theoretical arguments and numerical examples show that it is effective in practice.
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http://dx.doi.org/10.1007/s10985-017-9390-7DOI Listing
April 2018

Statistical monitoring-based alarming systems in modeling the AIDS epidemic in the United States, 1985-2011.

Curr HIV Res 2016 ;14(2):130-7

Department of Biostatistics, University of Florida, Gainesville, USA.

Background: Better decisions for the control of HIV/AIDS and other infectious diseases require better information. The large amount of available public health data makes it possible to extract such information to monitor and predict significant disease events in disease epidemic. The detection of unusual events often involves a combination of a forecasting and a decision mechanism assessing the extent to which an observed event differs significantly from a forecast event. A number of methods and models have been proposed to monitor the trend of infectious disease and to detect unusual events. Although these existing methods and models are useful, many new issues remain to be addressed, including the complicated data structure and the infectious disease dynamics. To overcome these issues, we introduced the statistical tool using statistical process control, and proposed a new method under that framework.

Methods: In this paper, we first reviewed the most commonly used methods and models, including the historical limit method, the time series analysis, the hidden Markov models, and the process control charts. Then, we further discussed issues with the current available methods. We proposed a new method using statistical process control. A major feature of the new method is that it prospectively monitors the disease incidence using sequentially collected data over time. It also takes into account a wide variety of longitudinal patterns and possible autocorrelation in the data.

Results: We test this novel method with the recorded data of the number of AIDS cases in different states of US from 1985 to 2011. The results show that our new method is effective in detecting and predicting the time trends of AIDS epidemic for individual states and for US as a whole. Although AIDS data are used in our demonstration, this method can be used for monitoring other infectious diseases.
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http://dx.doi.org/10.2174/1570162x13666151029102646DOI Listing
October 2016

Comparison of multiple hazard rate functions.

Biometrics 2016 Mar 22;72(1):39-45. Epub 2015 Sep 22.

Department of Biostatistics, College of Public Health & Health Professions and College of Medicine, University of Florida, Gainesville, Florida 32611, U.S.A.

Many robust tests have been proposed in the literature to compare two hazard rate functions, however, very few of them can be used in cases when there are multiple hazard rate functions to be compared. In this article, we propose an approach for detecting the difference among multiple hazard rate functions. Through a simulation study and a real-data application, we show that the new method is robust and powerful in many situations, compared with some commonly used tests.
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http://dx.doi.org/10.1111/biom.12412DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5912921PMC
March 2016

Statistical monitoring of the hand, foot and mouth disease in China.

Biometrics 2015 Sep 31;71(3):841-50. Epub 2015 Mar 31.

Department of Biostatistics, University of Florida, Gainesville, Florida 32611, U.S.A.

In a period starting around 2007, the Hand, Foot, and Mouth Disease (HFMD) became wide-spreading in China, and the Chinese public health was seriously threatened. To prevent the outbreak of infectious diseases like HFMD, effective disease surveillance systems would be especially helpful to give signals of disease outbreaks as early as possible. Statistical process control (SPC) charts provide a major statistical tool in industrial quality control for detecting product defectives in a timely manner. In recent years, SPC charts have been used for disease surveillance. However, disease surveillance data often have much more complicated structures, compared to the data collected from industrial production lines. Major challenges, including lack of in-control data, complex seasonal effects, and spatio-temporal correlations, make the surveillance data difficult to handle. In this article, we propose a three-step procedure for analyzing disease surveillance data, and our procedure is demonstrated using the HFMD data collected during 2008-2009 in China. Our method uses nonparametric longitudinal data and time series analysis methods to eliminate the possible impact of seasonality and temporal correlation before the disease incidence data are sequentially monitored by a SPC chart. At both national and provincial levels, our proposed method can effectively detect the increasing trend of disease incidence rate before the disease becomes wide-spreading.
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http://dx.doi.org/10.1111/biom.12301DOI Listing
September 2015

Surveillance of cardiovascular diseases using a multivariate dynamic screening system.

Stat Med 2015 Jun 11;34(14):2204-21. Epub 2015 Mar 11.

School of Finance and Statistics, East China Normal University, Shanghai, 200241, China.

In the SHARe Framingham Heart Study of the National Heart, Lung and Blood Institute, one major task is to monitor several health variables (e.g., blood pressure and cholesterol level) so that their irregular longitudinal pattern can be detected as soon as possible and some medical treatments applied in a timely manner to avoid some deadly cardiovascular diseases (e.g., stroke). To handle this kind of applications effectively, we propose a new statistical methodology called multivariate dynamic screening system (MDySS) in this paper. The MDySS method combines the major strengths of the multivariate longitudinal data analysis and the multivariate statistical process control, and it makes decisions about the longitudinal pattern of a subject by comparing it with other subjects cross sectionally and by sequentially monitoring it as well. Numerical studies show that MDySS works well in practice.
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http://dx.doi.org/10.1002/sim.6477DOI Listing
June 2015

Model selection and diagnostics for joint modeling of survival and longitudinal data with crossing hazard rate functions.

Stat Med 2014 Nov 7;33(26):4532-46. Epub 2014 Jul 7.

Department of Biostatistics, University of Florida, Gainesville, FL 32610, U.S.A.

Comparison of two hazard rate functions is important for evaluating treatment effect in studies concerning times to some important events. In practice, it may happen that the two hazard rate functions cross each other at one or more unknown time points, representing temporal changes of the treatment effect. Also, besides survival data, there could be longitudinal data available regarding some time-dependent covariates. When jointly modeling the survival and longitudinal data in such cases, model selection and model diagnostics are especially important to provide reliable statistical analysis of the data, which are lacking in the literature. In this paper, we discuss several criteria for assessing model fit that have been used for model selection and apply them to the joint modeling of survival and longitudinal data for comparing two crossing hazard rate functions. We also propose hypothesis testing and graphical methods for model diagnostics of the proposed joint modeling approach. Our proposed methods are illustrated by a simulation study and by a real-data example concerning two early breast cancer treatments.
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http://dx.doi.org/10.1002/sim.6259DOI Listing
November 2014

Efficient bias correction for magnetic resonance image denoising.

Stat Med 2013 May 17;32(12):2079-96. Epub 2012 Oct 17.

Department of Mathematics, Boise State University, Boise, ID, USA.

Magnetic resonance imaging (MRI) is a popular radiology technique that is used for visualizing detailed internal structure of the body. Observed MRI images are generated by the inverse Fourier transformation from received frequency signals of a magnetic resonance scanner system. Previous research has demonstrated that random noise involved in the observed MRI images can be described adequately by the so-called Rician noise model. Under that model, the observed image intensity at a given pixel is a nonlinear function of the true image intensity and of two independent zero-mean random variables with the same normal distribution. Because of such a complicated noise structure in the observed MRI images, denoised images by conventional denoising methods are usually biased, and the bias could reduce image contrast and negatively affect subsequent image analysis. Therefore, it is important to address the bias issue properly. To this end, several bias-correction procedures have been proposed in the literature. In this paper, we study the Rician noise model and the corresponding bias-correction problem systematically and propose a new and more effective bias-correction formula based on the regression analysis and Monte Carlo simulation. Numerical studies show that our proposed method works well in various applications.
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http://dx.doi.org/10.1002/sim.5661DOI Listing
May 2013

A correlation-matrix-based hierarchical clustering method for functional connectivity analysis.

J Neurosci Methods 2012 Oct 23;211(1):94-102. Epub 2012 Aug 23.

Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA.

In this study, a correlation matrix based hierarchical clustering (CMBHC) method is introduced to extract multiple correlation patterns from resting-state functional magnetic resonance imaging (fMRI) data. It was applied to spontaneous fMRI signals acquired from anesthetized rats, and the results were then compared with those obtained using independent component analysis (ICA), one of the most popular multivariate analysis method for analyzing spontaneous fMRI signals. It was demonstrated that the CMBHC has a higher sensitivity than the ICA, particularly on a single run data, for identifying correlation structures with relatively weak connections, for instance, the thalamocortical connections. Compared to the seed-based correlation analysis, the CMBHC does not require a priori information and thus can avoid potential biases caused by seed selection, and multiple patterns can be extracted at one time. In contrast to other multivariate methods, the CMBHC is based on spatiotemporal correlations of fMRI signals and its analysis outcomes are easy to interpret as the strength of functional connectivity. Moreover, its sensitivity of detecting patterns remains relatively high even for a single dataset. In conclusion, the CMBHC method could be a useful tool for investigating resting-state brain connectivity and function.
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http://dx.doi.org/10.1016/j.jneumeth.2012.08.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3477851PMC
October 2012

Edge structure preserving 3D image denoising by local surface approximation.

IEEE Trans Pattern Anal Mach Intell 2012 Aug;34(8):1457-68

School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA.

In various applications, including magnetic resonance imaging (MRI) and functional MRI (fMRI), 3D images are becoming increasingly popular. To improve the reliability of subsequent image analyses, 3D image denoising is often a necessary preprocessing step, which is the focus of the current paper. In the literature, most existing image denoising procedures are for 2D images. Their direct extensions to 3D cases generally cannot handle 3D images efficiently because the structure of a typical 3D image is substantially more complicated than that of a typical 2D image. For instance, edge locations are surfaces in 3D cases which would be much more challenging to handle compared to edge curves in 2D cases. We propose a novel 3D image denoising procedure in this paper, based on local approximation of the edge surfaces using a set of surface templates. An important property of this method is that it can preserve edges and major edge structures (e.g., intersections of two edge surfaces and pointed corners). Numerical studies show that it works well in various applications.
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http://dx.doi.org/10.1109/TPAMI.2011.261DOI Listing
August 2012

Intensity-Based Image Registration by Nonparametric Local Smoothing.

IEEE Trans Pattern Anal Mach Intell 2011 Oct 17;33(10):2081-92. Epub 2011 Feb 17.

Image registration is used widely in applications for mapping one image to another. Existing image registration methods are either feature-based or intensity-based. Feature-based methods first extract relevant image features and then find the geometrical transformation that best matches the two corresponding sets of features extracted from the two images. Because identification and extraction of image features is often a challenging and time-consuming process, intensity-based image registration, by which the mapping transformation is estimated directly from the observed image intensities of the two images, has received much attention recently. In the literature, most existing intensity-based image registration methods estimate the mapping transformation globally by solving a minimization/maximization problem defined by the two entire images to register. To this end, it needs to be assumed that the mapping transformation has a certain type of parametric form or it is a continuous bivariate function satisfying certain regularity conditions. In this paper, we propose a novel intensity-based image registration method using nonparametric local smoothing. By this method, the mapping transformation at a given pixel is estimated locally in a neighborhood after certain image features are accommodated in the estimation. Due to the flexibility of local smoothing, this method does not require any parametric form for the mapping transformation. It even allows the transformation to be a discontinuous function. Numerical examples show that it is effective in various applications.
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http://dx.doi.org/10.1109/TPAMI.2011.26DOI Listing
October 2011
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