Flexible Bayesian semiparametric mixed-effects model for skewed longitudinal data.

BACKGROUND: In clinical trials and epidemiological research, mixed-effects models are commonly used to examine population-level and subject-specific trajectories of biomarkers over time. Despite their increasing popularity and application, the specification of these models necessitates a great deal of care when analysing longitudinal data with non-linear patterns and asymmetry. Parametric (linear) mixed-effect models may not capture these complexities flexibly and adequately. Additionally, assuming a Gaussian distribution for random effects and/or model errors may be overly restrictive, as it lacks robustness against deviations from symmetry. METHODS: This paper presents a semiparametric mixed-effects model with flexible distributions for complex longitudinal data in the Bayesian paradigm. The non-linear time effect on the longitudinal response was modelled using a spline approach. The multivariate skew-t distribution, which is a more flexible distribution, is utilized to relax the normality assumptions associated with both random-effects and model errors. RESULTS: To assess the effectiveness of the proposed methods in various model settings, simulation studies were conducted. We then applied these models on chronic kidney disease (CKD) data and assessed the relationship between covariates and estimated glomerular filtration rate (eGFR). First, we compared the proposed semiparametric partially linear mixed-effect (SPPLM) model with the fully parametric one (FPLM), and the results indicated that the SPPLM model outperformed the FPLM model. We then further compared four different SPPLM models, each assuming different distributions for the random effects and model errors. The model with a skew-t distribution exhibited a superior fit to the CKD data compared to the Gaussian model. The findings from the application revealed that hypertension, diabetes, and follow-up time had a substantial association with kidney function, specifically leading to a decrease in GFR estimates. CONCLUSIONS: The application and simulation studies have demonstrated that our work has made a significant contribution towards a more robust and adaptable methodology for modeling intricate longitudinal data. We achieved this by proposing a semiparametric Bayesian modeling approach with a spline smoothing function and a skew-t distribution.

Unplanned hysterectomy: a comprehensive analysis of race, ethnicity, sociodemographic factors, pregnancy complications, and cardiovascular disease risk factors.

OBJECTIVE: To understand the risk of unplanned hysterectomy (UH) in pregnant women better in association with maternal sociodemographic characteristics, cardiovascular disease (CVD) risk factors, and current pregnancy complications. DESIGN: Using Florida birth data from 2005 to 2014, we investigated the possible interactions between known risk factors of having UH, including maternal sociodemographic characteristics, maternal medical history, and other pregnancy complications. Logistic regression models were constructed. Adjusted odds ratios and 95% confidence intervals were reported. RESULTS: Several interactions were observed that significantly affected odds of UH. Compared to non-Hispanic White women, Hispanic minority women were more likely to have an UH. The overall risk of UH for women with preterm birth (<37 weeks) and concurrently had premature rupture of membranes (PRoM), uterine rupture, or a previous cesarean delivery was significantly higher than women who delivered to term and had no pregnancy complications. Women who delivered via cesarean who also had preeclampsia, PRoM, or uterine rupture had an overall increased risk of UH. Significantly decreased risk of UH was seen for Black women less than 20 years old, women of other minority races with either less than a high school degree or a college degree or greater, women of other minority races with PRoM, and women with preterm birth and diabetes compared to respective reference groups. CONCLUSIONS: Maternal race, ethnicity, CVD risk factors, and current pregnancy complications affect the risk of UH in pregnant women through complex interactions that would not be seen in unadjusted models of risk analysis.

The Impact of Sparse Datasets When Harmonizing Data from Studies with Different Measures of the Same Construct.

Prevention science has increasingly turned to integrative data analysis (IDA) to combine individual participant-level data from multiple studies of the same topic, allowing us to evaluate overall effect size, test and model heterogeneity, and examine mediation. Studies included in IDA often use different measures for the same construct, leading to sparse datasets. We introduce a graph theory method for summarizing patterns of sparseness and use simulations to explore the impact of different patterns on measurement bias within three different measurement models: a single common factor, a hierarchical model, and a bifactor model. We simulated 1000 datasets with varying levels of sparseness and used Bayesian methods to estimate model parameters and evaluate bias. Results clarified that bias due to sparseness will depend on the strength of the general factor, the measurement model employed, and the level of indirect linkage among measures. We provide an example using a synthesis dataset that combined data on youth depression from 4146 youth who participated in 16 randomized field trials of prevention programs. Given that different synthesis datasets will embody different patterns of sparseness, we conclude by recommending that investigators use simulation methods to explore the potential for bias given the sparseness patterns they encounter.

Geographic variation and association of risk factors with incidence of colorectal cancer at small-area level.

PURPOSE: Examining spatial distribution of colorectal cancer (CRC) incidence or mortality is helpful for developing cancer control and prevention programs or for generating hypotheses. Such an investigation involves describing the spatial variation of risk factors for CRC and identifying hotspots. The aim of this study is to identify county-level risk factors that may be associated with the incidence of CRC and to map hotspots for CRC in Florida. METHODS: County-level CRC cases, recorded in 2018, were obtained from the Florida Department of Health, Division of Public Health Statistics & Performance Management (DPHSM). Data on county-level risk factors were also obtained from the same source. We used Bayesian spatial models for relative incidence rates and produced posterior predictive that indicates excess risk (hotspots) for CRC. RESULTS: The county-level unadjusted incidence rates range from .462 to 3.142. After fitting a Bayesian spatial model to the data, the results show that a decreasing risk of CRC is strongly associated with an increasing median income, higher percentage of Black population, and higher percentage of sedentary life at county level. Using exceedance probability, it is also observed that there are clustering and hotspots of high CRC incidence rates in Charlotte County in South Florida, Hernando, Sumter and Seminole counties in central Florida and Union and Washington counties in north Florida. CONCLUSION: Among few county-level variables that significantly explained the spatial variation of CRC, income disparity may need more attention for resource allocation and developing preventive intervention in high-risk areas for CRC.

Bayesian censored piecewise regression mixture models with skewness.

This paper presents censored mixture regression models with piecewise growth curves for assessing longitudinal data that exhibit multiphasic features. Such features may include censoring, skewness, measurement errors in covariates, and mixtures of unobserved subpopulations. In the process of describing those features, identification of differential effects of predictors on a response variable for a heterogeneous population (subpopulations) has recently been highly sought. Regression mixture models are key methods for assessing differential effects of predictors. In this article, we extend regression mixture models with normal distribution to incorporate (i) skew-normal distribution, (ii) left-censoring, (iii) measurement errors, and (iv) piecewise growth mixture modeling for describing multiphasic trajectories over time where the observed observations come from a mixture of unobserved subgroups. The proposed methods are illustrated using real data from an AIDS clinical study and a Bayesian approach.

Development of a Minority Prostate Cancer Research Digest: Communication Strategy Statement for Black Men.

Since prostate cancer incidence, prevalence and mortality are still highest among Black men in the United States, it is important to effectively address the factors that contribute to prostate cancer disparities in this at-risk population as well as their low participation in biomedical research/clinical trials. An effective communication strategy that can be used to disseminate information with high public health impact to Black men is one way to combat prostate cancer disparities. The objective of this study was to develop a Minority Prostate Cancer (MiCaP) research communication strategy using focus group methodology and expert in-depth interviews. The communication strategy statement developed in this study provides a guide for message concepts and materials for Black men, including communication content, source, channel, and location. Specifically, it provides recommendations on how to deliver information, how to choose the language and relevant images, how to gain attention, who is preferred to deliver messages, and other ways to engage Black men in health communication strategies. The communication strategy statement was used to develop the MiCaP Research Digest, a research communication program that is currently being tested in Orange County, Duval County, Leon County, Gadsden County, and the Tampa Bay area of Florida.

Neoadjuvant Chemotherapy for Intrahepatic Cholangiocarcinoma: A Propensity Score Survival Analysis Supporting Use in Patients with High-Risk Disease.

BACKGROUND: Upfront surgery is the current standard for resectable intrahepatic cholangiocarcinoma (ICC) despite high treatment failure with this approach. We sought to examine the use of neoadjuvant chemotherapy (NAC) as an alternative strategy for this population. METHODS: The National Cancer Database was used to identify patients with resectable ICC undergoing curative-intent surgery (2006-2014). Utilization trends were examined and survival estimates between NAC and upfront surgery were compared; propensity score-matched models were used to examine the association of NAC with overall survival (OS) for all patients and risk-stratified cohorts. Models accounted for clustering within hospitals, and results represent findings from a complete-case analysis. RESULTS: Among 881 patients with ICC, 8.3% received NAC, with no changes over time (Cochran-Armitage p = 0.7). Median follow-up was 50.9 months, with no difference in unadjusted survival with NAC versus upfront surgery (median OS 51.8 vs. 35.6 months, and 5-year OS rates of 38.2% vs. 36.6%; log rank p = 0.51), and no survival benefit in the propensity score-matched analysis (hazard ratio [HR] 0.78, 95% CI 0.54-1.11; p = 0.16). However, for patients with stage II-III disease, NAC was associated with a trend towards improved survival (median OS of 47.6 months vs. 25.9 months, and 5-year OS rates of 34% vs. 25.7%; log-rank p = 0.10) and a statistically significant survival benefit in the propensity score-matched analysis. (HR 0.58, 95% CI 0.37-0.91; p = 0.02). CONCLUSION: NAC is associated with improved OS over upfront surgery in patients with resectable ICC and high-risk of treatment failure. These data support the need for prospective studies to examine NAC as an alternative strategy to improve OS in this population.

Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012-2016 SEER data.

BACKGROUND: Prostate cancer (CaP) cases are high in the United States. According to the American Cancer Society, there are an estimated number of 174,650 CaP new cases in 2019. The estimated number of deaths from CaP in 2019 is 31,620, making CaP the second leading cause of cancer deaths among American men with lung cancer been the first. Our goal is to estimate and map prostate cancer relative risk, with the ultimate goal of identifying counties at higher risk where interventions and further research can be targeted. METHODS: The 2012-2016 Surveillance, Epidemiology, and End Results (SEER) Program data was used in this study. Analyses were conducted on 159 Georgia counties. The outcome variable is incident prostate cancer. We employed a Bayesian geospatial model to investigate both measured and unmeasured spatial risk factors for prostate cancer. We visualised the risk of prostate cancer by mapping the predicted relative risk and exceedance probabilities. We finally developed interactive web-based maps to guide optimal policy formulation and intervention strategies. RESULTS: Number of persons above age 65 years and below poverty, higher median family income, number of foreign born and unemployed were risk factors independently associated with prostate cancer risk in the non-spatial model. Except for the number of foreign born, all these risk factors were also significant in the spatial model with the same direction of effects. Substantial geographical variations in prostate cancer incidence were found in the study. The predicted mean relative risk was 1.20 with a range of 0.53 to 2.92. Individuals residing in Towns, Clay, Union, Putnam, Quitman, and Greene counties were at increased risk of prostate cancer incidence while those residing in Chattahoochee were at the lowest risk of prostate cancer incidence. CONCLUSION: Our results can be used as an effective tool in the identification of counties that require targeted interventions and further research by program managers and policy makers as part of an overall strategy in reducing the prostate cancer burden in Georgia State and the United States as a whole.

Determinants of malaria among under-five children in Ethiopia: Bayesian multilevel analysis.

BACKGROUND: In Ethiopia, malaria is one of the public health problems, and it is still among the ten top leading causes of morbidity and mortality among under-five children. However, the studies conducted in the country have been inconclusive and inconsistent. Thus, this study aimed to assess factors associated with malaria among under-five children in Ethiopia. METHODS: We retrieved secondary data from the malaria indicator survey data collected from September 30 to December 10, 2015, in Ethiopia. A total of 8301 under-five-year-old children who had microscopy test results were included in the study. Bayesian multilevel logistic regression models were fitted and Markov chain Monte Carlo simulation was used to estimate the model parameters using Gibbs sampling. Adjusted Odd Ratio with 95% credible interval in the multivariable model was used to select variables that have a significant association with malaria. RESULTS: In this study, sleeping under the insecticide-treated bed nets during bed time (ITN) [AOR 0.58,95% CI, 0.31-0.97)], having 2 and more ITN for the household [AOR 0.43, (95% CI, 0.17-0.88)], have radio [AOR 0.41, (95% CI, 0.19-0.78)], have television [AOR 0.19, (95% CI, 0.01-0.89)] and altitude [AOR 0.05, (95% CI, 0.01-0.13)] were the predictors of malaria among under-five children. CONCLUSIONS: The study revealed that sleeping under ITN, having two and more ITN for the household, altitude, availability of radio, and television were the predictors of malaria among under-five children in Ethiopia. Thus, the government should strengthen the availability and utilization of ITN to halt under-five mortality due to malaria.

Towards an Information Motivation and Behavioral Skills Model for New Sex Partners: Results of a Study of Condom Use as an HIV Prevention Method for Emerging Adults Who Met Partners on Dating and Sex-Seeking Platforms or Offline.

The information, motivation, behavioral Skills (IMB) model was used to identify factors that affect condom use with new sex partners that were met offline or online. Mixed methods data were collected from adults between the ages of 18 and 29 years who reported a new sex partner. A model was composed of participants' IMB scale scores to determine the effect of these variables on condom use. A subset of 20 survey participants completed interviews exploring how IMB model elements may have influenced their condom use. Mixed methods results showed condom use skills were influential for condom use during the first sexual encounter between new partners. Qualitative findings suggest the information and motivation may also influence condom use with new sex partners. The IMB model for new partners may be relevant model for the development of interventions that encourage emerging adults to use condoms at first sex with new sex partners.

Joint bent-cable Tobit models for longitudinal and time-to-event data.

In this article, we show how to estimate a transition period for the evolvement of drug resistance to antiretroviral (ARV) drug or other related treatments in the framework of developing a Bayesian method for jointly analyzing time-to-event and longitudinal data. For HIV/AIDS longitudinal data, developmental trajectories of viral loads tend to show a gradual change from a declining trend after initiation of treatment to an increasing trend without an abrupt change. Such characteristics of trajectories are also associated with a time-to-event process. To assess these clinically important features, we develop a joint bent-cable Tobit model for the time-to-event and left-censored response variable with skewness and phasic developments. Random effects are used to determine the stochastic dependence between the time-to-event process and response process. The proposed method is illustrated using real data from an AIDS clinical study.

Heterogeneous growth bent-cable models for time-to-event and longitudinal data: application to AIDS studies.

The major limitations of growth curve mixture models for HIV/AIDS data are the usual assumptions of normality and monophasic curves within latent classes. This article addresses these limitations by using non-normal skewed distributions and multiphasic patterns for outcomes of prospective studies. For such outcomes, new skew-t (ST) distributions are proposed for modeling heterogeneous growth trajectories, which exhibit not abrupt but gradual multiphasic changes from a declining trend to an increasing trend over time. We assess these clinically important features of longitudinal HIV/AIDS data using the bent-cable framework within a context of a joint modeling of time-to-event process and response process. A real dataset from an AIDS clinical study is used to illustrate the proposed methods.

Joint two-part Tobit models for longitudinal and time-to-event data.

In this article, we show how Tobit models can address problems of identifying characteristics of subjects having left-censored outcomes in the context of developing a method for jointly analyzing time-to-event and longitudinal data. There are some methods for handling these types of data separately, but they may not be appropriate when time to event is dependent on the longitudinal outcome, and a substantial portion of values are reported to be below the limits of detection. An alternative approach is to develop a joint model for the time-to-event outcome and a two-part longitudinal outcome, linking them through random effects. This proposed approach is implemented to assess the association between the risk of decline of CD4/CD8 ratio and rates of change in viral load, along with discriminating between patients who are potentially progressors to AIDS from patients who do not. We develop a fully Bayesian approach for fitting joint two-part Tobit models and illustrate the proposed methods on simulated and real data from an AIDS clinical study.

Bayesian analysis of piecewise growth mixture models with skew-t distributions: Application to AIDS studies.

A major problem in HIV/AIDS studies is the development of drug resistance to antiretroviral (ARV) drug or therapy. Estimating the time at which such drug resistance would develop is usually sought. The goal of this article is to perform this estimation by developing growth mixture models with change-points and skew-t distributions based on longitudinal data. For such data, following ARV treatment, the profile of each subject's viral load tends to follow a 'broken stick' like growth trajectory, indicating multiple phases of decline and increase in viral loads. These multiple phases with multiple change-points are captured by subject-specific random parameters of growth curve models. To account for heterogeneity of drug resistance among subjects, the change-points are also allowed to differ by subgroups (subpopulations) of patients classified into latent classes on the basis of trajectories of observed viral loads. The proposed methods are illustrated using real data from an AIDS clinical study.

Bayesian bent-cable growth mixture tobit models for longitudinal data with skewness and detection limit: application to AIDS studies.

This paper presents a new Bayesian methodology for identifying a transition period for the development of drug resistance to antiretroviral drug or therapy in HIV/AIDS studies or other related fields. Estimation of such a transition period requires an availability of longitudinal data where growth trajectories of a response variable tend to exhibit a gradual change from a declining trend to an increasing trend rather than an abrupt change. We assess this clinically important feature of the longitudinal HIV/AIDS data using the bent-cable framework within a growth mixture Tobit model. To account for heterogeneity of drug resistance among subjects, the parameters of the bent-cable growth mixture Tobit model are also allowed to differ by subgroups (subpopulations) of patients classified into latent classes on the basis of trajectories of observed viral load data with skewness and left-censoring. The proposed methods are illustrated using real data from an AIDS clinical study. Copyright © 2016 John Wiley & Sons, Ltd.

Testing moderation in network meta-analysis with individual participant data.

Meta-analytic methods for combining data from multiple intervention trials are commonly used to estimate the effectiveness of an intervention. They can also be extended to study comparative effectiveness, testing which of several alternative interventions is expected to have the strongest effect. This often requires network meta-analysis (NMA), which combines trials involving direct comparison of two interventions within the same trial and indirect comparisons across trials. In this paper, we extend existing network methods for main effects to examining moderator effects, allowing for tests of whether intervention effects vary for different populations or when employed in different contexts. In addition, we study how the use of individual participant data may increase the sensitivity of NMA for detecting moderator effects, as compared with aggregate data NMA that employs study-level effect sizes in a meta-regression framework. A new NMA diagram is proposed. We also develop a generalized multilevel model for NMA that takes into account within-trial and between-trial heterogeneity and can include participant-level covariates. Within this framework, we present definitions of homogeneity and consistency across trials. A simulation study based on this model is used to assess effects on power to detect both main and moderator effects. Results show that power to detect moderation is substantially greater when applied to individual participant data as compared with study-level effects. We illustrate the use of this method by applying it to data from a classroom-based randomized study that involved two sub-trials, each comparing interventions that were contrasted with separate control groups. Copyright © 2016 John Wiley & Sons, Ltd.

Piecewise Growth Mixture Tobit Models: Application to AIDS Studies.

Piecewise growth models are very flexible methods for assessing distinct phases of development or progression in longitudinal data. As an extension of these models, this paper presents piecewise growth mixture Tobit models (PGMTMs) for describing phasic changes of individual trajectories over time where the longitudinal data has a mixture of subpopulations and where left censoring due to a lower limit of detection (LOD) is also observed. There has been relatively little work done simultaneously modeling heterogeneous growth trajectories, segmented phases of progression, and left-censoring with skewed responses. The proposed methods are illustrated using real data from an AIDS clinical study. Analysis results suggested two classes of viral load growth trajectories: Class 1 started with a decline in viral load after treatment but rebound after change-point; Class 2 had a decrease the same as the Class 1 and continued a slower decrease over time.

Bayesian Two-Part Tobit Models with Left-Censoring, Skewness, and Nonignorable Missingness.

In a longitudinal HIV/AIDS study with response data, observations may be missing because of patient dropouts due to drug intolerance or other problems, resulting in nonignorable missing data. In addition to nonignorable missingness, there are also problems of skewness and left-censoring in the response variable because of a lower limit of detection (LOD). There has been relatively little work published simultaneously dealing with these features of longitudinal data. In particular, one of the features may sometimes be the existence of a larger proportion of left-censored data falling below LOD than expected under a usually assumed log-normal distribution. When this happens, an alternative model that can account for a high proportion of censored data should be considered. We present an extension of the random effects Tobit model that incorporates a mixture of true undetectable observations and the values from a skew-normal distribution for an outcome with left-censoring, skewness, and nonignorable missingness. A unifying modeling approach is used to assess the impact of left-censoring, skewness, nonignorable missingness and measurement error in covariates on a Bayesian inference. The proposed methods are illustrated using real data from an AIDS clinical study.

Bayesian bivariate linear mixed-effects models with skew-normal/independent distributions, with application to AIDS clinical studies.

Bivariate correlated (clustered) data often encountered in epidemiological and clinical research are routinely analyzed under a linear mixed-effected (LME) model with normality assumptions for the random-effects and within-subject errors. However, those analyses might not provide robust inference when the normality assumptions are questionable if the data set particularly exhibits skewness and heavy tails. In this article, we develop a Bayesian approach to bivariate linear mixed-effects (BLME) models replacing the Gaussian assumptions for the random terms with skew-normal/independent (SNI) distributions. The SNI distribution is an attractive class of asymmetric heavy-tailed parametric structure which includes the skew-normal, skew-t, skew-slash, and skew-contaminated normal distributions as special cases. We assume that the random-effects and the within-subject (random) errors, respectively, follow multivariate SNI and normal/independent (NI) distributions, which provide an appealing robust alternative to the symmetric normal distribution in a BLME model framework. The method is exemplified through an application to an AIDS clinical data set to compare potential models with different distribution specifications, and clinically important findings are reported.

Jointly modeling time-to-event and longitudinal data: A Bayesian approach.

This article explores Bayesian joint models of event times and longitudinal measures with an attempt to overcome departures from normality of the longitudinal response, measurement errors, and shortages of confidence in specifying a parametric time-to-event model. We allow the longitudinal response to have a skew distribution in the presence of measurement errors, and assume the time-to-event variable to have a nonparametric prior distribution. Posterior distributions of the parameters are attained simultaneously for inference based on Bayesian approach. An example from a recent AIDS clinical trial illustrates the methodology by jointly modeling the viral dynamics and the time to decrease in CD4/CD8 ratio in the presence of CD4 counts with measurement errors and to compare potential models with various scenarios and different distribution specifications. The analysis outcome indicates that the time-varying CD4 covariate is closely related to the first-phase viral decay rate, but the time to CD4/CD8 decrease is not highly associated with either the two viral decay rates or the CD4 changing rate over time. These findings may provide some quantitative guidance to better understand the relationship of the virological and immunological responses to antiretroviral treatments.