A Bayesian Procedure for File Linking to Analyze End-of-Life Medical Costs

End-of-life medical expenses are a significant proportion of all health care expenditures. These costs were studied using costs of services from Medicare claims and cause of death (CoD) from death certificates. In the absence of a unique identifier linking the two datasets, common variables identified unique matches for only 33% of deaths. The remaining cases formed cells with multiple cases (32% in cells with an equal number of cases from each file and 35% in cells with an unequal number). We sampled from the joint posterior distribution of model parameters and the permutations that link cases from the two files within each cell. The linking models included the regression of location of death on CoD and other parameters, and the regression of cost measures with a monotone missing data pattern on CoD and other demographic characteristics. Permutations were sampled by enumerating the exact distribution for small cells and by the Metropolis algorithm for large cells. Sparse matrix data structures enabled efficient calculations despite the large dataset (≈1.7 million cases). The procedure generates m datasets in which the matches between the two files are imputed. The m datasets can be analyzed independently and results combined using Rubin's multiple imputation rules. Our approach can be applied in other file linking applications.

Robust treatment comparison based on utilities of semi-competing risks in non-small-cell lung cancer

A design is presented for a randomized clinical trial comparing two second-line treatments, chemotherapy versus chemotherapy plus reirradiation, for treatment of recurrent non-small-cell lung cancer. The central research question is whether the potential efficacy benefit that adding reirradiation to chemotherapy may provide justifies its potential for increasing the risk of toxicity. The design uses two co-primary outcomes: time to disease progression or death, and time to severe toxicity. Because patients may be given an active third-line treatment at disease progression that confounds second-line treatment effects on toxicity and survival following disease progression, for the purpose of this comparative study follow-up ends at disease progression or death. In contrast, follow-up for disease progression or death continues after severe toxicity, so these are semi-competing risks. A conditionally conjugate Bayesian model that is robust to misspecification is formulated using piecewise exponential distributions. A numerical utility function is elicited from the physicians that characterizes desirabilities of the possible co-primary outcome realizations. A comparative test based on posterior mean utilities is proposed. A simulation study is presented to evaluate test performance for a variety of treatment differences, and a sensitivity assessment to the elicited utility function is performed. General guidelines are given for constructing a design in similar settings, and a computer program for simulation and trial conduct is provided.

Conditional Spectral Analysis of Replicated Multiple Time Series with Application to Nocturnal Physiology

This article considers the problem of analyzing associations between power spectra of multiple time series and cross-sectional outcomes when data are observed from multiple subjects. The motivating application comes from sleep medicine, where researchers are able to non-invasively record physiological time series signals during sleep. The frequency patterns of these signals, which can be quantified through the power spectrum, contain interpretable information about biological processes. An important problem in sleep research is drawing connections between power spectra of time series signals and clinical characteristics; these connections are key to understanding biological pathways through which sleep affects, and can be treated to improve, health. Such analyses are challenging as they must overcome the complicated structure of a power spectrum from multiple time series as a complex positive-definite matrix-valued function. This article proposes a new approach to such analyses based on a tensor-product spline model of Cholesky components of outcome-dependent power spectra. The approach exibly models power spectra as nonparametric functions of frequency and outcome while preserving geometric constraints. Formulated in a fully Bayesian framework, a Whittle likelihood based Markov chain Monte Carlo (MCMC) algorithm is developed for automated model fitting and for conducting inference on associations between outcomes and spectral measures. The method is used to analyze data from a study of sleep in older adults and uncovers new insights into how stress and arousal are connected to the amount of time one spends in bed.

A Study of Mexican Free-Tailed Bat Chirp Syllables: Bayesian Functional Mixed Models for Nonstationary Acoustic Time Series

We describe a new approach to analyze chirp syllables of free-tailed bats from two regions of Texas in which they are predominant: Austin and College Station. Our goal is to characterize any systematic regional differences in the mating chirps and assess whether individual bats have signature chirps. The data are analyzed by modeling spectrograms of the chirps as responses in a Bayesian functional mixed model. Given the variable chirp lengths, we compute the spectrograms on a relative time scale interpretable as the relative chirp position, using a variable window overlap based on chirp length. We use 2D wavelet transforms to capture correlation within the spectrogram in our modeling and obtain adaptive regularization of the estimates and inference for the regions-specific spectrograms. Our model includes random effect spectrograms at the bat level to account for correlation among chirps from the same bat, and to assess relative variability in chirp spectrograms within and between bats. The modeling of spectrograms using functional mixed models is a general approach for the analysis of replicated nonstationary time series, such as our acoustical signals, to relate aspects of the signals to various predictors, while accounting for between-signal structure. This can be done on raw spectrograms when all signals are of the same length, and can be done using spectrograms defined on a relative time scale for signals of variable length in settings where the idea of defining correspondence across signals based on relative position is sensible.

Modeling and correction of bolus dispersion effects in dynamic susceptibility contrast MRI

PURPOSE

Bolus dispersion in DSC-MRI can lead to errors in cerebral blood flow (CBF) estimation by up to 70% when using singular value decomposition analysis. However, it might be possible to correct for dispersion using two alternative methods: the vascular model (VM) and control point interpolation (CPI). Additionally, these approaches potentially provide a means to quantify the microvascular residue function.

METHODS

VM and CPI were extended to correct for dispersion by means of a vascular transport function. Simulations were performed at multiple dispersion levels and an in vivo analysis was performed on a healthy subject and two patients with carotid atherosclerotic disease.

RESULTS

Simulations showed that methods that could not address dispersion tended to underestimate CBF (ratio in CBF estimation, CBFratio = 0.57-0.77) in the presence of dispersion; whereas modified CPI showed the best performance at low-to-medium dispersion; CBFratio = 0.99 and 0.81, respectively. The in vivo data showed trends in CBF estimation and residue function that were consistent with the predictions from simulations.

CONCLUSION

In patients with atherosclerotic disease the estimated residue function showed considerable differences in the ipsilateral hemisphere. These differences could partly be attributed to dispersive effects arising from the stenosis when dispersion corrected CPI was used. It is thus beneficial to correct for dispersion in perfusion analysis using this method.

Dynamics of Covid-19 mortality and social determinants of health: a spatiotemporal analysis of exceedance probabilities

PURPOSE

To determine the association of social factors with Covid-19 mortality and identify high-risk clusters.

METHODS

Data on Covid-19 deaths across 3,108 contiguous U.S. counties from the Johns Hopkins University and social determinants of health (SDoH) data from the County Health Ranking and the Bureau of Labor Statistics were fitted to Bayesian semi-parametric spatiotemporal Negative Binomial models, and 95% credible intervals (CrI) of incidence rate ratios (IRR) were used to assess the associations. Exceedance probabilities were used for detecting clusters.

RESULTS

As of October 31, 2020, the median mortality rate was 40.05 per 100, 000. The monthly urban mortality rates increased with unemployment (IRRadjusted:1.41, 95% CrI: 1.24, 1.60), percent Black population (IRRadjusted:1.05, 95% CrI: 1.04, 1.07), and residential segregation (IRRadjusted:1.03, 95% CrI: 1.02, 1.04). The rural monthly mortality rates increased with percent female population (IRRadjusted: 1.17, 95% CrI: 1.11, 1.24) and percent Black population (IRRadjusted:1.07 95% CrI:1.06, 1.08). Higher college education rates were associated with decreased mortality rates in rural and urban counties. The dynamics of exceedance probabilities detected the shifts of high-risk clusters from the Northeast to Southern and Midwestern counties.

CONCLUSIONS

Spatiotemporal analyses enabled the inclusion of unobserved latent risk factors and aid in scientifically grounded decision-making at a granular level.

Bayesian Network Model to Evaluate the Effectiveness of Continuous Positive Airway Pressure Treatment of Sleep Apnea

OBJECTIVES

The association between obstructive sleep apnea (OSA) and mortality or serious cardiovascular events over a long period of time is not clearly understood. The aim of this observational study was to estimate the clinical effectiveness of continuous positive airway pressure (CPAP) treatment on an outcome variable combining mortality, acute myocardial infarction (AMI), and cerebrovascular insult (CVI) during a follow-up period of 15.5 years (186 ± 58 months).

METHODS

The data set consisted of 978 patients with an apnea-hypopnea index (AHI) ≥5.0. One-third had used CPAP treatment. For the first time, a data-driven causal Bayesian network (DDBN) and a hypothesis-driven causal Bayesian network (HDBN) were used to investigate the effectiveness of CPAP.

RESULTS

In the DDBN, coronary heart disease (CHD), congestive heart failure (CHF), and diuretic use were directly associated with the outcome variable. Sleep apnea parameters and CPAP treatment had no direct association with the outcome variable. In the HDBN, CPAP treatment showed an average improvement of 5.3 percentage points in the outcome. The greatest improvement was seen in patients aged ≤55 years. The effect of CPAP treatment was weaker in older patients (>55 years) and in patients with CHD. In CHF patients, CPAP treatment was associated with an increased risk of mortality, AMI, or CVI.

CONCLUSIONS

The effectiveness of CPAP is modest in younger patients. Long-term effectiveness is limited in older patients and in patients with heart disease (CHD or CHF).

The Pattern of Variation between Diarrhea and Malaria Coexistence with Corresponding Risk Factors in, Chikhwawa, Malawi: A Bivariate Multilevel Analysis

Developing countries face a huge burden of infectious diseases, a number of which co-exist. This paper estimates the pattern and variation of malaria and diarrhea coexistence in Chikhwawa, a district in Southern Malawi using bivariate multilevel modelling with Bayesian estimation. A probit link was employed to examine hierarchically built data from a survey of individuals (n = 6,727) nested within households (n = 1,380) nested within communities (n = 33). Results show significant malaria [σu12=0.901(95% CI:0.746,1.056)] and diarrhea [σu22=1.009(95% CI:0.860,1.158)] variations with a strong correlation between them [ru(1,2)=0.565] at household level. There are significant malaria [σv12=0.053(95% CI:0.018,0.088)] and diarrhea [σv22=0.099(95% CI:0.030,0.168)] variations at community level but with a small correlation [rv(1,2)=0.124] between them. There is also significant correlation between malaria and diarrhea at individual level [re(1,2)=0.241]. These results suggest a close association between reported malaria-like illness and diarrheal illness especially at household and individual levels in Southern Malawi.

Are there really many runners out there? Is the proportion of runners increasing over time? A population-based 12-year repeated cross-sectional study with 625,460 Brazilians

OBJECTIVES

To investigate the proportion of individuals who practice running, its temporal trend, and to describe the runners' characteristics.

DESIGN

Population-based repeated cross-sectional study.

METHODS

The data from the Protective and Risk Factors for Chronic Diseases by Telephone Survey (Vigitel) from Brazil were used. Telephone interviews were conducted with adults from the Brazilian capitals (27 cities) between 2006-2017. The interviewer read the questions and recorded the participants' responses immediately in a questionnaire. Data analyses were conducted using a Bayesian approach.

RESULTS

The Vigitel database consisted of 625,460 Brazilian participants composed of 295,681 exercisers and within them 15,529 runners. The mean yearly proportion of runners was about 2.45% (95% Bayesian credible interval [CrI] 1.93 to 3.11) and 5.32% (95%CrI 4.29 to 6.54) in the entire study population and within exercisers, respectively. The absolute increase in the proportion of runners per year over the 12-year period was 0.13% and 0.17% in the entire study population and within exercisers, respectively. Runners were more likely to be younger, men, within normal body mass index, highly educated, moderate alcohol drinkers, non-smokers, less exposed to TV, living near physical activity/sports facilities and less exposed to health conditions such as arterial hypertension, diabetes and dyslipidaemia.

CONCLUSION

There was a 95% probability that the yearly proportion of runners lies between 1.93% and 3.11% in the population of the 27 Brazilian capitals, and between 4.29% and 6.54% within exercisers. There has been an increase in the proportion of runners over time. Runners were associated with healthier characteristics compared to non-runners and non-exercisers.

Recent advances in Bayesian inference of isolation-with-migration models

Isolation-with-migration (IM) models have become popular for explaining population divergence in the presence of migrations. Bayesian methods are commonly used to estimate IM models, but they are limited to small data analysis or simple model inference. Recently three methods, IMa3, MIST, and AIM, resolved these limitations. Here, we describe the major problems addressed by these three software and compare differences among their inference methods, despite their use of the same standard likelihood function.

Bayesian logistic regression approaches to predict incorrect DRG assignment

Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode's probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.

Bayesian Inferences on Neural Activity in EEG-Based Brain-Computer Interface

A brain-computer interface (BCI) is a system that translates brain activity into commands to operate technology. A common design for an electroencephalogram (EEG) BCI relies on the classification of the P300 event-related potential (ERP), which is a response elicited by the rare occurrence of target stimuli among common non-target stimuli. Few existing ERP classifiers directly explore the underlying mechanism of the neural activity. To this end, we perform a novel Bayesian analysis of the probability distribution of multi-channel real EEG signals under the P300 ERP-BCI design. We aim to identify relevant spatial temporal differences of the neural activity, which provides statistical evidence of P300 ERP responses and helps design individually efficient and accurate BCIs. As one key finding of our single participant analysis, there is a 90% posterior probability that the target ERPs of the channels around visual cortex reach their negative peaks around 200 milliseconds post-stimulus. Our analysis identifies five important channels (PO7, PO8, Oz, P4, Cz) for the BCI speller leading to a 100% prediction accuracy. From the analyses of nine other participants, we consistently select the identified five channels, and the selection frequencies are robust to small variations of bandpass filters and kernel hyper-parameters.

Trend of Gastric Cancer after Bayesian Correction of Misclassification Error in Neighboring Provinces of Iran

BACKGROUND

Some errors may occur in the disease registry system. One of them is misclassification error in cancer registration. It occurs because some of the patients from deprived provinces travel to their adjacent provinces to receive better healthcare without mentioning their permanent residence. The aim of this study was to re-estimate the incidence of gastric cancer using the Bayesian correction for misclassification across Iranian provinces.

MATERIALS AND METHODS

Data of gastric cancer incidence were adapted from the Iranian national cancer registration reports from 2004 to 2008. Bayesian analysis was performed to estimate the misclassification rate with a beta prior distribution for misclassification parameter. Parameters of beta distribution were selected according to the expected coverage of new cancer cases in each medical university of the country.

RESULTS

There was a remarkable misclassification with reference to the registration of cancer cases across the provinces of the country. The average estimated misclassification rate was between 15% and 68%, and higher rates were estimated for more deprived provinces.

CONCLUSION

Misclassification error reduces the accuracy of the registry data, in turn causing underestimation and overestimation in the assessment of the risk of cancer in different areas. In conclusion, correcting the regional misclassification in cancer registry data is essential for discerning high-risk regions and making plans for cancer control and prevention.

Long-term trends and future projections of liver cancer burden in China from 1990 to 2030

Liver cancer remains a significant public health issue in China, exhibiting high incidence, mortality, and burden. Understanding its temporal trends and projections is essential for designing targeted prevention and treatment strategies. This study analyzes long-term trends in liver cancer incidence, prevalence, mortality, and burden from 1990 to 2021, assessing age, period, and cohort effects, and projecting future trends. Data on liver cancer incidence, prevalence, mortality, disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs) were analyzed from 1990 to 2021. Joinpoint regression analysis, age-period-cohort (APC) analysis, and BAPC modeling were applied to examine trends and project future trends. Decomposition analysis examined contributions of aging, epidemiological changes, and population growth. The study also compared China's liver cancer trends with global data. From 1990 to 2021, China experienced a decrease in age-standardized rates for liver cancer incidence, prevalence, mortality, and burden. Notably, Age-standardized incidence rates (ASIR) exhibited a decline after 2016, with a significant reduction in the male population. In 2021, there were 196,637 new cases of liver cancer in China, with a higher burden in males. ASIR were 14.34 per 100,000 for males and 4.89 per 100,000 for females. Mortality also followed a declining trend, with a notable decrease in age-standardized mortality rates. The age-standardized mortality rate (ASMR) for males was 12.4 per 100,000, significantly higher than for females (4.57 per 100,000) in 2021. Additionally, the age-standardized prevalence rate (ASPR) was 20.0 for males and 6.64 for females, with 265,539 total cases. The burden, measured by DALYs, YLDs, and YLLs, also showed a notable decline in age-standardized rates and significant gender disparities. Despite this, the absolute number of cases, deaths, and DALYs has continued to rise due to population growth and aging, with males bearing a significantly higher burden than females. The study also highlights the impact of aging, population growth, and epidemiological changes on liver cancer incidence and mortality in China. Projections for 2030 suggest a continued decrease in liver cancer incidence, especially among females, reflecting the effectiveness of public health interventions and medical advancements. However, gender disparities remain significant, and further efforts are needed to reduce the overall liver cancer burden, with an emphasis on early detection and prevention strategies.

Depression at the intersection of race/ethnicity, sex/gender, and sexual orientation in a nationally representative sample of US adults: A design-weighted MAIHDA

This study examined how race/ethnicity, sex/gender, and sexual orientation intersect to socially pattern depression among US adults. We used repeated, cross-sectional data from the 2015-2020 National Survey on Drug Use and Health (NSDUH; n=234,772) to conduct design-weighted multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) for two outcomes: past-year and lifetime major depressive episode (MDE). With 42 intersectional groups constructed from seven race/ethnicity, two sex/gender, and three sexual orientation categories, we estimated group-specific prevalence and excess/reduced prevalence attributable to intersectional effects (i.e., two-way or higher interactions between identity variables). Models revealed heterogeneity between intersectional groups, with prevalence estimates ranging from 3.4-31.4% (past-year) and 6.7-47.4% (lifetime). Model main effects indicated that people who were Multiracial, White, women, gay/lesbian, or bisexual had greater odds of MDE. Additive effects of race/ethnicity, sex/gender, and sexual orientation explained most between-group variance; however, approximately 3% (past-year) and 12% (lifetime) were attributable to intersectional effects, with some groups experiencing excess/reduced prevalence. For both outcomes, sexual orientation main effects (42.9-54.0%) explained a greater proportion of between-group variance relative to race/ethnicity (10.0-17.1%) and sex/gender (7.5-7.9%). Notably, we extend MAIHDA to calculate nationally representative estimates to open future opportunities to quantify intersectionality with complex sample survey data.

Flexible Regularized Estimation in High-Dimensional Mixed Membership Models

Mixed membership models are an extension of finite mixture models, where each observation can partially belong to more than one mixture component. A probabilistic framework for mixed membership models of high-dimensional continuous data is proposed with a focus on scalability and interpretability. The novel probabilistic representation of mixed membership is based on convex combinations of dependent multivariate Gaussian random vectors. In this setting, scalability is ensured through approximations of a tensor covariance structure through multivariate eigen-approximations with adaptive regularization imposed through shrinkage priors. Conditional weak posterior consistency is established on an unconstrained model, allowing for a simple posterior sampling scheme while keeping many of the desired theoretical properties of our model. The model is motivated by two biomedical case studies: a case study on functional brain imaging of children with autism spectrum disorder (ASD) and a case study on gene expression data from breast cancer tissue. These applications highlight how the typical assumption made in cluster analysis, that each observation comes from one homogeneous subgroup, may often be restrictive in several applications, leading to unnatural interpretations of data features.

Geospatial patterns by cancer stage across Australia for three common cancers

BACKGROUND

Monitoring cancer stage is vital to interpret cancer incidence and survival patterns, yet there are currently no cancer stage estimates by small areas across Australia, despite demonstrated large disparities in cancer incidence and survival. While cancer stage data is not routinely collected in Australia, a pilot project collected stage information nationwide in 2011.

METHODS

Data on all primary invasive melanoma, female breast and prostate cancers (stages 1-4) diagnosed during 2011 in Australia were categorised into early and intermediate/advanced stage at diagnosis. Bayesian spatial models were used to estimate standardised incidence rates (SIRs) and proportions of cancer stage across 2148 statistical areas level 2. The correlation between early and more advanced cancer rates was explored using exceedance probabilities.

RESULTS

Both melanoma and prostate cancer had mainly early stage diagnoses. There was large variation in rates across the nation, and also substantial correlation between SIRs of early and more advanced stage for melanoma and prostate cancer. In contrast, breast cancer had a higher proportion of advanced cancers diagnosed, less pronounced variation in rates and limited correlation between early and more advanced stage SIRs. The proportion of cases diagnosed as early stage varied across Australia by type of cancer.

CONCLUSION

This study uncovered important spatial patterns in the diagnosis of cancer by stage across the country, which varied by cancer type and location. There is an urgent need to have contemporary information about stage at diagnosis routinely included in population-based cancer registries across the country.

Unsafe Injection Is Associated with Higher HIV Testing after Bayesian Adjustment for Unmeasured Confounding

BACKGROUND

To apply a novel method to adjust for HIV knowledge as an unmeasured confounder for the effect of unsafe injection on future HIV testing.

METHODS

The data were collected from 601 HIV-negative persons who inject drugs (PWID) from a cohort in San Francisco. The panel-data generalized estimating equations (GEE) technique was used to estimate the adjusted risk ratio (RR) for the effect of unsafe injection on not being tested (NBT) for HIV. Expert opinion quantified the bias parameters to adjust for insufficient knowledge about HIV transmission as an unmeasured confounder using Bayesian bias analysis.

RESULTS

Expert opinion estimated that 2.5%-40.0% of PWID with unsafe injection had insufficient HIV knowledge; whereas 1.0%-20.0% who practiced safe injection had insufficient knowledge. Experts also estimated the RR for the association between insufficient knowledge and NBT for HIV as 1.1-5.0. The RR estimate for the association between unsafe injection and NBT for HIV, adjusted for measured confounders, was 0.96 (95% confidence interval: 0.89,1.03). However, the RR estimate decreased to 0.82 (95% credible interval: 0.64, 0.99) after adjusting for insufficient knowledge as an unmeasured confounder.

CONCLUSION

Our Bayesian approach that uses expert opinion to adjust for unmeasured confounders revealed that PWID who practice unsafe injection are more likely to be tested for HIV - an association that was not seen by conventional analysis.

Effective Factors on the Recurrence of Bipolar Mood Disorder I in an Iranian Population Sample Using the Frailty Model with Bayesian Approach

Objective: Bipolar I disorder is one of the most frequent mental disorders characterized by manic or mixed +/- depressive episodes. Drug treatment has been proved to diminish next episodes, but many other factors are important for exacerbating the conditions. This study aimed to investigate the effective factors on the time and number of episodes in these patients by applying the shared frailty model.

Method: In this retrospective longitudinal study, the information of 606 patients with bipolar I disorder, admitted for the first time in Ibn-e-Sina psychiatric hospital in Mashhad from the beginning of 2007 until the end of 2009 were used. These patients were followed up until the end of 2018 for readmission. The Cox model with gamma frailty and Bayesian approach were used to determine the effective factors of frequent recurrences.

Results: History of head trauma, substance abuse, and legal conflict had a positive impact on recurrences, while age had a negative effect on recurrences and the risk of recurrence was higher in younger people (P < 0.05). The variance estimation of frailty effect was 0.97 that indicates a correlation between the recurrence intervals of bipolar I patients, owing to a heterogeneity among patients.

Conclusion: Based on the results, a higher risk of recurrence of bipolar I disorder was found in younger patients and those with a history of head trauma, substance abuse, and legal conflicts. Further investigations are required to account for the genetic factor and psychosocial exposure during critical periods applying this model.

What Do You Think? Using Expert Opinion to Improve Predictions of Response Propensity Under a Bayesian Framework

Responsive survey designs introduce protocol changes to survey operations based on accumulating paradata. Case-level predictions, including response propensity, can be used to tailor data collection features in pursuit of cost or quality goals. Unfortunately, predictions based only on partial data from the current round of data collection can be biased, leading to ineffective tailoring. Bayesian approaches can provide protection against this bias. Prior beliefs, which are generated from data external to the current survey implementation, contribute information that may be lacking from the partial current data. Those priors are then updated with the accumulating paradata. The elicitation of the prior beliefs, then, is an important characteristic of these approaches. While historical data for the same or a similar survey may be the most natural source for generating priors, eliciting prior beliefs from experienced survey managers may be a reasonable choice for new surveys, or when historical data are not available. Here, we fielded a questionnaire to survey managers, asking about expected attempt-level response rates for different subgroups of cases, and developed prior distributions for attempt-level response propensity model coefficients based on the mean and standard error of their responses. Then, using respondent data from a real survey, we compared the predictions of response propensity when the expert knowledge is incorporated into a prior to those based on a standard method that considers accumulating paradata only, as well as a method that incorporates historical survey data.

Adaptive design clinical trials: current status by disease and trial phase in various perspectives

An adaptive design is a clinical trial design that allows for modification of a structured plan in a clinical trial based on data accumulated during pre-planned interim analyses. This flexible approach to clinical trial design improves the success rate of clinical trials while reducing time, cost, and sample size compared to conventional methods. The purpose of this study is to identify the current status of adaptive design and present key considerations for planning an appropriate adaptive design based on specific circumstances. We searched for clinical trials conducted between January 2006 to July 2021 in the Clinical Trials Registry (ClinicalTrials.gov) using keywords specified in the Food and Drug Administration Adaptive Design Clinical Trial Guidelines. In order to analyze the adaptive designs used in selected cases, we classified the results according to the phase of the clinical trial, type of indication, and the specific adaptation method employed. A total of 267 clinical trials were identified on ClinicalTrials.gov. Among them, 236 clinical trials actually applied adaptive designs and were classified according to phase, indication types, and adaptation methods. Adaptive designs were most frequently used in phase 2 clinical trials and oncology research. The most commonly used adaptation method was the adaptive treatment selection design. In the case of coronavirus disease 2019, the most frequently used designs were adaptive platform design and seamless design. Through this study, we expect to provide valuable insights and considerations for the implementation of adaptive design clinical trials in different diseases and stages.

Bayesian estimation of post-test probability of Candida glabrata fungemia by means of serum creatinine

Fungemia is a life-threatening condition associated with high mortality; the most frequently isolated genus is Candida. Candida glabrata is of particular concern because of its increasing resistance to azoles. We evaluated common lab tests accessible by almost all healthcare professionals to estimate the post-test probability of recovery of C. glabrata from a blood culture collected by venipuncture, positive for fungi identified by microscopic examination. Patients with blood cultures positive for C. glabrata had significantly higher median values of serum creatinine (P=0.006), and a value of ≥1.45 mg/dL was the best cut-off in discriminating C. glabrata from other Candida spp., with 0.67 [95% Confidence Interval (CI): 0.49-0.85] sensitivity and 0.75 (95% CI: 0.66-0.84) specificity; Youden's J statistic: 0.42. The receiver operator characteristic curve analysis showed an area under the curve of 0.718 (95% CI: 0.603-0.833); P=0.001. Therefore, given a pre-test probability of 24% and applying the Bayes' theorem, the post-test probability of C. glabrata fungemia with creatinine values ≥1.45 mg/dL increased to 45.8%. In conclusion, we showed how the probability of recovery of C. glabrata from blood cultures collected by venipuncture and positive for fungi can be better estimated using concurrent creatinine values.

Predictors of Developmental Defects of Enamel in Primary Maxillary Central Incisors Using Bayesian Model Selection

INTRODUCTION

Localized non-inheritable developmental defects of tooth enamel (DDE) are classified as enamel hypoplasia (EH), opacity (OP), and post-eruptive breakdown (PEB) using the enamel defects index. To better understand the etiology of DDE, we assessed the linkages amongst exposome variables for these defects during the specific time duration for enamel mineralization of the human primary maxillary central incisor enamel crowns. In general, these two teeth develop between 13 and 14 weeks in utero and 3-4 weeks' postpartum of a full-term delivery, followed by tooth eruption at about 1 year of age.

METHODS

We utilized existing datasets for mother-child dyads that encompassed 12 weeks' gestation through birth and early infancy, and child DDE outcomes from digital images of the erupted primary maxillary central incisor teeth. We applied a Bayesian modeling paradigm to assess the important predictors of EH, OP, and PEB.

RESULTS

The results of Gibbs variable selection showed a key set of predictors: mother's prepregnancy body mass index (BMI); maternal serum concentrations of calcium and phosphorus at gestational week 28; child's gestational age; and both mother's and child's functional vitamin D deficiency (FVDD). In this sample of healthy mothers and children, significant predictors for OP included the child having a gestational period >36 weeks and FVDD at birth, and for PEB included a mother's prepregnancy BMI <21.5 and higher serum phosphorus concentration at week 28.

CONCLUSION

In conclusion, our methodology and results provide a roadmap for assessing timely biomarker measures of exposures during specific tooth development to better understand the etiology of DDE for future prevention.