Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models

Compartmental models that describe infectious disease transmission across subpopulations are central for assessing the impact of non-pharmaceutical interventions, behavioral changes and seasonal effects on the spread of respiratory infections. We present a Bayesian workflow for such models, including four features: (1) an adjustment for incomplete case ascertainment, (2) an adequate sampling distribution of laboratory-confirmed cases, (3) a flexible, time-varying transmission rate, and (4) a stratification by age group. Within the workflow, we benchmarked the performance of various implementations of two of these features (2 and 3). For the second feature, we used SARS-CoV-2 data from the canton of Geneva (Switzerland) and found that a quasi-Poisson distribution is the most suitable sampling distribution for describing the overdispersion in the observed laboratory-confirmed cases. For the third feature, we implemented three methods: Brownian motion, B-splines, and approximate Gaussian processes (aGP). We compared their performance in terms of the number of effective samples per second, and the error and sharpness in estimating the time-varying transmission rate over a selection of ordinary differential equation solvers and tuning parameters, using simulated seroprevalence and laboratory-confirmed case data. Even though all methods could recover the time-varying dynamics in the transmission rate accurately, we found that B-splines perform up to four and ten times faster than Brownian motion and aGPs, respectively. We validated the B-spline model with simulated age-stratified data. We applied this model to 2020 laboratory-confirmed SARS-CoV-2 cases and two seroprevalence studies from the canton of Geneva. This resulted in detailed estimates of the transmission rate over time and the case ascertainment. Our results illustrate the potential of the presented workflow including stratified transmission to estimate age-specific epidemiological parameters. The workflow is freely available in the R package HETTMO, and can be easily adapted and applied to other infectious diseases.

Using leave-one-out cross validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A cautionary tale

In recent decades, multilevel regression and poststratification (MRP) has surged in popularity for population inference. However, the validity of the estimates can depend on details of the model, and there is currently little research on validation. We explore how leave-one-out cross validation (LOO) can be used to compare Bayesian models for MRP. We investigate two approximate calculations of LOO: Pareto smoothed importance sampling (PSIS-LOO) and a survey-weighted alternative (WTD-PSIS-LOO). Using two simulation designs, we examine how accurately these two criteria recover the correct ordering of model goodness at predicting population and small-area estimands. Focusing first on variable selection, we find that neither PSIS-LOO nor WTD-PSIS-LOO correctly recovers the models' order for an MRP population estimand, although both criteria correctly identify the best and worst model. When considering small-area estimation, the best model differs for different small areas, highlighting the complexity of MRP validation. When considering different priors, the models' order seems slightly better at smaller-area levels. These findings suggest that, while not terrible, PSIS-LOO-based ranking techniques may not be suitable to evaluate MRP as a method. We suggest this is due to the aggregation stage of MRP, where individual-level prediction errors average out. We validate these results by applying to the real world National Health and Nutrition Examination Survey (NHANES) data in the United States. Altogether, these results show that PSIS-LOO-based model validation tools need to be used with caution and might not convey the full story when validating MRP as a method.

A New Look at P Values for Randomized Clinical Trials

A New Look at P Values for Randomized Clinical TrialsUsing the primary results of 23,551 randomized clinical trials from the Cochrane Database, van Zwet et al. provide an empirical guide for the interpretation of an observed P value from a "typical" clinical trial in terms of the degree of overestimation of the reported effect, the probability of the effect's sign being wrong, and the predictive power of the trial.

Generically partisan: Polarization in political communication

American political parties continue to grow more polarized, but the extent of ideological polarization among the public is much less than the extent of perceived polarization (what the ideological gap is believed to be). Perceived polarization is concerning because of its link to interparty hostility, but it remains unclear what drives this phenomenon. We propose that a tendency for individuals to form broad generalizations about groups on the basis of inconsistent evidence may be partly responsible. We study this tendency by measuring the interpretation, endorsement, and recall of category-referring statements, also known as generics (e.g., "Democrats favor affirmative action"). In study 1 (n = 417), perceived polarization was substantially greater than actual polarization. Further, participants endorsed generics as long as they were true more often of the target party (e.g., Democrats favor affirmative action) than of the opposing party (e.g., Republicans favor affirmative action), even when they believed such statements to be true for well below 50% of the relevant party. Study 2 (n = 928) found that upon receiving information from political elites, people tended to recall these statements as generic, regardless of whether the original statement was generic or not. Study 3 (n = 422) found that generic statements regarding new political information led to polarized judgments and did so more than nongeneric statements. Altogether, the data indicate a tendency toward holding mental representations of political claims that exaggerate party differences. These findings suggest that the use of generic language, common in everyday speech, enables inferential errors that exacerbate perceived polarization.

Bayesian spatial modelling of localised SARS-CoV-2 transmission through mobility networks across England

In the early phases of growth, resurgent epidemic waves of SARS-CoV-2 incidence have been characterised by localised outbreaks. Therefore, understanding the geographic dispersion of emerging variants at the start of an outbreak is key for situational public health awareness. Using telecoms data, we derived mobility networks describing the movement patterns between local authorities in England, which we have used to inform the spatial structure of a Bayesian BYM2 model. Surge testing interventions can result in spatio-temporal sampling bias, and we account for this by extending the BYM2 model to include a random effect for each timepoint in a given area. Simulated-scenario modelling and real-world analyses of each variant that became dominant in England were conducted using our BYM2 model at local authority level in England. Simulated datasets were created using a stochastic metapopulation model, with the transmission rates between different areas parameterised using telecoms mobility data. Different scenarios were constructed to reproduce real-world spatial dispersion patterns that could prove challenging to inference, and we used these scenarios to understand the performance characteristics of the BYM2 model. The model performed better than unadjusted test positivity in all the simulation-scenarios, and in particular when sample sizes were small, or data was missing for geographical areas. Through the analyses of emerging variant transmission across England, we found a reduction in the early growth phase geographic clustering of later dominant variants as England became more interconnected from early 2022 and public health interventions were reduced. We have also shown the recent increased geographic spread and dominance of variants with similar mutations in the receptor binding domain, which may be indicative of convergent evolution of SARS-CoV-2 variants.

Tocilizumab for antibody-mediated rejection treatment in lung transplantation

Tocilizumab (TCZ), an IL-6 inhibitor, has shown promise in the treatment of donor-specific antibodies (DSA) and chronic antibody-mediated rejection (AMR) in renal transplant recipients. However, its use in lung transplantation has not been described. This retrospective case-control study compared AMR treatments containing TCZ in 9 bilateral lung transplant recipients to 18 patients treated for AMR without TCZ. Treatment with TCZ resulted in more clearance of DSA, lower recurrence of DSA, lower incidence of new DSA, and lower rates of graft failure when compared to those treated for AMR without TCZ. The incidence of infusion reactions, elevation in transaminases, and infections were similar between the 2 groups. These data support a role for TCZ in pulmonary AMR and establish preliminary evidence to design a randomized controlled trial of IL-6 inhibition for the management of AMR.

Inference from Nonrandom Samples Using Bayesian Machine Learning

We consider inference from nonrandom samples in data-rich settings where high-dimensional auxiliary information is available both in the sample and the target population, with survey inference being a special case. We propose a regularized prediction approach that predicts the outcomes in the population using a large number of auxiliary variables such that the ignorability assumption is reasonable and the Bayesian framework is straightforward for quantification of uncertainty. Besides the auxiliary variables, we also extend the approach by estimating the propensity score for a unit to be included in the sample and also including it as a predictor in the machine learning models. We find in simulation studies that the regularized predictions using soft Bayesian additive regression trees yield valid inference for the population means and coverage rates close to the nominal levels. We demonstrate the application of the proposed methods using two different real data applications, one in a survey and one in an epidemiologic study.

Toward a taxonomy of trust for probabilistic machine learning

Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond. To aid the development of trust in these decisions, we develop a taxonomy delineating where trust in an analysis can break down: (i) in the translation of real-world goals to goals on a particular set of training data, (ii) in the translation of abstract goals on the training data to a concrete mathematical problem, (iii) in the use of an algorithm to solve the stated mathematical problem, and (iv) in the use of a particular code implementation of the chosen algorithm. We detail how trust can fail at each step and illustrate our taxonomy with two case studies. Finally, we describe a wide variety of methods that can be used to increase trust at each step of our taxonomy. The use of our taxonomy highlights not only steps where existing research work on trust tends to concentrate and but also steps where building trust is particularly challenging.

A pilot randomized controlled trial of de novo belatacept-based immunosuppression following anti-thymocyte globulin induction in lung transplantation

The development of donor-specific antibodies (DSA) after lung transplantation is common and results in adverse outcomes. In kidney transplantation, Belatacept has been associated with a lower incidence of DSA, but experience with Belatacept in lung transplantation is limited. We conducted a two-center pilot randomized controlled trial of de novo immunosuppression with Belatacept after lung transplantation to assess the feasibility of conducting a pivotal trial. Twenty-seven participants were randomized to Control (Tacrolimus, Mycophenolate Mofetil, and prednisone, n = 14) or Belatacept-based immunosuppression (Tacrolimus, Belatacept, and prednisone until day 89 followed by Belatacept, Mycophenolate Mofetil, and prednisone, n = 13). All participants were treated with rabbit anti-thymocyte globulin for induction immunosuppression. We permanently stopped randomization and treatment with Belatacept after three participants in the Belatacept arm died compared to none in the Control arm. Subsequently, two additional participants in the Belatacept arm died for a total of five deaths compared to none in the Control arm (log rank p = .016). We did not detect a significant difference in DSA development, acute cellular rejection, or infection between the two groups. We conclude that the investigational regimen used in this study is associated with increased mortality after lung transplantation.

Beyond Vaccination Rates: A Synthetic Random Proxy Metric of Total SARS-CoV-2 Immunity Seroprevalence in the Community

BACKGROUND

Explicit knowledge of total community-level immune seroprevalence is critical to developing policies to mitigate the social and clinical impact of SARS-CoV-2. Publicly available vaccination data are frequently cited as a proxy for population immunity, but this metric ignores the effects of naturally acquired immunity, which varies broadly throughout the country and world. Without broad or random sampling of the population, accurate measurement of persistent immunity post-natural infection is generally unavailable.

METHODS

To enable tracking of both naturally acquired and vaccine-induced immunity, we set up a synthetic random proxy based on routine hospital testing for estimating total immunoglobulin G (IgG) prevalence in the sampled community. Our approach analyzed viral IgG testing data of asymptomatic patients who presented for elective procedures within a hospital system. We applied multilevel regression and poststratification to adjust for demographic and geographic discrepancies between the sample and the community population. We then applied state-based vaccination data to categorize immune status as driven by natural infection or by vaccine.

RESULTS

We validated the model using verified clinical metrics of viral and symptomatic disease incidence to show the expected biologic correlation of these entities with the timing, rate, and magnitude of seroprevalence. In mid-July 2021, the estimated immunity level was 74% with the administered vaccination rate of 45% in the two counties.

CONCLUSIONS

Our metric improves real-time understanding of immunity to COVID-19 as it evolves and the coordination of policy responses to the disease, toward an inexpensive and easily operational surveillance system that transcends the limits of vaccination datasets alone.

Stress Induced Eosinophilpoiesis Contributes to Mortality after Pulmonary Resection

Factors affecting mortality after lung surgery remain poorly explored. We performed pulmonary lobar resections(removing □ 1/5 of the total pulmonary volume) from C57BL/6 mice and evaluated quantitative changes in various leucocytes in the remaining lung, blood and spleen. A nearly four-fold increase in eosinophils was evident by day-7 post resection with a gradual return to baseline by day 30. Evaluation of the bone marrow demonstrated no change in common myeloid, granulocyte-monocyte or eosinophil progenitors. However the number of early stage and mature eosinophils doubled after lobar resection suggesting that pulmonary surgery augmented maturation of eosinophils. Neutralization of IL-5 decreased overall eosinophil numbers but the relative differences between resting and post-resection mice remained. Further evaluation of bone marrow demonstrated an increase in IL-25, IL-6, IL-9, KC and MCP-1 after pulmonary resection. We next performed right pneumonectomies in saline or diphtheria toxin-treated iPHIL mice, where diphtheria toxin receptor expression is limited to eosinophils, and noted a 70% mortality in eosinophil sufficient vs. 20% mortality in eosinophil depleted mice (p=.005). Severe hypoxia was noted in eosinophils sufficient vs. deficient mice (pO2 of 54.3+/−5.7 vs. 83.6+/−11.63; p=.04) four hours prior to resection. Taken together out data demonstrate that pulmonary surgery leads to bone marrow-specific maturation of eosinophils and systemic eosinophilia which directly contributes to perioperative mortality. Thus defining factors which may disrupt this process offers the opportunity to improve outcomes after lung surgery.

Supported by PO1 AI116501, RO1 AI145108-01

Computer-Aided Detection of Respiratory Sounds in Bronchial Asthma Patients Based on Machine Learning Method

The aim of the study is to develop a method for detection of pathological respiratory sound, caused by bronchial asthma, with the aid of machine learning techniques.

Materials and Methods

To build and train neural networks, we used the records of respiratory sounds of bronchial asthma patients at different stages of the disease (n=951) aged from several months to 47 years old and healthy volunteers (n=167). The sounds were recorded with calm breathing at four points: at the oral cavity, above the trachea, on the chest (second intercostal space on the right side), and at a point on the back.

Results

The method developed for computer-aided detection of respiratory sounds allows to diagnose sounds typical for bronchial asthma in 89.4% of cases with 89.3% sensitivity and 86.0% specificity regardless of sex and age of the patients, stage of the disease, and the point of sound recording.

Routine Hospital-based SARS-CoV-2 Testing Outperforms State-based Data in Predicting Clinical Burden

Throughout the coronavirus disease 2019 (COVID-19) pandemic, government policy and healthcare implementation responses have been guided by reported positivity rates and counts of positive cases in the community. The selection bias of these data calls into question their validity as measures of the actual viral incidence in the community and as predictors of clinical burden. In the absence of any successful public or academic campaign for comprehensive or random testing, we have developed a proxy method for synthetic random sampling, based on viral RNA testing of patients who present for elective procedures within a hospital system. We present here an approach under multilevel regression and poststratification to collecting and analyzing data on viral exposure among patients in a hospital system and performing statistical adjustment that has been made publicly available to estimate true viral incidence and trends in the community. We apply our approach to tracking viral behavior in a mixed urban-suburban-rural setting in Indiana. This method can be easily implemented in a wide variety of hospital settings. Finally, we provide evidence that this model predicts the clinical burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) earlier and more accurately than currently accepted metrics. See video abstract at, http://links.lww.com/EDE/B859.

A simple explanation for declining temperature sensitivity with warming

Recently, multiple studies have reported declining phenological sensitivities (∆ days per ℃) with higher temperatures. Such observations have been used to suggest climate change is reshaping biological processes, with major implications for forecasts of future change. Here, we show that these results may simply be the outcome of using linear models to estimate nonlinear temperature responses, specifically for events that occur after a cumulative thermal threshold is met-a common model for many biological events. Corrections for the nonlinearity of temperature responses consistently remove the apparent decline. Our results show that rising temperatures combined with linear estimates based on calendar time produce the observations of declining sensitivity-without any shift in the underlying biology. Current methods may thus undermine efforts to identify when and how warming will reshape biological processes.

Improving multilevel regression and poststratification with structured priors

A central theme in the field of survey statistics is estimating population-level quantities through data coming from potentially non-representative samples of the population. Multilevel regression and poststratification (MRP), a model-based approach, is gaining traction against the traditional weighted approach for survey estimates. MRP estimates are susceptible to bias if there is an underlying structure that the methodology does not capture. This work aims to provide a new framework for specifying structured prior distributions that lead to bias reduction in MRP estimates. We use simulation studies to explore the benefit of these prior distributions and demonstrate their efficacy on non-representative US survey data. We show that structured prior distributions offer absolute bias reduction and variance reduction for posterior MRP estimates in a large variety of data regimes.

Accounting for uncertainty during a pandemic

We discuss several issues of statistical design, data collection, analysis, communication, and decision-making that have arisen in recent and ongoing coronavirus studies, focusing on tools for assessment and propagation of uncertainty. This paper does not purport to be a comprehensive survey of the research literature; rather, we use examples to illustrate statistical points that we think are important.

Lung protective ventilation based on donor size is associated with a lower risk of severe primary graft dysfunction after lung transplantation

BACKGROUND

Mechanical ventilation immediately after lung transplantation may impact the development of primary graft dysfunction (PGD), particularly in cases of donor-recipient size mismatch as ventilation is typically based on recipient rather than donor size.

METHODS

We conducted a retrospective cohort study of adult bilateral lung transplant recipients at our center between January 2010 and January 2017. We defined donor-based lung protective ventilation (dLPV) as 6 to 8 ml/kg of donor ideal body weight and plateau pressure <30 cm H₂O. We calculated the donor-recipient predicted total lung capacity (pTLC) ratio and used logistic regression to examine relationships between pTLC ratio, dLPV and PGD grade 3 at 48 to 72 hours. We used Cox proportional hazards modelling to examine the relationship between pTLC ratio, dLPV and 1-year survival.

RESULTS

The cohort included 373 recipients; 24 (6.4%) developed PGD grade 3 at 48 to 72 hours, and 213 (57.3%) received dLPV. Mean pTLC ratio was 1.04 ± 0.18. dLPV was associated with significantly lower risks of PGD grade 3 (OR = 0.44; 95% CI: 0.29-0.68, p < 0.001) and 1-year mortality (HR = 0.49; 95% CI: 0.29-0.8, p = 0.018). There was a significant association between pTLC ratio and the risk of PGD grade 3, but this was attenuated by the use of dLPV.

CONCLUSIONS

dLPV is associated with decreased risk of PGD grade 3 at 48 to 72 hours and decreased 1-year mortality. Additionally, dLPV attenuates the association between pTLC and both PGD grade 3 and 1-year mortality. Donor-based ventilation strategies may help to mitigate the risk of PGD and other adverse outcomes associated with size mismatch after lung transplantation.

Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data

It is not always clear how to adjust for control data in causal inference, balancing the goals of reducing bias and variance. We show how, in a setting with repeated experiments, Bayesian hierarchical modeling yields an adaptive procedure that uses the data to determine how much adjustment to perform. The result is a novel analysis with increased statistical efficiency compared with the default analysis based on difference estimates. We demonstrate this procedure on two real examples, as well as on a series of simulated datasets. We show that the increased efficiency can have real-world consequences in terms of the conclusions that can be drawn from the experiments. We also discuss the relevance of this work to causal inference and statistical design and analysis more generally.

Know your population and know your model: Using model-based regression and poststratification to generalize findings beyond the observed sample

Psychology research often focuses on interactions, and this has deep implications for inference from nonrepresentative samples. For the goal of estimating average treatment effects, we propose to fit a model allowing treatment to interact with background variables and then average over the distribution of these variables in the population. This can be seen as an extension of multilevel regression and poststratification (MRP), a method used in political science and other areas of survey research, where researchers wish to generalize from a sparse and possibly nonrepresentative sample to the general population. In this article, we discuss areas where this method can be used in the psychological sciences. We use our method to estimate the norming distribution for the Big Five Personality Scale using open source data. We argue that large open data sources like this and other collaborative data sources can potentially be combined with MRP to help resolve current challenges of generalizability and replication in psychology. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey

BACKGROUND

Decisions about the continued need for control measures to contain the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on accurate and up-to-date information about the number of people testing positive for SARS-CoV-2 and risk factors for testing positive. Existing surveillance systems are generally not based on population samples and are not longitudinal in design.

METHODS

Samples were collected from individuals aged 2 years and older living in private households in England that were randomly selected from address lists and previous Office for National Statistics surveys in repeated cross-sectional household surveys with additional serial sampling and longitudinal follow-up. Participants completed a questionnaire and did nose and throat self-swabs. The percentage of individuals testing positive for SARS-CoV-2 RNA was estimated over time by use of dynamic multilevel regression and poststratification, to account for potential residual non-representativeness. Potential changes in risk factors for testing positive over time were also assessed. The study is registered with the ISRCTN Registry, ISRCTN21086382.

FINDINGS

Between April 26 and Nov 1, 2020, results were available from 1 191 170 samples from 280 327 individuals; 5231 samples were positive overall, from 3923 individuals. The percentage of people testing positive for SARS-CoV-2 changed substantially over time, with an initial decrease between April 26 and June 28, 2020, from 0·40% (95% credible interval 0·29-0·54) to 0·06% (0·04-0·07), followed by low levels during July and August, 2020, before substantial increases at the end of August, 2020, with percentages testing positive above 1% from the end of October, 2020. Having a patient-facing role and working outside your home were important risk factors for testing positive for SARS-CoV-2 at the end of the first wave (April 26 to June 28, 2020), but not in the second wave (from the end of August to Nov 1, 2020). Age (young adults, particularly those aged 17-24 years) was an important initial driver of increased positivity rates in the second wave. For example, the estimated percentage of individuals testing positive was more than six times higher in those aged 17-24 years than in those aged 70 years or older at the end of September, 2020. A substantial proportion of infections were in individuals not reporting symptoms around their positive test (45-68%, dependent on calendar time.

INTERPRETATION

Important risk factors for testing positive for SARS-CoV-2 varied substantially between the part of the first wave that was captured by the study (April to June, 2020) and the first part of the second wave of increased positivity rates (end of August to Nov 1, 2020), and a substantial proportion of infections were in individuals not reporting symptoms, indicating that continued monitoring for SARS-CoV-2 in the community will be important for managing the COVID-19 pandemic moving forwards.

FUNDING

Department of Health and Social Care.

Fallout of Lead Over Paris From the 2019 Notre-Dame Cathedral Fire

The roof and spire of Notre-Dame cathedral in Paris that caught fire and collapsed on 15 April 2019 were covered with 460 t of lead (Pb). Government reports documented Pb deposition immediately downwind of the cathedral and a twentyfold increase in airborne Pb concentrations at a distance of 50 km in the aftermath. For this study, we collected 100 samples of surface soil from tree pits, parks, and other sites in all directions within 1 km of the cathedral. Concentrations of Pb measured by X-ray fluorescence range from 30 to 9,000 mg/kg across the area, with a higher proportion of elevated concentrations to the northwest of the cathedral, in the direction of the wind prevailing during the fire. By integrating these observations with a Gaussian process regression model, we estimate that the average concentration of Pb in surface soil downwind of the cathedral is 430 (95% interval, 300-590) mg/kg, nearly double the average Pb concentration in the other directions of 240 (95% interval, 170-320) mg/kg. The difference corresponds to an integrated excess Pb inventory within a 1 km radius of 1.0 (95% interval, 0.5-1.5) t, about 0.2% of all the Pb covering the roof and spire. This is over 6 times the estimated amount of Pb deposited downwind 1-50 km from the cathedral. To what extent the concentrated fallout within 1 km documented here temporarily exposed the downwind population to Pb is difficult to confirm independently because too few soil, dust, and blood samples were collected immediately after the fire.

A consensus-based transparency checklist

We present a consensus-based checklist to improve and document the transparency of research reports in social and behavioural research. An accompanying online application allows users to complete the form and generate a report that they can submit with their manuscript or post to a public repository.

Childhood obesity intervention studies: A narrative review and guide for investigators, authors, editors, reviewers, journalists, and readers to guard against exaggerated effectiveness claims

Being able to draw accurate conclusions from childhood obesity trials is important to make advances in reversing the obesity epidemic. However, obesity research sometimes is not conducted or reported to appropriate scientific standards. To constructively draw attention to this issue, we present 10 errors that are commonly committed, illustrate each error with examples from the childhood obesity literature, and follow with suggestions on how to avoid these errors. These errors are as follows: using self-reported outcomes and teaching to the test; foregoing control groups and risking regression to the mean creating differences over time; changing the goal posts; ignoring clustering in studies that randomize groups of children; following the forking paths, subsetting, p-hacking, and data dredging; basing conclusions on tests for significant differences from baseline; equating "no statistically significant difference" with "equally effective"; ignoring intervention study results in favor of observational analyses; using one-sided testing for statistical significance; and stating that effects are clinically significant even though they are not statistically significant. We hope that compiling these errors in one article will serve as the beginning of a checklist to support fidelity in conducting, analyzing, and reporting childhood obesity research.

Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan

This report presents a new implementation of the Besag-York-Mollié (BYM) model in Stan, a probabilistic programming platform which does full Bayesian inference using Hamiltonian Monte Carlo (HMC). We review the spatial auto-correlation models used for areal data and disease risk mapping, and describe the corresponding Stan implementations. We also present a case study using Stan to fit a BYM model for motor vehicle crashes injuring school-age pedestrians in New York City from 2005 to 2014 localized to census tracts. Stan efficiently fit our multivariable BYM model having a large number of observations (n=2095 census tracts) with small outcome counts < 10 in the majority of tracts. Our findings reinforced that neighborhood income and social fragmentation are significant correlates of school-age pedestrian injuries. We also observed that nationally-available census tract estimates of commuting methods may serve as a useful indicator of underlying pedestrian densities.

The Implementation of Randomization Requires Corrected Analyses. Comment on “Comprehensive Nutritional and Dietary Intervention for Autism Spectrum Disorder—A Randomized, Controlled 12-Month Trial, Nutrients Nutrients 2018, 10, 369”

We commend Adams et al [...].

Impact of SLCO1B3 polymorphisms on clinical outcomes in lung allograft recipients receiving mycophenolic acid

Single-nucleotide polymorphisms (SNPs) in genes involved in mycophenolic acid (MPA) metabolism have been shown to contribute to variable MPA exposure, but their clinical effects are unclear. We aimed to determine if SNPs in key genes in MPA metabolism affect outcomes after lung transplantation. We performed a retrospective cohort study of 275 lung transplant recipients, 228 receiving mycophenolic acid and a control group of 47 receiving azathioprine. Six SNPs known to regulate MPA exposure from the SLCO, UGT and MRP2 families were genotyped. Primary outcome was 1-year survival. Secondary outcomes were 3-year survival, nonminimal (≥A2 or B2) acute rejection, and chronic lung allograft dysfunction (CLAD). Statistical analyses included time-to-event Kaplan-Meier with log-rank test and Cox regression modeling. We found that SLCO1B3 SNPs rs4149117 and rs7311358 were associated with decreased 1-year survival [rs7311358 HR 7.76 (1.37-44.04), p = 0.021; rs4149117 HR 7.28 (1.27-41.78), p = 0.026], increased risk for nonminimal acute rejection [rs4149117 TT334/T334G: OR 2.01 (1.06-3.81), p = 0.031; rs7311358 GG699/G699A: OR 2.18 (1.13-4.21) p = 0.019] and lower survival through 3 years for MPA patients but not for azathioprine patients. MPA carriers of either SLCO1B3 SNP had shorter survival after CLAD diagnosis (rs4149117 p = 0.048, rs7311358 p = 0.023). For the MPA patients, Cox regression modeling demonstrated that both SNPs remained independent risk factors for death. We conclude that hypofunctional SNPs in the SLCO1B3 gene are associated with an increased risk for acute rejection and allograft failure in lung transplant recipients treated with MPA.