Statistics as a Social Activity: Attitudes toward Amalgamating Evidence.

Amalgamation of evidence in statistics is conducted in several ways. Within a study, multiple observations are combined by averaging, or as factors in a likelihood or prediction algorithm. In multilevel modeling or Bayesian analysis, population or prior information is combined with data using the weighted averaging derived from probability modeling. In a scientific research project, inferences from data analysis are interpreted in light of mechanistic models and substantive theories. Within a scholarly or applied research community, data and conclusions from separate laboratories are amalgamated through a series of steps, including peer review, meta-analysis, review articles, and replication studies. These issues have been discussed for many years in the philosophy of science and statistics, gaining attention in recent decades first with the renewed popularity of Bayesian inference and then with concerns about the replication crisis in science. In this article, we review the amalgamation of statistical evidence from different perspectives, connecting the foundations of statistics to the social processes of validation, criticism, and consensus building.

Stress-induced eosinophil activation contributes to postoperative morbidity and mortality after lung resection.

Respiratory failure occurs more frequently after thoracic surgery than abdominal surgery. Although the etiology for this complication is frequently attributed to underlying lung disease present in patients undergoing thoracic surgery, this notion is often unfounded because many patients with normal preoperative pulmonary function often require prolonged oxygen supplementation even after minimal resection of lung tissue. Using a murine model of pulmonary resection and peripheral blood samples from patients undergoing resection of the lung or abdominal organs, we demonstrated that lung surgery initiates a proinflammatory loop that results in damage to the remaining lung tissue, noncardiogenic pulmonary edema, hypoxia, and even death. Specifically, we demonstrated that resection of murine lung tissue increased concentrations of the homeostatic cytokine interleukin-7, which led to local and systemic activation of type 2 innate lymphoid cells. This process activated lung-resident eosinophils and facilitated stress-induced eosinophil maturation in the bone marrow in a granulocyte-macrophage colony-stimulating factor-dependent manner, resulting in systemic eosinophilia in both mice and humans. Up-regulation of inducible nitric oxide synthase in lung-resident eosinophils led to tissue nitrosylation, pulmonary edema, hypoxia, and, at times, death. Disrupting this activation cascade at any stage ameliorated deleterious outcomes and improved survival after lung resection in the mouse model. Our data suggest that repurposing US Food and Drug Administration-approved eosinophil-targeting strategies may potentially offer a therapeutic intervention to improve outcomes for patients who require lung resection for benign or malignant etiology.

Intravital Two-Photon Microscopy of the Transplanted Mouse Lung.

Complications after lung transplantation are largely related to the host immune system responding to the graft. Such immune responses are regulated by crosstalk between donor and recipient cells. A better understanding of these processes relies on the use of preclinical animal models and is aided by an ability to study intra-graft immune cell trafficking in real-time. Intravital two-photon microscopy can be used to image tissues and organs for depths up to several hundred microns with minimal photodamage, which affords a great advantage over single-photon confocal microscopy. Selective use of transgenic mice with promoter-specific fluorescent protein expression and/or adoptive transfer of fluorescent dye-labeled cells during intravital two-photon microscopy allows for the dynamic study of single cells within their physiologic environment. Our group has developed a technique to stabilize mouse lungs, which has enabled us to image cellular dynamics in naïve lungs and orthotopically transplanted pulmonary grafts. This technique allows for detailed assessment of cellular behavior within the vasculature and in the interstitium, as well as for examination of interactions between various cell populations. This procedure can be readily learned and adapted to study immune mechanisms that regulate inflammatory and tolerogenic responses after lung transplantation. It can also be expanded to the study of other pathogenic pulmonary conditions.

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.

Nanoparticle targeting of neutrophil glycolysis prevents lung ischemia-reperfusion injury.

Neutrophils exacerbate pulmonary ischemia-reperfusion injury (IRI) resulting in poor short and long-term outcomes for lung transplant recipients. Glycolysis powers neutrophil activation, but it remains unclear if neutrophil-specific targeting of this pathway will inhibit IRI. Lipid nanoparticles containing the glycolysis flux inhibitor 2-deoxyglucose (2-DG) were conjugated to neutrophil-specific Ly6G antibodies (NP-Ly6G[2-DG]). Intravenously administered NP-Ly6G(2-DG) to mice exhibited high specificity for circulating neutrophils. NP-Ly6G(2-DG)-treated neutrophils were unable to adapt to hypoglycemic conditions of the lung airspace environment as evident by the loss of demand-induced glycolysis, reductions in glycogen and ATP content, and an increased vulnerability to apoptosis. NP-Ly6G(2-DG) treatment inhibited pulmonary IRI following hilar occlusion and orthotopic lung transplantation. IRI protection was associated with less airspace neutrophil extracellular trap generation, reduced intragraft neutrophilia, and enhanced alveolar macrophage efferocytotic clearance of neutrophils. Collectively, our data show that pharmacologically targeting glycolysis in neutrophils inhibits their activation and survival leading to reduced pulmonary IRI.

B cells mediate lung ischemia/reperfusion injury by recruiting classical monocytes via synergistic B cell receptor/TLR4 signaling.

Ischemia/reperfusion injury-mediated (IRI-mediated) primary graft dysfunction (PGD) adversely affects both short- and long-term outcomes after lung transplantation, a procedure that remains the only treatment option for patients suffering from end-stage respiratory failure. While B cells are known to regulate adaptive immune responses, their role in lung IRI is not well understood. Here, we demonstrated by intravital imaging that B cells are rapidly recruited to injured lungs, where they extravasate into the parenchyma. Using hilar clamping and transplant models, we observed that lung-infiltrating B cells produce the monocyte chemokine CCL7 in a TLR4-TRIF-dependent fashion, a critical step contributing to classical monocyte (CM) recruitment and subsequent neutrophil extravasation, resulting in worse lung function. We found that synergistic BCR-TLR4 activation on B cells is required for the recruitment of CMs to the injured lung. Finally, we corroborated our findings in reperfused human lungs, in which we observed a correlation between B cell infiltration and CM recruitment after transplantation. This study describes a role for B cells as critical orchestrators of lung IRI. As B cells can be depleted with currently available agents, our study provides a rationale for clinical trials investigating B cell-targeting therapies.

A New Look at P Values for Randomized Clinical Trials.

BACKGROUND: We have examined the primary efficacy results of 23,551 randomized clinical trials from the Cochrane Database of Systematic Reviews. METHODS: We estimate that the great majority of trials have much lower statistical power for actual effects than the 80 or 90% for the stated effect sizes. Consequently, "statistically significant" estimates tend to seriously overestimate actual treatment effects, "nonsignificant" results often correspond to important effects, and efforts to replicate often fail to achieve "significance" and may even appear to contradict initial results. To address these issues, we reinterpret the P value in terms of a reference population of studies that are, or could have been, in the Cochrane Database. RESULTS: This leads to 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. CONCLUSIONS: Such an interpretation provides additional insight about the effect under study and can guard medical researchers against naive interpretations of the P value and overoptimistic effect sizes. Because many research fields suffer from low power, our results are also relevant outside the medical domain. (Funded by the U.S. Office of Naval Research.)

Eosinophils restrain humoral alloimmunity after lung transplantation.

While the function of many leukocytes in transplant biology has been well defined, the role of eosinophils is controversial and remains poorly explored. Conflicting data exist regarding eosinophils' role in alloimmunity. Due to their prevalence in the lung, and their defined role in other pulmonary pathologies such as asthma, we set out to explore the role of eosinophils in the long-term maintenance of the lung allograft. We noted that depletion of eosinophils results in the generation of donor-specific antibodies. Eosinophil depletion increased memory B cell, plasma cell, and antibody-secreting cell differentiation and resulted in de novo generation of follicular germinal centers. Germinal center formation depended on the expansion of CD4+Foxp3-Bcl6+CXCR5+PD-1+ T follicular helper (Tfh) cells, which increase in number after eosinophil depletion. Mechanistically, we demonstrate that eosinophils prevent Tfh cell generation by acting as the dominant source of IFN-γ in an established lung allograft, thus facilitating Th1 rather than Tfh polarization of naive CD4+ T cells. Our data thus describe what we believe is a unique and previously unknown role for eosinophils in maintaining allograft tolerance and suggest that indiscriminate administration of eosinophil-lytic corticosteroids for treatment of acute cellular rejection may inadvertently promote humoral alloimmunity.

A Simple Cuff Technique for Murine Left Lung Transplantation.

Over the past decade, our laboratory has made significant progress in developing and refining vascularized mouse lung transplantation models using an efficient and highly reliable "cuff technique" of transplantation. This article describes a sophisticated and comprehensive method for orthotopic lung transplantation in a vascularized orthotopic lung model, representing the most physiologic and clinically relevant model of mouse lung transplantation to date. The transplantation process consists of two distinct stages: donor harvest and subsequent implantation into the recipient. The method has been successfully mastered, and with several months of sufficient training, a skilled practitioner can perform the procedure in approximately 90 min from skin-to-skin. Surprisingly, once individuals overcome the initial learning curve, the survival rate during the perioperative period approaches nearly 100%. The mouse model allows for the use of multiple commercially available transgenic and mutant strains of mice, enabling the study of tolerance and rejection. Additionally, the unique features of this model make it a valuable tool for investigating tumor biology and immunology.

Developing machine learning models to predict primary graft dysfunction after lung transplantation.

Primary graft dysfunction (PGD) is the leading cause of morbidity and mortality in the first 30 days after lung transplantation. Risk factors for the development of PGD include donor and recipient characteristics, but how multiple variables interact to impact the development of PGD and how clinicians should consider these in making decisions about donor acceptance remain unclear. This was a single-center retrospective cohort study to develop and evaluate machine learning pipelines to predict the development of PGD grade 3 within the first 72 hours of transplantation using donor and recipient variables that are known at the time of donor offer acceptance. Among 576 bilateral lung recipients, 173 (30%) developed PGD grade 3. The cohort underwent a 75% to 25% train-test split, and lasso regression was used to identify 11 variables for model development. A K-nearest neighbor's model showing the best calibration and performance with relatively small confidence intervals was selected as the final predictive model with an area under the receiver operating characteristics curve of 0.65. Machine learning models can predict the risk for development of PGD grade 3 based on data available at the time of donor offer acceptance. This may improve donor-recipient matching and donor utilization in the future.

Smoking exposure-induced bronchus-associated lymphoid tissue in donor lungs does not prevent tolerance induction after transplantation.

The presence of bronchus-associated lymphoid tissue (BALT) in donor lungs has been suggested to accelerate graft rejection after lung transplantation. Although chronic smoke exposure can induce BALT formation, the impact of donor cigarette use on alloimmune responses after lung transplantation is not well understood. Here, we show that smoking-induced BALT in mouse donor lungs contains Foxp3+ T cells and undergoes dynamic restructuring after transplantation, including recruitment of recipient-derived leukocytes to areas of pre-existing lymphoid follicles and replacement of graft-resident donor cells. Our findings from mouse and human lung transplant data support the notion that a donor's smoking history does not predispose to acute cellular rejection or prevent the establishment of allograft acceptance with comparable outcomes to nonsmoking donors. Thus, our work indicates that BALT in donor lungs is plastic in nature and may have important implications for modulating proinflammatory or tolerogenic immune responses following transplantation.

A Pilot Randomized Controlled Trial of De Novo Belatacept-based Immunosuppression After Lung Transplantation.

BACKGROUND: Chronic lung allograft dysfunction (CLAD) is the leading cause of death beyond the first year after lung transplantation. The development of donor-specific antibodies (DSA) is a recognized risk factor for CLAD. Based on experience in kidney transplantation, we hypothesized that belatacept, a selective T-cell costimulatory blocker, would reduce the incidence of DSA after lung transplantation, which may ameliorate the risk of CLAD. METHODS: We conducted a pilot randomized controlled trial (RCT) at 2 sites to assess the feasibility and inform the design of a large-scale RCT. All participants were treated with rabbit antithymocyte globulin for induction immunosuppression. Participants in the control arm were treated with tacrolimus, mycophenolate mofetil, and prednisone, and participants in the belatacept arm were treated with tacrolimus, belatacept, and prednisone through day 89 after transplant then converted to belatacept, mycophenolate mofetil, and prednisone for the remainder of year 1. RESULTS: After randomizing 27 participants, 3 in the belatacept arm died compared with none in the control arm. As a result, we stopped enrollment and treatment with belatacept, and all participants were treated with standard-of-care immunosuppression. Overall, 6 participants in the belatacept arm died compared with none in the control arm (log rank P = 0.008). We did not observe any differences in the incidence of DSA, acute cellular rejection, antibody-mediated rejection, CLAD, or infections between the 2 groups. CONCLUSIONS: We conclude that the investigational regimen used in this pilot RCT is associated with increased mortality after lung transplantation.

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.

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.

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.

Resolvin D1 prevents injurious neutrophil swarming in transplanted lungs.

Neutrophils are the primary cell type involved in lung ischemia-reperfusion injury (IRI), which remains a frequent and morbid complication after organ transplantation. Endogenous lipid mediators that become activated during acute inflammation-resolution have gained increasing recognition for their protective role(s) in promoting the restoration of homeostasis, but their influence on early immune responses following transplantation remains to be uncovered. Resolvin D1, 7S,8R,17S-trihydroxy-4Z,9E,11E,13Z,15E,19Z-docosahexaenoic acid (RvD1), is a potent stereoselective mediator that exhibits proresolving and anti-inflammatory actions in the setting of tissue injury. Here, using metabololipidomics, we demonstrate that endogenous proresolving mediators including RvD1 are increased in human and murine lung grafts immediately following transplantation. In mouse grafts, we observe lipid mediator class switching early after reperfusion. We use intravital two-photon microscopy to reveal that RvD1 treatment significantly limits early neutrophil infiltration and swarming, thereby ameliorating early graft dysfunction in transplanted syngeneic lungs subjected to severe IRI. Through integrated analysis of single-cell RNA sequencing data of donor and recipient immune cells from lung grafts, we identify transcriptomic changes induced by RvD1. These results support a role for RvD1 as a potent modality for preventing early neutrophil-mediated tissue damage after lung IRI that may be therapeutic in the clinics.

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.