Bayesian Hierarchical Model Research Articles

How Do Implausible Tensile Price Claims Affect Consumer Perceptions of Deceptiveness and Consumer Willingness to Purchase in Online Environments?

The study explores how and why an embedded implausible tensile price claim (TPC) affects consumers’ decisions as to whether to click on the associated online advertisement and their subsequent purchases. The authors conduct a field experiment by designing a structured set of search advertisements with implausible TPCs for a global apparel brand and launching them on Baidu, the largest search engine in China. Using a hierarchical Bayesian model, the authors quantify how implausible TPCs influence consumers’ two-stage decisions: clicking on the advertisement and purchasing the product. Using two laboratory experiments, the authors further demonstrate that the purchase decision is mediated by consumers’ perceptions of deception in the advertisement, despite the fact that the click decision is not. Results show that implausible TPCs in a search advertisement motivate consumers to click on the promotion. However, the discrepancy between the advertised and the actual discounts makes the advertisement seem deceptive to consumers, which reduces their likelihood of purchasing the product and leads them to search for other products and promotions. The results address advertising deception in online price claims, offer critical policy implications regarding implausible TPCs, and shed light on designing and executing implausible TPCs in online advertising.

First-line treatment preferences for advanced melanoma among oncologists and patients in the US: A discrete choice experiment.

9539 Background: NCCN guidelines recommend nivolumab + ipilimumab, nivolumab + relatlimab, and anti-PD-1 monotherapy as category 1 preferred first-line (1L) treatments (txs) for advanced melanoma. These immunotherapies (IOs) however differ in administration, efficacy, and safety. Given these differences, this study examines oncologists (oncs) and patients (pts) preferences when choosing 1L tx. Methods: Pts with self-reported unresectable or metastatic melanoma and oncs completed an online cross-sectional survey. A discrete choice experiment assessed preferences for three efficacy attributes, three safety attributes, and dosing schedule. Attributes (shown in table) were identified via literature and qualitative research. Each attribute had two or three levels based on clinical trial data of recommended IOs. Participants completed 12 tasks; each task showed two tx options with varying levels for each attribute. Hierarchical Bayesian modeling was used to estimate preference weights for each level and relative importance estimates, based on the range between levels, were computed. Results: 75 oncs (male 76%; mean age 46 years; community-based 51%) and 62 pts (male 68%; mean age 43 years) were analyzed (pt recruitment ongoing; target n = 75). For both oncs and pts, improvements in safety attributes was more important than improvements in efficacy attributes (pts: 50.3 vs. 33.9; oncs: 48.6 vs. 40.9). For safety, a decrease in risk of discontinuation due to treatment-related adverse events (TRAEs) was most important in oncs (24.7); while for pts, lower risk of grade 3/4 TRAEs was most important (22.6). For efficacy, an increase in overall survival (OS) was most influential for oncs (21.0); pts had similar estimates within efficacy. Dosing schedule was third most important for pts (15.8), while it was among the least important for oncs. Conclusions: Oncs and pts prioritize minimizing the risk of serious AEs when efficacy differences are minimal in 1L advanced melanoma tx choice. These findings highlight that oncs and pts value an IO with an efficacy benefit while ensuring a favorable risk-benefit profile. This information can inform tx counseling and selection discussions among oncs and pts. [Table: see text]

A Bayesian Approach to Uncertainty in Word Embedding Bias Estimation

Abstract Multiple measures, such as WEAT or MAC, attempt to quantify the magnitude of bias present in word embeddings in terms of a single-number metric. However, such metrics and the related statistical significance calculations rely on treating pre-averaged data as individual data points and utilizing bootstrapping techniques with low sample sizes. We show that similar results can be easily obtained using such methods even if the data are generated by a null model lacking the intended bias. Consequently, we argue that this approach generates false confidence. To address this issue, we propose a Bayesian alternative: hierarchical Bayesian modeling, which enables a more uncertainty-sensitive inspection of bias in word embeddings at different levels of granularity. To showcase our method, we apply it to Religion, Gender, and Race word lists from the original research, together with our control neutral word lists. We deploy the method using Google, GloVe, and Reddit embeddings. Further, we utilize our approach to evaluate a debiasing technique applied to the Reddit word embedding. Our findings reveal a more complex landscape than suggested by the proponents of single-number metrics. The datasets and source code for the paper are publicly available.1

A Bayesian hierarchical hidden Markov model for clustering and gene selection: Application to kidney cancer gene expression data.

We introduce a Bayesian approach for biclustering that accounts for the prior functional dependence between genes using hidden Markov models (HMMs). We utilize biological knowledge gathered from gene ontologies and the hidden Markov structure to capture the potential coexpression of neighboring genes. Our interpretable model-based clustering characterized each cluster of samples by three groups of features: overexpressed, underexpressed, and irrelevant features. The proposed methods have been implemented in an R package and are used to analyze both the simulated data and The Cancer Genome Atlas kidney cancerdata.

A BAYESIAN HIERARCHICAL SMALL AREA POPULATION MODEL ACCOUNTING FOR DATA SOURCE SPECIFIC METHODOLOGIES FROM AMERICAN COMMUNITY SURVEY, POPULATION ESTIMATES PROGRAM, AND DECENNIAL CENSUS DATA.

Small area population counts are necessary for many epidemiological studies, yet their quality and accuracy are often not assessed. In the United States, small area population counts are published by the United States Census Bureau (USCB) in the form of the decennial census counts, intercensal population projections (PEP), and American Community Survey (ACS) estimates. Although there are significant relationships between these three data sources, there are important contrasts in data collection, data availability, and processing methodologies such that each set of reported population counts may be subject to different sources and magnitudes of error. Additionally, these data sources do not report identical small area population counts due to post-survey adjustments specific to each data source. Consequently, in public health studies, small area disease/mortality rates may differ depending on which data source is used for denominator data. To accurately estimate annual small area population counts and their associated uncertainties, we present a Bayesian population (BPop) model, which fuses information from all three USCB sources, accounting for data source specific methodologies and associated errors. We produce comprehensive small area race-stratified estimates of the true population, and associated uncertainties, given the observed trends in all three USCB population estimates. The main features of our framework are: (1) a single model integrating multiple data sources, (2) accounting for data source specific data generating mechanisms and specifically accounting for data source specific errors, and (3) prediction of population counts for years without USCB reported data. We focus our study on the Black and White only populations for 159 counties of Georgia and produce estimates for years 2006-2023. We compare BPop population estimates to decennial census counts, PEP annual counts, and ACS multi-year estimates. Additionally, we illustrate and explain the different types of data source specific errors. Lastly, we compare model performance using simulations and validation exercises. Our Bayesian population model can be extended to other applications at smaller spatial granularity and for demographic subpopulations defined further by race, age, and sex, and/or for other geographical regions.

Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses

Cytometry is the standard multi-parameter assay for measuring single cell phenotype and functionality. It is commonly used for quantifying the relative frequencies of cell subsets in blood and disaggregated tissues. A typical analysis of cytometry data involves cell classification—that is, the identification of cell subgroups in the sample—and comparisons of the cell subgroups across samples or conditions. While modern experiments often necessitate the collection and processing of samples in multiple batches, analysis of cytometry data across batches is challenging because differences across samples may occur due to either true biological variation or technical reasons such as antibody lot effects or instrument optics across batches. Thus a critical step in comparative analyses of multi-sample cytometry data—yet missing in existing automated methods for analyzing such data—is cross-sample calibration, whose goal is to align corresponding cell subsets across multiple samples in the presence of technical variations, so that biological variations can be meaningfully compared. We introduce a Bayesian nonparametric hierarchical modeling approach for accomplishing both calibration and cell classification simultaneously in a unified probabilistic manner. Three important features of our method make it particularly effective for analyzing multi-sample cytometry data: a nonparametric mixture avoids prespecifying the number of cell clusters; a hierarchical skew normal kernel that allows flexibility in the shapes of the cell subsets and cross-sample variation in their locations; and finally the “coarsening” strategy makes inference robust to departures from the model not captured by the skew normal kernels. We demonstrate the merits of our approach in simulated examples and carry out a case study in the analysis of a multi-sample cytometry data set. We provide an R package for our method.

Dimension-free mixing times of Gibbs samplers for Bayesian hierarchical models

Dimension-free mixing times of Gibbs samplers for Bayesian hierarchical models

Early-Stage Alzheimer's Disease Affects Fast But Not Slow Adaptive Processes in Motor Learning.

Alzheimer's disease (AD) is characterized by an initial decline in declarative memory, while nondeclarative memory processing remains relatively intact. Error-based motor adaptation is traditionally seen as a form of nondeclarative memory, but recent findings suggest that it involves both fast, declarative, and slow, nondeclarative adaptive processes. If the declarative memory system shares resources with the fast process in motor adaptation, it can be hypothesized that the fast, but not the slow, process is disturbed in AD patients. To test this, we studied 20 early-stage AD patients and 21 age-matched controls of both sexes using a reach adaptation paradigm that relies on spontaneous recovery after sequential exposure to opposing force fields. Adaptation was measured using error clamps and expressed as an adaptation index (AI). Although patients with AD showed slightly lower adaptation to the force field than the controls, both groups demonstrated effects of spontaneous recovery. The time course of the AI was fitted by a hierarchical Bayesian two-state model in which each dynamic state is characterized by a retention and learning rate. Compared to controls, the retention rate of the fast process was the only parameter that was significantly different (lower) in the AD patients, confirming that the memory of the declarative, fast process is disturbed by AD. The slow adaptive process was virtually unaffected. Since the slow process learns only weakly from an error, our results provide neurocomputational evidence for the clinical practice of errorless learning of everyday tasks in people with dementia.

Bayesian Hierarchical Models of Basket Trials in the Context of Economic Evaluation

What Was the Problem or Question? With the availability of advanced genomic profiling methods, basket trials are more commonly used for the clinical assessment of treatments targeting multiple cancer types with a common biomarker. Basket trials study a primary intervention across a range of different patient subgroups (e.g., with specific cancer types) that share similar features (e.g., mutations or biomarkers), in which the effect of the primary intervention may be expected to differ. Heterogeneity among cancer types, limited sample sizes, a lack of comparators, and the use of surrogate end points pose challenges when considering the application to economic evaluations. Bayesian hierarchical models (BHMs) are well suited to account for the heterogeneity among cancer types while borrowing information across cancer types in basket trials. There is a need for clearer guidance about how these methods can be used in health technology assessment (HTA). What Did We Do? To better understand the role of BHMs in economic evaluations, an overview of basket trials and the current understanding of their use in economic evaluations was conducted, an illustration of how BHMs can inform economic evaluations was provided with a detailed example, and considerations for interpretating and appraising this information were listed. What Did We Find? BHMs offer an approach to use information from basket trials to inform economic evaluations; however, there are limitations to be aware of when using this information in economic evaluations and also when interpreting the results of the economic information. This document describes these limitations, and contains an appraisal list that was developed to highlight the implications. These are aspects that CADTH considers when assessing analyses based on BHMs. The field of BHMs is evolving, and advice in this document will need to be revisited as methods develop. What Does This Mean? As CADTH anticipates the increased need for appraisal of tumour-agnostic therapies, this document provides clarity around an important and useful set of methods for their evaluation, while also highlighting necessary areas for further research.

Viewpoint: Bayesian Hierarchical Models of Basket Trials in the Context of Economic Evaluation

Bayesian hierarchical models (BHMs) have emerged as a way to model information from basket trials, but there is limited information on how to use information from BHMs in economic evaluations. In consultation with researchers with expertise in this area, we have published a document that addresses this information gap. Our document describes areas for appraisal that we will consider when assessing economic evaluations informed by BHMs, outlines a set of methods that can be used for informing economic evaluations based on basket trials, and highlights areas for further research.

A hierarchical signal detection model with unequal variance for binary responses.

Gaussian signal detection models with equal variance are commonly used in simple yes-no detection and discrimination tasks whereas more flexible models with unequal variance require additional information. Here, a hierarchical Bayesian model with equal variance is extended to an unequal-variance model by exploiting variability of hit and false-alarm rates in a random sample of participants. This hierarchical model is investigated analytically, in simulations and in applications to existing data sets. The results suggest that signal variance and other parameters can be accurately estimated if plausible assumptions are met. It is concluded that the model provides a promising alternative to the ubiquitous equal-variance model for binary data.

Risk Factors for Development of Incisional Hernia after Aortic Aneurysm Repair: Secondary Analysis of the OVER Randomized Controlled Trial

Since the risk of mortality from rupture is elevated, elective repair of abdominal aortic aneurysms (AAAs) is often recommended. Currently, over 80% of elective repairs are carried out using an endovascular approach. While open repair has similar late survival and fewer reintervention outcomes when compared to endovascular repair, incisional hernia is a frequent complication with morbidity and cost implications. The Open versus Endovascular Repair (OVER) trial was the largest randomized trial of endovascular versus open repair of AAA in the United States. The purpose of this study was to determine risk factors associated with incisional hernia development following AAA repair via secondary analysis of the OVER data. This was a multisite trial conducted within the Veterans Affairs health-care system. Study participants (N=881) were enrolled from 2002 to 2008 and followed until 2011 with additional administrative data collection until 2016. Eligible patients had AAA for which elective repair was planned and randomized 1:1 to either open or endovascular repair. Incisional hernia was a prespecified end point in the OVER protocol, specifically assessed at each protocol follow-up visit. Technical details were extracted from each operative report, repair case report form(s), and adverse event form(s). Patient demographics, comorbid conditions, reported preoperative activity level, and operative details including initial approach, blood loss, and closure methods were analyzed using Bayesian hierarchical Weibull survival regression modeling. Incisional hernias were recorded among 46 participants (5.2%). The average time to hernia diagnosis was 3.5years. Of the 437 participants randomized to open treatment, 427 received an open repair including crossovers from endovascular treatment assignment. Transperitoneal repair was performed in 81%, running suture in 96%, and absorbable suture in 71% of cases. Randomization to endovascular repair was associated with reduced risk of hernia (hazard ratio [HR] 0.70, 95% credible interval [CI] 0.49-0.94). Higher activity level was associated with increased hernia risk (HR 1.39, 95% CI 1.06-1.84). Approach, suture closure techniques, body mass index, diabetes, and smoking status were not associated with increased risk of hernia development. Incisional hernia is a frequent complication associated with open repair of abdominal aortic aneurysm and commonly required reintervention. Endovascular repair was associated with reduced risk of hernia. Patients with increased activity experienced a higher incidence of hernia. However, no other modifiable patient, operative, or technical factors were found to be associated with hernia development.

Interactions between runoff volume, timing, and annual temperatures shape migration phenology of a threatened adfluvial sucker

AbstractMigratory fish species have been particularly impacted by changes to the hydrologic and climatic cues to which migration and spawning behaviours have been adapted across generations. While conservation and recovery programs increasingly implement flow management actions to promote successful migration and spawning, uncertainty regarding how spawning migration phenology responds to changing environmental conditions can limit the ability to effectively target such recovery actions. Here, we use a Bayesian hierarchical modelling framework to analyse spawning migration phenology of individually tagged June suckers (Chasmistes liorus) – a federally threatened, long‐lived, iteroparous, adfluvial species endemic to Utah Lake (Utah, USA) and its tributaries. We then examine how annual hydrologic and thermal conditions relate to different components of annual migration phenology, including peak migration date, in‐stream residence time, and among‐individual variation in migration timing. Peak migration date occurred earlier in years with warmer spring air temperatures (a proxy for water temperatures), though this effect interacted with peak runoff timing. Both residence time and among‐individual variation in migration timing were greater in years with larger spring discharge and later peak flows. Residence time was also longer in warmer years. These results highlight how natural and anthropogenic changes to river flow and thermal regimes are likely to impact June sucker migration timing and duration, and our approach can be applied to other migratory species to identify the external drivers of the different components of migration phenology.

Estimating ethanol correction factors for δ13C and δ15N isotopic signatures of freshwater zooplankton from multiple lakes

AbstractIn freshwater systems, δ13C and δ15N stable isotopes can be used to differentiate between pelagic and littoral energy sources and to quantify trophic position. In these ecosystems, crustacean zooplankton are frequently used to characterize the pelagic baseline. Zooplankton samples are often preserved prior to processing and analysis, which can affect isotopic signatures. Variability in preservation effects across studies make it difficult to determine if and how to correct for preservation effects. Here, we develop a correction factor for ethanol preservation and present a flexible statistical method that can be updated with additional data to increase its applicability. We collected zooplankton from five lakes in Minnesota, USA encompassing wide isotopic ranges (δ13C from −37.23‰ to −23.96‰; δ15N from 3.07‰ to 14.44‰). Changes in zooplankton δ13C and δ15N signatures were quantified using a Bayesian hierarchical model predicting fresh values from ethanol‐preserved values. Ethanol preservation increased δ13C by a factor of 1.158 (95% CI 0.866–1.441) and had a negligible effect on δ15N (slope = 1.077; 95% CI 0.833–1.359). Lake‐specific values did not differ from the overall relationship. K‐fold and leave‐one‐out cross validation tests verified that both models were accurate; RMSE of predicted δ13C = 0.701 and RMSE of predicted δ15N = 0.590. Our correction factors could be applied to other systems in which baseline δ13C and δ15N values fall within the range of our study, and this approach also enables the inclusion of data from additional lakes to estimate new corrections.

The Impact of Development on Antidepressant and Placebo Response in Anxiety Disorders: A Bayesian Hierarchical Meta-Analytic Examination of Randomized Controlled Trials in Children, Adolescents, and Adults.

Background: Understanding how development influences medication and placebo responses in anxiety disorders could inform treatment decisions, including age-specific first- versus second-line psychopharmacological interventions. Objective: To meta-analytically compare the trajectory of selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and placebo response in youth and adults with anxiety disorders. Methods: Weekly symptom severity data were extracted from prospective, randomized, parallel-group, placebo-controlled trials of SSRIs and SNRIs in children, adolescents, and adults with anxiety disorders (generalized, separation, and social anxiety disorders as well as panic disorder). Treatment response was modeled for the standardized change in continuous measures of anxiety using a Bayesian hierarchical model. Change in symptom severity was evaluated as a function of time, and post hoc analyses were conducted to determine the sensitivity of these results across sample heterogeneity and alternative functional forms. Results: Data were included from 11 trials of youth (SSRI, κ = 7; SNRI, κ = 4) and 71 studies of adults (SSRI, κ = 46; SNRI, κ = 25). In total, 1067 youth participated in SSRI trials and 1024 in SNRI trials. In total, 10,826 adults participated in SSRI trials (placebo, n = 5367; SSRI n = 5,459) and 6232 in SNRI trials (placebo, n = 3,128; SNRI n = 3,094). A logarithmic model best described the response. Placebo response was similar in youth and adults (mean difference = -1.98 ± 6.21, 95% credible interval [CrI]: -10.2 to 14.2, p = 0.750), and statistically significant improvement from baseline emerged by week 2 in both adults (mean difference: -18.34 + 1.017, 95% CrI: -20.3 to 16.3, p < 0.001) and youth (mean difference: -23.74 + 3.736, 95% CrI: -31.1 to -16.4, p < 0.001). SSRIs produced similar improvements for youth and adults (p = 0.129), but SNRIs produced slower improvement in youth than adults (p = 0.018). Conclusions: Antidepressant-related improvement occurs early in youth and adults with anxiety disorders. SSRI response is similar in adults and youth; however, SNRIs produce greater responses in adults than youth, potentially representing a developmental effect.

Domestic (re)infestation risk with the main vector Triatoma infestans increases with surrounding green vegetation and social vulnerability in the Argentine Chaco.

Chagas disease, caused by Trypanosoma cruzi, is still a public health problem in Latin America and in the Southern Cone countries, where Triatoma infestans is the main vector. We evaluated the relationships among the density of green vegetation around rural houses, sociodemographic characteristics, and domestic (re)infestation with T. infestans while accounting for their spatial dependence in the municipality of Pampa del Indio between 2007 and 2016. The study comprised sociodemographic and ecological variables from 734 rural houses with no missing data. Green vegetation density surrounding houses was estimated by the normalized difference vegetation index (NDVI). We used a hierarchical Bayesian logistic regression composed of fixed effects and spatial random effects to estimate domestic infestation risk and quantile regressions to evaluate the association between surrounding NDVI and selected sociodemographic variables. Qom ethnicity and the number of poultry were negatively associated with surrounding NDVI, whereas overcrowding was positively associated with surrounding NDVI. Hierarchical Bayesian models identified that domestic infestation was positively associated with surrounding NDVI, suitable walls for triatomines, and overcrowding over both intervention periods. Preintervention domestic infestation also was positively associated with Qom ethnicity. Models with spatial random effects performed better than models without spatial effects. The former identified geographic areas with a domestic infestation risk not accounted for by fixed-effect variables. Domestic infestation with T. infestans was associated with the density of green vegetation surrounding rural houses and social vulnerability over a decade of sustained vector control interventions. High density of green vegetation surrounding rural houses was associated with households with more vulnerable social conditions. Evaluation of domestic infestation risk should simultaneously consider social, landscape and spatial effects to control for their mutual dependency. Hierarchical Bayesian models provided a proficient methodology to identify areas for targeted triatomine and disease surveillance and control.

Combining prior and post‐release data while accounting for dispersal to improve predictions for reintroduction populations

AbstractAttempts to reintroduce species to managed areas may be compromised by dispersal into the surrounding landscape. Therefore, decisions regarding the selection and ongoing management of reintroduction areas require predicting dispersal as well as the survival and reproduction rates of the species to be reintroduced. Dispersal can potentially be measured directly by tracking animals, but this is often impractical. However, dispersal can also be inferred from re‐sighting surveys done within reintroduction areas if such data are available from multiple areas with varying connectivity to the surrounding landscape, allowing apparent survival and recruitment to be modelled as a function of connectivity metrics. Here, we show how data from 10 previous reintroductions of a New Zealand passerine, the toutouwai (Petroica longipes), were used to predict population dynamics at a predator‐controlled reintroduction area with high connectivity, and predictions then updated using post‐release data. Bayesian hierarchical modelling of the previous data produced prior distributions for productivity, adult survival and apparent juvenile survival rates that accounted for random variation among areas as well as rat density and connectivity. The modelling of apparent juvenile survival as a function of connectivity allowed it to be partitioned into estimates of survival and fidelity. Bayesian updating based on post‐release data produced posterior distributions for parameters that were consistent with the priors but much more precise. The prior data also allowed the recruitment rate estimated in the new area to be partitioned into separate estimates for productivity, juvenile survival and juvenile fidelity. Consequently, it was possible to not only estimate population growth under current management, but also predict the consequences of reducing the scale or intensity of predator control, facilitating adaptive management. The updated model could then be used to predict population growth as a function of the connectivity and predator control regime at proposed reintroduction areas while accounting for random variation among areas.

Enabling identification of component processes in perceptual learning with nonparametric hierarchical Bayesian modeling.

Perceptual learning is a multifaceted process, encompassing general learning, between-session forgetting or consolidation, and within-session fast relearning and deterioration. The learning curve constructed from threshold estimates in blocks or sessions, based on tens or hundreds of trials, may obscure component processes; high temporal resolution is necessary. We developed two nonparametric inference procedures: a Bayesian inference procedure (BIP) to estimate the posterior distribution of contrast threshold in each learning block for each learner independently and a hierarchical Bayesian model (HBM) that computes the joint posterior distribution of contrast threshold across all learning blocks at the population, subject, and test levels via the covariance of contrast thresholds across blocks. We applied the procedures to the data from two studies that investigated the interaction between feedback and training accuracy in Gabor orientation identification over 1920 trials across six sessions and estimated learning curve with block sizes L = 10, 20, 40, 80, 160, and 320 trials. The HBM generated significantly better fits to the data, smaller standard deviations, and more precise estimates, compared to the BIP across all block sizes. In addition, the HBM generated unbiased estimates, whereas the BIP only generated unbiased estimates with large block sizes but exhibited increased bias with small block sizes. With L = 10, 20, and 40, we were able to consistently identify general learning, between-session forgetting, and rapid relearning and adaptation within sessions. The nonparametric HBM provides a general framework for fine-grained assessment of the learning curve and enables identification of component processes in perceptual learning.

An Exact Bayesian Model for Meta-Analysis of the Standardized Mean Difference with Its Simultaneous Credible Intervals

While Bayesian methodology is increasingly favored in behavioral research for its clear probabilistic inference and model structure, its widespread acceptance as a standard meta-analysis approach remains limited. Although some conventional Bayesian hierarchical models are frequently used for analysis, their performance has not been thoroughly examined. This study evaluates two commonly used Bayesian models for meta-analysis of standardized mean difference and identifies significant issues with these models. In response, we introduce a new Bayesian model equipped with novel features that address existing model concerns and a broader limitation of the current Bayesian meta-analysis. Furthermore, we introduce a simple computational approach to construct simultaneous credible intervals for the summary effect and between-study heterogeneity, based on their joint posterior samples. This fully captures the joint uncertainty in these parameters, a task that is challenging or impractical with frequentist models. Through simulation studies rooted in a joint Bayesian/frequentist paradigm, we compare our model’s performance against existing ones under conditions that mirror realistic research scenarios. The results reveal that our new model outperforms others and shows enhanced statistical properties. We also demonstrate the practicality of our models using real-world examples, highlighting how our approach strengthens the robustness of inferences regarding the summary effect.

Evaluating human landing catches as a measure of mosquito biting and the importance of considering additional modes of action

Entomological evaluations of vector control tools often use human landing catches (HLCs) as a standard measure of a direct human-vector contact. However, some tools have additional characteristics, such as mortality, and HLCS are not sensitive for measuring other effects beyond landing inhibition. Therefore, additional measures may need to be considered when evaluating these tools for public health use. This study has two main aims (1) the evaluate the accuracy of HLCs as a proxy for feeding and (2) to compare the predicted reduction in vectorial capacity when we do and do not consider these additional characteristics. To achieve this, we analyse previously published semi-field data from an experiment which used HLCs and another where mosquitoes were allowed to feed in the presence of different dosages of the volatile pyrethroid spatial repellent, transfluthrin. We compare results for two mathematical models: one which only considers the reduction in feeding effect and one which also considers mortality before and after feeding (using data gathered by the aspiration of mosquitoes after the semi-field feeding/landing period and 24 h survival monitoring). These Bayesian hierarchical models are parameterised using Bayesian inference. We observe that, for susceptible mosquitoes, reduction in landing is underestimated by HLCs. For knockdown resistant mosquitoes the relationship is less clear; with HLCs sometimes appearing to overestimate this characteristic. We find HLCs tend to under-predict the relative reduction in vectorial capacity in susceptible mosquitoes while over-predicting this impact in knockdown-resistant mosquitoes. Models without secondary effects have lower predicted relative reductions in vectorial capacities. Overall, this study highlights the importance of considering additional characteristics to reduction in biting of volatile pyrethroid spatial repellents. We recommend that these are considered when evaluating novel vector control tools.