Bayesian Logistic Regression Model Research Articles

Most quantifiers have many meanings.

In this paper, we investigate, by means of a computational model, how individuals map quantifiers onto numbers and how they order quantifiers on a mental line. We selected five English quantifiers (few, fewer than half, many, more than half, and most) which differ in truth conditions and vagueness. We collected binary truth value judgment data in an online quantifier verification experiment. Using a Bayesian three-parameter logistic regression model, we separated three sources of individual differences: truth condition, vagueness, and response error. Clustering on one of the model's parameter that corresponds to truth conditions revealed four subgroups of participants with different quantifier-to-number mappings and different ranges of the mental line of quantifiers. Our findings suggest multiple sources of individual differences in semantic representations of quantifiers and support a conceptual distinction between different types of imprecision in quantifier meanings. We discuss the consequence of our findings for the main theoretical approaches to quantifiers: the bivalent truth-conditional approach and the fuzzy logic approach. We argue that the former approach neither can explain inter-individual differences nor intra-individual differences in truth conditions of vague quantifiers. The latter approach requires further specification to fully account for individual differences demonstrated in this study.

Abstract CT090: DAREON™-7: A phase 1, open-label, dose escalation and expansion cohort trial of the delta-like ligand (DLL3)-targeting T-cell engager BI 764532, plus first-line platinum-based chemotherapy in patients with DLL3-positive (+) neuroendocrine carcinomas

Abstract Background Neuroendocrine carcinomas (NECs) have limited treatment options. DLL3 is highly expressed in NECs and is a promising treatment target. In an ongoing phase 1 trial (NCT04429087), BI 764532, a DLL3/CD3 IgG-like T-cell engager, was tolerable with clinical activity in patients with DLL3-positive (+) tumors, including those with small cell lung cancer and other NECs. Purpose DAREON™-7 (NCT06132113), a phase 1, open-label, dose-escalation (Part A) and dose-expansion (Part B) trial, aims to determine the maximum tolerated dose (MTD), recommended dose for expansion (RDE)/recommended phase 2 dose (RP2D), and the safety and efficacy of BI 764532 + platinum and etoposide in patients with DLL3+ NEC. Experimental design Part A: ~25 patients will receive intravenous BI 764532 (target dose after step-in dosing) plus carboplatin/etoposide (CE) until intolerable toxicity, or progression per Response Evaluation Criteria in Solid Tumors (RECIST) v1.1, or for a maximum of 36 months. BI 764532 dose escalation will be guided by a Bayesian logistic regression model with overdose control. Part B: 2 cohorts (~15 patients each) will receive BI 764532 at the RDE/RP2D + CE or cisplatin/etoposide (carboplatin/etoposide or cisplatin/etoposide are the most-used standard of care [SOC] regimens). Key inclusion criteria: patients must have no prior systemic treatment for DLL3+ locally advanced or metastatic NEC (extrapulmonary or unknown primary) or large cell NEC of the lung except for 1 cycle of standard platinum/etoposide regimen as first-line treatment prior to entering the treatment period of the trial; patients with localized NEC or resectable disease who received prior adjuvant therapy can participate in the trial if they experienced a treatment-free interval of >6 months prior to the diagnosis of metastatic disease; and patients must have at least 1 measurable lesion as defined per RECIST v1.1. Patients must be adequate candidates to receive platinum/etoposide as the SOC treatment. Primary endpoints: occurrence of dose-limiting toxicities (DLTs) in the MTD evaluation (Part A) and on-treatment (Part B) periods. Secondary endpoints: occurrence of DLTs and adverse events during the on-treatment period (Part B), and efficacy measured by objective response and duration of response (Part B). Enrollment is ongoing. Citation Format: Jaume Capdevila, Timon Vandamme, Patricia Niccoli, Saori Mishima, Ken Kato, Yiyuan Ma, Ping Sun, Lijiang Geng, Ulrich M. Lauer. DAREON™-7: A phase 1, open-label, dose escalation and expansion cohort trial of the delta-like ligand (DLL3)-targeting T-cell engager BI 764532, plus first-line platinum-based chemotherapy in patients with DLL3-positive (+) neuroendocrine carcinomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT090.

Abstract CT184: First-in-human trial of TIGIT inhibitor M6223 as monotherapy or in combination with bintrafusp alfa (BA) in patients (pts) with advanced solid unresectable tumors

Abstract Introduction: M6223 is an intravenous (IV), fully human, antagonistic, anti-TIGIT antibody with an Fc-mediated effector region. In preclinical studies, M6223 combined with BA (a bifunctional fusion protein that simultaneously targets the TGF-β and PD-(L)1 pathways) enhanced antitumor efficacy compared to either agent alone. Methods: This first-in-human, dose escalation study of M6223 as monotherapy (M6223; Part 1A) or in combination with BA (M6223+BA; Part 1B) included pts with advanced solid tumors (aged ≥18 years, ECOG PS ≤1) (NCT04457778). In Part 1A, pts received M6223 at 10 mg, 30 mg, 100 mg, 300 mg, 900 mg, 1600 mg, 2400 mg (all Q2W) or M6223 2400 mg Q3W. In Part 1B, pts received M6223 at 300 mg, 900 mg, or 1600 mg, all in combination with BA 1200 mg (both Q2W, IV). Dose escalation decisions by the Safety Monitoring Committee were assisted by a Bayesian 2-parameter logistic regression model. Primary objectives were safety, tolerability, maximum tolerated dose (MTD), and recommended dose for expansion (RDE). Additional objectives included pharmacokinetics (PK), pharmacodynamics (PD), and clinical activity. Results: At final analysis, 40 pts (21 male, age range 24-79 years) had received M6223 (Q2W: n=32; Q3W: n=8), and 18 pts (7 male, age range 34-80 years) had received M6223+BA. Overall, two dose-limiting toxicities were observed: a grade 3 adrenal insufficiency (M6223, 900 mg) and a grade 3 anemia (M6223+BA, 300 mg; unrelated to M6223). In the M6223 group, grade ≥3 TEAEs were observed in 14 (35.0%) pts and M6223-related grade ≥3 TEAEs in 2 (5.0%) pts. In the M6223+BA group, grade ≥3 TEAEs were observed in 14 (77.8%) pts and M6223-related grade ≥3 TEAEs in 4 (22.2%) pts. No MTD was identified. RDEs were 1600 mg Q2W or 2400 mg Q3W for M6223 monotherapy, and 1600 mg+1200 mg Q2W for M6223+BA. Half-life for the two monotherapy RDEs were 9.0 and 14.6 days, respectively, with moderate accumulation at the selected RDEs. Linear PK was observed for M6223 at 100-2400 mg Q2W and at 2400 mg Q3W; co-administration with BA did not change the PK profile of M6223. PD analyses in blood showed full and sustained TIGIT target occupancy, and depletion of suppressive Tregs at M6223 ≥900 mg. Paired biopsies in the M6223 group (900 mg Q2W, 1600 mg Q2W, 2400 mg Q3W; n=12) showed a trend of decrease in TIGIT+ and increase in CD226+ cells. Median overall survival was 7.6 months (95% CI: 4.9, 12.0) and median progression-free survival was 1.4 months (95% CI: 1.3, 1.8). No pt achieved an objective response (per RECIST v1.1); stable disease as the best response was observed in 13 (32.5%) pts in M6223 and 5 (27.8%) pts in M6223+BA. Conclusion: M6223 monotherapy and in combination with BA had a manageable safety profile, and RDEs for both mono- and combination therapy were defined. Further evaluation of M6223 is ongoing in combination with PD-L1 inhibitor avelumab (JAVELIN Bladder Medley; NCT05327530). Citation Format: Aung Naing, Meredith McKean, Anthony Tolcher, Anja Victor, Ping Hu, Keyvan Tadjalli Mehr, Thomas Kitzing, Daniel Holland, Emilia Richter, Lillian Siu. First-in-human trial of TIGIT inhibitor M6223 as monotherapy or in combination with bintrafusp alfa (BA) in patients (pts) with advanced solid unresectable tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT184.

Abstract CT067: The anti-BCMA antibody-drug conjugate HDP-101 with a novel amanitin payload shows promising initial first in human results in relapsed multiple myeloma

Abstract Background: HDP-101 is a new antibody drug conjugatetargeting B-cell maturation antigen (BCMA) that carries a synthetic amanitin payload with a new mode of action. Amanitin inhibits RNA polymerase II, effectively stopping transcription and inducing apoptosis in tumor cells, regardless of their proliferation status. It's shown cytotoxicity in vitro against BCMA-positive myeloma cell lines and non-proliferating primary CD138+ cells from refractory myeloma patients withefficacy even in cells with low BCMA density. Clinical Study: HDP-101-01 is a first-in-human, open label, non-randomized, multicenter, phase 1/2a trial in patients with multiple myeloma whose disease has progressed or is refractory, aiming to determine the Maximum Tolerated Dose and/or the Recommended phase 2 Dose in Phase 1. Dose escalation is guided by an adaptive Bayesian logistic regression model with overdose control. The primary objective in phase 2 is to assess anti-tumor activity. Study Progress: As of November 2023, 18 patients (7 females, 11 males) were enrolled in 5 consecutive dose cohorts of 20, 30, 60, 80, and 100µg/kg. Patients had a median age of 70 years (48-82), were heavily pre-treated and multidrug-resistant, and had a median of 6,5 prior treatments (2-15). Study Results: Preliminary data shows that pharmacokinetics of HDP-101 aligns with expectations, and exposure to HDP-101 is dose proportional. Free payload was not detected in serum at a limit of detection of 30 ng/mL, and no anti-drug antibodiesor immunogenic reactions were noted. 17 of 18 patients were evaluable for dose limiting toxicities (DLT) in the 5 treatment cohorts. Initial cohorts were well tolerated, without any DLTs, including no signs of hepatic and renal toxicities, infusions reactions, or ocular disorders. All patients in Cohort 5 had Grade 1-4 transient thrombocytopeniawith platelet reductions starting on Cycle 1/Day 2 (C1/D2), a nadir on C1D5, and full recovery by C1D15 at the latest without any clinical sequelae or interventions. Notably, subsequent dosing with HDP-101 did not produce similarly deep episodes of thrombocytopenia, supporting the possibility that this event is not due to a direct cytotoxic effect against megakaryocytes. After Cohort 5, mitigation strategies are being considered and further dose and schedule optimization is planned with fractionated dosing and/or with premedication. Efficacy: Cohort3 has a patient with stable disease (SD) after 15 cycles. Cohort 5 has 4 ongoing patients with promising results after 3-4 cycles: 2 patients achieved PR, and 2 have SD. Updated data will be presented at the AACR2024 meeting. Citation Format: Jonathan Kaufman, Marc Raab, Shambavi Richard, Sebastian Grosicki, István Takács, András Strassz, Andreas Pahl, Michael Kulke, Thorsten Michael, Anette Last, Hajnalka Szabóki, Garrit Jentsch, Oliver Schönborn-Kellenberger, Robert Orlowski. The anti-BCMA antibody-drug conjugate HDP-101 with a novel amanitin payload shows promising initial first in human results in relapsed multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT067.

Optimization of EWOC principle in BLRM design for phase 1 oncology trials

ABSTRACT Bayesian logistic regression model (BLRM) is widely used to guide dose escalation decisions in phase 1 oncology trials. An important feature of BLRM design is the appealing safety performance due to its escalation with overdose control (EWOC). However, some recent literature indicates that BLRM with EWOC may have a relatively low probability to find the maximum tolerated dose (MTD) compared to some other dose escalation designs. This work discusses this design problem and proposes a practical solution to improve the performance of BLRM design. Specifically, we suggest increasing the EWOC cutoff from routine value 0.25 to a value between 0.3 and 0.4, which will increase the chance of finding the correct MTD with minimal compromise to overdosing risk. Our comparative simulation studies indicate that BLRM with an increased EWOC cutoff has comparable operating characteristics on the correct MTD selection and over-toxicity control as other dose escalation designs (BOIN, mTPI, keyboard, etc.). Moreover, we compare the methodology and operating characteristics of BLRM designs with various decision rules that allow more flexible overdosing control. A case study of dose escalation in a recent phase 1 oncology trial is provided to show how BLRM with optimal EWOC cutoff operates well in practice.

Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model

BackgroundMultidrug resistant tuberculosis (MDR-TB) represents a major public health concern in the Republic of Moldova, with an estimated 31% of new and 56% of previously treated TB cases having MDR disease in 2022. A recent genomic epidemiology study of incident TB occurring in 2018 and 2019 found that 92% of MDR-TB was the result of transmission. The MDR phenotype was concentrated among two M. tuberculosis (Mtb) lineages: L2.2.1 (Beijing) and L4.2.1 (Ural). MethodsWe developed and applied a hierarchical Bayesian multinominal logistic regression model to Mtb genomic, spatial, and epidemiological data collected from all individuals with diagnosed TB in Moldova in 2018 and 2019 to identify locations in which specific Mtb strains are being transmitted. We then used a logistic regression model to estimate locality-level factors associated with local transmission. FindingsWe found differences in the spatial distribution and degree of local concentration of disease due to specific strains of Beijing and Ural lineage Mtb. Foci of transmission for four strains of Beijing lineage Mtb, predominantly of the MDR-TB phenotype, were located in several regions, but largely concentrated in Transnistria. In contrast, transmission of Ural lineage Mtb had less marked patterns of spatial aggregation, with a single strain (also of the MDR phenotype) spatially clustered in southern Transnistria. We found a 30% (95% credible interval 2%-80%) increase in odds of a locality being a transmission cluster for each increase of 100 persons per square kilometer, while higher local tuberculosis incidence and poverty were not associated with a locality being a transmission focus. InterpretationOur results identified localities where specific Mtb transmission networks were concentrated and quantified the association between locality-level factors and focal transmission. This analysis revealed Transnistria as the primary area where specific Mtb strains (predominantly of the MDR-TB phenotype) were locally transmitted and suggests that targeted intensified case finding in this region may be an attractive policy option.

56 Impact of ACTIV-6 treatment on PROMIS-29 at 7, 14, 28, and 90 days

OBJECTIVES/GOALS: As mortality and morbidity from acute COVID-19 decline, the impact of COVID-19 on short- and long-term quality of life (QoL) becomes critical to address. We assessed the impact of re-purposed COVID-19 therapies on QoL as a secondary outcome measure in ACTIV-6, a decentralized platform trial. METHODS/STUDY POPULATION: Adults aged ≥30 with mild-to-moderate COVID-19 enroll in ACTIV-6 online or through a study site. Patients are randomized to a medication of interest or placebo. Medications are mailed and symptoms are tracked using electronic diaries. QoL is measured#_msocom_1 using the PROMIS-29 questionnaire. Adjusted Bayesian logistic regression models are used to measure effects of treatment on the seven PROMIS-29 QoL domains at days 7, 14, 28#_msocom_2 and 90. Covariates are treatment, age, gender, symptom duration and severity, vaccination status, geographic region, call center#_msocom_3#_msocom_4, and calendar time. Treatment effects are described using ORs, 95% credible intervals, and posterior probabilities of efficacy, P(eff). RESULTS/ANTICIPATED RESULTS: There are 5,362 patients included, representing four of the study arms in ACTIV-6. We report results where P(eff)<0.025 and P(eff)>0.975 in the table below. Table 1. Scale Day: OR* (95% credible interval, P(eff)) Therapy Physical Anxiety Depression Fatigue Sleep Social Pain Ivermectin 400 — Ivermectin 600 D7: 0.77 (0.61-0.96, 0.01) D14: 0.65 (0.49-0.85, <0.01) D28: 0.69 (0.52-0.92, 0.01) — D7: 0.79 (0.64-0.97, 0.01) — D14 0.78 (0.60-1.00, 0.02) D28: 0.66 (0.50-0.87, <0.01) Fluticasone - D14: 0.77 (0.60-0.99, 0.02) — D7: 0.76 (0.62-0.93, <0.01) D90: 0.79 (0.64-0.98, 0.01) — D7: 0.74 (0.59-0.93, 0.01) Fluvoxamine D7: 0.66 (0.51-0.84, 0.01) — D28: 1.38 (1.02, 1.85, 0.98) D7: 0.78 (0.63-0.97, 0.01) D7: 0.77 (0.62-0.95, 0.01) — *OR > 1 favors active intervention DISCUSSION/SIGNIFICANCE: Results suggest fluvoxamine may improve depression scores by day 28, while placebo is favored in several other scales across treatments. Differences between treatment and placebo are not seen at most other timepoints. This trial is ongoing and future work will include results from additional ACTIV-6 study arms.

A comparison of Bayesian and frequentist approaches to incorporating clinical and biological information for the prediction of response to standardized pediatric colitis therapy.

The prospective cohort study PROTECT is the largest study in pediatric ulcerative colitis (UC) with standardized treatments, providing valuable data for predicting clinical outcomes. PROTECT and previous studies have identified characteristics associated with clinical outcomes. In this study, we aimed to compare predictive modeling between Bayesian analysis including machine learning and frequentist analysis. The key outcomes for this analysis were week 4, 12 and 52 corticosteroid (CS)-free remission following standardized treatment from diagnosis. We developed predictive modeling with multivariable Bayesian logistic regression (BLR), Bayesian additive regression trees (BART) and frequentist logistic regression (FLR). The effect estimate of each risk factor was estimated and compared between the BLR and FLR models. The predictive performance of the models was assessed including area under curve (AUC) of the receiver operating characteristic (ROC) curve. Ten-fold cross-validation was performed for internal validation of the models. The estimation contained 95% credible (or confidence) interval (CI). The statistically significant associations between the risk factors and early or late outcomes were consistent between all BLR and FLR models. The model performance was similar while BLR and BART models had narrower credible intervals of AUCs. To predict week 4 CS-free remission, the BLR model had AUC of 0.69 (95% CI 0.67-0.70), the BART model had AUC of 0.70 (0.67-0.72), and the FLR had AUC of 0.70 (0.65-0.76). To predict week 12 CS-free remission, the BLR model had AUC of 0.78 (0.77-0.79), the BART model had AUC of 0.78 (0.77-0.79), and the FLR model had AUC of 0.79 (0.74-0.83). To predict week 52 CS-free remission, the BLR model had AUC of 0.69 (0.68-0.70), the BART model had AUC of 0.69 (0.67-0.70), and the FLR model had AUC of 0.69 (0.64-0.74). The BART model identified nonlinear associations. BLR and BART models had intuitive interpretation on interval estimation, better precision in estimating the AUC and can be alternatives for predicting clinical outcomes in pediatric patients with UC. BART model can estimate nonlinear nonparametric association.

Molecular detection of SARS-CoV-2 and other respiratory viruses in saliva and classroom air: a two winters tale

ObjectivesTo compare the prevalence of SARS-CoV-2 and other respiratory viruses in saliva and bioaerosols between two winters and to model the probability of virus detection in classroom air for different viruses. MethodsWe analysed saliva, air, and air cleaner filter samples from studies conducted in two Swiss secondary schools (students aged 14–17 years) over 7 weeks during the winters of 2021/22 and 2022/23. Two bioaerosol sampling devices and high efficiency particulate air (HEPA) filters from air cleaners were used to collect airborne virus particles in four classrooms. Daily bioaerosol samples were pooled for each sampling device before PCR analysis of a panel of 19 respiratory viruses and viral subtypes. The probability of detection of airborne viruses was modelled using an adjusted Bayesian logistic regression model. ResultsThree classes (58 students) participated in 2021/22, and two classes (38 students) in 2022/23. During winter 2021/22, SARS-CoV-2 dominated in saliva (19 of 21 positive samples) and bioaerosols (9 of 10). One year later, there were 50 positive saliva samples, mostly influenza B, rhinovirus, and adenovirus, and two positive bioaerosol samples, one rhinovirus and one adenovirus. The weekly probability of airborne detection was 34% (95% credible interval [CrI] 22–47%) for SARS-CoV-2 and 10% (95% CrI 5–16%) for other respiratory viruses. DiscussionThere was a distinct shift in the distribution of respiratory viruses from SARS-CoV-2 during the omicron wave to other respiratory viruses one year later. SARS-CoV-2 is more likely to be detected in the air than other endemic respiratory viruses, possibly reflecting differences in viral characteristics and the composition of virus-carrying particles that facilitate airborne long-range transmission.

Sampling design methods for making improved lake management decisions

AbstractThe ecological status of lakes is important for understanding an ecosystem's biodiversity as well as for service water quality and policies related to land use and agricultural run‐off. If the status is weak, then decisions about management alternatives need to be made. We assess the value of information of lake monitoring in Finland, where lakes are abundant. With reasonable ecological values and restoration costs, the value of information analysis can be compared with the survey's costs. Data are worth gathering if the expected value from the data exceeds the costs. From existing data, we specify a hierarchical Bayesian spatial logistic regression model for the ecological status of lakes. We then rely on functional approximations and Laplace approximations to get closed‐form expressions for the value of information of a sampling design. The case study contains thousands of lakes. The combinatorially difficult design problem is to wisely pick the right subset of lakes for data gathering. To solve this optimization problem, we study the performance of various heuristics: greedy forward algorithms, exchange algorithms and Bayesian optimization approaches. The value of information increases quickly when adding lakes to a small design but then flattens out. Good designs are usually composed of lakes that are difficult to manage, while also balancing a variety of covariates and geographic coverage. The designs achieved by forward selection are reasonably good, but we can outperform them with the more nuanced search algorithms. Statistical designs clearly outperform other designs selected according to simpler criteria.

Modifying the false discovery rate procedure based on the information theory under arbitrary correlation structure and its performance in high-dimensional genomic data

BackgroundControlling the False Discovery Rate (FDR) in Multiple Comparison Procedures (MCPs) has widespread applications in many scientific fields. Previous studies show that the correlation structure between test statistics increases the variance and bias of FDR. The objective of this study is to modify the effect of correlation in MCPs based on the information theory. We proposed three modified procedures (M1, M2, and M3) under strong, moderate, and mild assumptions based on the conditional Fisher Information of the consecutive sorted test statistics for controlling the false discovery rate under arbitrary correlation structure. The performance of the proposed procedures was compared with the Benjamini–Hochberg (BH) and Benjamini–Yekutieli (BY) procedures in simulation study and real high-dimensional data of colorectal cancer gene expressions. In the simulation study, we generated 1000 differential multivariate Gaussian features with different levels of the correlation structure and screened the significance features by the FDR controlling procedures, with strong control on the Family Wise Error Rates.ResultsWhen there was no correlation between 1000 simulated features, the performance of the BH procedure was similar to the three proposed procedures. In low to medium correlation structures the BY procedure is too conservative. The BH procedure is too liberal, and the mean number of screened features was constant at the different levels of the correlation between features. The mean number of screened features by proposed procedures was between BY and BH procedures and reduced when the correlations increased. Where the features are highly correlated the number of screened features by proposed procedures reached the Bonferroni (BF) procedure, as expected. In real data analysis the BY, BH, M1, M2, and M3 procedures were done to screen gene expressions of colorectal cancer. To fit a predictive model based on the screened features the Efficient Bayesian Logistic Regression (EBLR) model was used. The fitted EBLR models based on the screened features by M1 and M2 procedures have minimum entropies and are more efficient than BY and BH procedures.ConclusionThe modified proposed procedures based on information theory, are much more flexible than BH and BY procedures for the amount of correlation between test statistics. The modified procedures avoided screening the non-informative features and so the number of screened features reduced with the increase in the level of correlation.

On the relative conservativeness of Bayesian logistic regression method in oncology dose-finding studies.

The Bayesian logistic regression method (BLRM) is a widely adopted and flexible design for finding the maximum tolerated dose in oncology phase I studies. However, the BLRM design has been criticized in the literature for being overly conservative due to the use of the overdose control rule. Recently, a discussion paper titled "Improving the performance of Bayesian logistic regression model with overall control in oncology dose-finding studies" in Statistics in Medicine has proposed an overall control rule to address the "excessive conservativeness" of the standard BLRM design. In this short communication, we discuss the relative conservativeness of the standard BLRM design and also suggest a dose-switching rule to further enhance its performance.

Influence of organization environment on contractor’s inter-regional market entry practice in China: an industry-level approach based on Bayesian logistic regression model

Inter-regional market entry poses a complex challenge for contractors, particularly in the construction industry with its unique characteristics. Existing research predominantly focuses on an international scale and target markets, often overlooking the influence of the organizational environment, especially the home market. This study aims to fill this gap by analyzing how the construction organizational environment affects contractors’ inter-regional market practices. Grounded in theories of organizational management, we developed a theoretical model with seven hypotheses examining the impact of organizational environment. We tested hypotheses with Bayesian logistic regression model based on data from 426 construction organizations in China. Our findings reveal that a contractor’s size significantly impacts the practice compared to other attributes like ownership and experience. Additionally, a thriving local economy strongly supports contractors in exploring other geographical markets. Interestingly, while local competition intensity and protectionism create barriers for external firms, they do not significantly affect the diversification activities of local contractors. These insights contribute to understanding the dynamics of inter-regional market entry in the construction industry and can guide practitioners in strategizing for sustainable growth. Future research could explore more detailed strategies such as market selection and entry mode, enhancing the depth of understanding in this area.

Eco-epidemiology of Rickettsia amblyommatis and Rickettsia parkeri in naturally infected ticks (Acari: Ixodida) from South Carolina

BackgroundSpotted fever group Rickettsia (SFGR) is the largest group of Rickettsia species of clinical and veterinary importance emerging worldwide. Historically, SFGR cases were linked to Rickettsia rickettsii, the causal agent of Rocky Mountain spotted fever; however, recently discovered species Rickettsia parkeri and Rickettsia amblyommatis have been shown to cause a wide range of clinical symptoms. The role of R. amblyommatis in SFGR eco-epidemiology and the possible public health implications remain unknown.MethodsThis study evaluated statewide tick surveillance and land-use classification data to define the eco-epidemiological relationships between R. amblyommatis and R. parkeri among questing and feeding ticks collected across South Carolina between 2021 and 2022. Questing ticks from state parks and feeding ticks from animal shelters were evaluated for R. parkeri and R. amblyommatis using reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) on pooled samples. A Bayesian multivariable logistic regression model for pool testing data was used to assess associations between R. parkeri or R. amblyommatis infection and land-use classification variables among questing ticks. The Spearman correlation was used to evaluate the relationship between the two tested pathogens.ResultsThe infection prevalence for R. amblyommatis was 24.8% (23.4–26.3%) among questing ticks, and 39.5% (37.4–42.0%) among feeding ticks; conversely, for R. parkeri it was 19.0% (17.6–20.5%) among questing ticks and 22.4% (20.3–24.5%) among feeding ticks. A negative, refractory correlation was found between the species, with ticks significantly more likely to contain one or the other pathogen, but not both simultaneously. The Bayesian analysis revealed that R. amblyommatis infection was positively associated with deciduous, evergreen, and mixed forests, and negatively associated with hay and pasture fields, and emergent herbaceous wetlands. Rickettsia parkeri infection was positively associated with deciduous, mixed, and evergreen forests, herbaceous vegetation, cultivated cropland, woody wetlands, and emergent herbaceous wetlands, and negatively associated with hay and pasture fields.ConclusionsThis is the first study to evaluate the eco-epidemiological factors driving tick pathogenicity in South Carolina. The negative interactions between SFGR species suggest the possible inhibition between the two pathogens tested, which could have important public health implications. Moreover, land-use classification factors revealed environments associated with tick pathogenicity, highlighting the need for tick vector control in these areas.Graphical

Determinants and spatial factors of anemia in women of reproductive age in Democratic Republic of Congo (drc): a Bayesian multilevel ordinal logistic regression model approach

BackgroundAs a global public health problem, anemia affects more than 400 million women of reproductive age worldwide, mostly in Africa and India. In the DRC, the prevalence of anemia has decreased slightly from 52.9% in 2007, to 46.4% in 2012 and 42.4% in 2019. However, there is considerable regional variation in its distribution. The aim of this study is to determine the factors contributing to anemia in women of reproductive age and to explore its spatial distribution in the DRC.MethodsBased on the Bayesian Multilevel Spatial Ordinal Logistic Regression Model, we used the 2013 Democratic Republic of Congo Demographic and Health Survey (DHS-DRC II) data to investigate individual and environmental characteristics contributing to the development of anemia in women of reproductive age and the mapping of anemia in terms of residual spatial effects.ResultsAge, pregnancy status, body mass index, education level, current breastfeeding, current marital status, contraceptive and insecticide-treated net use, source of drinking water supply and toilet/latrine use including the province of residence were the factors contributing to anemia in women of reproductive age in DRC. With Global Moran's I = -0.00279, p-value ≥ 0.05, the spatial distribution of anemia in women of reproductive age in DRC results from random spatial processes. Thus, the observed spatial pattern is completely random.ConclusionThe Bayesian Multilevel Spatial Ordinal Logistic Regression statistical model is able to adjust for risk and spatial factors of anemia in women of reproductive age in DRC highlighting the combined role of individual and environmental factors in the development of anemia in DRC.

Gender differences, environmental pressures, tumor characteristics, and death rate in a lung cancer cohort: a seven-years Bayesian survival analysis using cancer registry data from a contaminated area in Italy.

In Taranto, Southern Italy, adverse impacts on the environment and human health due to industrial installations have been studied. In the literature, associations have been reported between gender, environmental factors, and lung cancer mortality in women and men. The aim of this study was to investigate the relationships between gender, residence in areas with high environmental pressures, bronchus/lung cancer characteristics, and death rate. Data from the Taranto Cancer Registry were used, including all women and men with invasive bronchus/lung cancer diagnosed between 1 January 2016 and 31 December 2020 and with follow-up to 31 December 2022. Bayesian mixed effects logistic and Cox regression models were fitted with the approach of integrated nested Laplace approximation, adjusting for patients and disease characteristics. A total of 2,535 person-years were observed. Male gender was associated with a higher prevalence of histological grade 3 (OR 2.45, 95% CrI 1.35-4.43) and lung squamous-cell carcinoma (OR 3.04, 95% CrI 1.97-4.69). Variables associated with higher death rate were male gender (HR 1.24, 95% CrI 1.07-1.43), pathological/clinical stage II (HR 2.49, 95% CrI 1.63-3.79), III (HR 3.40, 95% CrI 2.33-4.97), and IV (HR 8.21, 95% CrI 5.95-11.34), histological grade 3 (HR 1.80, 95% CrI 1.25-2.59), lung squamous-cell carcinoma (HR 1.18, 95% CrI 1.00-1.39), and small-cell lung cancer (HR 1.62, 95% CrI 1.31-1.99). Variables associated with lower death rate were other-type lung cancer (HR 0.65, 95% CrI 0.44-0.95), high immune checkpoint ligand expression (HR 0.75, 95% CrI 0.59-0.95), lung localization (HR 0.73, 95% CrI 0.62-0.86), and left localization (HR 0.85, 95% CrI 0.75-0.95). The results among patients with lung cancer did not show an association between residence in the contaminated site of national interest (SIN) and the prevalence of the above mentioned prognostic factors, nor between residence in SIN and death rate. The findings confirmed the independent prognostic values of different lung cancer characteristics. Even after adjusting for patients and disease characteristics, male gender appeared to be associated with a higher prevalence of poorly differentiated cancer and squamous-cell carcinoma, and with an increased death rate.

Environmental pressures, tumor characteristics, and death rate in a female breast cancer cohort: a seven-years Bayesian survival analysis using cancer registry data from a contaminated area in Italy.

In Taranto, Southern Italy, adverse impacts on the environment and human health due to industrial installations have been studied. In the literature, few associations have been reported between environmental factors and breast cancer mortality in women. The aim of this study was to investigate the relationships between residence in areas with high environmental pressures, female breast cancer characteristics, and death rate. Data from the Taranto Cancer Registry were used, including all women with invasive breast cancer diagnosed between 01 January 2015 and 31 December 2020 and with follow-up to 31 December 2021. Bayesian mixed effects logistic and Cox regression models were fitted with the approach of integrated nested Laplace approximation, adjusting for patients and disease characteristics. A total of 10,445 person-years were observed. Variables associated with higher death rate were residence in the contaminated site of national interest (SIN) (HR 1.22, 95% CrI 1.01-1.48), pathological/clinical stage III (HR 2.77, 95% CrI 1.93-3.97) and IV (HR 17.05, 95% CrI 11.94-24.34), histological grade 3 (HR 2.50, 95% CrI 1.20-5.23), Ki-67 proliferation index of 21-50% (HR 1.42, 95% CrI 1.10-1.83) and > 50% (HR 1.81, 95% CrI 1.29-2.55), and bilateral localization (HR 1.65, 95% CrI 1.01-2.68). Variables associated with lower death rate were estrogen and/or progesterone receptor positivity (HR 0.61, 95% CrI 0.45-0.81) and HER2/neu oncogene positivity (HR 0.59, 95% CrI 0.44-0.79). The findings confirmed the independent prognostic values of different female breast cancer characteristics. Even after adjusting for patients and disease characteristics, residence in the SIN of Taranto appeared to be associated with an increased death rate.

Cutaneous Leishmaniasis Hampers COVID-19: A Controlled Cross-Sectional Study in High-Burden Endemic Areas of Iran.

Emerging infectious diseases such as SARS-CoV-2 can cause pandemics and create a critical risk for humans. In a previous pilot study, we reported that the immunological responses induced bycutaneous leishmaniasis (CL) could decrease the incidence and severity of COVID-19. In this large-scale case-control study, we assessed the possible relationship between mortality and morbidity of COVID-19 in healed CL persons suffering scars compared to cases without CL history. This controlled cross-sectional study was conducted between July 2020 and December 2022 in the endemic and high-burden areas of CL in southeastern Iran. In the study, 1400 previous CL cases with scars and 1,521,329 subjects who had no previous CL were analyzed. We used R 4.0.2 to analyze the data. Firth's bias reduction approach corresponding to the penalization of likelihood logistic regression by Jeffreys was also employed to influence the variables in the dataset. Also, a Bayesian ordinal logistic regression model was performed to explore the COVID-19 severity in both case and referent groups. The occurrence and severity rate of COVID-19 in CL scar cases are significantly less than in the non-CL control group, while in the CL scar subjects, patients with critical conditions and mortality were not observed. The morbidity (OR = 0.11, CI 0.06-0.20 and P < 0.001) and severity of COVID-19 in previous cases with CL scars were significantly diminished than that in the control group (credible interval - 2.57, - 1.62). The results represented a durable negative relationship between cured CL and COVID-19 incidence and severity. Additional studies seem necessary and should be designed to further validate the true impact and underlying mechanistic action of CL on COVID-19.

Seismic fragility analysis of nuclear containment structure using Bayesian logistic regression model

Nuclear containment structure serves as the critical shielding structure in nuclear power plants, and must maintain structural integrity under various conditions. Following the Fukushima nuclear accident, seismic safety of nuclear power plant structures has become a significant concern in nuclear engineering community. This study presents seismic fragility analysis of the nuclear containment structure subjected to far-fault ground motion based on Bayesian logistic regression model. A refined finite element model of the nuclear containment structure was developed using layered shell element with considering material nonlinear behavior. The maximum tensile strain and roof drift of the nuclear containment structure were analyzed. MATLAB scripts for the seismic fragility analysis method based on Bayesian logistic regression model were developed. Posterior samples for the parameter of fragility function parameters were obtained using developed scripts. The sensitivity of the proposed Bayesian logistic regression based seismic fragility analysis method was verified. Seismic fragility curves of the nuclear containment structure obtained with the minimized sum of squared error, maximum likelihood estimation and Bayesian logistic regression methods were compared in depth.

Curb the spread: Leveraging intensive longitudinal data to unravel within- and between-person processes in adherence to nonpharmaceutical interventions during COVID-19.

Adhering to behavioral recommendations and nonpharmaceutical interventions (NPIs) is a key to control COVID-19 infection rates. However, rates have decreased globally, and potentially modifiable determinants of ongoing adherence and their interaction with social and physical momentary environments are still poorly understood. Here, we comprehensively examine within-person variations and between-person differences in known behavioral determinants (capability and motivation), as well as the moderating role of situational variable environmental factors (opportunity) in predicting adherence to hygiene and social distancing behaviors. Ecological momentary assessment study over 6 months with monthly assessment bouts (4 days each and five daily assessments) in 623 German adults. Repeated daily assessments of capability, opportunity, motivation, and behavior (COM-B) model factors. Bayesian multilevel logistic regression models were estimated to examine main effects of COM-B factors and moderating effects of momentary environmental factors. Momentary adherence to NPIs was predicted by within-person changes in COM-B factors (motivation: intentions, goal conflict, and control beliefs; opportunities: regulations and norms). Between-person differences in capabilities (habit strength) and motivation (intentions and control beliefs) predicted adherence across situations. Situation-specific environmental factors moderated the motivation-behavior association (regulation measures increased; goal conflict and nonadherent others decreased the association). Individual momentary (within-person) and stable (between-person) motivation indicators predicted adherence. However, situational environmental factors such as regulations or norms have strong main effects and moderate the motivation-behavior translation. These findings have policy implications, supporting recent claims to not rely on the narrative of "personal responsibility," but instead combine health education measures to increase individual motivation with consistent regulation. (PsycInfo Database Record (c) 2023 APA, all rights reserved).