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Bather JR, Han L, Bennett AS, Elliott L, Goodman MS. Detecting univariate, bivariate, and overall effects of drug mixtures using Bayesian kernel machine regression. THE AMERICAN JOURNAL OF DRUG AND ALCOHOL ABUSE 2024:1-8. [PMID: 39042906 DOI: 10.1080/00952990.2024.2380463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 07/03/2024] [Indexed: 07/25/2024]
Abstract
Background: Innovative analytic approaches to drug studies are needed to understand better the co-use of opioids with non-opioids among people using illicit drugs. One approach is the Bayesian kernel machine regression (BKMR), widely applied in environmental epidemiology to study exposure mixtures but has received far less attention in substance use research.Objective: To describe the utility of the BKMR approach to study the effects of drug substance mixtures on health outcomes.Methods: We simulated data for 200 individuals. Using the Vale and Maurelli method, we simulated multivariate non-normal drug exposure data: xylazine (mean = 300 ng/mL, SD = 100 ng/mL), fentanyl (mean = 200 ng/mL, SD = 71 ng/mL), benzodiazepine (mean = 300 ng/mL, SD = 55 ng/mL), and nitazene (mean = 200 ng/mL, SD = 141 ng/mL) concentrations. We performed 10,000 MCMC sampling iterations with three Markov chains. Model diagnostics included trace plots, r-hat values, and effective sample sizes. We also provided visual relationships of the univariate and bivariate exposure-response and the overall mixture effect.Results: Higher levels of fentanyl and nitazene concentrations were associated with higher levels of the simulated health outcome, controlling for age. Trace plots, r-hat values, and effective sample size statistics demonstrated BKMR stability across multiple Markov chains.Conclusions: Our understanding of drug mixtures tends to be limited to studies of single-drug models. BKMR offers an innovative way to discern which substances pose a greater health risk than other substances and can be applied to assess univariate, bivariate, and cumulative drug effects on health outcomes.
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Affiliation(s)
- Jemar R Bather
- Center for Anti-Racism, Social Justice & Public Health, New York University School of Global Public Health, New York, NY, USA
- Department of Biostatistics, New York University School of Global Public Health, New York, NY, USA
| | - Larry Han
- Department of Health Sciences, Northeastern University, Boston, MA, USA
| | - Alex S Bennett
- Center for Anti-Racism, Social Justice & Public Health, New York University School of Global Public Health, New York, NY, USA
- Department of Social and Behavioral Sciences, New York University School of Global Public Health, New York, NY, USA
- Center for Drug Use and HIV/HCV Research, New York University School of Global Public Health, New York, NY, USA
| | - Luther Elliott
- Department of Social and Behavioral Sciences, New York University School of Global Public Health, New York, NY, USA
- Center for Drug Use and HIV/HCV Research, New York University School of Global Public Health, New York, NY, USA
| | - Melody S Goodman
- Center for Anti-Racism, Social Justice & Public Health, New York University School of Global Public Health, New York, NY, USA
- Department of Biostatistics, New York University School of Global Public Health, New York, NY, USA
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Ulitzsch E, Shin HJ, Lüdtke O. Accounting for careless and insufficient effort responding in large-scale survey data-development, evaluation, and application of a screen-time-based weighting procedure. Behav Res Methods 2024; 56:804-825. [PMID: 36867339 PMCID: PMC10830617 DOI: 10.3758/s13428-022-02053-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/09/2022] [Indexed: 03/04/2023]
Abstract
Careless and insufficient effort responding (C/IER) poses a major threat to the quality of large-scale survey data. Traditional indicator-based procedures for its detection are limited in that they are only sensitive to specific types of C/IER behavior, such as straight lining or rapid responding, rely on arbitrary threshold settings, and do not allow taking the uncertainty of C/IER classification into account. Overcoming these limitations, we develop a two-step screen-time-based weighting procedure for computer-administered surveys. The procedure allows considering the uncertainty in C/IER identification, is agnostic towards the specific types of C/IE response patterns, and can feasibly be integrated with common analysis workflows for large-scale survey data. In Step 1, we draw on mixture modeling to identify subcomponents of log screen time distributions presumably stemming from C/IER. In Step 2, the analysis model of choice is applied to item response data, with respondents' posterior class probabilities being employed to downweigh response patterns according to their probability of stemming from C/IER. We illustrate the approach on a sample of more than 400,000 respondents being administered 48 scales of the PISA 2018 background questionnaire. We gather supporting validity evidence by investigating relationships between C/IER proportions and screen characteristics that entail higher cognitive burden, such as screen position and text length, relating identified C/IER proportions to other indicators of C/IER as well as by investigating rank-order consistency in C/IER behavior across screens. Finally, in a re-analysis of the PISA 2018 background questionnaire data, we investigate the impact of the C/IER adjustments on country-level comparisons.
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Affiliation(s)
- Esther Ulitzsch
- IPN-Leibniz Institute for Science and Mathematics Education, Educational Measurement, Olshausenstraße 62, 24118, Kiel, Germany.
- Centre for International Student Assessment, Munich, Germany.
| | | | - Oliver Lüdtke
- IPN-Leibniz Institute for Science and Mathematics Education, Educational Measurement, Olshausenstraße 62, 24118, Kiel, Germany
- Centre for International Student Assessment, Munich, Germany
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3
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Schumacher L, Bürkner PC, Voss A, Köthe U, Radev ST. Neural superstatistics for Bayesian estimation of dynamic cognitive models. Sci Rep 2023; 13:13778. [PMID: 37612320 PMCID: PMC10447473 DOI: 10.1038/s41598-023-40278-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 08/08/2023] [Indexed: 08/25/2023] Open
Abstract
Mathematical models of cognition are often memoryless and ignore potential fluctuations of their parameters. However, human cognition is inherently dynamic. Thus, we propose to augment mechanistic cognitive models with a temporal dimension and estimate the resulting dynamics from a superstatistics perspective. Such a model entails a hierarchy between a low-level observation model and a high-level transition model. The observation model describes the local behavior of a system, and the transition model specifies how the parameters of the observation model evolve over time. To overcome the estimation challenges resulting from the complexity of superstatistical models, we develop and validate a simulation-based deep learning method for Bayesian inference, which can recover both time-varying and time-invariant parameters. We first benchmark our method against two existing frameworks capable of estimating time-varying parameters. We then apply our method to fit a dynamic version of the diffusion decision model to long time series of human response times data. Our results show that the deep learning approach is very efficient in capturing the temporal dynamics of the model. Furthermore, we show that the erroneous assumption of static or homogeneous parameters will hide important temporal information.
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Affiliation(s)
- Lukas Schumacher
- Institute of Psychology, Heidelberg University, Heidelberg, Germany.
| | | | - Andreas Voss
- Institute of Psychology, Heidelberg University, Heidelberg, Germany
| | - Ullrich Köthe
- Computer Vision and Learning Lab, Heidelberg University, Heidelberg, Germany
| | - Stefan T Radev
- Cluster of Excellence STRUCTURES, Heidelberg University, Heidelberg, Germany
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4
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Giordano KR, Saber M, Green TR, Rojas-Valencia LM, Ortiz JB, Murphy SM, Lifshitz J, Rowe RK. Colony-Stimulating Factor-1 Receptor Inhibition Transiently Attenuated the Peripheral Immune Response to Experimental Traumatic Brain Injury. Neurotrauma Rep 2023; 4:284-296. [PMID: 37139183 PMCID: PMC10150725 DOI: 10.1089/neur.2022.0092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023] Open
Abstract
To investigate microglial mechanisms in central and peripheral inflammation after experimental traumatic brain injury (TBI), we inhibited the colony-stimulating factor-1 receptor (CSF-1R) with PLX5622 (PLX). We hypothesized that microglia depletion would attenuate central inflammation acutely with no effect on peripheral inflammation. After randomization, male mice (n = 105) were fed PLX or control diets (21 days) and then received midline fluid percussion injury or sham injury. Brain and blood were collected at 1, 3, or 7 days post-injury (DPI). Immune cell populations were quantified in the brain and blood by flow cytometry. Cytokines (interleukin [IL]-6, IL-1β, tumor necrosis factor-α, interferon-γ, IL-17A, and IL-10) were quantified in the blood using a multi-plex enzyme-linked immunosorbent assay. Data were analyzed using Bayesian multi-variate, multi-level models. PLX depleted microglia at all time points and reduced neutrophils in the brain at 7 DPI. PLX also depleted CD115+ monocytes, reduced myeloid cells, neutrophils, and Ly6Clow monocytes in blood, and elevated IL-6. TBI induced a central and peripheral immune response. TBI elevated leukocytes, microglia, and macrophages in the brain and elevated peripheral myeloid cells, neutrophils, Ly6Cint monocytes, and IL-1β in the blood. TBI lowered peripheral CD115+ and Ly6Clow monocytes in the blood. TBI PLX mice had fewer leukocytes and microglia in the brain at 1 DPI, with elevated neutrophils at 7 DPI compared to TBI mice on a control diet. TBI PLX mice also had fewer peripheral myeloid cells, CD115+, and Ly6Clow monocytes in the blood at 3 DPI, but elevated Ly6Chigh, Ly6Cint, and CD115+ monocyte populations at 7 DPI, compared to TBI mice on a control diet. TBI PLX mice had elevated proinflammatory cytokines and lower anti-inflammatory cytokines in the blood at 7 DPI compared to TBI mice on a control diet. CSF-1R inhibition reduced the immune response to TBI at 1 and 3 DPI, but elevated peripheral inflammation at 7 DPI.
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Affiliation(s)
- Katherine R. Giordano
- BARROW Neurological Institute at Phoenix Children's Hospital, Phoenix, Arizona, USA
- Department of Child Health, University of Arizona College of Medicine–Phoenix, Phoenix, Arizona, USA
- Phoenix Veteran Affairs Health Care System, Phoenix, Arizona, USA
| | - Maha Saber
- BARROW Neurological Institute at Phoenix Children's Hospital, Phoenix, Arizona, USA
- Department of Child Health, University of Arizona College of Medicine–Phoenix, Phoenix, Arizona, USA
| | - Tabitha R.F. Green
- Department of Child Health, University of Arizona College of Medicine–Phoenix, Phoenix, Arizona, USA
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, Colorado, USA
| | - Luisa M. Rojas-Valencia
- BARROW Neurological Institute at Phoenix Children's Hospital, Phoenix, Arizona, USA
- Department of Child Health, University of Arizona College of Medicine–Phoenix, Phoenix, Arizona, USA
- Phoenix Veteran Affairs Health Care System, Phoenix, Arizona, USA
| | - J. Bryce Ortiz
- BARROW Neurological Institute at Phoenix Children's Hospital, Phoenix, Arizona, USA
- Department of Child Health, University of Arizona College of Medicine–Phoenix, Phoenix, Arizona, USA
- Phoenix Veteran Affairs Health Care System, Phoenix, Arizona, USA
| | - Sean M. Murphy
- Department of Child Health, University of Arizona College of Medicine–Phoenix, Phoenix, Arizona, USA
| | - Jonathan Lifshitz
- BARROW Neurological Institute at Phoenix Children's Hospital, Phoenix, Arizona, USA
- Department of Child Health, University of Arizona College of Medicine–Phoenix, Phoenix, Arizona, USA
- Phoenix Veteran Affairs Health Care System, Phoenix, Arizona, USA
| | - Rachel K. Rowe
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, Colorado, USA
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Feng Y, Wang J, Zhang H, Lu G. Incrementally Stochastic and Accelerated Gradient Information Mixed Optimization for Manipulator Motion Planning. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3191206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Yichang Feng
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China
| | - Jin Wang
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China
| | - Haiyun Zhang
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China
| | - Guodong Lu
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China
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Ulitzsch E, Pohl S, Khorramdel L, Kroehne U, von Davier M. A Response-Time-Based Latent Response Mixture Model for Identifying and Modeling Careless and Insufficient Effort Responding in Survey Data. PSYCHOMETRIKA 2022; 87:593-619. [PMID: 34855118 PMCID: PMC9166878 DOI: 10.1007/s11336-021-09817-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 10/11/2021] [Indexed: 06/13/2023]
Abstract
Careless and insufficient effort responding (C/IER) can pose a major threat to data quality and, as such, to validity of inferences drawn from questionnaire data. A rich body of methods aiming at its detection has been developed. Most of these methods can detect only specific types of C/IER patterns. However, typically different types of C/IER patterns occur within one data set and need to be accounted for. We present a model-based approach for detecting manifold manifestations of C/IER at once. This is achieved by leveraging response time (RT) information available from computer-administered questionnaires and integrating theoretical considerations on C/IER with recent psychometric modeling approaches. The approach a) takes the specifics of attentive response behavior on questionnaires into account by incorporating the distance-difficulty hypothesis, b) allows for attentiveness to vary on the screen-by-respondent level, c) allows for respondents with different trait and speed levels to differ in their attentiveness, and d) at once deals with various response patterns arising from C/IER. The approach makes use of item-level RTs. An adapted version for aggregated RTs is presented that supports screening for C/IER behavior on the respondent level. Parameter recovery is investigated in a simulation study. The approach is illustrated in an empirical example, comparing different RT measures and contrasting the proposed model-based procedure against indicator-based multiple-hurdle approaches.
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Affiliation(s)
- Esther Ulitzsch
- IPN-Leibniz Institute for Science and Mathematics Education, Olshausenstraße 62, 24118, Kiel, Germany.
| | | | | | - Ulf Kroehne
- DIPF-Leibniz Institute for Research and Information in Education, Frankfurt, Germany
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Bayesian Framework for Multi-Wave COVID-19 Epidemic Analysis Using Empirical Vaccination Data. MATHEMATICS 2021. [DOI: 10.3390/math10010021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The COVID-19 pandemic has highlighted the necessity of advanced modeling inference using the limited data of daily cases. Tracking a long-term epidemic trajectory requires explanatory modeling with more complexities than the one with short-time forecasts, especially for the highly vaccinated scenario in the latest phase. With this work, we propose a novel modeling framework that combines an epidemiological model with Bayesian inference to perform an explanatory analysis on the spreading of COVID-19 in Israel. The Bayesian inference is implemented on a modified SEIR compartmental model supplemented by real-time vaccination data and piecewise transmission and infectious rates determined by change points. We illustrate the fitted multi-wave trajectory in Israel with the checkpoints of major changes in publicly announced interventions or critical social events. The result of our modeling framework partly reflects the impact of different stages of mitigation strategies as well as the vaccination effectiveness, and provides forecasts of near future scenarios.
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Harris RC, Sumner T, Knight GM, Zhang H, White RG. Potential impact of tuberculosis vaccines in China, South Africa, and India. Sci Transl Med 2020; 12:eaax4607. [PMID: 33028708 DOI: 10.1126/scitranslmed.aax4607] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 11/12/2019] [Accepted: 09/16/2020] [Indexed: 12/11/2022]
Abstract
More effective tuberculosis vaccines are needed to help reach World Health Organization tuberculosis elimination goals. Insufficient evidence exists on the potential impact of future tuberculosis vaccines with varying characteristics and in different epidemiological settings. To inform vaccine development decision making, we modeled the impact of hypothetical tuberculosis vaccines in three high-burden countries. We calibrated Mycobacterium tuberculosis (M.tb) transmission models to age-stratified demographic and epidemiological data from China, South Africa, and India. We varied vaccine efficacy to prevent infection or disease, effective in persons M.tb uninfected or infected, and duration of protection. We modeled routine early-adolescent vaccination and 10-yearly mass campaigns from 2025. We estimated median percentage population-level tuberculosis incidence rate reduction (IRR) in 2050 compared to a no new vaccine scenario. In all settings, results suggested vaccines preventing disease in M.tb-infected populations would have greatest impact by 2050 (10-year, 70% efficacy against disease, IRR 51%, 52%, and 54% in China, South Africa, and India, respectively). Vaccines preventing reinfection delivered lower potential impact (IRR 1, 12, and 17%). Intermediate impact was predicted for vaccines effective only in uninfected populations, if preventing infection (IRR 21, 37, and 50%) or disease (IRR 19, 36, and 51%), with greater impact in higher-transmission settings. Tuberculosis vaccines have the potential to deliver substantial population-level impact. For prioritizing impact by 2050, vaccine development should focus on preventing disease in M.tb-infected populations. Preventing infection or disease in uninfected populations may be useful in higher transmission settings. As vaccine impact depended on epidemiology, different development strategies may be required.
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Affiliation(s)
- Rebecca C Harris
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
| | - Tom Sumner
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Gwenan M Knight
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Hui Zhang
- Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Richard G White
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
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Albuquerque PH, do Valle DR, Li D. Bayesian LDA for mixed-membership clustering analysis: The Rlda package. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2018.10.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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10
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Gonsalves GS, Copple JT, Johnson T, Paltiel AD, Warren JL. Bayesian adaptive algorithms for locating HIV mobile testing services. BMC Med 2018; 16:155. [PMID: 30173667 PMCID: PMC6120098 DOI: 10.1186/s12916-018-1129-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 07/13/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND We have previously conducted computer-based tournaments to compare the yield of alternative approaches to deploying mobile HIV testing services in settings where the prevalence of undetected infection may be characterized by 'hotspots'. We report here on three refinements to our prior assessments and their implications for decision-making. Specifically, (1) enlarging the number of geographic zones; (2) including spatial correlation in the prevalence of undetected infection; and (3) evaluating a prospective search algorithm that accounts for such correlation. METHODS Building on our prior work, we used a simulation model to create a hypothetical city consisting of up to 100 contiguous geographic zones. Each zone was randomly assigned a prevalence of undetected HIV infection. We employed a user-defined weighting scheme to correlate infection levels between adjacent zones. Over 180 days, search algorithms selected a zone in which to conduct a fixed number of HIV tests. Algorithms were permitted to observe the results of their own prior testing activities and to use that information in choosing where to test in subsequent rounds. The algorithms were (1) Thompson sampling (TS), an adaptive Bayesian search strategy; (2) Besag York Mollié (BYM), a Bayesian hierarchical model; and (3) Clairvoyance, a benchmarking strategy with access to perfect information. RESULTS Over 250 tournament runs, BYM detected 65.3% (compared to 55.1% for TS) of the cases identified by Clairvoyance. BYM outperformed TS in all sensitivity analyses, except when there was a small number of zones (i.e., 16 zones in a 4 × 4 grid), wherein there was no significant difference in the yield of the two strategies. Though settings of no, low, medium, and high spatial correlation in the data were examined, differences in these levels did not have a significant effect on the relative performance of BYM versus TS. CONCLUSIONS BYM narrowly outperformed TS in our simulation, suggesting that small improvements in yield can be achieved by accounting for spatial correlation. However, the comparative simplicity with which TS can be implemented makes a field evaluation critical to understanding the practical value of either of these algorithms as an alternative to existing approaches for deploying HIV testing resources.
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Affiliation(s)
- Gregg S. Gonsalves
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, 60 College Street, New Haven, CT USA
| | - J. Tyler Copple
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, 60 College Street, New Haven, CT USA
- Independent Consultant, Yale School of Public Health, 60 College Street, New Haven, CT USA
| | - Tyler Johnson
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, 60 College Street, New Haven, CT USA
| | - A. David Paltiel
- Department of Health Policy and Management, Yale School of Public Health, 60 College Street, New Haven, CT USA
| | - Joshua L. Warren
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT USA
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