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  1. Article ; Online: Probability Samples Provide a Means of Benchmarking and Adjusting for Data Collected From Nonprobability Samples.

    Elliott, Michael R

    American journal of public health

    2023  Volume 113, Issue 7, Page(s) 721–723

    MeSH term(s) Humans ; Sampling Studies ; Benchmarking ; Research Design
    Language English
    Publishing date 2023-06-07
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 121100-6
    ISSN 1541-0048 ; 0090-0036 ; 0002-9572
    ISSN (online) 1541-0048
    ISSN 0090-0036 ; 0002-9572
    DOI 10.2105/AJPH.2023.307317
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Un I Zs.279: Show issues Location:
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  2. Article ; Online: Man-made marine structures - Agents of marine environmental change or just other bits of the hard stuff?

    Elliott, Michael / Birchenough, Silvana N R

    Marine pollution bulletin

    2022  Volume 176, Page(s) 113468

    Language English
    Publishing date 2022-02-18
    Publishing country England
    Document type Editorial
    ZDB-ID 2001296-2
    ISSN 1879-3363 ; 0025-326X
    ISSN (online) 1879-3363
    ISSN 0025-326X
    DOI 10.1016/j.marpolbul.2022.113468
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  3. Article ; Online: Latent Classes of Tobacco and Cannabis Use among Youth and Young Adults in the United States.

    Mattingly, Delvon T / Elliott, Michael R / Fleischer, Nancy L

    Substance use & misuse

    2023  Volume 58, Issue 10, Page(s) 1235–1245

    Abstract: Background: ...

    Abstract Background:
    MeSH term(s) Humans ; United States/epidemiology ; Adolescent ; Young Adult ; Tobacco Use/epidemiology ; Cannabis ; Ethnicity ; Minority Groups ; Tobacco Products ; Electronic Nicotine Delivery Systems
    Language English
    Publishing date 2023-06-01
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1310358-1
    ISSN 1532-2491 ; 1082-6084
    ISSN (online) 1532-2491
    ISSN 1082-6084
    DOI 10.1080/10826084.2023.2215312
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  4. Article ; Online: Surrogacy validation for time-to-event outcomes with illness-death frailty models.

    Roberts, Emily K / Elliott, Michael R / Taylor, Jeremy M G

    Biometrical journal. Biometrische Zeitschrift

    2023  Volume 66, Issue 1, Page(s) e2200324

    Abstract: A common practice in clinical trials is to evaluate a treatment effect on an intermediate outcome when the true outcome of interest would be difficult or costly to measure. We consider how to validate intermediate outcomes in a causally-valid way when ... ...

    Abstract A common practice in clinical trials is to evaluate a treatment effect on an intermediate outcome when the true outcome of interest would be difficult or costly to measure. We consider how to validate intermediate outcomes in a causally-valid way when the trial outcomes are time-to-event. Using counterfactual outcomes, those that would be observed if the counterfactual treatment had been given, the causal association paradigm assesses the relationship of the treatment effect on the surrogate outcome with the treatment effect on the true, primary outcome. In particular, we propose illness-death models to accommodate the censored and semicompeting risk structure of survival data. The proposed causal version of these models involves estimable and counterfactual frailty terms. Via these multistate models, we characterize what a valid surrogate would look like using a causal effect predictiveness plot. We evaluate the estimation properties of a Bayesian method using Markov chain Monte Carlo and assess the sensitivity of our model assumptions. Our motivating data source is a localized prostate cancer clinical trial where the two survival outcomes are time to distant metastasis and time to death.
    MeSH term(s) Humans ; Models, Statistical ; Bayes Theorem ; Frailty ; Biomarkers
    Chemical Substances Biomarkers
    Language English
    Publishing date 2023-09-29
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 131640-0
    ISSN 1521-4036 ; 0323-3847 ; 0006-3452
    ISSN (online) 1521-4036
    ISSN 0323-3847 ; 0006-3452
    DOI 10.1002/bimj.202200324
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  5. Article ; Online: Estimating the effect of latent time-varying count exposures using multiple lists.

    Won, Jung Yeon / Elliott, Michael R / Sanchez-Vaznaugh, Emma V / Sánchez, Brisa N

    Biometrics

    2024  Volume 80, Issue 1

    Abstract: A major challenge in longitudinal built-environment health studies is the accuracy of commercial business databases that are used to characterize dynamic food environments. Different databases often provide conflicting exposure measures on the same ... ...

    Abstract A major challenge in longitudinal built-environment health studies is the accuracy of commercial business databases that are used to characterize dynamic food environments. Different databases often provide conflicting exposure measures on the same subject due to different source credibilities. As on-site verification is not feasible for historical data, we suggest combining multiple databases to correct the bias in health effect estimates due to measurement error in any 1 datasource. We propose a joint model for the time-varying health outcomes, observed count exposures, and latent true count exposures. Our model estimates the time-specific quality of sources and incorporates time dependence of true count exposure by Poisson integer-valued first-order autoregressive process. We take a Bayesian nonparametric approach to flexibly account for location-specific exposures. By resolving the discordance between different databases, our method reduces the bias in the longitudinal health effect of the true exposures. Our method is demonstrated with childhood obesity data in California public schools with respect to convenience store exposures in school neighborhoods from 2001 to 2008.
    MeSH term(s) Child ; Humans ; Bayes Theorem ; Pediatric Obesity ; Databases, Factual ; Schools
    Language English
    Publishing date 2024-02-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 213543-7
    ISSN 1541-0420 ; 0099-4987 ; 0006-341X
    ISSN (online) 1541-0420
    ISSN 0099-4987 ; 0006-341X
    DOI 10.1093/biomtc/ujad027
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    Zs.B 72: Show issues Location:
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  6. Article: Weighted Dirichlet Process Mixture Models to Accommodate Complex Sample Designs for Linear and Quantile Regression.

    Elliott, Michael R / Xia, Xi

    Journal of official statistics

    2021  Volume 37, Issue 1, Page(s) 71–95

    Abstract: Standard randomization-based inference conditions on the data in the population and makes inference with respect to the repeating sampling properties of the sampling indicators. In some settings these estimators can be quite unstable; Bayesian model- ... ...

    Abstract Standard randomization-based inference conditions on the data in the population and makes inference with respect to the repeating sampling properties of the sampling indicators. In some settings these estimators can be quite unstable; Bayesian model-based approaches focus on the posterior predictive distribution of population quantities, potentially providing a better balance between bias correction and efficiency. Previous work in this area has focused on estimation of means and linear and generalized linear regression parameters; these methods do not allow for a general estimation of distributional functions such as quantile or quantile regression parameters. Here we adapt an extended Dirichlet Process Mixture model that allows the DP prior to be a mixture of DP random basis measures that are a function of covariates. These models allow many mixture components when necessary to accommodate the sample design, but can shrink to few components for more efficient estimation when the data allow. We provide an application to the estimation of relationships between serum dioxin levels and age in the US population, either at the mean level (via linear regression) or across the dioxin distribution (via quantile regression) using the National Health and Nutrition Examination Survey.
    Language English
    Publishing date 2021-03-12
    Publishing country Sweden
    Document type Journal Article
    ZDB-ID 2013985-8
    ISSN 2001-7367 ; 0282-423X
    ISSN (online) 2001-7367
    ISSN 0282-423X
    DOI 10.2478/jos-2021-0004
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  7. Article ; Online: Improving transportability of randomized controlled trial inference using robust prediction methods.

    Elliott, Michael R / Carroll, Orlagh / Grieve, Richard / Carpenter, James

    Statistical methods in medical research

    2023  Volume 32, Issue 12, Page(s) 2365–2385

    Abstract: Randomized trials have been the gold standard for assessing causal effects since their introduction by Fisher in the 1920s, since they can eliminate both observed and unobserved confounding. Estimates of causal effects at the population level from ... ...

    Abstract Randomized trials have been the gold standard for assessing causal effects since their introduction by Fisher in the 1920s, since they can eliminate both observed and unobserved confounding. Estimates of causal effects at the population level from randomized controlled trials can still be biased if there are both effect modification and systematic differences between the trial sample and the ultimate population of inference with respect to these modifiers. Recent advances in the survey statistics literature to improve inference in nonprobability samples by using information from probability samples can provide an avenue for improving population causal inference in randomized controlled trials when relevant probability samples of the patient population are available. We review some recent work in "transporting" causal effect estimates from trials to populations, focusing on the setting where there is a "benchmark" or population-representative sample along with the RCT sample. We then propose estimators using either inverse probability weighting (IPWT) or prediction that can accommodate unequal probability of selection in the "benchmark" or population, and use Bayesian additive regression trees for both inverse probability of treatment weighting and prediction estimation that do not require specification of functional form or interaction. We also consider how the assumption of ignorability may be assessed from observed data and propose a sensitivity analysis under the failure of this assumption. We compare our proposed approach with existing methods in simulation and apply these alternative approaches to a study of pulmonary artery catheterization in critically ill patients. We also suggest next steps for future work.
    MeSH term(s) Humans ; Bayes Theorem ; Computer Simulation ; Probability ; Causality ; Randomized Controlled Trials as Topic
    Language English
    Publishing date 2023-11-07
    Publishing country England
    Document type Review ; Journal Article
    ZDB-ID 1136948-6
    ISSN 1477-0334 ; 0962-2802
    ISSN (online) 1477-0334
    ISSN 0962-2802
    DOI 10.1177/09622802231210944
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3564: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
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  8. Article: Data cleaning for clinician researchers: Application and explanation of a data-quality framework.

    Pilowsky, Julia K / Elliott, Rosalind / Roche, Michael A

    Australian critical care : official journal of the Confederation of Australian Critical Care Nurses

    2024  

    Abstract: Background: Data cleaning is the series of procedures performed before a formal statistical analysis, with the aim of reducing the number of error values in a dataset and improving the overall quality of subsequent analyses. Several study-reporting ... ...

    Abstract Background: Data cleaning is the series of procedures performed before a formal statistical analysis, with the aim of reducing the number of error values in a dataset and improving the overall quality of subsequent analyses. Several study-reporting guidelines recommend the inclusion of data-cleaning procedures; however, little practical guidance exists for how to conduct these procedures.
    Objectives: This paper aimed to provide practical guidance for how to perform and report rigorous data-cleaning procedures.
    Methods: A previously proposed data-quality framework was identified and used to facilitate the description and explanation of data-cleaning procedures. The broader data-cleaning process was broken down into discrete tasks to create a data-cleaning checklist. Examples of the how the various tasks had been undertaken for a previous study using data from the Australia and New Zealand Intensive Care Society Adult Patient Database were also provided.
    Results: Data-cleaning tasks were described and grouped according to four data-quality domains described in the framework: data integrity, consistency, completeness, and accuracy. Tasks described include creation of a data dictionary, checking consistency of values across multiple variables, quantifying and managing missing data, and the identification and management of outlier values. The data-cleaning task checklist provides a practical summary of the various aspects of the data-cleaning process and will assist clinician researchers in performing this process in the future.
    Conclusions: Data cleaning is an integral part of any statistical analysis and helps ensure that study results are valid and reproducible. Use of the data-cleaning task checklist will facilitate the conduct of rigorous data-cleaning processes, with the aim of improving the quality of future research.
    Language English
    Publishing date 2024-04-09
    Publishing country Australia
    Document type Journal Article
    ZDB-ID 1159493-7
    ISSN 1878-1721 ; 1036-7314
    ISSN (online) 1878-1721
    ISSN 1036-7314
    DOI 10.1016/j.aucc.2024.03.004
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    Zs.A 3762: Show issues Location:
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  9. Article: The anchoring method: Estimation of interviewer effects in the absence of interpenetrated sample assignment.

    Elliott, Michael R / West, Brady T / Zhang, Xinyu / Coffey, Stephanie

    Survey methodology

    2023  Volume 48, Issue 1, Page(s) 25–48

    Abstract: Methodological studies of the effects that human interviewers have on the quality of survey data have long been limited by a critical assumption: that interviewers in a given survey are assigned random subsets of the larger overall sample (also known as ... ...

    Abstract Methodological studies of the effects that human interviewers have on the quality of survey data have long been limited by a critical assumption: that interviewers in a given survey are assigned random subsets of the larger overall sample (also known as interpenetrated assignment). Absent this type of study design, estimates of interviewer effects on survey measures of interest may reflect differences between interviewers in the characteristics of their assigned sample members, rather than recruitment or measurement effects specifically introduced by the interviewers. Previous attempts to approximate interpenetrated assignment have typically used regression models to condition on factors that might be related to interviewer assignment. We introduce a new approach for overcoming this lack of interpenetrated assignment when estimating interviewer effects. This approach, which we refer to as the "anchoring" method, leverages correlations between observed variables that are unlikely to be affected by interviewers ("anchors") and variables that may be prone to interviewer effects to remove components of within-interviewer correlations that lack of interpenetrated assignment may introduce. We consider both frequentist and Bayesian approaches, where the latter can make use of information about interviewer effect variances in previous waves of a study, if available. We evaluate this new methodology empirically using a simulation study, and then illustrate its application using real survey data from the Behavioral Risk Factor Surveillance System (BRFSS), where interviewer IDs are provided on public-use data files. While our proposed method shares some of the limitations of the traditional approach - namely the need for variables associated with the outcome of interest that are also free of measurement error - it avoids the need for conditional inference and thus has improved inferential qualities when the focus is on marginal estimates, and it shows evidence of further reducing overestimation of larger interviewer effects relative to the traditional approach.
    Language English
    Publishing date 2023-02-08
    Publishing country Canada
    Document type Journal Article
    ZDB-ID 2276693-5
    ISSN 1492-0921 ; 0714-0045
    ISSN (online) 1492-0921
    ISSN 0714-0045
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  10. Article ; Online: Past-Year Use Prevalence of Cannabidiol, Cannabigerol, Cannabinol, and Δ8-Tetrahydrocannabinol Among US Adults.

    Wilson-Poe, Adrianne R / Smith, Tristin / Elliott, Michael R / Kruger, Daniel J / Boehnke, Kevin F

    JAMA network open

    2023  Volume 6, Issue 12, Page(s) e2347373

    MeSH term(s) Adult ; Humans ; Cannabinol ; Dronabinol ; Cannabidiol ; Prevalence
    Chemical Substances cannabigerol (J1K406072N) ; Cannabinol (7UYP6MC9GH) ; Dronabinol (7J8897W37S) ; Cannabidiol (19GBJ60SN5)
    Language English
    Publishing date 2023-12-01
    Publishing country United States
    Document type Journal Article
    ISSN 2574-3805
    ISSN (online) 2574-3805
    DOI 10.1001/jamanetworkopen.2023.47373
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