Epidemiologic Methods最新文献

{"title":"An ensemble modeling approach for estimating expected patterns in mortality in the United States.","authors":"Lauren M Rossen, Jodi A Cisewski, Farida B Ahmad, Robert N Anderson","doi":"10.1515/em-2025-0035","DOIUrl":"https://doi.org/10.1515/em-2025-0035","url":null,"abstract":"<p><strong>Background: </strong>Excess mortality statistics are critical for quantifying the impact of various public health disasters and epidemics, particularly when deaths directly attributable to a given event may be underestimated. The COVID-19 pandemic created challenges for monitoring excess mortality due to the prolonged elevations in mortality throughout the pandemic, and the difficulties in accurately forecasting expected mortality over long time horizons. Additionally, prior research has found that excess mortality estimates can vary depending on the model used and various analytic decisions. Ensemble modeling approaches can mitigate these limitations by incorporating information from multiple models and better accounting for the uncertainty around analytic choices.</p><p><strong>Objective: </strong>To evaluate the performance of an ensemble modeling approach to estimating excess mortality relative to methods that rely on single models.</p><p><strong>Methods: </strong>This study assesses an ensemble modeling approach to estimate excess mortality in the United States, based on weekly mortality data from the National Vital Statistics System from 2008 through 2024. We evaluated 76 candidate models with various long-term and seasonal trend specifications along with ensemble models.</p><p><strong>Results: </strong>Results indicate that an ensemble modeling approach offers more consistent and robust performance, with improved accuracy and coverage compared to individual models.</p><p><strong>Conclusions: </strong>This analysis highlights the utility of ensemble modeling in providing robust estimates of expected mortality, a critical resource for public health preparedness and response in the face of disasters or epidemics.</p>","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13693783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149274402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Primer on Large-Sample Statistical Inference for Epidemiologists.","authors":"Bonnie E Shook-Sa, Stephen R Cole, Paul N Zivich, Jessie K Edwards, Taylor J Krajewski, Timothy Feeney, Adaora Adimora, Michael G Hudgens","doi":"10.1515/em-2025-0036","DOIUrl":"10.1515/em-2025-0036","url":null,"abstract":"<p><p>Statistical theory forms a foundation for how epidemiologists learn about populations in public health and medical studies and is fundamental for the understanding of more advanced epidemiological methods (e.g., in causal inference and machine learning). Textbooks provide in-depth coverage of probability and statistical theory, but with such comprehensive coverage that it can be easy to miss the forest for the trees. Here, we provide a summary of fundamental concepts from large-sample statistical theory to allow for more focused understanding tailored to epidemiologists and health science researchers. This primer aims to promote appropriate understanding and application of statistical methods in epidemiologic research. We clarify several often-confused statistical topics and provide a motivation for the application of large-sample inferential methods to data from population health and medical studies. Assumptions underlying commonly used statistical methods that must be considered for valid inference are also discussed. These ideas are contextualized with an example from the Women's Interagency HIV Study.</p>","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13026506/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147575801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new method to estimate factors associated with tuberculosis transmission using whole genome sequencing and other additional metadata.","authors":"Anne N Shapiro, ChuanChin Huang, Meredith B Brooks, Samantha Malatesta, Leonid Lecca, Mercedes C Becerra, Roger I Calderon, Carmen C Contreras, Judith Jimenez, Rosa Yataco, Zibiao Zhang, Megan B Murray, Helen E Jenkins, Laura F White","doi":"10.1515/em-2026-0009","DOIUrl":"10.1515/em-2026-0009","url":null,"abstract":"<p><strong>Background: </strong>Understanding tuberculosis (TB) transmission dynamics is necessary to interrupt disease spread. We developed a model to estimate adjusted odds ratios (ORs) for factors associated with genetic relatedness, as proxy for transmission.</p><p><strong>Methods: </strong>We build upon an existing iterative model that modifies genetically linked tuberculosis case data to better represent true transmission links. We incorporate bootstrapped logistic regression to calculate adjusted ORs with confidence intervals that account for correlation from individuals present across multiple transmission pairs. We assess model performance with simulation studies and apply the method to cohort data from Lima, Peru.</p><p><strong>Results: </strong>Iterative algorithm estimates resembled those from logistic regression but had larger confidence intervals, reflecting the data correlation adjustment. Transmission pairs where at least one member was >34 years had decreased transmission odds. Pairs with at least one incarcerated or male member had increased adjusted transmission odds.</p><p><strong>Conclusions: </strong>We produce adjusted ORs accounting for the correlation of pairwise genetic relatedness data. These ORs are an accurate proxy for the association between covariates and transmission and further our understanding of factors associated with tuberculosis transmission.</p>","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637481/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regression calibration for time-to-event outcomes: mitigating bias due to measurement error in real-world endpoints.","authors":"Benjamin Ackerman, Ryan W Gan, Youyi Zhang, Juned Siddique, James Roose, Jennifer L Lund, Janick Weberpals, Jocelyn R Wang, Craig S Meyer, Jennifer Hayden, Khaled Sarsour, Ashita S Batavia","doi":"10.1515/em-2025-0009","DOIUrl":"10.1515/em-2025-0009","url":null,"abstract":"<p><strong>Objectives: </strong>In drug development, there is increasing interest in leveraging real-world data (RWD) to augment trial data and generate evidence about treatment efficacy. However, comparing patient outcomes across trial and routine clinical care settings can be susceptible to bias, namely due to differences in how and when disease assessments occur. This can introduce measurement error in RWD relative to trial standards and lead to bias when comparing endpoints. We develop a novel statistical method, survival regression calibration (SRC), to mitigate measurement error bias in time-to-event RWD outcomes and improve inferences when combining trials with RWD in oncology.</p><p><strong>Methods: </strong>SRC extends upon existing regression calibration methods to address measurement error in time-to-event RWD outcomes. The method entails fitting separate Weibull regression models using trial-like ('true') and real-world-like ('mismeasured') outcome measures in a validation sample, and then calibrating parameter estimates in the full study according to the estimated bias in Weibull parameters. We evaluate performance of SRC under varying degrees of existing measurement error bias via simulation, and then illustrate how SRC can address measurement error when estimating median progression-free survival (mPFS) in newly diagnosed multiple myeloma RWD.</p><p><strong>Results: </strong>When measurement error exists between trial and real-world mPFS, SRC can effectively account for its resulting bias. SRC yields greater reduction in measurement error bias than standard regression calibration methods, due to its suitability for time-to-event outcomes.</p><p><strong>Conclusions: </strong>Outcome measurement error is important to address when combining trials and RWD, as it may lead to biased results. Our SRC method helps mitigate such bias, improving comparability between real-world and trial endpoints and strengthening evidence of treatment efficacy.</p>","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"14 1","pages":"20250009"},"PeriodicalIF":0.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12464481/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145186746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the association between school substance programs and student substance use: accounting for informative cluster size.","authors":"Aya A Mitani, Yushu Zou, Scott T Leatherdale, Karen A Patte","doi":"10.1515/em-2024-0028","DOIUrl":"https://doi.org/10.1515/em-2024-0028","url":null,"abstract":"<p><strong>Objectives: </strong>The use of substances in adolescents is an increasing public health problem. Many high schools in Canada have implemented school-based programs to mitigate student substance use, but their utility is not conclusive. Polysubstance use data collected on students from multiple schools may be subject to informative cluster size (ICS). The objective of this study was to investigate whether a multivariate analysis approach that addresses ICS provides different conclusions from univariate analyses and methods that do not account for ICS.</p><p><strong>Methods: </strong>We used data from the 2018/2019 cycle of the Cannabis, Obesity, Mental health, Physical activity, Alcohol, Smoking, and Sedentary Behaviour (COMPASS) study, an ongoing prospective cohort study that annually collects data from Canadian high schools and students. We compared results from four analytical approaches that estimate marginal associations between each school substance program and the four substance use behaviours (binge drinking, cannabis, e-cigarette, and cigarette): univariate generalized estimating equations (GEE), univariate cluster-weighted GEE (CWGEE), multivariate GEE, and multivariate CWGEE.</p><p><strong>Results: </strong>We observed that the proportion of students who engage in each of the four behaviours was higher in small schools and lower in large schools. In general, the univariate and multivariate analyses produced comparable results. Some differences existed between multivariate CWGEE and GEE. CWGEE indicated that the school program on cannabis had an odds ratio (OR) and 95 % confidence interval (CI) of 0.83 (0.73, 0.95) on all substance use, but GEE produced a null association with an OR (95 % CI) of 0.92 (0.79, 1.07).</p><p><strong>Conclusions: </strong>When ICS is present in clustered school data, weighted and unweighted analyses may produce different results. Care is needed to investigate the relationship between cluster size and the outcome, and use appropriate methods for analysis. Certain substance programs may influence student behaviour in other substances, highlighting the need for a multivariate analytical approach when studying the use of substances by adolescents.</p>","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"14 1","pages":"20240028"},"PeriodicalIF":0.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12376993/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The quantiles of extreme differences matrix for evaluating discriminant validity.","authors":"Tyler J VanderWeele, R Noah Padgett","doi":"10.1515/em-2025-0006","DOIUrl":"https://doi.org/10.1515/em-2025-0006","url":null,"abstract":"<p><p>When data on multiple indicators of underlying psychosocial constructs are collected, they are often intended as closely related assessments of a relatively unified phenomenon, or alternatively as capturing distinct facets of the phenomenon. Establishing distinctions among construct phenomena, assessments, or indicators is sometimes described as establishing discriminant validity. In the philosophical literature, often extreme instances or limit cases, actual or hypothetical, are used to identify settings in which one phenomenon is present and the other is not, to establish distinctions. We put forward an empirical analogue of this philosophical principle applied to distinctions amongst survey item responses. The quantiles of extreme differences matrix characterizes, for each pair of indicators, how large differences are between indicators at relatively extreme quantiles of the distribution of those differences. We discuss potential uses and properties of this matrix and related matrices for identifying relevant distinctions among indicators or facets of underlying construct phenomena.</p>","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"14 1","pages":"20250006"},"PeriodicalIF":0.0,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12372585/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-varying reproductive number estimation for practical application in structured populations.","authors":"Erin Clancey, Eric T Lofgren","doi":"10.1515/em-2024-0020","DOIUrl":"https://doi.org/10.1515/em-2024-0020","url":null,"abstract":"<p><strong>Objectives: </strong>EpiEstim is a popular statistical framework designed to produce real-time estimates of the time-varying reproductive number, <math> <msub><mrow><mi>ℛ</mi></mrow> <mrow><mi>t</mi></mrow> </msub> </math> . However, the methods in EpiEstim have not been tested in small, non-randomly mixing populations to determine if the resulting <math> <msub> <mrow> <mover><mrow><mi>ℛ</mi></mrow> <mi>ˆ</mi></mover> </mrow> <mrow><mi>t</mi></mrow> </msub> </math> estimates are temporally biased. Thus, we evaluate the temporal performance of EpiEstim <math> <msub> <mrow> <mover><mrow><mi>ℛ</mi></mrow> <mi>ˆ</mi></mover> </mrow> <mrow><mi>t</mi></mrow> </msub> </math> estimates when population structure is present, and then demonstrate how to recover temporal accuracy using an approximation with EpiEstim.</p><p><strong>Methods: </strong>Following a real-world example of a COVID-19 outbreak in a small university town, we generate simulated case report data from a two-population mechanistic model with an explicit generation interval distribution and expression to compute true <math> <msub><mrow><mi>ℛ</mi></mrow> <mrow><mi>t</mi></mrow> </msub> </math> . To quantify the temporal bias, we compare the time points when true <math> <msub><mrow><mi>ℛ</mi></mrow> <mrow><mi>t</mi></mrow> </msub> </math> and estimated <math> <msub> <mrow> <mover><mrow><mi>ℛ</mi></mrow> <mi>ˆ</mi></mover> </mrow> <mrow><mi>t</mi></mrow> </msub> </math> from EpiEstim fall below the critical threshold of 1.</p><p><strong>Results: </strong>When population structure is present but not accounted for <math> <msub> <mrow> <mover><mrow><mi>ℛ</mi></mrow> <mi>ˆ</mi></mover> </mrow> <mrow><mi>t</mi></mrow> </msub> </math> estimates from EpiEstim prematurely fall below 1. When incidence data is aggregated over weeks the estimates from EpiEstim fall below the critical threshold at a later time point than estimates from daily data, however, population structure does not further affect timing differences between aggregated and daily data. Last, we show it is possible to recover the correct timing when by using the lagging subpopulation outbreak to estimate <math> <msub> <mrow> <mover><mrow><mi>ℛ</mi></mrow> <mi>ˆ</mi></mover> </mrow> <mrow><mi>t</mi></mrow> </msub> </math> for the total population with EpiEstim.</p><p><strong>Conclusions: </strong><math> <msub><mrow><mi>ℛ</mi></mrow> <mrow><mi>t</mi></mrow> </msub> </math> is a key parameter used for epidemic response. Since population structure can bias <math> <msub><mrow><mi>ℛ</mi></mrow> <mrow><mi>t</mi></mrow> </msub> </math> near the critical threshold of 1, EpiEstim should be prudently applied to incidence data from structured populations.</p>","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12383560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linked shrinkage to improve estimation of interaction effects in regression models.","authors":"Mark A van de Wiel, Matteo Amestoy, Jeroen Hoogland","doi":"10.1515/em-2023-0039","DOIUrl":"10.1515/em-2023-0039","url":null,"abstract":"<p><strong>Objectives: </strong>The addition of two-way interactions is a classic problem in statistics, and comes with the challenge of quadratically increasing dimension. We aim to a) devise an estimation method that can handle this challenge and b) to aid interpretation of the resulting model by developing computational tools for quantifying variable importance.</p><p><strong>Methods: </strong>Existing strategies typically overcome the dimensionality problem by only allowing interactions between relevant main effects. Building on this philosophy, and aiming for settings with moderate n to p ratio, we develop a local shrinkage model that links the shrinkage of interaction effects to the shrinkage of their corresponding main effects. In addition, we derive a new analytical formula for the Shapley value, which allows rapid assessment of individual-specific variable importance scores and their uncertainties.</p><p><strong>Results: </strong>We empirically demonstrate that our approach provides accurate estimates of the model parameters and very competitive predictive accuracy. In our Bayesian framework, estimation inherently comes with inference, which facilitates variable selection. Comparisons with key competitors are provided. Large-scale cohort data are used to provide realistic illustrations and evaluations. The implementation of our method in RStan is relatively straightforward and flexible, allowing for adaptation to specific needs.</p><p><strong>Conclusions: </strong>Our method is an attractive alternative for existing strategies to handle interactions in epidemiological and/or clinical studies, as its linked local shrinkage can improve parameter accuracy, prediction and variable selection. Moreover, it provides appropriate inference and interpretation, and may compete well with less interpretable machine learners in terms of prediction.</p>","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"13 1","pages":"20230039"},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11232106/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Population dynamic study of two prey one predator system with disease in first prey using fuzzy impulsive control","authors":"Khushbu Singh, K. Kolla","doi":"10.1515/em-2023-0037","DOIUrl":"https://doi.org/10.1515/em-2023-0037","url":null,"abstract":"\u0000 \u0000 \u0000 The prey-predator model provides a mathematical framework for understanding the population dynamics of interacting species, highlighting the delicate balance between predator and prey populations in ecological systems. The four-species predator-prey model extends the Lotka-Volterra framework to explore the dynamics of ecosystems with multiple interacting species. It provides a theoretical foundation for understanding how the populations of multiple prey and predator species influence each other over time. Apart from the traditional methods like direct approach for solving the non-linear system of equations, recent Fuzzy method approaches have been developed. The solution of non-linear systems using classical methods is not easy due to its non-linearity, analytical complexity, chaotic behavior, etc. and the T-S method is very much effective to analyze the non-linear models.\u0000 \u0000 \u0000 \u0000 In this study, we considered an eco-epidemic model with two populations of prey and one population of predator, with the only infectious disease infecting the first prey population. The four-dimensional Lotka-Volterra predator-prey system’s model stability has been examined using the Takagi-Sugeno (T-S) impulsive control model and the Fuzzy impulsive control model. Following the formulation of the model, the global stability and the Fuzzy solution are carried out through numerical simulations and graphical representations with appropriate discussion for a better understanding the dynamics of our proposed model.\u0000 \u0000 \u0000 \u0000 The Takagi-Sugeno method has diverse applications in modeling, control, pattern recognition, and decision-making in systems where uncertainty and non-linearity play a significant role. Its ability to combine fuzzy logic with traditional mathematical models provides a powerful tool for addressing complex real-world problems.\u0000 \u0000 \u0000 \u0000 The impulse control approach, what is considered within the foundation of fuzzy systems established on T-S model, is found to be suitable for extremely complex and non-linear systems with impulse effects.\u0000","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"27 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140525695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bounds for selection bias using outcome probabilities","authors":"Stina Zetterstrom","doi":"10.1515/em-2023-0033","DOIUrl":"https://doi.org/10.1515/em-2023-0033","url":null,"abstract":"\u0000 \u0000 \u0000 Determining the causal relationship between exposure and outcome is the goal of many observational studies. However, the selection of subjects into the study population, either voluntary or involuntary, may result in estimates that suffer from selection bias. To assess the robustness of the estimates as well as the magnitude of the bias, bounds for the bias can be calculated. Previous bounds for selection bias often require the specification of unknown relative risks, which might be difficult to provide. Here, alternative bounds based on observed data and unknown outcome probabilities are proposed. These unknown probabilities may be easier to specify than unknown relative risks.\u0000 \u0000 \u0000 \u0000 I derive alternative bounds from the definitions of the causal estimands using the potential outcomes framework, under specific assumptions. The bounds are expressed using observed data and unobserved outcome probabilities. The bounds are compared to previously reported bounds in a simulation study. Furthermore, a study of perinatal risk factors for type 1 diabetes is provided as a motivating example.\u0000 \u0000 \u0000 \u0000 I show that the proposed bounds are often informative when the exposure and outcome are sufficiently common, especially for the risk difference in the total population. It is also noted that the proposed bounds can be uninformative when the exposure and outcome are rare. Furthermore, it is noted that previously proposed assumption-free bounds are special cases of the new bounds when the sensitivity parameters are set to their most conservative values.\u0000 \u0000 \u0000 \u0000 Depending on the data generating process and causal estimand of interest, the proposed bounds can be tighter or wider than the reference bounds. Importantly, in cases with sufficiently common outcome and exposure, the proposed bounds are often informative, especially for the risk difference in the total population. It is also noted that, in some cases, the new bounds can be wider than the reference bounds. However, the proposed bounds based on unobserved probabilities may in some cases be easier to specify than the reference bounds based on unknown relative risks.\u0000","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"128 5-6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140516970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}