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Relevant bibliographies by topics / Unmeasured confounders

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Academic literature on the topic 'Unmeasured confounders'

Author: Grafiati

Published: 25 January 2025

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Journal articles on the topic "Unmeasured confounders"

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Burne, Rebecca M., and Michal Abrahamowicz. "Adjustment for time-dependent unmeasured confounders in marginal structural Cox models using validation sample data." Statistical Methods in Medical Research 28, no. 2 (2017): 357–71. http://dx.doi.org/10.1177/0962280217726800.

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Large databases used in observational studies of drug safety often lack information on important confounders. The resulting unmeasured confounding bias may be avoided by using additional confounder information, frequently available in smaller clinical “validation samples”. Yet, no existing method that uses such validation samples is able to deal with unmeasured time-varying variables acting as both confounders and possible mediators of the treatment effect. We propose and compare alternative methods which control for confounders measured only in a validation sample within marginal structural C

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Handorf, Elizabeth A., Daniel F. Heitjan, Justin E. Bekelman, and Nandita Mitra. "Estimating cost-effectiveness from claims and registry data with measured and unmeasured confounders." Statistical Methods in Medical Research 28, no. 7 (2018): 2227–42. http://dx.doi.org/10.1177/0962280218759137.

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The analysis of observational data to determine the cost-effectiveness of medical treatments is complicated by the need to account for skewness, censoring, and the effects of measured and unmeasured confounders. We quantify cost-effectiveness as the Net Monetary Benefit (NMB), a linear combination of the treatment effects on cost and effectiveness that denominates utility in monetary terms. We propose a parametric estimation approach that describes cost with a Gamma generalized linear model and survival time (the canonical effectiveness variable) with a Weibull accelerated failure time model.

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Rodday, Angie Mae, Theresa Hahn, Peter K. Lindenauer, and Susan K. Parsons. "67409 Quantifying Unmeasured Confounding in Relationship between Treatment Intensity and Outcomes among Older Patients with Hodgkin Lymphoma (HL) using Surveillance, Epidemiology and End Results (SEER)-Medicare Data." Journal of Clinical and Translational Science 5, s1 (2021): 49–50. http://dx.doi.org/10.1017/cts.2021.531.

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ABSTRACT IMPACT: E-values can help quantify the amount of unmeasured confounded necessary to fully explain away a relationship between treatment and outcomes in observational data. OBJECTIVES/GOALS: Older patients with HL have worse outcomes than younger patients, which may reflect treatment choice (e.g., fewer chemotherapy cycles). We studied the relationship between treatment intensity and 3-year overall survival (OS) in SEER-Medicare. We calculated an E-value to quantify the unmeasured confounding needed to explain away any relationship. METHODS/STUDY POPULATION: This retrospective cohort s

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Yin, Xiang, Elizabeth Stuart, Mehmet Burcu, Mark Stewart, Elizabeth B. Lamont, and Ruthanna Davi. "Assessing the impact of unmeasured confounding in external control arms via tipping point analyses." Journal of Clinical Oncology 42, no. 16_suppl (2024): e23065-e23065. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.e23065.

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e23065 Background: Estimates of the comparative efficacy of new therapies from single-arm settings can be obtained in advance of RCTs through use of external control arms (ECAs).[1] ECAs are collections of patients with the index disease who were treated outside of the single-arm trial, whose measured baseline attributes are matched to the single-arm trial patients and whose outcomes are compared to trial patients’ to estimate comparative efficacy. Without randomization, observed treatment-outcome associations may be confounded by unmeasured patient attributes. Applying established statistical

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Palta, Mari, and Tzy-Jyun Yao. "Analysis of Longitudinal Data with Unmeasured Confounders." Biometrics 47, no. 4 (1991): 1355. http://dx.doi.org/10.2307/2532391.

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Szarewski, A., and D. Mansour. "Study subject to unmeasured confounders and biases." BMJ 342, may31 1 (2011): d3349. http://dx.doi.org/10.1136/bmj.d3349.

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Navadeh, Soodabeh, Ali Mirzazadeh, Willi McFarland, et al. "Unsafe Injection Is Associated with Higher HIV Testing after Bayesian Adjustment for Unmeasured Confounding." Archives of Iranian Medicine 23, no. 12 (2020): 848–55. http://dx.doi.org/10.34172/aim.2020.113.

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Background: To apply a novel method to adjust for HIV knowledge as an unmeasured confounder for the effect of unsafe injection on future HIV testing. Methods: The data were collected from 601 HIV-negative persons who inject drugs (PWID) from a cohort in San Francisco. The panel-data generalized estimating equations (GEE) technique was used to estimate the adjusted risk ratio (RR) for the effect of unsafe injection on not being tested (NBT) for HIV. Expert opinion quantified the bias parameters to adjust for insufficient knowledge about HIV transmission as an unmeasured confounder using Bayesia

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McCandless, Lawrence C. "Meta-Analysis of Observational Studies with Unmeasured Confounders." International Journal of Biostatistics 8, no. 2 (2012): 1–31. http://dx.doi.org/10.2202/1557-4679.1350.

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Flanders, W. Dana. "Negative-Control Exposures: Adjusting for Unmeasured and Measured Confounders With Bounds for Remaining Bias." Epidemiology 34, no. 6 (2023): 850–53. http://dx.doi.org/10.1097/ede.0000000000001650.

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Negative-control exposures can be used to detect and even adjust for confounding that remains after control of measured confounders. A newly described method allows the analyst to reduce residual confounding by unmeasured confounders U by using negative-control exposures to define and select a subcohort wherein the U-distribution among the exposed is similar to that among the unexposed. Here, we show that conventional methods can be used to control for measured confounders in conjunction with the new method to control for unmeasured ones. We also derive an expression for bias that remains afte

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Luiz, Ronir Raggio, and Maria Deolinda Borges Cabral. "Sensitivity analysis for an unmeasured confounder: a review of two independent methods." Revista Brasileira de Epidemiologia 13, no. 2 (2010): 188–98. http://dx.doi.org/10.1590/s1415-790x2010000200002.

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One of the main purposes of epidemiological studies is to estimate causal effects. Causal inference should be addressed by observational and experimental studies. A strong constraint for the interpretation of observational studies is the possible presence of unobserved confounders (hidden biases). An approach for assessing the possible effects of unobserved confounders may be drawn up through the use of a sensitivity analysis that determines how strong the effects of an unmeasured confounder should be to explain an apparent association, and which should be the characteristics of this confounde

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Dissertations / Theses on the topic "Unmeasured confounders"

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Wang, Yingbo. "Using propensity score to adjust for unmeasured confounders in small area studies of environmental exposures and health." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/51497.

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Small area studies are commonly used in epidemiology to assess the impact of risk factors on health outcomes when data are available at the aggregated level. However the estimates are often biased due to unmeasured confounders which cannot be taken into account. Integrating individual-level information into area-level data in ecological studies may help reduce bias. To investigate this, I develop an area/ecological level propensity score (PS) to integrate individual-level data and then synthesise the area-level PS with routinely available area-level datasets, such as hospital episode statistic

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Duong, Chi-Hong. "Approches statistiques en pharmacoépidémiologie pour la prise en compte des facteurs de confusion indirectement mesurés dans les bases de données médico-administratives : Application aux médicaments pris au cours de la grossesse." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASR028.

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Les bases de données médico-administratives sont de plus en plus utilisées en pharmacoépidémiologie. Néanmoins, l'existence de facteurs de confusion (FDC) non mesurés et non pris en compte peut biaiser les analyses. Dans ce travail, nous explorons l'intérêt d'exploiter la richesse des données avec la sélection à large échelle d'un grand nombre de covariables mesurées corrélées avec d'éventuels facteurs manquants pour les ajuster indirectement. Ce concept est à la base du score de propension en grande dimension (hdPS), et nous appliquons la même démarche à la G-computation (GC) et l'estimation

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Chien-ChouSu and 蘇建州. "Comparative Mortality Risk of Antipsychotic Medications in Elderly Patients with Stroke: Adjusting for Unmeasured Confounders with Stroke Registry Database." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/mjf9ea.

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博士<br>國立成功大學<br>臨床藥學與藥物科技研究所<br>107<br>Background: Elderly patients are at risk for developing psychosis after stroke, including delusions, hallucinations, agitation, and disorganized behavior. According to previous guidelines, antipsychotics are the first-line pharmacological intervention for psychosis, but elderly patients who are treated with antipsychotics might have an increased risk of mortality based on US FDA safety communications. However, there are limited studies examining mortality risk associated with antipsychotic use in elderly patients who have had a stroke. The major limitatio

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Book chapters on the topic "Unmeasured confounders"

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Lash, Timothy L., Aliza K. Fink, and Matthew P. Fox. "Unmeasured and Unknown Confounders." In Statistics for Biology and Health. Springer New York, 2009. http://dx.doi.org/10.1007/b97920_5.

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Lash, Timothy L., Aliza K. Fink, and Matthew P. Fox. "Unmeasured and Unknown Confounders." In Statistics for Biology and Health. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-87959-8_5.

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Rothman, Kenneth J., Krista F. Huybrechts, and Eleanor J. Murray. "An Introduction to Some Advanced Topics." In Epidemiology, 3rd ed. Oxford University PressNew York, 2025. http://dx.doi.org/10.1093/oso/9780197751541.003.0015.

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Abstract Many topics in epidemiology are too specialized for an introductory text. In this final chapter, a few such topics are introduced, providing an on-ramp to further study for those wishing to pursue a deeper knowledge of epidemiologic methods. Since every dataset has some missing data, the chapter starts by outlining approaches to handling missing data in an analysis. Second, it describes how causal diagrams can be helpful to distinguish causal and noncausal relations between study variables and to identify variables that should be accounted for in the analyses. Third, it briefly explains why novel approaches, g-methods, are needed to account for time-varying confounding, which results from a feedback loop between exposure and confounders. Fourth, instrumental variables are introduced as an approach that can theoretically control for confounding even if some confounders remain unmeasured. Finally, quantitative bias analyses offer a structured approach to quantifying the potential impact of systematic errors on study findings.

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Chib, Siddhartha. "On Inferring Effects of Binary Treatments with Unobserved Confounders." In Bayesian Statistics 7. Oxford University PressOxford, 2003. http://dx.doi.org/10.1093/oso/9780198526155.003.0004.

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Abstract One of the most pervasive problems in statistics is the following. Suppose x is a binary {0,1} indicator of a treatment (where the term treatment is intended to embrace a covariate of interest, not necessarily one arising in a medical or epidemiological setting) and y is a response and the objective is to isolate the effect (“treatment effect” ) of x on y. To fix ideas, x may be an indicator of smoking status taking the value one if the subject is a current smoker and the value zero otherwise and y may be some measure of subject health. For simplicity we assume that both the treatment and response are univariate although this can obviously be relaxed. It is well understood that outside of the experimental setting inferring effects of this kind raise a multitude of challenges that are not easy to address (even in a randomized treatment setting, inference is difficult when human subjects are involved due to dropouts, non-compliance and other such complications). The problem is that when the treatment intake is non-random, as in an observational setting where subjects self-select into a treatment state, the choice of treatment may be influenced by unmeasured or unmeasurable or unobservable covariates that also affect the response.

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Savarese, Gianluigi, Marija Polovina, and Gerasimos Filippatos. "Clinical trial design and interpretation." In The ESC Textbook of Heart Failure, edited by Petar M. Seferović, Andrew J. S. Coats, Gerasimos Filippatos, Stefan D. Anker, Johann Bauersachs, and Giuseppe Rosano. Oxford University PressOxford, 2023. http://dx.doi.org/10.1093/med/9780198891628.003.0083.

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Abstract Randomized controlled trials (RCTs) represent the gold standard to test treatment benefit in medicine because randomization, unlike adjustments in observational studies, allows controlling for any known and unknown, measured and unmeasured confounder. Blinding is a further procedure used in RCTs to reduce risk of bias. Setting adequate selection criteria in RCTs is key to test the population that the intervention is intended for and to enrich the trial for the occurrence of cardiovascular endpoints and reduce the risk of competing events. However, too strict selection criteria might lead to questions bout the generalizability of RCTs findings to real-world populations. Intention-to-treat analysis is the gold standard in RCT analysis but use of per-protocol as supporting sensitivity analysis is highly recommended. Pre-specification when approaching subgroup analysis prevents the selective reporting of results and leads to more credible and valuable findings compared with post-hoc analyses. Novel and more dynamic trial design might make possible running more, more effective, and cheaper randomized controlled trials in the heart failure field.

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Conference papers on the topic "Unmeasured confounders"

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Shimizu, Tatsuhiro. "Diffusion Model in Causal Inference with Unmeasured Confounders." In 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2023. http://dx.doi.org/10.1109/ssci52147.2023.10372009.

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McCann, Cameron. "Multilevel Mediation With Unmeasured Cluster-Level Confounders: Evaluating Propensity Score Models." In AERA 2024. AERA, 2024. http://dx.doi.org/10.3102/ip.24.2150208.

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Batista, Bernardo Pinheiro de Senna Nogueira, Suzana Sales de Aguiar, Ana Carolina Padula Ribeiro Pereira, Rosalina Jorge Koifman, and Anke Bergmann. "IMPACT OF BREAST RECONSTRUCTION ON MORTALITY AFTER BREAST CANCER: SURVIVAL ANALYSIS IN A COHORT OF 620 CONSECUTIVE PATIENTS." In Abstracts from the Brazilian Breast Cancer Symposium - BBCS 2021. Mastology, 2021. http://dx.doi.org/10.29289/259453942021v31s2094.

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Background: Access to breast reconstruction is a complex and poorly understood aspect of survival. In the United States, although the rate of immediate reconstruction has tripled in the past 20 years, less than 40% of women undergoing a mastectomy will do so as part of the same procedure. Although there is common understanding that breast reconstruction is oncologically safe, published data on its impact on survival show conflicting and unjustified observations. Methods: We performed a secondary survival analysis in a fixed cohort of 620 consecutive patients who underwent mastectomy between Au

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Ding, Sihao, Peng Wu, Fuli Feng, et al. "Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis." In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2022. http://dx.doi.org/10.1145/3534678.3539240.

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Reports on the topic "Unmeasured confounders"

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Hertel, Thomas, David Hummels, Maros Ivanic, and Roman Keeney. How Confident Can We Be in CGE-Based Assessments of Free Trade Agreements? GTAP Working Paper, 2003. http://dx.doi.org/10.21642/gtap.wp26.

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With the proliferation of Free Trade Agreements (FTAs) over the past decade, demand for quantitative analysis of their likely impacts has surged. The main quantitative tool for performing such analysis is Computable General Equilibrium (CGE) modeling. Yet these models have been widely criticized for performing poorly (Kehoe, 2002) and having weak econometric foundations (McKitrick, 1998; Jorgenson, 1984). FTA results have been shown to be particularly sensitive to the trade elasticities, with small trade elasticities generating large terms of trade effects and relatively modest efficiency gain

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