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Adjusting for indirectly measured confounding using large-scale propensity scores [article]

Linying Zhang, Yixin Wang, Martijn Schuemie, David Blei, George Hripcsak
2022 arXiv   pre-print
To help answer this question, this paper examines the performance of the large-scale propensity score (LSPS) approach on causal analysis of medical data.  ...  Confounding remains one of the major challenges to causal inference with observational data.  ...  Acknowledgements This work was supported by NIH R01LM006910, c-01; ONR N00014-17-1-2131, N00014-15-1-2209; NSF CCF-1740833; DARPA SD2 FA8750-18-C-0130; Amazon; NVIDIA; and Simons Foundation.  ... 
arXiv:2110.12235v2 fatcat:x7tsiodpijh6jjsj5kg3vv4zae

Contextualizing E-values for Interpretable Sensitivity to Unmeasured Confounding Analyses [article]

Lucy D'Agostino McGowan and Robert A. Greevy, Jr
2020 arXiv   pre-print
The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding.  ...  We introduce a sensitivity analysis figure that presents the Observed Covariate E-values, on the E-value scale, next to their corresponding observed bias effects, on the original scale of the study results  ...  balance pre-and post-propensity score adjustment (Austin and Stuart 2015; Joffe et al. 2004) .  ... 
arXiv:2011.07030v1 fatcat:5ggps6wm2nfjllpdhxwwnwxmne

The ACCE method: an approach for obtaining quantitative or qualitative estimates of residual confounding that includes unmeasured confounding

Eric G. Smith
2015 F1000Research  
studies typically cannot account for Background: confounding from unmeasured factors.  ...  Discuss this article (0) Comments 2 1 METHOD ARTICLE The ACCE method: an approach for obtaining quantitative or qualitative estimates of residual confounding [v1; ref status: indexed, Abstract Nonrandomized  ...  Some degree of change between Model 1 and Model 2 is expected in the balance of each of the propensity score covariates present in common between Model 1 and Model 2 (the "included covariates").  ... 
doi:10.12688/f1000research.4801.2 pmid:25580226 pmcid:PMC4288424 fatcat:knr44ojedjdavmuhqrnjupu2ri

The ACCE method: an approach for obtaining quantitative or qualitative estimates of residual confounding

Eric G. Smith
2014 F1000Research  
Nonrandomized studies typically cannot account for Background: confounding from unmeasured factors.  ...  Some degree of change between Model 1 and Model 2 is expected in the balance of each of the propensity score covariates present in common between Model 1 and Model 2 (the "included covariates").  ...  Treatment Effect Estimate observed between Model 1 and Model 2 is attributable to the increased balance in the Introduced Variable (resulting from stratification or matching on the propensity score  ... 
doi:10.12688/f1000research.4801.1 pmid:25580226 pmcid:PMC4288424 fatcat:uatnpavrbnggzi55m5ivu2sk6m

Using Non-Experimental Data to Estimate Treatment Effects

Elizabeth A. Stuart, Sue M. Marcus, Marcela V. Horvitz-Lennon, Robert D. Gibbons, Sharon-Lise T. Normand, C. Hendricks Brown
2009 Psychiatric annals  
Rather, the success of a propensity score model (and subsequent matching or stratifi cation procedure) is determined by the covariate balance achieved.  ...  Once the treatment group, comparison group, and potential confounders are identifi ed, researchers need to identify data on those groups and the confounders.  ... 
doi:10.3928/00485713-20090625-07 pmid:20563313 pmcid:PMC2886294 fatcat:rchvxubeobdrhkasxyb5s5sjaa

Applied comparison of large‐scale propensity score matching and cardinality matching for causal inference in observational research

Stephen P. Fortin, Stephen S Johnston, Martijn J Schuemie
2021 BMC Medical Research Methodology  
The current study proposes a framework for large-scale CM (LS-CM); and compares large-scale PSM (LS-PSM) and LS-CM in terms of post-match sample size, covariate balance and residual confounding at progressively  ...  Propensity scores were calculated using LASSO regression, and candidate covariates with non-zero beta coefficients in the propensity model were defined as matching covariates for use in LS-CM.  ...  large-scale propensity score matching using machine learning to calculate propensity scores [8, 9] .  ... 
doi:10.1186/s12874-021-01282-1 pmid:34030640 fatcat:pp7f5z5ipve75akaswghjy5kba

Using high-dimensional propensity scores to automate confounding control in a distributed medical product safety surveillance system

Jeremy A. Rassen, Sebastian Schneeweiss
2012 Pharmacoepidemiology and Drug Safety  
The high-dimensional propensity score (hd-PS) algorithm is one option for automated confounding control in longitudinal healthcare databases.  ...  of the safety evaluation process to achieve the necessary speed and scale at reasonable cost without sacrificing validity.  ...  ACKNOWLEDGEMENTS The authors acknowledge the members of the Mini-Sentinel Methods Development Signal Evaluation Workgroup for their insights on collier bias and other variable selection challenges.  ... 
doi:10.1002/pds.2328 pmid:22262592 fatcat:7zkodg75rnarhibv636xpyavuq

Adjusting for Unmeasured Spatial Confounding with Distance Adjusted Propensity Score Matching [article]

Georgia Papadogeorgou, Christine Choirat, Corwin Zigler
2017 arXiv   pre-print
Propensity score matching is a common tool for adjusting for observed confounding in observational studies, but is known to have limitations in the presence of unmeasured confounding.  ...  In many settings, researchers are confronted with spatially-indexed data where the relative locations of the observational units may serve as a useful proxy for unmeasured confounding that varies according  ...  The contents of this work are solely the responsibility of the grantee and do not necessarily represent the official  ... 
arXiv:1610.07583v3 fatcat:5pglgapt4jeyjaxzeftk7hpmmi

Evaluating long-term effects of a psychiatric treatment using instrumental variable and matching approaches

Bo Lu, Sue Marcus
2012 Health Services & Outcomes Research Methodology  
Evaluating treatment effects in non-randomized studies is challenging due to the potential unmeasured confounding and complex form of observed confounding.  ...  Propensity score based approaches, such as matching or weighting, are commonly used to handle observed confounding variables.  ...  Acknowledgments The research is supported through grants from the National Institute on Drug Abuse Award Number R03DA030662 to B.L.  ... 
doi:10.1007/s10742-012-0101-2 pmid:23483774 pmcid:PMC3587666 fatcat:3n5srusbnjh5zey7o7twn5zmti

Regularized Regression Versus the High-Dimensional Propensity Score for Confounding Adjustment in Secondary Database Analyses

Jessica M. Franklin, Wesley Eddings, Robert J. Glynn, Sebastian Schneeweiss
2015 American Journal of Epidemiology  
Simulation scenarios varied the true underlying outcome model, treatment effect, prevalence of exposure and outcome, and presence of unmeasured confounding.  ...  However, including the variables selected by lasso regression in a regular propensity score model also performed well and may provide a promising alternative variable selection method.  ...  This work was funded by the Patient-Centered Outcomes Research Institute (grant ME-1303-5796). Conflict of interest: none declared.  ... 
doi:10.1093/aje/kwv108 pmid:26233956 fatcat:jm76vks6r5bsvggq5jwdv2c2dy

Supplementary_Material – Supplemental material for The use and quality of reporting of propensity score methods in multiple sclerosis literature: A review

Mohammad Ehsanul Karim, Fabio Pellegrini, Robert W Platt, Gabrielle Simoneau, Julie Rouette, Carl De Moor
2020 Figshare  
Supplemental material, Supplementary_Material for The use and quality of reporting of propensity score methods in multiple sclerosis literature: A review by Mohammad Ehsanul Karim, Fabio Pellegrini, Robert  ...  W Platt, Gabrielle Simoneau, Julie Rouette and Carl de Moor in Multiple Sclerosis Journal  ...  Supplementary Methods Background on Propensity Scores Propensity Scores In a study, each subject has observed pre-treatment covariates (L), receives a treatment at baseline, and has an outcome recorded  ... 
doi:10.25384/sage.13234824.v2 fatcat:7ru5andrmzapnib7xk7epe2aiy

Impact of influenza vaccination on mortality risk among the elderly

R. H. H. Groenwold, A. W. Hoes, E. Hak
2009 European Respiratory Journal  
After adjustment for measured confounders using multivariable regression analysis, propensity score matching and propensity score regression analysis, influenza vaccination reduced mortality risk (odds  ...  Estimates of influenza vaccine effectiveness have mostly been derived from nonrandomised studies and therefore are potentially confounded.  ...  Large-scale trials evaluating more serious outcomes such as mortality are not available, in part because of the large sample size needed, and also due to ethical constraints.  ... 
doi:10.1183/09031936.00190008 pmid:19213779 fatcat:fvgz766fwrhcpnsdmxtwdwoaim

Observational Research Using Propensity Scores

Karthik Raghunathan, J. Bradley Layton, Tetsu Ohnuma, Andrew D. Shaw
2016 Advances in Chronic Kidney Disease  
Propensity scores (PS) represent an intuitive set of approaches to reduce the influence of such "confounding" factors.  ...  Despite several advantages, PS-based methods cannot account for unmeasured confounding, ie, for factors that are not being included in the computation of PS.  ...  IN THE FIELD OF PS There is considerable ongoing research on the estimation and application of PS in observational research.  ... 
doi:10.1053/j.ackd.2016.11.010 pmid:28115080 fatcat:oot3hepidfao3oaelksp5addde

Methods for Constructing and Assessing Propensity Scores

Melissa M. Garrido, Amy S. Kelley, Julia Paris, Katherine Roza, Diane E. Meier, R. Sean Morrison, Melissa D. Aldridge
2014 Health Services Research  
groups; (3) balance of covariates across treatment and comparison groups within blocks of the propensity score; (4) choice of matching and weighting strategies; (5) balance of covariates after matching  ...  Guidance, Stata code, and empirical examples are given to illustrate (1) the process of choosing variables to include in the propensity score; (2) balance of propensity score across treatment and comparison  ...  Propensity scores only balance measured covariates, and balance in measured covariates does not necessarily indicate balance in unmeasured covariates.  ... 
doi:10.1111/1475-6773.12182 pmid:24779867 pmcid:PMC4213057 fatcat:cu73boug7jda5otjyo4aixvxsq

Assessing causal treatment effect estimation when using large observational datasets

E. R. John, K. R. Abrams, C. E. Brightling, N. A. Sheehan
2019 BMC Medical Research Methodology  
Recently, there has been a heightened interest in developing and evaluating different methods for analysing observational data.  ...  In this paper we will compare some common approaches to estimating treatment effects from observational data in order to highlight the importance of considering, and justifying, the relevant assumptions  ...  Takeda, and has received research funding from ABPI, EFPIA, Pfizer and Sanofi.  ... 
doi:10.1186/s12874-019-0858-x pmid:31726969 pmcid:PMC6854791 fatcat:rtcagbiafzezbkfspl3m5zql7q
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