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Adjusting for indirectly measured confounding using large-scale propensity scores
[article]
2022
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2015
F1000Research
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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
2014
F1000Research
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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
2009
Psychiatric annals
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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
2021
BMC Medical Research Methodology
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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
2012
Pharmacoepidemiology and Drug Safety
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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]
2017
arXiv
pre-print
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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
2012
Health Services & Outcomes Research Methodology
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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
2015
American Journal of Epidemiology
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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
2020
Figshare
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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
2009
European Respiratory Journal
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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
2016
Advances in Chronic Kidney Disease
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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
2014
Health Services Research
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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
2019
BMC Medical Research Methodology
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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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