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Статті в журналах з теми "Unmeasured confounders"

Автор: Grafiati

Опубліковано: 25 січня 2025

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1

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 (August 24, 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 Cox models. Each method corrects the time-varying inverse probability of treatment weights for all subject-by-time observations using either regression calibration of the propensity score, or multiple imputation of unmeasured confounders. Two proposed methods rely on martingale residuals from a Cox model that includes only confounders fully measured in the large database, to correct inverse probability of treatment weight for imputed values of unmeasured confounders. Simulation demonstrates that martingale residual-based methods systematically reduce confounding bias over naïve methods, with multiple imputation including the martingale residual yielding, on average, the best overall accuracy. We apply martingale residual-based imputation to re-assess the potential risk of drug-induced hypoglycemia in diabetic patients, where an important laboratory test is repeatedly measured only in a small sub-cohort.

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2

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 (February 22, 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. To account for correlation between cost and survival, we propose a bootstrap procedure to compute confidence intervals for NMB. To examine sensitivity to unmeasured confounders, we derive simple approximate relationships between naïve parameters, assuming only measured confounders, and the values those parameters would take if there was further adjustment for a single unmeasured confounder with a specified distribution. A simulation study shows that the method returns accurate estimates for treatment effects on cost, survival, and NMB under the assumed model. We apply our method to compare two treatments for Stage II/III bladder cancer, concluding that the NMB is sensitive to hypothesized unmeasured confounders that represent smoking status and personal income.

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3

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 (March 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 study of SEER-Medicare data from 1999-2016 included 1131 patients diagnosed with advanced stage HL at age ≥65 years. Treatment was categorized as: (1) full chemotherapy regimens (‘full regimen’, n=689); (2) partial chemotherapy regimen (‘partial regimen’, n=175); (3) single chemotherapy agent or radiotherapy (‘single agent/RT’, n=102), or (4) no treatment (n=165). A multivariable Cox regression model estimated the relationship between treatment and 3-year OS, adjusting for disease and patient factors. An E-value was computed to quantify the minimum strength of association that an unmeasured confounder would need to have with both the treatment and OS to completely explain away a significant association between treatment and OS based on the multivariable model. RESULTS/ANTICIPATED RESULTS: Results from the multivariable model found higher hazards of death for partial regimens (HR=1.81, 95% CI=1.43, 2.29), single agent/RT (HR=1.74, 95% CI=1.30, 2.34), or no treatment (HR=1.98, 95% CI=1.56, 2.552) compared to full regimens. We calculated an E-value for single agent/RT because it has the smallest HR of the treatment levels. The observed HR of 1.74 could be explained away by an unmeasured confounder that was associated with both treatment and OS with a HR of 2.29, above and beyond the measured confounders; the 95% CI could be moved to include the null by an unmeasured confounder that was associated with both the treatment and OS with a HR of 1.69. Of the measured confounders, B symptoms had the strongest relationship with treatment (HR=2.08) and OS (HR=1.38), which was below the E-value. DISCUSSION/SIGNIFICANCE OF FINDINGS: Patients with advanced stage HL who did not receive full chemotherapy regimens had worse 3-year OS, even after adjusting for potential confounders related to the patient and disease. The E-value analysis made explicit the amount of unmeasured confounding necessary to fully explain away the relationship between treatment and OS.

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4

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 (June 1, 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 methods, we estimate the magnitude and prevalence of unmeasured confounding required to move an apparently favorable hazard ratio (HR) to the “tipping point” where the new drug is no longer associated with a favorable survival statistically or clinically. Methods: Studying previously reported patients with multiple myeloma treated with an experimental therapy in a clinical trial (N = 290) and patients treated with standard of care therapy from a rigorously matched ECA (N = 290), we applied the method of Lin et al. to adjust the observed treatment effect (HR and 95% CIs) for overall survival to reflect the impact of a (set of) unmeasured confounder(s).[1,2] Lin’s formula incorporates both the unmeasured confounder’s theoretical association with mortality and its prevalence according to treatment group (i.e., single-arm trial vs. ECA). Results: The observed treatment effect for the single-arm trial treated vs. ECA patients was HR 0.76 (95% CI 0.63-0.91). We estimated the impact of an unmeasured confounder (where HR for overall survival of those patients with and without the confounder is set to 1.5) by its prevalence in each group. When the prevalence of the unmeasured confounder is balanced across groups there is no change in the observed treatment effect. When the presence of the confounder is 70% for ECA patients and absent in the trial patients, the clinical tipping point occurs with loss of the favorable HR (i.e., HR 1.02, 95% CI: 0.85-1.23). Conclusions: While novel analytic methods like ECAs have the potential to accelerate drug development, the lack of randomization raises concern for potential unmeasured confounding. Applying Lin’s method, we illustrate that the impact of unmeasured confounding on HR estimates from a single-arm trial vs.an ECA is a function of both the association with mortality and asymmetries in prevalence. Consistency in the efficacy conclusion for all clinically tenable assumptions indicates a qualitatively reliable conclusion. Friends of Cancer Research whitepaper (2019): available online at https://www.focr.org/sites/default/files/Panel-1_External_Control_Arms2019AM.pdf. Lin D, Psaty B, Kronmal R. Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics.1998; 54:948–963.

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5

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

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6

Szarewski, A., and D. Mansour. "Study subject to unmeasured confounders and biases." BMJ 342, may31 1 (May 31, 2011): d3349. http://dx.doi.org/10.1136/bmj.d3349.

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7

Navadeh, Soodabeh, Ali Mirzazadeh, Willi McFarland, Phillip Coffin, Mohammad Chehrazi, Kazem Mohammad, Maryam Nazemipour, Mohammad Ali Mansournia, Lawrence C. McCandless, and Kimberly Page. "Unsafe Injection Is Associated with Higher HIV Testing after Bayesian Adjustment for Unmeasured Confounding." Archives of Iranian Medicine 23, no. 12 (December 1, 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 Bayesian bias analysis. Results: Expert opinion estimated that 2.5%–40.0% of PWID with unsafe injection had insufficient HIV knowledge; whereas 1.0%–20.0% who practiced safe injection had insufficient knowledge. Experts also estimated the RR for the association between insufficient knowledge and NBT for HIV as 1.1-5.0. The RR estimate for the association between unsafe injection and NBT for HIV, adjusted for measured confounders, was 0.96 (95% confidence interval: 0.89,1.03). However, the RR estimate decreased to 0.82 (95% credible interval: 0.64, 0.99) after adjusting for insufficient knowledge as an unmeasured confounder. Conclusion: Our Bayesian approach that uses expert opinion to adjust for unmeasured confounders revealed that PWID who practice unsafe injection are more likely to be tested for HIV – an association that was not seen by conventional analysis.

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8

McCandless, Lawrence C. "Meta-Analysis of Observational Studies with Unmeasured Confounders." International Journal of Biostatistics 8, no. 2 (January 6, 2012): 1–31. http://dx.doi.org/10.2202/1557-4679.1350.

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9

Flanders, W. Dana. "Negative-Control Exposures: Adjusting for Unmeasured and Measured Confounders With Bounds for Remaining Bias." Epidemiology 34, no. 6 (September 26, 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 after applying this approach. We express remaining bias in terms of a “balancing” parameter and show that this parameter is bounded by a summary variational distance between the U-distribution in the exposed and the unexposed. These measures describe and bound the extent of remaining confounding after using negative controls to adjust for unmeasured confounders with conventional control of measured confounders.

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10

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 (June 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 confounder to exhibit such an effect. The purpose of this paper is to review and integrate two independent sensitivity analysis methods. The two methods are presented to assess the impact of an unmeasured confounder variable: one developed by Greenland under an epidemiological perspective, and the other developed from a statistical standpoint by Rosenbaum. By combining (or merging) epidemiological and statistical issues, this integration became a more complete and direct sensitivity analysis, encouraging its required diffusion and additional applications. As observational studies are more subject to biases and confounding than experimental settings, the consideration of epidemiological and statistical aspects in sensitivity analysis strengthens the causal inference.

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11

Cabral, Maria Deolinda Borges, and Ronir Raggio Luiz. "Sensitivity analysis for unmeasured confounders using an electronic spreadsheet." Revista de Saúde Pública 41, no. 3 (June 2007): 446–52. http://dx.doi.org/10.1590/s0034-89102007000300017.

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In studies assessing the effects of a given exposure variable and a specific outcome of interest, confusion may arise from the mistaken impression that the exposure variable is producing the outcome of interest, when in fact the observed effect is due to an existing confounder. However, quantitative techniques are rarely used to determine the potential influence of unmeasured confounders. Sensitivity analysis is a statistical technique that allows to quantitatively measuring the impact of an unmeasured confounding variable on the association of interest that is being assessed. The purpose of this study was to make it feasible to apply two sensitivity analysis methods available in the literature, developed by Rosenbaum and Greenland, using an electronic spreadsheet. Thus, it can be easier for researchers to include this quantitative tool in the set of procedures that have been commonly used in the stage of result validation.

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12

Groenwold, Rolf H. H., Jonathan A. C. Sterne, Debbie A. Lawlor, Karel G. M. Moons, Arno W. Hoes, and Kate Tilling. "Sensitivity analysis for the effects of multiple unmeasured confounders." Annals of Epidemiology 26, no. 9 (September 2016): 605–11. http://dx.doi.org/10.1016/j.annepidem.2016.07.009.

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13

Hauptman, Paul J., and John T. Chibnall. "Unmeasured Confounders and Predictive Models: What's Your C-Statistic?" Journal of Cardiac Failure 21, no. 11 (November 2015): 857–58. http://dx.doi.org/10.1016/j.cardfail.2015.09.006.

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14

Wyss, Richard, Mark Lunt, M. Alan Brookhart, Robert J. Glynn, and Til Stürmer. "Reducing Bias Amplification in the Presence of Unmeasured Confounding through Out-of-Sample Estimation Strategies for the Disease Risk Score." Journal of Causal Inference 2, no. 2 (September 1, 2014): 131–46. http://dx.doi.org/10.1515/jci-2014-0009.

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AbstractThe prognostic score, or disease risk score (DRS), is a summary score that is used to control for confounding in non-experimental studies. While the DRS has been shown to effectively control for measured confounders, unmeasured confounding continues to be a fundamental obstacle in non-experimental research. Both theory and simulations have shown that in the presence of unmeasured confounding, controlling for variables that affect treatment (both instrumental variables and measured confounders) amplifies the bias caused by unmeasured confounders. In this paper, we use causal diagrams and path analysis to review and illustrate the process of bias amplification. We show that traditional estimation strategies for the DRS do not avoid bias amplification when controlling for predictors of treatment. We then discuss estimation strategies for the DRS that can potentially reduce bias amplification that is caused by controlling both instrumental variables and measured confounders. We show that under certain assumptions, estimating the DRS in populations outside the defined study cohort where treatment has not been introduced, or in outside populations with reduced treatment prevalence, can control for the confounding effects of measured confounders while at the same time reduce bias amplification.

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15

Leow, Jeffrey J., Alexander P. Cole, Maxine Sun, and Quoc-Dien Trinh. "Association of Androgen Deprivation Therapy With Alzheimer’s Disease: Unmeasured Confounders." Journal of Clinical Oncology 34, no. 23 (August 10, 2016): 2801–3. http://dx.doi.org/10.1200/jco.2016.66.6594.

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16

McGowan, Lucy D’Agostino. "tipr: An R package for sensitivity analyses for unmeasured confounders." Journal of Open Source Software 7, no. 77 (September 5, 2022): 4495. http://dx.doi.org/10.21105/joss.04495.

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17

Li, Yun, Yoonseok Lee, Friedrich K. Port, and Bruce M. Robinson. "The impact of unmeasured within- and between-cluster confounding on the bias of effect estimatorsof a continuous exposure." Statistical Methods in Medical Research 29, no. 8 (November 7, 2019): 2119–39. http://dx.doi.org/10.1177/0962280219883323.

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Unmeasured confounding almost always exists in observational studies and can bias estimates of exposure effects. Instrumental variable methods are popular choices in combating unmeasured confounding to obtain less biased effect estimates. However, we demonstrate that alternative methods may give less biased estimates depending on the nature of unmeasured confounding. Treatment preferences of clusters (e.g. physician practices) are the most frequently used instruments in instrumental variable analyses. These preference-based instrumental variable analyses are usually conducted on data clustered by region, hospital/facility, or physician, where unmeasured confounding often occurs within or between clusters. We aim to quantify the impact of unmeasured confounding on the bias of effect estimators in instrumental variable analysis, as well as several common alternative methods including ordinary least squares regression, linear mixed models, and fixed-effect models to study the effect of a continuous exposure (e.g. treatment dose) on a continuous outcome. We derive closed-form expressions of asymptotic bias of estimators from these four methods in the presence of unmeasured within- and/or between-cluster confounders. Simulations demonstrate that the asymptotic bias formulae well approximate bias in finite samples for all methods. The bias formulae show that instrumental variable analyses can provide consistent estimates when unmeasured within-cluster confounding exists, but not when between-cluster confounding exists. On the other hand, fixed-effect models and linear mixed models can provide consistent estimates when unmeasured between-cluster confounding exits, but not for within-cluster confounding. Whether instrumental variable analyses are advantageous in reducing bias over fixed-effect models and linear mixed models depends on the extent of unmeasured within-cluster confounding relative to between-cluster confounding. Furthermore, the impact of unmeasured between-cluster confounding on instrumental variable analysis estimates is larger than the impact of unmeasured within-cluster confounding on fixed-effect model and linear mixed model estimates. We illustrate the use of these methods in estimating the effect of erythropoiesis stimulating agents on hemoglobin levels. Our findings provide guidance for choosing appropriate methods to combat the dominant types of unmeasured confounders and help interpret statistical results in the context of unmeasured confounding.

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18

Liang, Wenbin, and Tanya Chikritzhs. "Examining the Relationship between Heavy Alcohol Use and Assaults: With Adjustment for the Effects of Unmeasured Confounders." BioMed Research International 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/596179.

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Background. Experimental studies suggest that alcohol can lead to aggression in laboratory settings; however, it is impossible to test the causal relationship between alcohol use and real-life violence among humans in randomized clinical trials.Objectives. (i) To examine the relationship between heavy alcohol use and assaults in a population based study; (ii) to demonstrate the proxy outcome method, as a means of controlling the effects of unknown/unmeasured confounders in observational studies.Methods. This study used data collected from three waves of the National Survey on Drug Use and Health (NSDUH). The effects of heavy alcohol use on assault were measured using multivariable logistic regressions in conjunction with the proxy outcome method.Results. Application of the proxy outcome method indicated that effect sizes of heavy alcohol use on the risk of assault were overestimated in the standard models. After adjusting for the effects of unknown/unmeasured confounders, the risk of assault remained 43% and 63% higherP<0.05among participants who consumed 5+ drinks/day for 5–8 days/month and 9–30 days/month, respectively.Conclusions. Even after adjustment for unknown/unmeasured confounders the association between heavy alcohol use and risk of violence remained significant. These findings support the hypothesis that heavy alcohol use can cause violence.

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19

Kuroki, Manabu. "Identificability Criteria for Total Effects in the Presence of Unmeasured Confounders." Japanese journal of applied statistics 36, no. 2/3 (2007): 71–85. http://dx.doi.org/10.5023/jappstat.36.71.

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20

Stürmer, Til, Robert J. Glynn, Kenneth J. Rothman, Jerry Avorn, and Sebastian Schneeweiss. "Adjustments for Unmeasured Confounders in Pharmacoepidemiologic Database Studies Using External Information." Medical Care 45, Suppl 2 (October 2007): S158—S165. http://dx.doi.org/10.1097/mlr.0b013e318070c045.

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21

Schneeweiss, Sebastian, Robert J. Glynn, Elizabeth H. Tsai, Jerry Avorn, and Daniel H. Solomon. "Adjusting for Unmeasured Confounders in Pharmacoepidemiologic Claims Data Using External Information." Epidemiology 16, no. 1 (January 2005): 17–24. http://dx.doi.org/10.1097/01.ede.0000147164.11879.b5.

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22

Arah, Onyebuchi A., Yasutaka Chiba, and Sander Greenland. "Bias Formulas for External Adjustment and Sensitivity Analysis of Unmeasured Confounders." Annals of Epidemiology 18, no. 8 (August 2008): 637–46. http://dx.doi.org/10.1016/j.annepidem.2008.04.003.

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23

Kuroki, Manabu, and Zhihong Cai. "Formulating tightest bounds on causal effects in studies with unmeasured confounders." Statistics in Medicine 27, no. 30 (September 9, 2008): 6597–611. http://dx.doi.org/10.1002/sim.3430.

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24

Huang, Lihong, Jianbing Ma, Xiaochun Qiu, and Tao Suo. "Assess the Application of the E-Value in the Unmeasured Confounder Evaluation of Observational Pharmaceutical Studies." Scientific Programming 2021 (October 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/9618161.

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Public health is very important in big cities, and data analysis on public health studies is always a demanding issue that determines the study effectiveness. E-value was proposed as a standard sensitivity analysis tool to assess unmeasured confounders in observational studies, but its value is doubted. To evaluate the usefulness of E-value, in this paper, we collected 368 observational studies on drug effectiveness evaluation published from 1998 to September 2019 (out of 3426 searched studies) and evaluated the features of E-value. We selected the effects of primary outcomes or the largest effects in terms of hazard ratio, risk ratio, or odds ratio. Effects were transformed into estimated effect sizes following a standard E-value computation. In all 368 studies, the disease with the highest percentage was infections and infestations, at 21.7% (80/368). Our results showed that the median relative effect size was 1.89 (Q1-Q3: 1.41–2.95), and the corresponding median E-value was 3.19 with 95% confidence interval lower bound 1.77. Smaller studies yielded larger E-values for the effect size estimate and the relationship was considerably attenuated when considering the E-value for the lower bound of 95% confidence interval on the effect size. Notably, E-values have a monotonic, almost linear relationship with effect estimates. We found that E-value may cause misimpressions on the unmeasured confounder, and the same E-value does not reflect the varying nature of the unmeasured confounders in different studies, and there lacks a guidance on how E-value can be deemed as small or large, all of which limits the capability of E-value as a standard sensitivity analysis tool in real applications.

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25

Lin, D. Y., B. M. Psaty, and R. A. Kronmal. "Assessing the Sensitivity of Regression Results to Unmeasured Confounders in Observational Studies." Biometrics 54, no. 3 (September 1998): 948. http://dx.doi.org/10.2307/2533848.

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26

Guertin, Jason R., Elham Rahme, and Jacques LeLorier. "Performance of the high-dimensional propensity score in adjusting for unmeasured confounders." European Journal of Clinical Pharmacology 72, no. 12 (August 30, 2016): 1497–505. http://dx.doi.org/10.1007/s00228-016-2118-x.

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27

Groenwold, R., A. Hoes, K. Nichol, and E. Hak. "Quantifying the potential role of unmeasured confounders: the example of influenza vaccination." International Journal of Epidemiology 37, no. 6 (August 25, 2008): 1422–29. http://dx.doi.org/10.1093/ije/dyn173.

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28

Zöller, Daniela, Leesa F. Wockner, and Harald Binder. "Automatic variable selection for exposure‐driven propensity score matching with unmeasured confounders." Biometrical Journal 62, no. 3 (March 23, 2020): 868–84. http://dx.doi.org/10.1002/bimj.201800190.

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29

Kaloth, Srivarsha, and Aayush Visaria. "The implications of unmeasured confounders on sleep's complex relationship with cardiometabolic health." Journal of Hypertension 42, no. 2 (January 4, 2024): 383–84. http://dx.doi.org/10.1097/hjh.0000000000003491.

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30

McCandless, Lawrence C., and Julian M. Somers. "Bayesian sensitivity analysis for unmeasured confounding in causal mediation analysis." Statistical Methods in Medical Research 28, no. 2 (September 7, 2017): 515–31. http://dx.doi.org/10.1177/0962280217729844.

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Causal mediation analysis techniques enable investigators to examine whether the effect of the exposure on an outcome is mediated by some intermediate variable. Motivated by a data example from epidemiology, we consider estimation of natural direct and indirect effects on a survival outcome. An important concern is bias from confounders that may be unmeasured. Estimating natural direct and indirect effects requires an elaborate series of assumptions in order to identify the target quantities. The analyst must carefully measure and adjust for important predictors of the exposure, mediator and outcome. Omitting important confounders may bias the results in a way that is difficult to predict. In recent years, several methods have been proposed to explore sensitivity to unmeasured confounding in mediation analysis. However, many of these methods limit complexity by relying on a handful of sensitivity parameters that are difficult to interpret, or alternatively, by assuming that specific patterns of unmeasured confounding are absent. Instead, we propose a simple Bayesian sensitivity analysis technique that is indexed by four bias parameters. Our method has the unique advantage that it is able to simultaneously assess unmeasured confounding in the mediator–outcome, exposure–outcome and exposure–mediator relationships. It is a natural Bayesian extension of the sensitivity analysis methodologies of VanderWeele, which have been widely used in the epidemiology literature. We present simulation findings, and additionally, we illustrate the method in an epidemiological study of mortality rates in criminal offenders from British Columbia.

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Li, Lin, Tyra Lagerberg, Zheng Chang, Samuele Cortese, Mina A. Rosenqvist, Catarina Almqvist, Brian M. D’Onofrio, et al. "Maternal pre-pregnancy overweight/obesity and the risk of attention-deficit/hyperactivity disorder in offspring: a systematic review, meta-analysis and quasi-experimental family-based study." International Journal of Epidemiology 49, no. 3 (April 26, 2020): 857–75. http://dx.doi.org/10.1093/ije/dyaa040.

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Abstract Background Previous studies are inconclusive concerning the association between maternal pre-pregnancy overweight/obesity and risk of attention-deficit/hyperactivity disorder (ADHD) in offspring. We therefore conducted a systematic review and meta-analysis to clarify this association. To address the variation in confounding adjustment between studies, especially inadequate adjustment of unmeasured familial confounding in most studies, we further performed cousin and sibling comparisons in a nationwide population-based cohort in Sweden. Methods We searched PubMed, Embase and PsycINFO during 1975–2018. We used random-effects models to calculate pooled risk ratios (RRs) with 95% confidence interval. In the population-based study, Cox proportional hazard models were used to calculate the unadjusted hazard ratios (HRs) and HRs adjusted for all confounders identified in previous studies. Stratified Cox models were applied to data on full cousins and full siblings to further control for unmeasured familial confounding. Results Eight cohorts with a total of 784 804 mother–child pairs were included in the meta-analysis. Maternal overweight [RRoverweight = 1.31 (1.25–1.38), I2 = 6.80%] and obesity [RRobesity = 1.92 (1.84–2.00), I2 = 0.00%] were both associated with an increased risk of ADHD in offspring. In the population-based cohort of 971 501 individuals born between 1992 and 2004, unadjusted Cox models revealed similar associations [HRoverweight = 1.30 (1.28–1.34), HRobesity = 1.92 (1.87–1.98)]. These associations gradually attenuated towards the null when adjusted for measured confounders [HRoverweight = 1.21 (1.19–1.25), HRobesity = 1.60 (1.55–1.65)], unmeasured factors shared by cousins [HRoverweight = 1.10 (0.98–1.23), HRobesity = 1.44 (1.22–1.70)] and unmeasured factors shared by siblings [HRoverweight = 1.01 (0.92–1.11), HRobesity = 1.10 (0.94–1.27)]. Conclusion Pre-pregnancy overweight/obesity is associated with an increased risk of ADHD in offspring. The observed association is largely due to unmeasured familial confounding.

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Zhang, Xiang, James D. Stamey, and Maya B. Mathur. "Assessing the impact of unmeasured confounders for credible and reliable real‐world evidence." Pharmacoepidemiology and Drug Safety 29, no. 10 (September 14, 2020): 1219–27. http://dx.doi.org/10.1002/pds.5117.

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Ma, Yiqun, Federica Nobile, Anne Marb, Robert Dubrow, Massimo Stafoggia, Susanne Breitner, Patrick L. Kinney, and Kai Chen. "Short-Term Exposure to Fine Particulate Matter and Nitrogen Dioxide and Mortality in 4 Countries." JAMA Network Open 7, no. 3 (March 1, 2024): e2354607. http://dx.doi.org/10.1001/jamanetworkopen.2023.54607.

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ImportanceThe association between short-term exposure to air pollution and mortality has been widely documented worldwide; however, few studies have applied causal modeling approaches to account for unmeasured confounders that vary across time and space.ObjectiveTo estimate the association between short-term changes in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations and changes in daily all-cause mortality rates using a causal modeling approach.Design, Setting, and ParticipantsThis cross-sectional study used air pollution and mortality data from Jiangsu, China; California; central-southern Italy; and Germany with interactive fixed-effects models to control for both measured and unmeasured spatiotemporal confounders. A total of 8 963 352 deaths in these 4 regions from January 1, 2015, to December 31, 2019, were included in the study. Data were analyzed from June 1, 2021, to October 30, 2023.ExposureDay-to-day changes in county- or municipality-level mean PM2.5 and NO2 concentrations.Main Outcomes and MeasuresDay-to-day changes in county- or municipality-level all-cause mortality rates.ResultsAmong the 8 963 352 deaths in the 4 study regions, a 10-μg/m3 increase in daily PM2.5 concentration was associated with an increase in daily all-cause deaths per 100 000 people of 0.01 (95% CI, 0.001-0.01) in Jiangsu, 0.03 (95% CI, 0.004-0.05) in California, 0.10 (95% CI, 0.07-0.14) in central-southern Italy, and 0.04 (95% CI, 0.02- 0.05) in Germany. The corresponding increases in mortality rates for a 10-μg/m3 increase in NO2 concentration were 0.04 (95% CI, 0.03-0.05) in Jiangsu, 0.03 (95% CI, 0.01-0.04) in California, 0.10 (95% CI, 0.05-0.15) in central-southern Italy, and 0.05 (95% CI, 0.04-0.06) in Germany. Significant effect modifications by age were observed in all regions, by sex in Germany (eg, 0.05 [95% CI, 0.03-0.06] for females in the single-pollutant model of PM2.5), and by urbanicity in Jiangsu (0.07 [95% CI, 0.04-0.10] for rural counties in the 2-pollutant model of NO2).Conclusions and RelevanceThe findings of this cross-sectional study contribute to the growing body of evidence that increases in short-term exposures to PM2.5 and NO2 may be associated with increases in all-cause mortality rates. The interactive fixed-effects model, which controls for unmeasured spatial and temporal confounders, including unmeasured time-varying confounders in different spatial units, can be used to estimate associations between changes in short-term exposure to air pollution and changes in health outcomes.

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Shaheen, Seif O., Cecilia Lundholm, Bronwyn K. Brew, and Catarina Almqvist. "Prescribed analgesics in pregnancy and risk of childhood asthma." European Respiratory Journal 53, no. 5 (March 17, 2019): 1801090. http://dx.doi.org/10.1183/13993003.01090-2018.

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Many epidemiological studies have reported a positive association between prenatal exposure to paracetamol and childhood wheezing and asthma. We investigated whether the link between prenatal analgesic exposure and asthma/wheeze is specific to paracetamol, and whether it is causal or confounded.Using linked Swedish health register data we investigated the relation between various prescribed analgesics in pregnancy and the risk of childhood asthma/wheeze in a population of 492 999, and used negative paternal control and sibling comparison approaches to explore unmeasured confounding.After controlling for potential confounders, prescribed opioids, antimigraine drugs and paracetamol were all positively associated with childhood asthma/wheeze risk at all ages (e.g. for asthma/wheeze at age 4 years: adjusted OR 1.39 (95% CI 1.30–1.49), 1.19 (95% CI 1.01–1.40) and 1.47 (95% CI 1.36–1.59) for opioids, antimigraine drugs and paracetamol, respectively). The results of the paternal control analysis did not suggest the presence of unmeasured confounding by genetics or shared environment. However, the sibling control analysis broadly suggested that associations between prenatal exposure to the analgesics and asthma/wheeze were confounded by specific maternal factors (e.g. for asthma/wheeze at age 4 years: adjusted OR 0.91 (95% CI 0.62–1.31), 0.50 (95% CI 0.17–1.45) and 0.80 (95% CI 0.50–1.29) for opioids, antimigraine drugs and paracetamol, respectively).We propose that analgesic use in pregnancy does not cause childhood asthma/wheeze and that the association is confounded by unmeasured factors that are intrinsic to the mother, such as chronic pain or anxiety.

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Brew, Bronwyn K., Tong Gong, Dylan M. Williams, Henrik Larsson, and Catarina Almqvist. "Using fathers as a negative control exposure to test the Developmental Origins of Health and Disease Hypothesis: A case study on maternal distress and offspring asthma using Swedish register data." Scandinavian Journal of Public Health 45, no. 17_suppl (July 2017): 36–40. http://dx.doi.org/10.1177/1403494817702324.

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Background: Developmental Origins of Health and Disease Hypothesis (DOHaD) studies are often observational in nature and are therefore prone to biases from loss to follow-up and unmeasured confounding. Register-based studies can reduce these issues since they allow almost complete follow-up and provide information on fathers that can be used in a negative control analysis to assess the impact of unmeasured confounding. Aim: The aim of this study was to propose a causal model for testing DOHaD using paternal exposure as a negative control, and its application to maternal distress in pregnancy and offspring asthma. Methods: A causal diagram including shared and parent-specific measured and unmeasured confounders for maternal (fetal) and paternal exposures is proposed. The case study consisted of all children born in Sweden from July 2006 to December 2008 ( n=254,150). Information about childhood asthma, parental distress and covariates was obtained from the Swedish national health registers. Associations between maternal and paternal distress during pregnancy and offspring asthma at age five years were assessed separately and with mutual adjustment for the other parent’s distress measure, as well as for shared confounders. Results: Maternal distress during pregnancy was associated with offspring asthma risk; mutually adjusted odds ratio (OR) (OR 1.32, 95% CI 1.23, 1.43). The mutually adjusted paternal distress−offspring asthma analysis (OR 1.05, 95% CI 0.97, 1.13) indicated no evidence for unmeasured confounding shared by the mother and father. Conclusions: Using paternal exposure in a negative control model to test the robustness of fetal programming hypotheses can be a relatively simple extension of conventional observational studies but limitations need to be considered.

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Tozzi, Viola, Aitana Lertxundi, Jesus M. Ibarluzea, and Michela Baccini. "Causal Effects of Prenatal Exposure to PM2.5 on Child Development and the Role of Unobserved Confounding." International Journal of Environmental Research and Public Health 16, no. 22 (November 9, 2019): 4381. http://dx.doi.org/10.3390/ijerph16224381.

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Prenatal exposure to airborne particles is a potential risk factor for infant neuropsychological development. This issue is usually explored by regression analysis under the implicit assumption that all relevant confounders are accounted for. Our aim is to estimate the causal effect of prenatal exposure to high concentrations of airborne particles with a diameter < 2.5 µm (PM2.5) on children’s psychomotor and mental scores in a birth cohort from Gipuzkoa (Spain), and investigate the robustness of the results to possible unobserved confounding. We adopted the propensity score matching approach and performed sensitivity analyses comparing the actual effect estimates with those obtained after adjusting for unobserved confounders simulated to have different strengths. On average, mental and psychomotor scores decreased of −2.47 (90% CI: −7.22; 2.28) and −3.18 (90% CI: −7.61; 1.25) points when the prenatal exposure was ≥17 μg/m3 (median). These estimates were robust to the presence of unmeasured confounders having strength similar to that of the observed ones. The plausibility of having omitted a confounder strong enough to drive the estimates to zero was poor. The sensitivity analyses conferred solidity to our findings, despite the large sampling variability. This kind of sensitivity analysis should be routinely implemented in observational studies, especially in exploring new relationships.

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VanderWeele, Tyler J., and Onyebuchi A. Arah. "Bias Formulas for Sensitivity Analysis of Unmeasured Confounding for General Outcomes, Treatments, and Confounders." Epidemiology 22, no. 1 (January 2011): 42–52. http://dx.doi.org/10.1097/ede.0b013e3181f74493.

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Cai, Zhuangyu, and Babette A. Brumback. "Model-based standardization to adjust for unmeasured cluster-level confounders with complex survey data." Statistics in Medicine 34, no. 15 (April 8, 2015): 2368–80. http://dx.doi.org/10.1002/sim.6504.

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Schneeweiss, Sebastian. "Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics." Pharmacoepidemiology and Drug Safety 15, no. 5 (2006): 291–303. http://dx.doi.org/10.1002/pds.1200.

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Hwang, S., A. Verhoek, M. Diamond, and M. Rutherford. "MSR130 Current Trends in Quantitative Bias Analysis for Unmeasured Confounders: A Targeted Literature Review." Value in Health 26, no. 12 (December 2023): S418. http://dx.doi.org/10.1016/j.jval.2023.09.2189.

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Ezzalfani, Monia, Raphaël Porcher, Alexia Savignoni, Suzette Delaloge, Thomas Filleron, Mathieu Robain, and David Pérol. "Addressing the issue of bias in observational studies: Using instrumental variables and a quasi-randomization trial in an ESME research project." PLOS ONE 16, no. 9 (September 15, 2021): e0255017. http://dx.doi.org/10.1371/journal.pone.0255017.

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Purpose Observational studies using routinely collected data are faced with a number of potential shortcomings that can bias their results. Many methods rely on controlling for measured and unmeasured confounders. In this work, we investigate the use of instrumental variables (IV) and quasi-trial analysis to control for unmeasured confounders in the context of a study based on the retrospective Epidemiological Strategy and Medical Economics (ESME) database, which compared overall survival (OS) with paclitaxel plus bevacizumab or paclitaxel alone as first-line treatment in patients with HER2-negative metastatic breast cancer (MBC). Patients and methods Causal interpretations and estimates can be made from observation data using IV and quasi-trial analysis. Quasi-trial analysis has the same conceptual basis as IV, however, instead of using IV in the analysis, a “superficial” or “pseudo” randomized trial is used in a Cox model. For instance, in a multicenter trial, instead of using the treatment variable, quasi-trial analysis can consider the treatment preference in each center, which can be informative, and then comparisons of results between centers or clinicians can be informative. Results In the original analysis, the OS adjusted for major factors was significantly longer with paclitaxel and bevacizumab than with paclitaxel alone. Using the center-treatment preference as an instrument yielded to concordant results. For the quasi-trial analysis, a Cox model was used, adjusted on all factors initially used. The results consolidate those obtained with a conventional multivariate Cox model. Conclusion Unmeasured confounding is a major concern in observational studies, and IV or quasi-trial analysis can be helpful to complement analysis of studies of this nature.

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Byun, Garam, Ho Kim, Sun-Young Kim, Seung-Sup Kim, Hannah Oh, and Jong-Tae Lee. "Validity evaluation of indirect adjustment method for multiple unmeasured confounders: A simulation and empirical study." Environmental Research 204 (March 2022): 111992. http://dx.doi.org/10.1016/j.envres.2021.111992.

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Björk, Jonas, and Ulf Strömberg. "Model specification and unmeasured confounders in partially ecologic analyses based on group proportions of exposed." Scandinavian Journal of Work, Environment & Health 31, no. 3 (June 2005): 184–90. http://dx.doi.org/10.5271/sjweh.868.

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McCandless, Lawrence C., Paul Gustafson, and Adrian R. Levy. "A sensitivity analysis using information about measured confounders yielded improved uncertainty assessments for unmeasured confounding." Journal of Clinical Epidemiology 61, no. 3 (March 2008): 247–55. http://dx.doi.org/10.1016/j.jclinepi.2007.05.006.

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Erly, Steven J., Christine M. Khosropour, Anjum Hajat, Monisha Sharma, Jennifer R. Reuer, and Julia C. Dombrowski. "AIDS Drug Assistance Program disenrollment is associated with loss of viral suppression beyond differences in homelessness, mental health, and substance use disorders: An evaluation in Washington state 2017–2019." PLOS ONE 18, no. 5 (May 4, 2023): e0285326. http://dx.doi.org/10.1371/journal.pone.0285326.

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AIDS Drug Assistance Programs (ADAPs) are state-administered programs that pay for medical care for people living with HIV in the US. Maintaining enrollment in the programs is challenging, and a large proportion of clients in Washington state (WA) fail to recertify and are disenrolled. In this study we sought to quantify the impact of disenrollment from ADAPs on viral suppression. We conducted a retrospective cohort study of the 5238 clients in WA ADAP from 2017 to 2019 and estimated the risk difference (RD) of viral suppression before and after disenrollment. We performed a quantitative bias analysis (QBA) to assess the effect of unmeasured confounders, as the factors that contribute to disenrollment and medication discontinuation may overlap. Of the 1336 ADAP clients who disenrolled ≥1 time, 83% were virally suppressed before disenrollment versus 69% after (RD 12%, 95%CI 9–15%). The RD was highest among clients with dual Medicaid-Medicare insurance (RD 22%, 95%CI 9–35%) and lowest among privately insured individuals (RD 8%, 95%CI 5–12%). The results of the QBA suggest that unmeasured confounders do not negate the overall RD. The ADAP recertification procedures negatively impact the care of clients who struggle to stay in the program; alternative procedures may reduce this impact.

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Duke, Naomi, and Ross Macmillan. "Schooling, Skills, and Self-rated Health." Sociology of Education 89, no. 3 (June 22, 2016): 171–206. http://dx.doi.org/10.1177/0038040716653168.

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Education is a key sociological variable in the explanation of health and health disparities. Conventional wisdom emphasizes a life course–human capital perspective with expectations of causal effects that are quasi-linear, large in magnitude for high levels of educational attainment, and reasonably robust in the face of measured and unmeasured explanatory factors. We challenge this wisdom by offering an alternative theoretical account and an empirical investigation organized around the role of measured and unmeasured cognitive and noncognitive skills as confounders in the association between educational attainment and health. Based on longitudinal data from the National Longitudinal Survey of Youth-1997 spanning mid-adolescence through early adulthood, results indicate that (1) effects of educational attainment are vulnerable to issues of omitted variable bias, (2) measured indicators of cognitive and noncognitive skills account for a significant proportion of the traditionally observed effect of educational attainment, (3) such skills have effects larger than that of even the highest levels of educational attainment when appropriate controls for unmeasured heterogeneity are incorporated, and (4) models that most stringently control for such time-stable abilities show little evidence of a substantive association between educational attainment and health.

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Arah, Onyebuchi A. "Bias Analysis for Uncontrolled Confounding in the Health Sciences." Annual Review of Public Health 38, no. 1 (March 20, 2017): 23–38. http://dx.doi.org/10.1146/annurev-publhealth-032315-021644.

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Uncontrolled confounding due to unmeasured confounders biases causal inference in health science studies using observational and imperfect experimental designs. The adoption of methods for analysis of bias due to uncontrolled confounding has been slow, despite the increasing availability of such methods. Bias analysis for such uncontrolled confounding is most useful in big data studies and systematic reviews to gauge the extent to which extraneous preexposure variables that affect the exposure and the outcome can explain some or all of the reported exposure-outcome associations. We review methods that can be applied during or after data analysis to adjust for uncontrolled confounding for different outcomes, confounders, and study settings. We discuss relevant bias formulas and how to obtain the required information for applying them. Finally, we develop a new intuitive generalized bias analysis framework for simulating and adjusting for the amount of uncontrolled confounding due to not measuring and adjusting for one or more confounders.

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Schroeder, Mary C., Cole G. Chapman, Elizabeth A. Chrischilles, June Wilwert, Kathleen M. Schneider, Jennifer G. Robinson, and John M. Brooks. "Generating Practice-Based Evidence in the Use of Guideline-Recommended Combination Therapy for Secondary Prevention of Acute Myocardial Infarction." Pharmacy 10, no. 6 (November 3, 2022): 147. http://dx.doi.org/10.3390/pharmacy10060147.

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Background: Clinical guidelines recommend beta-blockers, angiotensin-converting enzyme inhibitors/angiotensin-receptor blockers, and statins for the secondary prevention of acute myocardial infarction (AMI). It is not clear whether variation in real-world practice reflects poor quality-of-care or a balance of outcome tradeoffs across patients. Methods: The study cohort included Medicare fee-for-service beneficiaries hospitalized 2007–2008 for AMI. Treatment within 30-days post-discharge was grouped into one of eight possible combinations for the three drug classes. Outcomes included one-year overall survival, one-year cardiovascular-event-free survival, and 90-day adverse events. Treatment effects were estimated using an Instrumental Variables (IV) approach with instruments based on measures of local-area practice style. Pre-specified data elements were abstracted from hospital medical records for a stratified, random sample to create “unmeasured confounders” (per claims data) and assess model assumptions. Results: Each drug combination was observed in the final sample (N = 124,695), with 35.7% having all three, and 13.5% having none. Higher rates of guideline-recommended treatment were associated with both better survival and more adverse events. Unmeasured confounders were not associated with instrumental variable values. Conclusions: The results from this study suggest that providers consider both treatment benefits and harms in patients with AMIs. The investigation of estimator assumptions support the validity of the estimates.

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Xu, Stanley, Christina L. Clarke, Sophia R. Newcomer, Matthew F. Daley, and Jason M. Glanz. "Sensitivity analyses of unmeasured and partially‐measured confounders using multiple imputation in a vaccine safety study." Pharmacoepidemiology and Drug Safety 30, no. 9 (May 31, 2021): 1200–1213. http://dx.doi.org/10.1002/pds.5294.

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Thygesen, Lau Caspar, Anton Pottegård, Annette Kjaer Ersbøll, Søren Friis, Til Stürmer, and Jesper Hallas. "External adjustment of unmeasured confounders in a case-control study of benzodiazepine use and cancer risk." British Journal of Clinical Pharmacology 83, no. 11 (July 12, 2017): 2517–27. http://dx.doi.org/10.1111/bcp.13342.

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