confounding bias
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BMC Surgery ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Juan Liu ◽  
Chunyan Gao ◽  
Hailong Fu ◽  
Xiaonan Zhou ◽  
Li Zhang ◽  
...  

Abstract Background Spinal tumor surgery usually involved long operation time, large area of soft tissue resection and long wound, and was prone to hypothermia during the operation. Therefore, actively promoting insulation and optimizing the intraoperative insulation program have great potential in reducing the incidence of hypothermia and reducing the incidence of postoperative complications. In this study, we compared patients who did not implement multi-mode nursing insulation program (MNIP) with those who implemented MNIP, observing and comparing clinical outcomes, and complications in both groups, with the aim of developing an optimal management plan for the preoperative, intraoperative, and postoperative periods, respectively. Methods We selected 2 periods of 1 year, before (n = 120 patients) and after MINP implementation (n = 120 patients). Data were collected on patient demographics, operative, perioperative details, temperature changes, anesthesia recovery effect, incidence of postoperative wound infection, length of hospital stay and complications. PS analyses were used for dealing with confounding bias in this retrospective observational study. Results After PS matching, the outcomes of 120 well-balanced pairs of patients were compared (No-MNIP vs MNIP). There was no significant difference concerning the satisfaction survey. The results indicated that the MNIP had better insulation effect at 90 min, 120 min, 150 min after anesthesia induction and after surgery. There were 16 cases of complications in the No-MNIP group and 5 cases in the MNIP group postoperative, which have significant statistical difference. Conclusion In this study, the incidence of intraoperative hypothermia was effectively reduced by adopting the multi-mode insulation scheme, thus reducing the incidence of incision infection and shortening the length of hospital stay of patients.


2021 ◽  
Author(s):  
Julie M. Petersen ◽  
Malcolm Barrett ◽  
Katherine A. Ahrens ◽  
Eleanor J. Murray ◽  
Allison S. Bryant ◽  
...  

2021 ◽  
Vol 12 ◽  
Author(s):  
Davood Tofighi

Mediation analysis relies on an untestable assumption of the no omitted confounders, which posits that an omitted variable that confounds the relationships between the antecedent, mediator, and outcome variables cannot exist. One common model in alcohol addiction studies is a nonrandomized latent growth curve mediation model (LGCMM), where the antecedent variable is not randomized, the two covarying mediators are latent intercept and slope modeling longitudinal effect of the repeated measures mediator, and an outcome variable that measures alcohol use. An important gap in the literature is lack of sensitivity analysis techniques to assess the effect of the violation of the no omitted confounder assumption in a nonrandomized LGCMM. We extend a sensitivity analysis technique, termed correlated augmented mediation sensitivity analysis (CAMSA), to a nonrandomized LGCMM. We address several unresolved issues in conducting CAMSA for the nonrandomized LGCMM and present: (a) analytical results showing how confounder correlations model a confounding bias, (b) algorithms to address admissible values for confounder correlations, (c) accessible R code within an SEM framework to conduct our proposed sensitivity analysis, and (d) an empirical example. We conclude that conducting sensitivity analysis to ascertain robustness of the mediation analysis is critical.


2021 ◽  
pp. 096228022199596
Author(s):  
Tyrel Stokes ◽  
Russell Steele ◽  
Ian Shrier

Recent theoretical work in causal inference has explored an important class of variables which, when conditioned on, may further amplify existing unmeasured confounding bias (bias amplification). Despite this theoretical work, existing simulations of bias amplification in clinical settings have suggested bias amplification may not be as important in many practical cases as suggested in the theoretical literature. We resolve this tension by using tools from the semi-parametric regression literature leading to a general characterization in terms of the geometry of OLS estimators which allows us to extend current results to a larger class of DAGs, functional forms, and distributional assumptions. We further use these results to understand the limitations of current simulation approaches and to propose a new framework for performing causal simulation experiments to compare estimators. We then evaluate the challenges and benefits of extending this simulation approach to the context of a real clinical data set with a binary treatment, laying the groundwork for a principled approach to sensitivity analysis for bias amplification in the presence of unmeasured confounding.


2021 ◽  
Author(s):  
Margaret K. K Doll ◽  
Stacy M. Pettigrew ◽  
Julia Ma ◽  
Aman Verma

Background: The test-negative design is commonly used to estimate influenza and COVID-19 vaccine effectiveness (VE). In these studies, correlated COVID-19 and influenza vaccine behaviors may introduce a confounding bias where controls are included with the other vaccine-preventable acute respiratory illness (ARI). We quantified the impact of this bias on VE estimates in studies where this bias is not addressed. Methods: We simulated study populations under varying vaccination probabilities, COVID-19 VE, influenza VE, and proportions of controls included with the other vaccine-preventable ARI. Mean bias was calculated as the difference between true and estimated VE. Absolute mean bias in VE estimates was classified as low (<10%), moderate (10% to <20%), and high (≥20%). Results: Where vaccination probabilities are positively correlated, COVID-19 and influenza VE test-negative studies with influenza and SARS-CoV-2 ARI controls, respectively, underestimate VE. For COVID-19 VE studies, mean bias was low for all scenarios where influenza represented ≤50% of controls. For influenza VE studies, mean bias was low for all scenarios where SARS-CoV-2 represented ≤10% of controls. Although bias was driven by the conditional probability of vaccination, low VE of the vaccine of interest and high VE of the confounding vaccine increase its magnitude. Conclusions: Where a low percentage of controls are included with the other vaccine-preventable ARI, bias in COVID-19 and influenza VE estimates is low. However, influenza VE estimates are likely more susceptible to bias. Researchers should consider potential bias and its implications in their respective study settings to make informed methodological decisions in test-negative VE studies.


2021 ◽  
Author(s):  
Maya B Mathur ◽  
Tyler VanderWeele

In a recent concept paper (Verbeek et al., 2021), the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group provides a preliminary proposal to improve its existing guidelines for assessing sensitivity to uncontrolled confounding in meta-analyses of nonrandomized studies. The new proposal centers on reporting the E-value for the meta-analytic mean and on comparing this E-value to a measured “reference confounder” to determine whether residual uncontrolled confounding in the meta-analyzed studies could or could not plausibly explain away the meta-analytic mean. Although we agree that E-value analogs for meta-analyses could be an informative addition to future GRADE guidelines, we suggest improvements to the Verbeek et al. (2021)’s specific proposal regarding: (1) their interpretation of comparisons between the E-value and the strengths of associations of a reference confounder; (2) their characterization of evidence strength in meta-analyses in terms of only the meta-analytic mean; and (3) the possibility of confounding bias that is heterogeneous across studies.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhifeng Zhao ◽  
Yiming Zhu ◽  
Xiaochun Ni ◽  
Jiayun Lin ◽  
Hongjie Li ◽  
...  

Abstract Background The gamma-glutamyl transferase (GGT) to alanine aminotransferase (ALT) ratio has been reported as an effective predictor of the severity of hepatitis and HCC. The purpose of this study was to determine the role of the GGT/ALT ratio in the prediction of vascular invasion and survival outcomes in patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). Methods The risk factors for vascular invasion were determined by univariate/multivariate logistic analysis. The cut-off value of GGT/ALT in predicting vascular invasion was calculated using the receiver operating characteristic (ROC) curve. The prognostic value of GGT/ALT was examined by Cox analysis and Kaplan–Meier curves. Sensitivity analysis, such as subgroup analysis and propensity score matching (PSM), was performed to reduce potential confounding bias. Results A high GGT/ALT ratio was identified as an independent risk factor for vascular invasion (P = 0.03). The correlation analysis suggested that higher GGT/ALT was associated with more severe tumour burdens, including vascular invasion (P < 0.001), tumour volume > 5 cm (P < 0.001), poor pathological differentiation (P = 0.042), more severe BCLC (P < 0.001) and ALBI grade (P = 0.007). In the survival analysis, a high GGT/ALT ratio was associated with poor overall survival (OS) (HR: 1.38; 95% CI 1.03, 1.87; P < 0.0001) and disease-free survival (DFS) (HR: 1.32; 95% CI 1.03, 1.87; P < 0.0001). In the subgroup analysis, similar results were consistently observed across most subgroups. In PSM analysis, GGT/ALT remained independently associated with vascular invasion (OR, 186; 95% CI 1.23, 3.33). Conclusion The GGT/ALT ratio was a potential effective factor in the prediction of vascular invasion and prognosis in patients with HBV-related HCC.


2021 ◽  
Author(s):  
Dugang Liu ◽  
Pengxiang Cheng ◽  
Hong Zhu ◽  
Zhenhua Dong ◽  
Xiuqiang He ◽  
...  

2021 ◽  
Vol 33 (1) ◽  
Author(s):  
Maria A. Barceló ◽  
Marc Saez

Abstract Background While numerous studies have assessed the effects of environmental (meteorological variables and air pollutants) and socioeconomic variables on the spread of the COVID-19 pandemic, many of them, however, have significant methodological limitations and errors that could call their results into question. Our main objective in this paper is to assess the methodological limitations in studies that evaluated the effects of environmental and socioeconomic variables on the spread of COVID-19. Main body We carried out a systematic review by conducting searches in the online databases PubMed, Web of Science and Scopus up to December 31, 2020. We first excluded those studies that did not deal with SAR-CoV-2 or COVID-19, preprints, comments, opinion or purely narrative papers, reviews and systematic literature reviews. Among the eligible full-text articles, we then excluded articles that were purely descriptive and those that did not include any type of regression model. We evaluated the risk of bias in six domains: confounding bias, control for population, control of spatial and/or temporal dependence, control of non-linearities, measurement errors and statistical model. Of the 5631 abstracts initially identified, we were left with 132 studies on which to carry out the qualitative synthesis. Of the 132 eligible studies, we evaluated 63.64% of the studies as high risk of bias, 19.70% as moderate risk of bias and 16.67% as low risk of bias. Conclusions All the studies we have reviewed, to a greater or lesser extent, have methodological limitations. These limitations prevent conclusions being drawn concerning the effects environmental (meteorological and air pollutants) and socioeconomic variables have had on COVID-19 outcomes. However, we dare to argue that the effects of these variables, if they exist, would be indirect, based on their relationship with social contact.


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