scholarly journals Assess the Application of the E-Value in the Unmeasured Confounder Evaluation of Observational Pharmaceutical Studies

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lihong Huang ◽  
Jianbing Ma ◽  
Xiaochun Qiu ◽  
Tao Suo
Keyword(s):  
Public Health ◽  
Sensitivity Analysis ◽  
Lower Bound ◽  
Confidence Interval ◽  
Effect Size ◽  
Observational Studies ◽  
Relative Effect ◽  
Analysis Tool ◽  
Unmeasured Confounder ◽  
Unmeasured Confounders

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.

scholarly journals Systematic Review and Meta-Analysis of the Efficacy of Appropriate Empiric Antibiotic Therapy for Sepsis

2010 ◽  
Vol 54 (11) ◽  
pp. 4851-4863 ◽  
Author(s):  
Mical Paul ◽  
Vered Shani ◽  
Eli Muchtar ◽  
Galia Kariv ◽  
Eyal Robenshtok ◽  
...  
Keyword(s):  
Systematic Review ◽  
Confidence Interval ◽  
Antibiotic Treatment ◽  
Effect Size ◽  
Observational Studies ◽  
Meta Analysis ◽  
Empiric Antibiotic Therapy ◽  
Clinical Variable ◽  
Empirical Antibiotic Treatment ◽  
All Cause Mortality

ABSTRACT Quantifying the benefit of early antibiotic treatment is crucial for decision making and can be assessed only in observational studies. We performed a systematic review of prospective studies reporting the effect of appropriate empirical antibiotic treatment on all-cause mortality among adult inpatients with sepsis. Two reviewers independently extracted data. Risk of bias was assessed using the Newcastle-Ottawa score. We calculated unadjusted odds ratios (ORs) with 95% confidence intervals for each study and extracted adjusted ORs, with variance, methods, and covariates being used for adjustment. ORs were pooled using random-effects meta-analysis. We examined the effects of methodological and clinical confounders on results through subgroup analysis or mixed-effect meta-regression. Seventy studies were included, of which 48 provided an adjusted OR for inappropriate empirical antibiotic treatment. Inappropriate empirical antibiotic treatment was associated with significantly higher mortality in the unadjusted and adjusted comparisons, with considerable heterogeneity occurring in both analyses (I 2 > 70%). Study design, time of mortality assessment, the reporting methods of the multivariable models, and the covariates used for adjustment were significantly associated with effect size. Septic shock was the only clinical variable significantly affecting results (it was associated with higher ORs). Studies adjusting for background conditions and sepsis severity reported a pooled adjusted OR of 1.60 (95% confidence interval = 1.37 to 1.86; 26 studies; number needed to treat to prevent one fatal outcome, 10 patients [95% confidence interval = 8 to 15]; I 2 = 46.3%) given 34% mortality with inappropriate empirical treatment. Appropriate empirical antibiotic treatment is associated with a significant reduction in all-cause mortality. However, the methods used in the observational studies significantly affect the effect size reported. Methods of observational studies assessing the effects of antibiotic treatment should be improved and standardized.

scholarly journals A note on a sensitivity analysis for unmeasured confounding, and the related E-value

2020 ◽  
Vol 8 (1) ◽  
pp. 229-248
Author(s):  
Arvid Sjölander
Keyword(s):  
Sensitivity Analysis ◽  
Observational Studies ◽  
Real Data ◽  
Feasible Region ◽  
Maximal Strength ◽  
Unmeasured Confounding ◽  
Unmeasured Confounder ◽  
Two Parameters ◽  
Strength Of Association

Abstract Unmeasured confounding is one of the most important threats to the validity of observational studies. In this paper we scrutinize a recently proposed sensitivity analysis for unmeasured confounding. The analysis requires specification of two parameters, loosely defined as the maximal strength of association that an unmeasured confounder may have with the exposure and with the outcome, respectively. The E-value is defined as the strength of association that the confounder must have with the exposure and the outcome, to fully explain away an observed exposure-outcome association. We derive the feasible region of the sensitivity analysis parameters, and we show that the bounds produced by the sensitivity analysis are not always sharp. We finally establish a region in which the bounds are guaranteed to be sharp, and we discuss the implications of this sharp region for the interpretation of the E-value. We illustrate the theory with a real data example and a simulation.

scholarly journals On the use of the E-value for sensitivity analysis in epidemiologic studies

2021 ◽  
Vol 37 (6) ◽  
Author(s):  
Conceição Christina Rigo Vale ◽  
Nubia Karla de Oliveira Almeida ◽  
Renan Moritz Varnier Rodrigues de Almeida
Keyword(s):  
Sensitivity Analysis ◽  
Prenatal Care ◽  
Low Birthweight ◽  
Epidemiological Studies ◽  
Epidemiologic Studies ◽  
Sensitivity Index ◽  
Analysis Tool ◽  
Causal Inferences ◽  
Unmeasured Confounder ◽  
Increased Risk

Abstract: This study illustrates the use of a recently developed sensitivity index, the E-value, helpful in strengthening causal inferences in observational epidemiological studies. The E-value aims to determine the minimum required strength of association between an unmeasured confounder and an exposure/outcome to explain the observed association as non-causal. Such parameter is defined as E - v a l u e = R R + R R R R - 1, where RR is the risk ratio between the exposure and the outcome. Our work illustrates the E-value using observational data from a recently published study on the relationship between indicators of prenatal care adequacy and the outcome low birthweight. The E-value ranged between 1.45 and 5.63 according to the category and prenatal care index evaluated, showing the highest value for the “no prenatal care” category of the GINDEX index and the minimum value for “intermediate prenatal care” of the APNCU index. For “inappropriate prenatal care” (all indexes), the E-value ranged between 2.76 (GINDEX) and 4.99 (APNCU). These findings indicate that only strong confounder/low birthweight associations (more than 400% increased risk) would be able to fully explain the prenatal care vs. low birthweight association observed. The E-value is a useful, intuitive sensitivity analysis tool that may help strengthening causal inferences in epidemiological observational studies.

scholarly journals Sensitivity analysis for an unmeasured confounder: a review of two independent methods

2010 ◽  
Vol 13 (2) ◽  
pp. 188-198 ◽  
Author(s):  
Ronir Raggio Luiz ◽  
Maria Deolinda Borges Cabral
Keyword(s):  
Sensitivity Analysis ◽  
Causal Inference ◽  
Observational Studies ◽  
Experimental Studies ◽  
Epidemiological Studies ◽  
Strong Constraint ◽  
Unmeasured Confounder ◽  
Direct Sensitivity ◽  
Hidden Biases ◽  
The Impact

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.

scholarly journals Assessing the impact of unmeasured confounders for credible and reliable real-world evidence

2020 ◽  
Author(s):  
Xiang Zhang ◽  
James Stamey ◽  
Maya B Mathur
Keyword(s):  
Sensitivity Analysis ◽  
Statistical Methods ◽  
Real World ◽  
Sensitivity Analyses ◽  
Real World Data ◽  
Unmeasured Confounding ◽  
Suggested Approach ◽  
Unmeasured Confounder ◽  
The Impact ◽  
Unmeasured Confounders

Purpose: We review statistical methods for assessing the possible impact of bias due to unmeasured confounding in real world data analysis and provide detailed recommendations for choosing among the methods. Methods: By updating an earlier systematic review, we summarize modern statistical best practices for evaluating and correcting for potential bias due to unmeasuredconfounding in estimating causal treatment effect from non-interventional studies. Results: We suggest a hierarchical structure for assessing unmeasured confounding.First, for initial sensitivity analyses, we strongly recommend applying a recently developed method, the E-value, that is straightforward to apply and does not require prior knowledge or assumptions about the unmeasured confounder(s). When some such knowledge is available, the E-value could be supplemented by the rule-out or array method at this step. If these initial analyses suggest results may not be robust to unmeasured confounding, subsequent analyses could be conducted using more specialized statistical methods, which we categorize based on whether they requireaccess to external data on the suspected unmeasured confounder(s), internal data, or no data. Other factors for choosing the subsequent sensitivity analysis methods arealso introduced and discussed, including the types of unmeasured confounders and whether the subsequent sensitivity analysis is intended to provide a corrected causaltreatment effect. Conclusion: Various analytical methods have been proposed to address unmeasured confounding, but little research has discussed a structured approach to select appropriate methods in practice. In providing practical suggestions for choosing appropriate initial and, potentially, more specialized subsequent sensitivity analyses, we hopeto facilitate the widespread reporting of such sensitivity analyses in non-interventional studies. The suggested approach also has the potential to inform pre-specificationof sensitivity analyses before executing the analysis, and therefore increase the transparency and limit selective study reporting.

scholarly journals Clinical laboratory tests and five-year incidence of major depressive disorder: a prospective cohort study of 433,890 participants from the UK Biobank

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael Wainberg ◽  
Stefan Kloiber ◽  
Breno Diniz ◽  
Roger S. McIntyre ◽  
Daniel Felsky ◽  
...  
Keyword(s):  
Public Health ◽  
Major Depressive Disorder ◽  
Cohort Study ◽  
Confidence Interval ◽  
Depressive Disorder ◽  
Blood Cell ◽  
Biological Processes ◽  
Uk Biobank ◽  
Major Depressive ◽  
The Uk

AbstractPrevention of major depressive disorder (MDD) is a public health priority. Identifying biomarkers of underlying biological processes that contribute to MDD onset may help address this public health need. This prospective cohort study encompassed 383,131 white British participants from the UK Biobank with no prior history of MDD, with replication in 50,759 participants of other ancestries. Leveraging linked inpatient and primary care records, we computed adjusted odds ratios for 5-year MDD incidence among individuals with values below or above the 95% confidence interval (<2.5th or >97.5th percentile) on each of 57 laboratory measures. Sensitivity analyses were performed across multiple percentile thresholds and in comparison to established reference ranges. We found that indicators of liver dysfunction were associated with increased 5-year MDD incidence (even after correction for alcohol use and body mass index): elevated alanine aminotransferase (AOR = 1.35, 95% confidence interval [1.16, 1.58]), aspartate aminotransferase (AOR = 1.39 [1.19, 1.62]), and gamma glutamyltransferase (AOR = 1.52 [1.31, 1.76]) as well as low albumin (AOR = 1.28 [1.09, 1.50]). Similar observations were made with respect to endocrine dysregulation, specifically low insulin-like growth factor 1 (AOR = 1.34 [1.16, 1.55]), low testosterone among males (AOR = 1.60 [1.27, 2.00]), and elevated glycated hemoglobin (HbA1C; AOR = 1.23 [1.05, 1.43]). Markers of renal impairment (i.e. elevated cystatin C, phosphate, and urea) and indicators of anemia and macrocytosis (i.e. red blood cell enlargement) were also associated with MDD incidence. While some immune markers, like elevated white blood cell and neutrophil count, were associated with MDD (AOR = 1.23 [1.07, 1.42]), others, like elevated C-reactive protein, were not (AOR = 1.04 [0.89, 1.22]). The 30 significant associations validated as a group in the multi-ancestry replication cohort (Wilcoxon p = 0.0005), with a median AOR of 1.235. Importantly, all 30 significant associations with extreme laboratory test results were directionally consistent with an increased MDD risk. In sum, markers of liver and kidney dysfunction, growth hormone and testosterone deficiency, innate immunity, anemia, macrocytosis, and insulin resistance were associated with MDD incidence in a large community-based cohort. Our results support a contributory role of diverse biological processes to MDD onset.

A sensitivity analysis tool for improving the capacity of amusement rides

2008 ◽  
Vol 2 (2) ◽  
pp. 117-126 ◽  
Author(s):  
S S Desai ◽  
J L Hunsucker
Keyword(s):  
Sensitivity Analysis ◽  
Analysis Tool

scholarly journals Descriptive Epidemiology of Rescue-Related Fatal Drowning in Turkey

2021 ◽  
Vol 18 (12) ◽  
pp. 6613
Author(s):  
Ali Işın ◽  
Adnan Turgut ◽  
Amy E. Peden
Keyword(s):  
Public Health ◽  
Retrospective Study ◽  
Relative Risk ◽  
Confidence Interval ◽  
Descriptive Epidemiology ◽  
Death Registry ◽  
Public Health Threat ◽  
Reduction Strategies ◽  
Media Reports ◽  
Risk Reduction Strategies

Drowning is a public-health threat and a leading cause of injury-related death. In Turkey, drowning results in 900 fatalities annually, and the rate is rising. As data on rescue-related drowning are scarce, this retrospective study explores the epidemiology of fatal drowning among rescuers in Turkey. As there are no routinely collected death registry data on drowning in Turkey, data were sourced from media reports of incidents between 2015 and 2019. Rescuer fatalities were analysed by age, sex, activity prior to rescue, location, incident day of week and season, and place of death. Statistical analyses comprised X2 tests of significance (p < 0.05) and calculation of relative risk (95% confidence interval) using fatality rates. In total, 237 bystander rescuers drowned (90% male; 35% 15–24 years). In 33% of cases, the primary drowning victim (PDV) was successfully rescued, while in 46% of cases the rescue resulted in multiple drowning fatalities (mean = 2.29; range 1–5 rescuers). Rescues were more likely to be successful in saving the PDV if undertaken at the beach/sea (X2 = 29.147; p < 0.001), while swimming (X2 = 12.504; p = 0.001), or during summer (X2 = 8.223; p = 0.029). Risk of bystander rescue-related fatal drowning was twice as high on weekdays compared to on weekends (RR = 2.04; 95%CI: 1.56–2.67). While bystanders play an important role in reducing drowning, undertaking a rescue is not without risk and can lead to multiple drowning incidents. Training in rescue and resuscitation skills (especially the prioritization of non-contact rescues) coupled with increasing awareness of drowning risk, are risk-reduction strategies which should be explored in Turkey.

scholarly journals Safety of Systemic Medications Among Older Adults With Psoriasis and Atopic Dermatitis: A Systematic Review of Observational Studies

2021 ◽  
pp. 120347542199377
Author(s):  
Evan Tang ◽  
Talha Maqbool ◽  
Megan Lam ◽  
Gaelen P. Adam ◽  
Mina Tadrous ◽  
...  
Keyword(s):  
Systematic Review ◽  
Older Adults ◽  
Atopic Dermatitis ◽  
Observational Studies ◽  
Relative Effect ◽  
Emergency Department Visits ◽  
Major Cardiovascular Events ◽  
Quantitative Synthesis ◽  
Newcastle Ottawa Scale ◽  
Ottawa Scale

Background Psoriasis and atopic dermatitis are common among older adults (≥65 years old), but clinical trials often exclude that population. Objective To synthesize evidence from observational studies on the safety of systemic therapies (conventional or biologic) for psoriasis and atopic dermatitis among older adults in a systematic review. Methods We searched MEDLINE and EMBASE (inception to October 31, 2019) and included observational studies reporting adverse events among older people treated with systemic therapy for psoriasis or atopic dermatitis. Outcomes were death, hospitalization, emergency department visits, infections, major cardiovascular events, renal toxicity, hepatotoxicity, and cytopenias. We assessed study quality using the Newcastle-Ottawa Scale. Results We included 22 studies on treatment for psoriasis and 2 for atopic dermatitis. Most studies were small and non-comparative and 20 of 24 were low quality. Studies comparing safety between medications or medication classes or between older and younger adults did not show apparent differences but had wide confidence intervals around relative effect estimates. Heterogeneity of study design and reporting precluded quantitative synthesis. Conclusions There is scant evidence on the safety of conventional systemic and biologic medications for older adults with psoriasis or atopic dermatitis; older adults and their clinicians should be aware of this evidence gap.

scholarly journals Building Drainage System Design for Tall Buildings: Current Limitations and Public Health Implications

Buildings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 70
Author(s):  
Michael Gormley ◽  
David Kelly ◽  
David Campbell ◽  
Yunpeng Xue ◽  
Colin Stewart
Keyword(s):  
Public Health ◽  
System Design ◽  
Drainage System ◽  
Tall Buildings ◽  
Health Concern ◽  
Analysis Tool ◽  
Water Trap ◽  
Current Design ◽  
Cross Transmission ◽  
Transmission Of Disease

National design guides provide essential guidance for the design of building drainage systems, which primarily ensure the basic objectives of preventing odor ingress and cross-transmission of disease through water-trap seal retention. Current building drainage system design guides only extend to buildings of 30 floors, while modern tall buildings frequently extend to over 100 floors, exceeding the predictive capability of current design guides in terms of operating system conditions. However, the same design guides are being used for tall buildings as would be used for low-rise buildings. A complicating factor is the historic roots of current design guides and standards (including the interpretation of the governing fluid mechanics principles and margins of safety), causing many design differences to exist for the same conditions internationally, such as minimum trap seal retention requirements, stack-to-vent cross-vent spacing, and even stack diameter. The design guides also differ in the size and scale of the systems they cover, and most make no allowance for the specific building drainage system requirements of tall buildings. This paper assesses the limitations of applying current building drainage system design guides when applied to the case of tall buildings. Primarily, the assessments used in this research are based on codes from Europe, the USA and Australia/New Zealand as representative of the most common approaches and from which many other codes and standards are derived. The numerical simulation model, AIRNET, was used as the analysis tool. Our findings confirm that current design guides, which have been out of date for a number of decades, are now in urgent need of updating as code-compliant systems have been shown to be susceptible to water-trap seal depletion, a risk to cross-transmission of disease, which is a major public health concern, particularly in view of the current COVID-19 pandemic.

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