Breastfeeding and the risk of childhood asthma: A two-stage instrumental variable analysis to address endogeneity

2017 ◽  
Vol 28 (6) ◽  
pp. 564-572 ◽  
Author(s):  
Nivita D. Sharma
Keyword(s):  
Childhood Asthma ◽  
Instrumental Variable ◽  
Two Stage ◽  
Instrumental Variable Analysis ◽  
Variable Analysis

scholarly journals Two-step Randomisation: Applying the Results of Small Feasibility Studies of Interventions to Large-scale Mendelian Randomisation Studies to Robustly Infer Causal Effects on Clinical Endpoints

2019 ◽  
Author(s):  
Meda R Sandu ◽  
Rhona Beynon ◽  
Rebecca Richmond ◽  
Diana L. Santos Ferreira ◽  
Lucy Hackshaw-McGeagh ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Green Tea ◽  
Instrumental Variable ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Two Stage ◽  
Instrumental Variable Analysis ◽  
Metabolic Traits ◽  
Randomization Analysis ◽  
Variable Analysis

Background: Feasibility trials are preliminary trials that assess the viability and acceptability of intervention studies and the effects of the intervention on intermediate endpoints. Due to their short duration, they are unable to establish the effects of the intervention on long-term clinical outcomes. We propose a novel method that could transform the interpretation of feasibility trials using modified two-stage randomisation analyses. Methods In this two-stage process, we explored the effects of a 6-month feasibility factorial randomised controlled trial (RCT) of lycopene and green tea dietary interventions (ProDiet) on 159 serum metabolic traits in 133 men with raised PSA levels but prostate cancer (PCA) free. In the first stage, we conducted an intention-to-treat analysis, using linear regression to examine the effects of the interventions on metabolic traits, compared to the placebo group and instrumental variable analysis to assess the causal effect of the intervention on the outcomes. In the second stage, we used a two-sample Mendelian Randomization (MR) approach to assess the causal effect of metabolic traits altered by the interventions, on PCA risk, using summary statistics data from an international PCA consortium of 44,825 cancer cases and 27,904 controls. ResultsThe systemic effects of lycopene and green tea supplementation on serum metabolic profile were comparable to the effects of the respective dietary advice interventions (R2= 0.65 and 0.76 for lycopene and green tea respectively). Metabolites which were altered in response to lycopene supplementation were acetate (standard deviation difference versus placebo (β)): 0.69; 95% CI= 0.24, 1.15; p=0.003), valine (β: -0.62; -1.03, -0.02; p=0.004), pyruvate (β: -0.56; -0.95, -0.16; p=0.006), and docosahexaenoic acid (β: -0.50; -085, -0.14; p=0.006). The instrumental variable analysis showed there was no evidence that green tea altered the metabolome, but lycopene was associated with an increase in acetate (β=2.13; p=0.006) and decreases in pyruvate (β=-1.90; p=0.009), valine (β=-1.79; p=0.023), diacylglycerol (β=-1.81; p=0.026), alanine (β=-1.55; p=0.015) and DHA (p=0.097), where the regression coefficient represents the standard deviation (SD) difference in metabolite measures per unit change in lycopene (µmol/L) or EGCG (nM).Using MR, a genetically instrumented SD increase in pyruvate increased the odds of PCA by 1.29 (1.03, 1.62; p=0.027). Conclusion Using a two-stage randomization analysis in a feasibility RCT, we found that lycopene lowered levels of pyruvate, which our Mendelian randomization analysis suggests may be causally related to reduced PCA risk.

Using instrumental variables to estimate the attributable fraction

2019 ◽  
Vol 29 (8) ◽  
pp. 2063-2073
Author(s):  
Elisabeth Dahlqwist ◽  
Zoltán Kutalik ◽  
Arvid Sjölander
Keyword(s):  
Coronary Heart Disease ◽  
Risk Factor ◽  
Heart Disease ◽  
Instrumental Variables ◽  
Instrumental Variable ◽  
Mendelian Randomization ◽  
Attributable Fraction ◽  
Two Stage ◽  
Instrumental Variable Analysis ◽  
Variable Analysis

In order to design efficient interventions aimed to improve public health, policy makers need to be provided with reliable information of the health burden of different risk factors. For this purpose, we are interested in the proportion of cases that could be prevented had some harmful exposure been eliminated from the population, i.e. the attributable fraction. The attributable fraction is a causal measure; thus, to estimate the attributable fraction from observational data, we have to make appropriate adjustment for confounding. However, some confounders may be unobserved, or even unknown to the investigator. A possible solution to this problem is to use instrumental variable analysis. In this work, we present how the attributable fraction can be estimated with instrumental variable methods based on the two-stage estimator or the G-estimator. One situation when the problem of unmeasuredconfounding may be particularly severe is when assessing the effect of low educational qualifications on coronary heart disease. By using Mendelian randomization, a special case of instrumental variable analysis, it has been claimed that low educational qualifications is a causal risk factor for coronary heart disease. We use Mendelian randomization to estimate the causal risk ratio and causal odds ratio of low educational qualifications as a risk factor for coronary heart disease with data from the UK Biobank. We compare the two-stage and G-estimator as well as the attributable fraction based on the two estimators. The plausibility of drawing causal conclusion in this analysis is thoroughly discussed and alternative genetic instrumental variables are tested.

P0157GENETICALLY DETERMINED HYPOALBUMINEMIA AND THE RISK OF HYPERTENSION: GWAS-BASED INSTRUMENTAL VARIABLE ANALYSIS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Sang-Woong Han ◽  
Mi-yeon Yu ◽  
Jong Wook Choi ◽  
Joo-Hark Yi ◽  
Chang Hwa Lee ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Serum Albumin ◽  
Instrumental Variable ◽  
Serum Albumin Level ◽  
Albumin Level ◽  
Albumin Concentration ◽  
Two Stage ◽  
Instrumental Variable Analysis ◽  
Increased Risk ◽  
Variable Analysis

Abstract Background and Aims Hypoalbuminemia is a clinical indicator of vascular endothelial dysfunction. However, the genetic association of hypoalbuminemia with hypertension has not yet been clarified. We investigated whether genetic variation(s) associated with decreased serum albumin level is deeply related with increased risk of hypertension. Method Mild hypoalbuminemia was defined as a serum albumin concentration of less than 4.0 g/dL. We performed GWAS-based instrumental variable analysis using the population-based cohort data collected from KoGES. Eligible as cases were all native Koreans without significant medical illness and a total 4326 participants were divided in into control (n = 3157) and hypoalbuminemic (n = 1168) according to their serum albumin level. Results Our GWAS revealed that 71 susceptibility loci were associated with mild hypoalbuminemia and subsequent two-stage least squares estimation analysis showed that both genetic variations at rs2894536 in LOC107986598 gene and rs10972486 in the ATP8B5P gene were related with systolic blood pressure. In subsequent Cox-proportional hazards model, we found that not only low serum albumin (HR = 0.654, 95% CI = 0.521-0.820), but also polymorphisms of rs2894536 (HR = 1.176, 95% CI = 1.015-1.361) and rs10972486 (HR = 1.152, 95% CI = 1.009-1.316) were significant predictor of hypertension. Our two-stage residual inclusion analysis indicated that candidate genetic variation attenuated the association between serum albumin and hypertension, showing hazard ratios of hypoalbuminemia on development of hypertension were 0.762 (0.659-0.882) for rs2894536 and 0.759 (0.656-0.878) for rs10972486. Conclusion These findings support a causal relationship between hypoalbuminemia-associated genetic variation, hypoalbuminemia and development of hypertension.

Abstract 2323: Statin Use and Discharge Disposition After Ischemic Stroke Hospitalization

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Alexander Flint ◽  
Hooman Kamel ◽  
Babak Navi ◽  
Vivek Rao ◽  
Bonnie Faigeles ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Instrumental Variable ◽  
Multivariable Analysis ◽  
Treatment Patterns ◽  
High Dose ◽  
Instrumental Variable Analysis ◽  
Discharge Disposition ◽  
Usual Dose ◽  
Statin Use ◽  
Variable Analysis

Objective: To determine whether statin use is associated with improved discharge disposition after ischemic stroke. Methods: We analyzed 12,689 patients with ischemic stroke over a 7 year period at 17 hospitals in Kaiser Permanente Northern California. We used multivariable generalized ordinal logistic regression and instrumental variable analysis of treatment patterns by hospital to control for the possibility of confounding. Results: Statin users before and during stroke hospitalization were more likely to be discharged home (54.9% for statin users, 46.3% for statin non-users) and less likely to die in hospital (5.3% for statin users, 10.3% for statin non-users). Patients who underwent statin withdrawal in-hospital were less likely to be discharged home (39.1% for statin withdrawal, 54.9% for statin continuation) and more likely to die in hospital (22.3% for statin withdrawal, 5.3% for statin continuation). Users of higher statin doses (>60 mg / day) were even more likely to be discharged home (62.5% for high dose statin, 56.5% for usual dose statin, and 47.4% for no statin) and less likely to die in hospital in-hospital (3.5% for high dose statin, 5.6% for usual dose statin, and 10.6% for no statin). These results were confirmed by multivariable analysis. The association of statin use and improved outcomes was also confirmed by instrumental variable analysis of treatment patterns by hospital, and thus this association cannot be explained by confounding at the individual patient level. Conclusions: Statin use is associated with improved discharge disposition after ischemic stroke, particularly at higher doses.

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