Smoking, Nicotine, and COVID-19: Triangulation of Methods and Preregistration Are Required for Robust Causal Inference.
Ontology highlight
ABSTRACT:
SUBMITTER: Perski O
PROVIDER: S-EPMC9825305 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
Publications
Smoking, Nicotine, and COVID-19: Triangulation of Methods and Preregistration Are Required for Robust Causal Inference.
Perski Olga O Simons David D Shahab Lion L Brown Jamie J
Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco 20230101 2
Similar Datasets
Project description:In this paper we review the methodological underpinnings of the general pharmacogenetic approach for uncovering genetically-driven treatment effect heterogeneity. This typically utilises only individuals who are treated and relies on fairly strong baseline assumptions to estimate what we term the 'genetically moderated treatment effect' (GMTE). When these assumptions are seriously violated, we show that a robust but less efficient estimate of the GMTE that incorporates information on the population of untreated individuals can instead be used. In cases of partial violation, we clarify when Mendelian randomization and a modified confounder adjustment method can also yield consistent estimates for the GMTE. A decision framework is then described to decide when a particular estimation strategy is most appropriate and how specific estimators can be combined to further improve efficiency. Triangulation of evidence from different data sources, each with their inherent biases and limitations, is becoming a well established principle for strengthening causal analysis. We call our framework 'Triangulation WIthin a STudy' (TWIST)' in order to emphasise that an analysis in this spirit is also possible within a single data set, using causal estimates that are approximately uncorrelated, but reliant on different sets of assumptions. We illustrate these approaches by re-analysing primary-care-linked UK Biobank data relating to CYP2C19 genetic variants, Clopidogrel use and stroke risk, and data relating to APOE genetic variants, statin use and Coronary Artery Disease.
| S-EPMC8452063 | biostudies-literature
Project description:An association between maternal smoking in pregnancy and autism may be biologically plausible, but the evidence to date is inconsistent. We aimed to investigate the causal relationship between maternal smoking during pregnancy and offspring autism using conventional analysis and causal inference methods. In the Avon Longitudinal Study of Parents and Children we investigated the association of maternal smoking during pregnancy (exposure) with offspring autism spectrum disorder (ASD) or possible ASD diagnosis (n = 11,946) and high scores on four autism-related traits (outcomes) (n = 7402-9152). Maternal smoking was self-reported and also measured using an epigenetic score (n = 866-964). Partner's smoking was used as a negative control for intrauterine exposure (n = 6616-10,995). Mendelian randomisation (n = 1002-2037) was carried out using a genetic variant at the CHRNA3 locus in maternal DNA as a proxy for heaviness of smoking. In observational analysis, we observed an association between smoking during pregnancy and impairments in social communication [OR = 1.56, 95% CI = 1.29, 1.87] and repetitive behaviours, but multivariable adjustment suggested evidence for confounding. There was weaker evidence of such association for the other traits or a diagnosis of autism. The magnitude of association for partner's smoking with impairments in social communication was similar [OR = 1.56, 95% CI = 1.30, 1.87] suggesting potential for shared confounding. There was weak evidence for an association of the epigenetic score or genetic variation at CHRNA3 with ASD or any of the autism-related traits. In conclusion, using several analytic methods, we did not find enough evidence to support a causal association between maternal smoking during pregnancy and offspring autism or related traits.
| S-EPMC6265272 | biostudies-literature
Project description:The usual development cycles are too slow for the development of vaccines, diagnostics and treatments in pandemics such as the ongoing SARS-CoV-2 pandemic. Given the pressure in such a situation, there is a risk that findings of early clinical trials are overinterpreted despite their limitations in terms of size and design. Motivated by a non-randomized open-label study investigating the efficacy of hydroxychloroquine in patients with COVID-19, we describe in a unified fashion various alternative approaches to the analysis of non-randomized studies. A widely used tool to reduce the impact of treatment-selection bias are so-called propensity score (PS) methods. Conditioning on the propensity score allows one to replicate the design of a randomized controlled trial, conditional on observed covariates. Extensions include the g-computation approach, which is less frequently applied, in particular in clinical studies. Moreover, doubly robust estimators provide additional advantages. Here, we investigate the properties of propensity score based methods including three variations of doubly robust estimators in small sample settings, typical for early trials, in a simulation study. R code for the simulations is provided.
| S-EPMC7834813 | biostudies-literature
Project description:BackgroundMediation analysis aims at estimating to what extent the effect of an exposure on an outcome is explained by a set of mediators on the causal pathway between the exposure and the outcome. The total effect of the exposure on the outcome can be decomposed into an indirect effect, i.e. the effect explained by the mediators jointly, and a direct effect, i.e. the effect unexplained by the mediators. However finer decompositions are possible in presence of independent or sequential mediators.MethodsWe review four statistical methods to analyse multiple sequential mediators, the inverse odds ratio weighting approach, the inverse probability weighting approach, the imputation approach and the extended imputation approach. These approaches are compared and implemented using a case-study with the aim to investigate the mediating role of adverse reproductive outcomes and infant respiratory infections in the effect of maternal pregnancy mental health on infant wheezing in the Ninfea birth cohort.ResultsUsing the inverse odds ratio weighting approach, the direct effect of maternal depression or anxiety in pregnancy is equal to a 59% (95% CI: 27%,94%) increased prevalence of infant wheezing and the mediated effect through adverse reproductive outcomes is equal to a 3% (95% CI: -6%,12%) increased prevalence of infant wheezing. When including infant lower respiratory infections in the mediation pathway, the direct effect decreases to 57% (95% CI: 25%,92%) and the indirect effect increases to 5% (95% CI: -5%,15%). The estimates of the effects obtained using the weighting and the imputation approaches are similar. The extended imputation approach suggests that the small joint indirect effect through adverse reproductive outcomes and lower respiratory infections is due entirely to the contribution of infant lower respiratory infections, and not to an increased prevalence of adverse reproductive outcomes.ConclusionsThe four methods revealed similar results of small mediating role of adverse reproductive outcomes and early respiratory tract infections in the effect of maternal pregnancy mental health on infant wheezing. The choice of the method depends on what is the effect of main interest, the type of the variables involved in the analysis (binary, categorical, count or continuous) and the confidence in specifying the models for the exposure, the mediators and the outcome.
| S-EPMC9686042 | biostudies-literature
Project description:The problem of estimating the average treatment effects is important when evaluating the effectiveness of medical treatments or social intervention policies. Most of the existing methods for estimating the average treatment effect rely on some parametric assumptions about the propensity score model or the outcome regression model one way or the other. In reality, both models are prone to misspecification, which can have undue influence on the estimated average treatment effect. We propose an alternative robust approach to estimating the average treatment effect based on observational data in the challenging situation when neither a plausible parametric outcome model nor a reliable parametric propensity score model is available. Our estimator can be considered as a robust extension of the popular class of propensity score weighted estimators. This approach has the advantage of being robust, flexible, data adaptive, and it can handle many covariates simultaneously. Adopting a dimension reduction approach, we estimate the propensity score weights semiparametrically by using a non-parametric link function to relate the treatment assignment indicator to a low-dimensional structure of the covariates which are formed typically by several linear combinations of the covariates. We develop a class of consistent estimators for the average treatment effect and study their theoretical properties. We demonstrate the robust performance of the estimators on simulated data and a real data example of investigating the effect of maternal smoking on babies' birth weight.
| S-EPMC6089681 | biostudies-literature
Project description:Major biotechnological advances have facilitated a tremendous boost to the collection of (gen-/transcript-/prote-/methyl-/metabol-)omics data in very large sample sizes worldwide. Coordinated efforts have yielded a deluge of studies associating diseases with genetic markers (genome-wide association studies) or with molecular phenotypes. Whereas omics-disease associations have led to biologically meaningful and coherent mechanisms, the identified (non-germline) disease biomarkers may simply be correlates or consequences of the explored diseases. To move beyond this realm, Mendelian randomization provides a principled framework to integrate information on omics- and disease-associated genetic variants to pinpoint molecular traits causally driving disease development. In this review, we show the latest advances in this field, flag up key challenges for the future, and propose potential solutions.
| S-EPMC8091955 | biostudies-literature
Project description:Empirical measurement of interventions to address significant global health and development problems is necessary to ensure that resources are applied appropriately. Such intervention programs are often deployed at the group or community level. The gold standard design to measure the effectiveness of community-level interventions is the community-randomized trial, but the conditions of these trials often make it difficult to assess their external validity and sustainability. The sheer number of community interventions, relative to randomized studies, speaks to a need for rigorous observational methods to measure their impact. In this article, we use the potential outcomes model for causal inference to motivate a matched cohort design to study the impact and sustainability of nonrandomized, preexisting interventions. We illustrate the method using a sanitation mobilization, water supply, and hygiene intervention in rural India. In a matched sample of 25 villages, we enrolled 1,284 children <5 y old and measured outcomes over 12 mo. Although we found a 33 percentage point difference in new toilet construction [95% confidence interval (CI) = 28%, 39%], we found no impacts on height-for-age Z scores (adjusted difference = 0.01, 95% CI = -0.15, 0.19) or diarrhea (adjusted longitudinal prevalence difference = 0.003, 95% CI = -0.001, 0.008) among children <5 y old. This study demonstrates that matched cohort designs can estimate impacts from nonrandomized, preexisting interventions that are used widely in development efforts. Interpreting the impacts as causal, however, requires stronger assumptions than prospective, randomized studies.
| S-EPMC3012527 | biostudies-literature
Project description:Confounding bias arises when a treatment and outcome share a common cause. In randomised controlled experiments (trials), treatment assignment is random, ostensibly eliminating confounding bias. Here, we use causal directed acyclic graphs to unveil eight structural sources of bias that nevertheless persist in these trials. This analysis highlights the crucial role of causal inference methods in the design and analysis of experiments, ensuring the validity of conclusions drawn from experimental data.
| S-EPMC11658928 | biostudies-literature
Project description:BackgroundObservational studies have found associations between smoking and both poorer cognitive ability and lower educational attainment; however, evaluating causality is challenging. We used two complementary methods to explore this.MethodsWe conducted observational analyses of up to 12 004 participants in a cohort study (Study One) and Mendelian randomisation (MR) analyses using summary and cohort data (Study Two). Outcome measures were cognitive ability at age 15 and educational attainment at age 16 (Study One), and educational attainment and fluid intelligence (Study Two).ResultsStudy One: heaviness of smoking at age 15 was associated with lower cognitive ability at age 15 and lower educational attainment at age 16. Adjustment for potential confounders partially attenuated findings (e.g. fully adjusted cognitive ability β -0.736, 95% CI -1.238 to -0.233, p = 0.004; fully adjusted educational attainment β -1.254, 95% CI -1.597 to -0.911, p < 0.001). Study Two: MR indicated that both smoking initiation and lifetime smoking predict lower educational attainment (e.g. smoking initiation to educational attainment inverse-variance weighted MR β -0.197, 95% CI -0.223 to -0.171, p = 1.78 × 10-49). Educational attainment results were robust to sensitivity analyses, while analyses of general cognitive ability were less so.ConclusionWe find some evidence of a causal effect of smoking on lower educational attainment, but not cognitive ability. Triangulation of evidence across observational and MR methods is a strength, but the genetic variants associated with smoking initiation may be pleiotropic, suggesting caution in interpreting these results. The nature of this pleiotropy warrants further study.
| S-EPMC9226381 | biostudies-literature
Project description:This work proposes a new approach to determine the spatial location and orientation of an object using measurements performed on the object itself. The on-board triangulation algorithm we outline could be implemented in lieu of, or in addition to, well-known alternatives such as Global Positioning System (GPS) or standard triangulation, since both of these correspond to significantly different geometric pictures and necessitate different hardware and algorithms. We motivate the theory by describing situations in which on-board triangulation would be useful and even preferable to standard methods. The on-board triangulation algorithm we outline involves utilizing dumb beacons which broadcast omnidirectional single frequency radio waves, and smart antenna arrays on the object itself to infer the direction of the beacon signals, which may be used for onboard calculation of the position and orientation of the object. Numerical examples demonstrate the utility of the method and its noise tolerance.
| S-EPMC5482487 | biostudies-literature