Causal inference concepts applied to three observational studies in the context of vaccine development: from theory to practice

Gvozdenovic, E.; Malvisi, Lucio; Cinconze, E.; Vansteelandt, Stijn; Nakanwagi, Phoebe; Aris, Emmanuel; Rosillon, Dominique

doi:10.1186/s12874-021-01220-1

Cited by 5 publications

(2 citation statements)

References 52 publications

(66 reference statements)

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“…[17][18][19] We will explore this inconsistency by reviewing the literature under the principles of causal inference. We here use counterfactual reasoning as proposed by Rubin, 20 Balke and Pearl 21 and as recently revised by Gvozdenović et al 22 Treatment effects are considered causal, under the proviso of certain assumptions: exchangeability, positivity and consistency. We will start by defining causality under these assumptions.…”

Section: Key Messagesmentioning

confidence: 99%

Biological DMARDs and disease modification in axial spondyloarthritis: a review through the lens of causal inference

et al. 2021

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Axial spondyloarthritis (axSpA) is a chronic rheumatic disease characterised by inflammation predominantly involving the spine and the sacroiliac joints. In some patients, axial inflammation leads to irreversible structural damage that in the spine is usually quantified by the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS). Available therapeutic options include biological disease-modifying antirheumatic drugs (bDMARDs), which have been proven effective in suppressing inflammation in several randomised controlled trials (RCT), the gold standard for evaluating causal treatment effects. RCTs are, however, unfeasible for testing structural effects in axSpA mainly due to the low sensitivity to change of the mSASSS. The available literature therefore mainly includes observational research, which poses serious challenges to the determination of causality. Here, we review the studies testing the effect of bDMARDs on spinal radiographic progression, making use of the principles of causal inference. By exploring the assumptions of causality under counterfactual reasoning (exchangeability, positivity and consistency), we distinguish between studies that likely have reported confounded treatment effects and studies that, on the basis of their design, have more likely reported causal treatment effects. We conclude that bDMARDs might, indirectly, interfere with spinal radiographic progression in axSpA by their effect on inflammation. Innovations in imaging are expected, so that placebo-controlled trials can in the future become a reality. In the meantime, causal inference analysis using observational data may contribute to a better understanding of whether disease modification is possible in axSpA.

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Section: Key Messagesmentioning

confidence: 99%

Biological DMARDs and disease modification in axial spondyloarthritis: a review through the lens of causal inference

et al. 2021

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“…Randomised controlled trials (RCT) are often employed to explore causal relationships [ 6 ], but an RCT would be unethical and impractical in this scenario–non-smokers would need to be unnecessarily exposed to nicotine for decades to understand the (potentially harmful) long-term effects of regular nicotine use without confounding from exposure to cigarette smoke. Mendelian randomisation (MR) is an alternative method which can be used in scenarios where RCTs are implausible or unethical.…”

Section: Introductionmentioning

confidence: 99%

Estimating the health impact of nicotine exposure by dissecting the effects of nicotine versus non-nicotine constituents of tobacco smoke: A multivariable Mendelian randomisation study

Khouja,

Sanderson,

Wootton

et al. 2024

PLoS Genet

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The detrimental health effects of smoking are well-known, but the impact of regular nicotine use without exposure to the other constituents of tobacco is less clear. Given the increasing daily use of alternative nicotine delivery systems, such as e-cigarettes, it is increasingly important to understand and separate the effects of nicotine use from the impact of tobacco smoke exposure. Using a multivariable Mendelian randomisation framework, we explored the direct effects of nicotine compared with the non-nicotine constituents of tobacco smoke on health outcomes (lung cancer, chronic obstructive pulmonary disease [COPD], forced expiratory volume in one second [FEV-1], forced vital capacity [FVC], coronary heart disease [CHD], and heart rate [HR]). We used Genome-Wide Association Study (GWAS) summary statistics from Buchwald and colleagues, the GWAS and Sequencing Consortium of Alcohol and Nicotine, the International Lung Cancer Consortium, and UK Biobank. Increased nicotine metabolism increased the risk of COPD, lung cancer, and lung function in the univariable analysis. However, when accounting for smoking heaviness in the multivariable analysis, we found that increased nicotine metabolite ratio (indicative of decreased nicotine exposure per cigarette smoked) decreases heart rate (b = -0.30, 95% CI -0.50 to -0.10) and lung function (b = -33.33, 95% CI -41.76 to -24.90). There was no clear evidence of an effect on the remaining outcomes. The results suggest that these smoking-related outcomes are not due to nicotine exposure but are caused by the other components of tobacco smoke; however, there are multiple potential sources of bias, and the results should be triangulated using evidence from a range of methodologies.

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A survey of deep causal models and their industrial applications

Li,

Guo,

Qiang

2024

Artif Intell Rev

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The notion of causality assumes a paramount position within the realm of human cognition. Over the past few decades, there has been significant advancement in the domain of causal effect estimation across various disciplines, including but not limited to computer science, medicine, economics, and industrial applications. Given the continous advancements in deep learning methodologies, there has been a notable surge in its utilization for the estimation of causal effects using counterfactual data. Typically, deep causal models map the characteristics of covariates to a representation space and then design various objective functions to estimate counterfactual data unbiasedly. Different from the existing surveys on causal models in machine learning, this review mainly focuses on the overview of the deep causal models based on neural networks, and its core contributions are as follows: (1) we cast insight on a comprehensive overview of deep causal models from both timeline of development and method classification perspectives; (2) we outline some typical applications of causal effect estimation to industry; (3) we also endeavor to present a detailed categorization and analysis on relevant datasets, source codes and experiments.

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Causal inference concepts applied to three observational studies in the context of vaccine development: from theory to practice

Cited by 5 publications

References 52 publications

Biological DMARDs and disease modification in axial spondyloarthritis: a review through the lens of causal inference

Biological DMARDs and disease modification in axial spondyloarthritis: a review through the lens of causal inference

Estimating the health impact of nicotine exposure by dissecting the effects of nicotine versus non-nicotine constituents of tobacco smoke: A multivariable Mendelian randomisation study

A survey of deep causal models and their industrial applications

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