Causal inference in perioperative medicine observational research: part 1, a graphical introduction

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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Causal Inference and Observational Research

Perspectives on Psychological Science ◽

10.1177/1745691610383511 ◽

2010 ◽

Vol 5 (5) ◽

pp. 546-556 ◽

Cited By ~ 232

Author(s):

Matt McGue ◽

Merete Osler ◽

Kaare Christensen

Keyword(s):

Causal Inference ◽

Observational Research

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Causal inference with observational data: the need for triangulation of evidence

Psychological Medicine ◽

10.1017/s0033291720005127 ◽

2021 ◽

pp. 1-16

Author(s):

Gemma Hammerton ◽

Marcus R. Munafò

Keyword(s):

Causal Inference ◽

Observational Data ◽

Causal Effect ◽

Observational Research ◽

Potential Bias ◽

Definite Answer ◽

Single Method ◽

Statistical Approaches ◽

Scientific Infrastructure ◽

Different Sources

Abstract The goal of much observational research is to identify risk factors that have a causal effect on health and social outcomes. However, observational data are subject to biases from confounding, selection and measurement, which can result in an underestimate or overestimate of the effect of interest. Various advanced statistical approaches exist that offer certain advantages in terms of addressing these potential biases. However, although these statistical approaches have different underlying statistical assumptions, in practice they cannot always completely remove key sources of bias; therefore, using design-based approaches to improve causal inference is also important. Here it is the design of the study that addresses the problem of potential bias – either by ensuring it is not present (under certain assumptions) or by comparing results across methods with different sources and direction of potential bias. The distinction between statistical and design-based approaches is not an absolute one, but it provides a framework for triangulation – the thoughtful application of multiple approaches (e.g. statistical and design based), each with their own strengths and weaknesses, and in particular sources and directions of bias. It is unlikely that any single method can provide a definite answer to a causal question, but the triangulation of evidence provided by different approaches can provide a stronger basis for causal inference. Triangulation can be considered part of wider efforts to improve the transparency and robustness of scientific research, and the wider scientific infrastructure and system of incentives.

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Determining causality in epidemiology: Why observational studies can be misleading and the case for experiments

10.31236/osf.io/9q5hm ◽

2018 ◽

Author(s):

Craig Perrin ◽

James Steele

Keyword(s):

Causal Inference ◽

Observational Studies ◽

Observational Research ◽

Observational Methods ◽

Alternative Approach

In epidemiology, the purpose of causal inference is often to identify variables that appear to induce the event of interest, through observational methods. This is usually because it is not feasible to experimentally induce such an event. By identifying causes, interventions can be developed to prevent the effects from manifesting. However, observational research is not without limitations. This article discusses the ways in which observational research can be misleading and how these problems can be overcome with an alternative approach to experimenting on the event of interest.

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Accounting for Confounding in Observational Studies

Annual Review of Clinical Psychology ◽

10.1146/annurev-clinpsy-032816-045030 ◽

2020 ◽

Vol 16 (1) ◽

pp. 25-48 ◽

Cited By ~ 2

Author(s):

Brian M. D'Onofrio ◽

Arvid Sjölander ◽

Benjamin B. Lahey ◽

Paul Lichtenstein ◽

A. Sara Öberg

Keyword(s):

Risk Factors ◽

Causal Inference ◽

Behavioral Health ◽

Clinical Psychology ◽

Observational Studies ◽

Health Problems ◽

Observational Research ◽

Causal Diagrams ◽

Competing Hypotheses ◽

Theoretical Issues

The goal of this review is to enable clinical psychology researchers to more rigorously test competing hypotheses when studying risk factors in observational studies. We argue that there is a critical need for researchers to leverage recent advances in epidemiology/biostatistics related to causal inference and to use innovative approaches to address a key limitation of observational research: the need to account for confounding. We first review theoretical issues related to the study of causation, how causal diagrams can facilitate the identification and testing of competing hypotheses, and the current limitations of observational research in the field. We then describe two broad approaches that help account for confounding: analytic approaches that account for measured traits and designs that account for unmeasured factors. We provide descriptions of several such approaches and highlight their strengths and limitations, particularly as they relate to the etiology and treatment of behavioral health problems.

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Observational research rigour alone does not justify causal inference

European Journal of Clinical Investigation ◽

10.1111/eci.12681 ◽

2016 ◽

Vol 46 (12) ◽

pp. 985-993 ◽

Cited By ~ 14

Author(s):

Keisuke Ejima ◽

Peng Li ◽

Daniel L. Smith ◽

Tim R. Nagy ◽

Inga Kadish ◽

...

Keyword(s):

Causal Inference ◽

Observational Research

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Beyond metaphors and semantics: A framework for causal inference in neuroscience

Behavioral and Brain Sciences ◽

10.1017/s0140525x19001389 ◽

2019 ◽

Vol 42 ◽

Author(s):

Roberto A. Gulli

Keyword(s):

Causal Inference ◽

Causal Power

Abstract The long-enduring coding metaphor is deemed problematic because it imbues correlational evidence with causal power. In neuroscience, most research is correlational or conditionally correlational; this research, in aggregate, informs causal inference. Rather than prescribing semantics used in correlational studies, it would be useful for neuroscientists to focus on a constructive syntax to guide principled causal inference.

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