scholarly journals 1282Causal inference in multi-cohort studies using the target trial approach

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Marnie Downes ◽  
Meredith O’Connor ◽  
Craig A Olsson ◽  
David Burgner ◽  
Sharon Goldfeld ◽  
...  
Keyword(s):  
Cohort Studies ◽  
Causal Inference ◽  
Emotional Reactivity ◽  
Maternal Mental Health ◽  
Target Trial ◽  
Causal Inferences ◽  
Definition Of ◽  
Effect Heterogeneity ◽  
Casual Inference ◽  
Single Cohort

Abstract Focus of Presentation Utilising data from multiple cohorts to address causal questions in health research has become increasingly widespread due to a number of advantages. These include improved precision of estimates, in particular to investigate effect heterogeneity as well as rare events and exposures, and the ability to examine the replicability of findings. However, undertaking causal inference in multi-cohort studies also faces several challenges, which makes clear causal thinking even more important than in single-cohort studies. We propose the use of the “target trial” framework for the conduct of causal inference in multi-cohort studies. Findings Using two case studies, the first considering the effect of maternal mental health on emotional reactivity and the second examining the influence of exposure to adversity on inflammatory outcomes in childhood, we describe and demonstrate how the target trial approach enables clear definition of the target estimand and systematic consideration of sources of bias. Considering the target trial as the reference point allows the identification of potential biases within each study, so that analysis can be planned to reduce them. Furthermore, the interpretation of findings is assisted by an understanding of the unavoidable biases that may be compounded when pooling data from multiple cohorts, or that may explain discrepant findings across cohorts. Conclusions/Implications Use of the target trial framework in multi-cohort studies helps strengthen causal inferences through improved analysis design and clarity in the interpretation of findings. Key messages The target trial framework, already well-established for casual inference in single-cohort studies, is recommended for the conduct of causal inference in multi-cohort studies.

scholarly journals Reactivity in social scientific experiments: what is it and how is it different (and worse) than a Placebo effect?

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
María Jiménez-Buedo
Keyword(s):  
Experimental Data ◽  
Conceptual Framework ◽  
Placebo Effect ◽  
Placebo Effects ◽  
Causal Inferences ◽  
Scientific Experiments ◽  
Social Scientific ◽  
The Many ◽  
Definition Of ◽  
In Virtue Of

AbstractReactivity, or the phenomenon by which subjects tend to modify their behavior in virtue of their being studied upon, is often cited as one of the most important difficulties involved in social scientific experiments, and yet, there is to date a persistent conceptual muddle when dealing with the many dimensions of reactivity. This paper offers a conceptual framework for reactivity that draws on an interventionist approach to causality. The framework allows us to offer an unambiguous definition of reactivity and distinguishes it from placebo effects. Further, it allows us to distinguish between benign and malignant forms of the phenomenon, depending on whether reactivity constitutes a danger to the validity of the causal inferences drawn from experimental data.

Emotion Dysregulation: A Review of the Concept and Implications for Clinical Practice

2016 ◽  
Vol 33 (S1) ◽  
pp. S528-S529
Author(s):  
A. D’Agostino ◽  
S. Covanti ◽  
M. Rossi Monti ◽  
V. Starcevic
Keyword(s):  
Clinical Practice ◽  
Psychiatric Disorders ◽  
Emotion Dysregulation ◽  
Emotional Reactivity ◽  
Emotional Awareness ◽  
Future Research ◽  
Competing Interest ◽  
Expression Of Emotions ◽  
Psychology Literature ◽  
Definition Of

IntroductionOver the past decade, emotion dysregulation has become a very popular term in the psychiatric and clinical psychology literature and it has been described as a key component in a range of mental disorders. For this reason, it has been recently called the “hallmark of psychopathology” (Beauchaine et al., 2007). However, many issues make this concept controversial.ObjectivesTo explore emotion dysregulation, focusing on problems related to its definition, meanings and role in many psychiatric disorders.AimsTo clarify the psychopathological core of emotion dysregulation and to discuss potential implications for clinical practice.MethodsA literature review was carried out by examining articles published in English between January 2003 and June 2015. A search of the databases PubMed, PsycINFO, Science Direct, Medline, EMBASE and Google Scholar was performed to identify the relevant papers.ResultsAlthough, there is no agreement about the definition of emotion dysregulation, the following five overlapping, not mutually exclusive dimensions were identified: decreased emotional awareness, inadequate emotional reactivity, intense experience and expression of emotions, emotional rigidity and cognitive reappraisal difficulty. These dimensions characterise a number of psychiatric disorders in different proportions, with borderline personality disorder and eating disorders seemingly more affected than other conditions.ConclusionsThis review highlights a discrepancy between the widespread clinical use of emotion dysregulation and inadequate conceptual status of this construct. Better understanding of the various dimensions of emotion dysregulation has implications for treatment. Future research needs to address emotion dysregulation in all its multifaceted complexity.Disclosure of interestThe authors have not supplied their declaration of competing interest.

On Cognitive Models of Causal Inferences and Causation Networks

2011 ◽  
Vol 3 (1) ◽  
pp. 50-60 ◽  
Author(s):  
Yingxu Wang
Keyword(s):  
Problem Solving ◽  
Mathematical Models ◽  
Causal Inference ◽  
Computational Intelligence ◽  
Theoretical Foundation ◽  
Cognitive Models ◽  
Human Reasoning ◽  
Cognitive Computing ◽  
Causal Inferences ◽  
Human Thought

Human thought, perception, reasoning, and problem solving are highly dependent on causal inferences. This paper presents a set of cognitive models for causation analyses and causal inferences. The taxonomy and mathematical models of causations are created. The framework and properties of causal inferences are elaborated. Methodologies for uncertain causal inferences are discussed. The theoretical foundation of humor and jokes as false causality is revealed. The formalization of causal inference methodologies enables machines to mimic complex human reasoning mechanisms in cognitive informatics, cognitive computing, and computational intelligence.

scholarly journals Rate of normal lung function decline in ageing adults: a systematic review of prospective cohort studies

BMJ Open ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. e028150 ◽  
Author(s):  
Elizabeth T Thomas ◽  
Michelle Guppy ◽  
Sharon E Straus ◽  
Katy J L Bell ◽  
Paul Glasziou
Keyword(s):  
Systematic Review ◽  
Lung Function ◽  
Cohort Studies ◽  
Lung Disease ◽  
Prospective Cohort ◽  
Normal Lung ◽  
Prospective Cohort Studies ◽  
Age Related ◽  
Disease Definition ◽  
Definition Of

ObjectiveTo conduct a systematic review investigating the normal age-related changes in lung function in adults without known lung disease.DesignSystematic review.Data sourcesMEDLINE, Embase and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched for eligible studies from inception to February 12, 2019, supplemented by manual searches of reference lists and clinical trial registries.Eligibility criteriaWe planned to include prospective cohort studies and randomised controlled trials (control arms) that measured changes in lung function over time in asymptomatic adults without known respiratory disease.Data extraction and synthesisTwo authors independently determined the eligibility of studies, extracted data and assessed the risk of bias of included studies using the modified Newcastle–Ottawa Scale.ResultsFrom 4385 records screened, we identified 16 cohort studies with 31 099 participants. All included studies demonstrated decline in lung function—forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and peak expiratory flow rate (PEFR) with age. In studies with longer follow-up (>10 years), rates of FEV1decline ranged from 17.7 to 46.4 mL/year (median 22.4 mL/year). Overall, men had faster absolute rates of decline (median 43.5 mL/year) compared with women (median 30.5 mL/year). Differences in relative FEV1change, however, were not observed between men and women. FEV1/FVC change was reported in only one study, declining by 0.29% per year. An age-specific analysis suggested the rate of FEV1function decline may accelerate with each decade of age.ConclusionsLung function—FEV1, FVC and PEFR—decline with age in individuals without known lung disease. The definition of chronic airway disease may need to be reconsidered to allow for normal ageing and ensure that people likely to benefit from interventions are identified rather than healthy people who may be harmed by potential overdiagnosis and overtreatment. The first step would be to apply age, sex and ethnicity-adjusted FEV1/FVC thresholds to the disease definition of chronic obstructive pulmonary disease.PROSPERO registration numberCRD42018087066.

scholarly journals Invited Commentary: What Social Epidemiology Brings to the Table—Reconciling Social Epidemiology and Causal Inference

2019 ◽  
Author(s):  
Whitney R Robinson ◽  
Zinzi D Bailey
Keyword(s):  
Causal Inference ◽  
Social Epidemiology ◽  
Methodological Challenges ◽  
Scientific Goal ◽  
Definition Of

Abstract In response to the Galea and Hernán article, “Win-Win: Reconciling Social Epidemiology and Causal Inference” (Am J Epidemiol. 2020;189(XX):XXXX–XXXX), we offer a definition of social epidemiology. We then argue that methodological challenges most salient to social epidemiology have not been adequately addressed in quantitative causal inference, that identifying causes is a worthy scientific goal, and that quantitative causal inference can learn from social epidemiology’s methodological innovations. Finally, we make 3 recommendations for quantitative causal inference.

scholarly journals Maternal mental health and infant emotional reactivity: a 20-year two-cohort study of preconception and perinatal exposures

2019 ◽  
Vol 50 (5) ◽  
pp. 827-837 ◽  
Author(s):  
Elizabeth Spry ◽  
Margarita Moreno-Betancur ◽  
Denise Becker ◽  
Helena Romaniuk ◽  
John B. Carlin ◽  
...  
Keyword(s):  
Mental Health ◽  
Depressive Symptoms ◽  
Emotional Reactivity ◽  
Mental Health Problems ◽  
Maternal Mental Health ◽  
Health Problems ◽  
Prior Exposure ◽  
Differential Sensitivity ◽  
Perinatal Mental Health ◽  
Pregnancy And Postpartum

AbstractBackgroundMaternal mental health during pregnancy and postpartum predicts later emotional and behavioural problems in children. Even though most perinatal mental health problems begin before pregnancy, the consequences of preconception maternal mental health for children's early emotional development have not been prospectively studied.MethodsWe used data from two prospective Australian intergenerational cohorts, with 756 women assessed repeatedly for mental health problems before pregnancy between age 13 and 29 years, and during pregnancy and at 1 year postpartum for 1231 subsequent pregnancies. Offspring infant emotional reactivity, an early indicator of differential sensitivity denoting increased risk of emotional problems under adversity, was assessed at 1 year postpartum.ResultsThirty-seven percent of infants born to mothers with persistent preconception mental health problems were categorised as high in emotional reactivity, compared to 23% born to mothers without preconception history (adjusted OR 2.1, 95% CI 1.4–3.1). Ante- and postnatal maternal depressive symptoms were similarly associated with infant emotional reactivity, but these perinatal associations reduced somewhat after adjustment for prior exposure. Causal mediation analysis further showed that 88% of the preconception risk was a direct effect, not mediated by perinatal exposure.ConclusionsMaternal preconception mental health problems predict infant emotional reactivity, independently of maternal perinatal mental health; while associations between perinatal depressive symptoms and infant reactivity are partially explained by prior exposure. Findings suggest that processes shaping early vulnerability for later mental disorders arise well before conception. There is an emerging case for expanding developmental theories and trialling preventive interventions in the years before pregnancy.

scholarly journals Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference

2007 ◽  
Vol 15 (3) ◽  
pp. 199-236 ◽  
Author(s):  
Daniel E. Ho ◽  
Kosuke Imai ◽  
Gary King ◽  
Elizabeth A. Stuart
Keyword(s):  
Causal Inference ◽  
Mean Squared Error ◽  
Statistical Properties ◽  
Parametric Models ◽  
Unified Approach ◽  
Causal Inferences ◽  
Fast Growing ◽  
Squared Error ◽  
Unique Model ◽  
Functional Forms

Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and other modeling assumptions, how can researchers ensure that the few estimates presented are accurate or representative? How do readers know that publications are not merely demonstrations that it ispossibleto find a specification that fits the author's favorite hypothesis? And how do we evaluate or even define statistical properties like unbiasedness or mean squared error when no unique model or estimator even exists? Matching methods, which offer the promise of causal inference with fewer assumptions, constitute one possible way forward, but crucial results in this fast-growing methodological literature are often grossly misinterpreted. We explain how to avoid these misinterpretations and propose a unified approach that makes it possible for researchers to preprocess data with matching (such as with the easy-to-use software we offer) and then to apply the best parametric techniques they would have used anyway. This procedure makes parametric models produce more accurate and considerably less model-dependent causal inferences.

Studying Mechanisms To Strengthen Causal Inferences In Quantitative Research

2009 ◽  
Author(s):  
Peter Hedström
Keyword(s):  
Causal Inference ◽  
Political Science ◽  
Statistical Inference ◽  
Statistical Models ◽  
Quantitative Research ◽  
Social Outcomes ◽  
Causal Inferences ◽  
The Social

This article emphasizes various ways by which the study of mechanisms can make quantitative research more useful for causal inference. It concentrates on three aspects of the role of mechanisms in causal and statistical inference: how an understanding of the mechanisms at work can improve statistical inference by guiding the specification of the statistical models to be estimated; how mechanisms can strengthen causal inferences by improving our understanding of why individuals do what they do; and how mechanism-based models can strengthen causal inferences by showing why, acting as they do, individuals bring about the social outcomes they do. There has been a surge of interest in mechanism-based explanations, in political science as well as in sociology. Most of this work has been vital and valuable in that it has sought to clarify the distinctiveness of the approach and to apply it empirically.

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