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

Abstract Focus of presentation Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. We present a practical framework for handling and reporting the analysis of incomplete data in observational studies, which we illustrate using a case study from the Avon Longitudinal Study of Parents and Children. Findings The framework consists of three steps: 1) Develop an analysis plan specifying the analysis model and how missing data are going to be addressed. Important considerations are whether a complete records analysis is likely to be valid, whether multiple imputation or an alternative approach is likely to offer benefits, and whether a sensitivity analysis regarding the missingness mechanism is required. 2) Explore the data, checking the methods outlined in the analysis plan are appropriate, and conduct the pre-planned analysis. 3) Report the results, including a description of the missing data, details on how missing data were addressed, and the results from all analyses, interpreted in light of the missing data and clinical relevance. Conclusions/Implications This framework encourages researchers to think carefully about their missing data and be transparent about the potential effect on the study results. This will increase confidence in the reliability and reproducibility of results from published papers. Key messages Researchers need to develop a plan for missing data prior to conducting their analysis, and be transparent about how they handled the missing data and its potential effect when reporting their results.

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98Framework for the Treatment And Reporting of Missing data in Observational Studies: The TARMOS framework

International Journal of Epidemiology ◽

10.1093/ije/dyab168.371 ◽

2021 ◽

Vol 50 (Supplement_1) ◽

Author(s):

Katherine Lee ◽

Kate Tilling ◽

Rosie Cornish ◽

James Carpenter

Keyword(s):

Missing Data ◽

Observational Studies ◽

Potential Effect ◽

Observational Research ◽

Analysis Model ◽

Parents And Children ◽

Alternative Approach ◽

Study Results ◽

Data Checking

Abstract Focus of presentation Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. We present a practical framework for handling and reporting the analysis of incomplete data in observational studies, which we illustrate using a case study from the Avon Longitudinal Study of Parents and Children. Findings The framework consists of three steps: 1) Develop an analysis plan specifying the analysis model and how missing data are going to be addressed. Important considerations are whether a complete records analysis is likely to be valid, whether multiple imputation or an alternative approach is likely to offer benefits, and whether a sensitivity analysis regarding the missingness mechanism is required. 2) Explore the data, checking the methods outlined in the analysis plan are appropriate, and conduct the pre-planned analysis. 3) Report the results, including a description of the missing data, details on how missing data were addressed, and the results from all analyses, interpreted in light of the missing data and clinical relevance. Conclusions/Implications This framework encourages researchers to think carefully about their missing data and be transparent about the potential effect on the study results. This will increase confidence in the reliability and reproducibility of results from published papers. Key messages Researchers need to develop a plan for missing data prior to conducting their analysis, and be transparent about how they handled the missing data and its potential effect when reporting their results.

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What Emotion Dysregulation Looks Like

The Oxford Handbook of Emotion Dysregulation ◽

10.1093/oxfordhb/9780190689285.013.5 ◽

2018 ◽

pp. 52-68

Author(s):

K. Ashana Ramsook ◽

Pamela M. Cole ◽

Margaret A. Fields-Olivieri

Keyword(s):

Observational Studies ◽

Emotion Dysregulation ◽

Emotional Reaction ◽

Observational Research ◽

Multiple Time Scales ◽

Emotional Expressions ◽

Multiple Time ◽

Observational Methods ◽

New Directions

Recent conceptualizations of emotion dysregulation define it as a process that unfolds over multiple time scales and that leads to short- or long-term impairments. This chapter discusses the advantages of observational methods for measuring emotion dysregulation as a process, focusing on three patterns and associated evidence of them from observational studies. First, the chapter discusses context-inappropriate emotion, the absence of an expected emotional reaction or an atypical reaction for the situational context. Second, it discusses atypical emotion dynamics, specifically emotional expressions that change abruptly, including but not limited to emotional lability. Third, it discusses ways in which emotions endure and are difficult to modify, pointing to ineffective strategy use as a mechanism. It concludes by discussing new directions for observational research, including creative study design and analytic methods that can capture emotion dysregulation.

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Observational Research in Face-to-Face Small Groupwork: Capturing Affect as Socio-Dynamic Interpersonal Phenomena

Small Group Research ◽

10.1177/1046496420985920 ◽

2021 ◽

pp. 104649642098592

Author(s):

Cheryl Jones ◽

Simone Volet ◽

Deborah Pino-Pasternak

Keyword(s):

Observational Studies ◽

Work Environments ◽

Observational Research ◽

Observational Methods ◽

Organizational Research ◽

Face To Face ◽

Fine Grained ◽

Observation Methods ◽

Analytical Tools ◽

Affect Dynamics

Interpersonal affect in face-to-face small groupwork, though pervasive in university and work environments, is rarely examined as the fine-grained sequential interactions in which it manifests. This review synthesized 21 recent studies in tertiary collaborative learning and organizational research that have used observation methods to investigate affect in face-to-face small groupwork. The analysis focused on examining the extent to which observational studies captured affect as social (interactive) and dynamic (temporally unfolding). Findings showed that observational methods elicit information about affect dynamics in groupwork that is unique and complementary to other methods. Key affect constructs, behavioral operationalizations, and analytical tools used to capture affect are discussed.

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Observational studies in health professional education research

Focus on Health Professional Education A Multi-Professional Journal ◽

10.11157/fohpe.v22i1.536 ◽

2021 ◽

Vol 22 (1) ◽

pp. 94-111

Author(s):

Joanna Tai ◽

Juan Fischer ◽

Christy Noble

Keyword(s):

Qualitative Research ◽

Education Research ◽

Health Professional ◽

Professional Education ◽

Observational Studies ◽

Observational Research ◽

Health Professional Education ◽

Observational Methods ◽

Contemporary World ◽

Decision Points

Observational studies are not uncommon in health professional education and are frequently associated with ethnography as a methodology. This article aims to provide an overview of how observational studies are used in health professional education research. Firstly, we explore some ways in which observational methods can be used in association with a range of qualitative research stances, and then we focus on the practicalities of undertaking observational research. Next, we use two case studies to illustrate some of the key decision points when designing observational research. Finally, we collate resources and consider the implications of contemporary world events on observational research.

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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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Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration. Translation to Russian

Digital Diagnostics ◽

10.17816/dd70821 ◽

2021 ◽

pp. 1-10

Author(s):

Jan P. Vandenbroucke ◽

Erik Von Elm ◽

Douglas G. Altman ◽

Peter C. Gotzsche ◽

Cynthia D. Mulrow ◽

...

Keyword(s):

Observational Studies ◽

Empirical Studies ◽

Observational Research ◽

Case Control Studies ◽

Quality Of Reporting ◽

Cross Sectional ◽

Useful Flow ◽

Study Designs ◽

Strobe Statement

Much medical research is observational. The reporting of observational studies is often of insufficient quality. Poor reporting hampers the assessment of the strengths and weaknesses of a study and the generalisability of its results. Taking into account empirical evidence and theoretical considerations, a group of methodologists, researchers, and editors developed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations to improve the quality of reporting of observational studies. The STROBE Statement consists of a checklist of 22 items, which relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to cohort studies, case-control studies and cross-sectional studies and four are specific to each of the three study designs. The STROBE Statement provides guidance to authors about how to improve the reporting of observational studies and facilitates critical appraisal and interpretation of studies by reviewers, journal editors and readers. This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the STROBE Statement. The meaning and rationale for each checklist item are presented. For each item, one or several published examples and, where possible, references to relevant empirical studies and methodological literature are provided. Examples of useful flow diagrams are also included. The STROBE Statement, this document, and the associated Web site (http://www. strobe-statement.org/) should be helpful resources to improve reporting of observational research. This article is the reprint with Russian translation of the original that can be observed here: Vandenbroucke JP, von Elm E, Altman DG, Gotzsche PC, Mulrow CD, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration. PLoS Med 2007;4(10):e297. doi:10.1371/journal.pmed.0040297

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External Validity Reconsidered

Population-Based Survey Experiments ◽

10.23943/princeton/9780691144511.003.0008 ◽

2011 ◽

Author(s):

Diana C. Mutz

Keyword(s):

Political Science ◽

Real World ◽

External Validity ◽

Observational Studies ◽

Internal Validity ◽

Conventional Wisdom ◽

Observational Research ◽

Convincing Case

This chapter talks about the significance of generalizability. Experimentalists often go to great lengths to argue that student or other convenience samples are not problematic in terms of external validity. Likewise, a convincing case for causality is often elusive with observational research, no matter how stridently one might argue to the contrary. The conventional wisdom is that experiments are widely valued for their internal validity, and experiments lack external validity. These assumptions are so widespread as to go without question in most disciplines, particularly those emphasizing external validity, such as political science and sociology. But observational studies, such as surveys, are still supposed to be better for purposes of maximizing external validity because this method allows studying people in real world settings.

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SOCIAL AND BEHAVIORAL FACTORS IN COGNITIVE AGING: APPLYING THE CAUSAL INFERENCE FRAMEWORK IN OBSERVATIONAL STUDIES

Innovation in Aging ◽

10.1093/geroni/igz038.1178 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

pp. S323-S323

Author(s):

Anja K Leist

Keyword(s):

Machine Learning ◽

Cognitive Decline ◽

Causal Inference ◽

Cognitive Aging ◽

Behavioral Interventions ◽

Observational Studies ◽

Intervention Research ◽

Learning Approaches ◽

Behavioral Factors

Abstract Rationale: There is an urgent need to better understand how to maintain cognitive functioning at older ages with social and behavioral interventions, given that there is currently no medical cure available to prevent, halt or reverse the progression of cognitive decline and dementia. However, in current models, it is still not well established which factors (e.g. education, BMI, physical activity, sleep, depression) matter most at which ages, and which behavioral profiles are most protective against cognitive decline. In the last years, advances in the fields of causal inference and machine learning have equipped epidemiology and social sciences with methods and models to approach causal questions in observational studies. Method: The presentation will give an overview of the causal inference framework and different machine learning approaches to investigate cognitive aging. First, we will present relevant research questions on the role of social and behavioral factors in cognitive aging in observational studies. Second, we will introduce the causal inference framework and recent methods to visualize and compute the strength of causal paths. Third, promising machine learning approaches to arrive at robust predictions are presented. The 13-year follow-up from the European SHARE survey that employs well-established cognitive performance tests is used to demonstrate the usefulness of the approach. Discussion: The causal inference framework, combined with recent machine learning approaches and applied in observational studies, provides a robust alternative to intervention research. Advantages for investigations under the new framework, e.g., fewer ethical considerations compared to intervention research, as well as limitations are discussed.

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