scholarly journals 1274The application of simulation to quantifying the influence of bias in reproductive and perinatal epidemiology

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Jennifer Dunne ◽  
Gizachew Assefa Tessema ◽  
Milica Ognjenovic ◽  
Gavin Pereira
Keyword(s):  
Observational Studies ◽  
Reference Lists ◽  
Postnatal Period ◽  
Causal Effects ◽  
Simulation Methods ◽  
Perinatal Epidemiology ◽  
Causal Diagrams ◽  
Practical Guidance ◽  
The Many ◽  
Development Analysis

Abstract Background Establishing causal effects in reproductive and perinatal epidemiology is challenging due to the many selection and attrition processes from preconception to the postnatal period. Further challenging, is the potential for the misclassification of exposures, outcomes and confounders, contributing to measurement error. The application of simulation enables the illustration and quantification of the magnitude of various types of bias commonly found in observational studies. Methods A systematic search was conducted in PubMed, Medline, Embase, CINAHL and Scopus in August 2020. A gray literature search of Google and Google Scholar, followed by a search of the reference lists of included studies, was undertaken. Results Thirty-nine studies, covering information (n = 14), selection (n = 14), confounding (n = 9), protection (n = 1), and attenuation bias (n = 1) were identified. The methods of simulating data and reporting of results varied, with more recent studies including causal diagrams. Few studies included code for replication. Although there has been an increasing application of simulation in reproductive and perinatal epidemiology since 2015, overall this remains an underexplored area. Conclusions The studies demonstrated effectiveness in the quantification of multiple types of bias using simulation. The limited use implies that further effort is required to increase knowledge of the application of simulation, which will thereby improve causal interpretation in reproductive and perinatal studies. Key messages Practical guidance for researchers is required in the development, analysis and reporting of simulation methods for the quantification of bias.

Lifestyle intervention strategies in early life to improve pregnancy outcomes and long-term health of offspring: a narrative review

2018 ◽  
Vol 10 (3) ◽  
pp. 314-321 ◽  
Author(s):  
Romy Gaillard ◽  
John Wright ◽  
Vincent W.V. Jaddoe
Keyword(s):  
Health Outcomes ◽  
Pregnancy Outcomes ◽  
Early Life ◽  
Observational Studies ◽  
Postnatal Period ◽  
Modifiable Risk Factors ◽  
Lifestyle Interventions ◽  
Randomized Controlled ◽  
Offspring Health

AbstractAdverse exposures during fetal life and the postnatal period influence physical, cognitive and emotional development, and predispose to an increased risk of various chronic diseases throughout the life course. Findings from large observational studies in various populations and experimental animal studies have identified different modifiable risk factors in early life. Adverse maternal lifestyle factors, including overweight, unhealthy diet, sedentary behavior, smoking, alcohol consumption and stress in the preconception period and during pregnancy, are the most common modifiable risk factors leading to a suboptimal in-utero environment for fetal development. In the postnatal period, breastfeeding, infant growth and infant dietary intake are important modifiable factors influencing long-term offspring health outcomes. Despite the large amount of findings from observational studies, translation to lifestyle interventions seems to be challenging. Currently, randomized controlled trials focused on the influence of lifestyle interventions in these critical periods on short-term and long-term maternal and offspring health outcomes are scarce, have major limitations and do not show strong effects on maternal and offspring outcomes. New and innovative approaches are needed to move from describing these causes of ill-health to start tackling them using intervention approaches. Future randomized controlled lifestyle intervention studies and innovative observational studies, using quasi-experimental designs, are needed focused on the effects of an integrated lifestyle advice from preconception onwards on pregnancy outcomes and long-term health outcomes in offspring on a population level.

scholarly journals Novel bounds for causal effects based on sensitivity parameters on the risk difference scale

2021 ◽  
Vol 9 (1) ◽  
pp. 190-210
Author(s):  
Arvid Sjölander ◽  
Ola Hössjer
Keyword(s):  
Risk Ratio ◽  
Simulation Study ◽  
Observational Studies ◽  
Causal Effect ◽  
Real Data ◽  
Risk Difference ◽  
Causal Effects ◽  
Ratio Scale ◽  
Unmeasured Confounding ◽  
Range Of Values

Abstract Unmeasured confounding is an important threat to the validity of observational studies. A common way to deal with unmeasured confounding is to compute bounds for the causal effect of interest, that is, a range of values that is guaranteed to include the true effect, given the observed data. Recently, bounds have been proposed that are based on sensitivity parameters, which quantify the degree of unmeasured confounding on the risk ratio scale. These bounds can be used to compute an E-value, that is, the degree of confounding required to explain away an observed association, on the risk ratio scale. We complement and extend this previous work by deriving analogous bounds, based on sensitivity parameters on the risk difference scale. We show that our bounds can also be used to compute an E-value, on the risk difference scale. We compare our novel bounds with previous bounds through a real data example and a simulation study.

scholarly journals 1230Should multiple imputation be stratified by exposure group when estimating causal effects via outcome regression?

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Jiaxin Zhang ◽  
S. Ghazaleh Dashti ◽  
John B. Carlin ◽  
Katherine J. Lee ◽  
Margarita Moreno-Betancur
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Observational Studies ◽  
Controlled Trial ◽  
Simulated Data ◽  
Causal Effects ◽  
Exposure Group ◽  
Complete Case ◽  
Main Effect ◽  
Health Cohort Study

Abstract Background Outcome regression remains widely applied for estimating causal effects in observational studies, in which causal inference is conceptualised as emulating a randomized controlled trial (RCT). Multiple imputation (MI) is a commonly used method for handling missing data, but while in RCTs it has been shown that MI should be conducted by treatment group to reduce bias, whether imputation should be conducted by exposure group in observational studies has not been studied. Methods We conducted a simulation study to evaluate the performance of seven methods for handling missing data: Complete-case analysis (CCA), MI of main effect, MI with interactions (between exposure and: outcome, a strong confounder, outcome and a strong confounder, all incomplete), and MI conducted by exposure group. We simulated data based on an example from the Victorian Adolescent Health Cohort Study. Three exposure prevalences and seven outcome generation models were considered, the latter ranging from no interaction to strong-positive or negative exposure-confounder interaction. Various missingness scenarios were examined: with incomplete outcome only or also incomplete confounders, and three levels of complexity regarding the missingness mechanism. Results For all scenarios, MI by exposure led to the least bias, followed by MI approaches that included exposure-confounder interactions. Conclusions If MI is adopted in outcome regression, we recommend conducting MI by exposure group and, when not feasible, including exposure-confounder interactions in the imputation model. Key messages Similar to RCTs, MI should be conducted by exposure group when estimating average causal effects using outcome regression in observational studies.

Digital Ethics as Translational Ethics

2022 ◽  
pp. 1-15
Author(s):  
David Danks
Keyword(s):  
Digital Technology ◽  
Digital Technologies ◽  
Ethical Research ◽  
Digital Ethics ◽  
Ethical Decisions ◽  
Different Types ◽  
Practical Guidance ◽  
The Creation ◽  
The Many

There are growing calls for more digital ethics, largely in response to the many problems that have occurred with digital technologies. However, there has been less clarity about exactly what this might mean. This chapter argues first that ethical decisions and considerations are ubiquitous within the creation of digital technology. Ethical analyses cannot be treated as a secondary or optional aspect of technology creation. This argument does not specify the content of digital ethics, though, and so further research is needed. This chapter then argues that this research must take the form of translational ethics: a robust, multi-disciplinary effort to translate the abstract results of ethical research into practical guidance for technology creators. Examples are provided of this kind of translation from principles to different types of practices.

scholarly journals A Graphical Approximation to Generalization: Definitions and Diagrams

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Fernando Martel García ◽  
Leonard Wantchekon
Keyword(s):  
External Validity ◽  
Fundamental Problem ◽  
Systematic Variation ◽  
Causal Effects ◽  
Graphical Language ◽  
Causal Diagrams ◽  
Causal Approach

AbstractThe fundamental problem of external validity is not to generalize from one experiment, so much as to experimentally test generalizable theories. That is, theories that explain the systematic variation of causal effects across contexts. Here we show how the graphical language of causal diagrams can be used in this endeavour. Specifically we show how generalization is a causal problem, how a causal approach is more robust than a purely predictive one, and how causal diagrams can be adapted to convey partial parametric information about interactions.

Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies

2004 ◽  
Vol 29 (3) ◽  
pp. 343-367 ◽  
Author(s):  
Donald B. Rubin
Keyword(s):  
Public Policy ◽  
Causal Inference ◽  
Statistical Inference ◽  
Undergraduate Students ◽  
Observational Studies ◽  
Causal Effects ◽  
Designed Experiments ◽  
Critical Activity ◽  
The One

Inference for causal effects is a critical activity in many branches of science and public policy. The field of statistics is the one field most suited to address such problems, whether from designed experiments or observational studies. Consequently, it is arguably essential that departments of statistics teach courses in causal inference to both graduate and undergraduate students. This article discusses an outline of such courses based on repeated experience over more than a decade.

The Many Faces of Survivor Bias in Observational Studies on Trauma Resuscitation Requiring Massive Transfusion

2015 ◽  
Vol 66 (1) ◽  
pp. 45-48 ◽  
Author(s):  
Anthony M.-H. Ho ◽  
Jorge E. Zamora ◽  
John B. Holcomb ◽  
Calvin S.H. Ng ◽  
Manoj K. Karmakar ◽  
...  
Keyword(s):  
Observational Studies ◽  
Massive Transfusion ◽  
Trauma Resuscitation ◽  
Survivor Bias ◽  
The Many

scholarly journals Counterfactual Reasoning in Observational Studies

2019 ◽  
Vol 33 ◽  
pp. 9886-9887
Author(s):  
Negar Hassanpour
Keyword(s):  
Alternative Treatment ◽  
Observational Studies ◽  
Causal Effect ◽  
Intelligent Agent ◽  
Population Based ◽  
Causal Effects ◽  
Medical Procedure ◽  
Counterfactual Reasoning ◽  
Effect Estimation ◽  
The Individual

To identify the appropriate action to take, an intelligent agent must infer the causal effects of every possible action choices. A prominent example is precision medicine that attempts to identify which medical procedure will benefit each individual patient the most. This requires answering counterfactual questions such as: ""Would this patient have lived longer, had she received an alternative treatment?"". In my PhD, I attempt to explore ways to address the challenges associated with causal effect estimation; with a focus on devising methods that enhance performance according to the individual-based measures (as opposed to population-based measures).

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