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

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.

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

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Rosie Cornish ◽  
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.

A New Model for Energy-Saving in Cast Irons Production Introduced Technology Contribution

2011 ◽  
Vol 71-78 ◽  
pp. 2420-2423
Author(s):  
Dan Huang ◽  
Wu Zhao ◽  
Wei Ping Chen
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Analysis Model ◽  
Cast Irons ◽  
New Model ◽  
Yield Increase ◽  
Study Results ◽  
Technological Level ◽  
Lost Foam

A new model for energy-saving in cast irons production introduced technology contribution has been developed. According to the analysis model, in case of keeping same energy efficiency of device, the higher technological level increases, the easier the R increases; even if keep the same melting and heat treatment devices, significant reduction of production energy consumption would be implemented just depending on the production yield increase. A case study results show that technology measurements which has no direct effect on energy consumption play an important role in energy conservation, where the contribution rates of lost-foam casting and computer technology are 20% and 17%. The technological measurements play an important role in cast irons production which cannot be ignored.

scholarly journals 1376Modern concepts in the handling and reporting of missing data

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Katherine Lee ◽  
James Carpenter ◽  
Roderick Little ◽  
Cattram Nguyen ◽  
Rosie Cornish
Keyword(s):  
Missing Data ◽  
Observational Studies ◽  
Reliability And Validity ◽  
Sensitivity Analyses ◽  
Observational Research ◽  
Policy And Practice ◽  
Analytical Thinking ◽  
Research Findings ◽  
Practical Guidance ◽  
Research To Policy

Abstract Focus and outcomes for participants Missing data are ubiquitous in observational studies, and the simple solution of restricting the analyses to the subset with complete records will often result in bias and loss of power. The seriousness of these issues for resulting inferences depends on both the mechanism causing the missing data and the form of the substantive question and associated model. The methodological literature on methods for the analysis of partially observed data has grown substantially over the last twenty years, and although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. Importantly, the lack of transparency around methodological decisions regarding the analysis is threatening the validity and reproducibility of modern research. In this symposium leading researchers in missing data methodology will present practical guidance on how to select an appropriate method to handle missing data, describe how to report the results from such an analysis and describe how to conduct sensitivity analyses in the multiple imputation framework. Rationale for the symposium, including for its inclusion in the Congress One of the sub-themes of WCE 2021 is “Translation from research to policy and practice”. Although there is a growing body of literature surrounding missing data methodology, evidence from systematic reviews suggests that missing data is still often not handled appropriately. If practice is to change, it is important to educate applied researchers regarding the available methodology and provide practical guidance on how to determine the best method for handling missing data. An important part of this is providing guidance on the reporting of results from analyses with missing data. This is particularly pertinent given the current emphasis on reproducibility of research findings. In this symposium we bring some of the latest research from the Missing Data Topic Group of the STRengthening Analytical Thinking for Observational Studies (STRATOS) initiative whose aim is to provide accessible and accurate guidance in the design and analysis of observational studies in order to increasie the reliability and validity of observational research. Presentation program Names of presenters

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

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.

Evaluation of Multiple Imputation Approaches for Handling Missing Covariate Information in a Case-Cohort Study.

2021 ◽  
Author(s):  
Melissa Middleton ◽  
Cattram Nguyen ◽  
Margarita Moreno-Betancur ◽  
John B Carlin ◽  
Katherine J Lee
Keyword(s):  
Missing Data ◽  
Cohort Studies ◽  
Multiple Imputation ◽  
Epidemiological Studies ◽  
Analysis Model ◽  
Imputation Model ◽  
Selection Probability ◽  
Covariate Data ◽  
Unequal Sampling

Abstract Background In case-cohort studies a random subcohort is selected from the inception cohort and acts as the sample of controls for several outcome investigations. Analysis is conducted using only the cases and the subcohort, with inverse probability weighting (IPW) used to account for the unequal sampling probabilities resulting from the study design. Like all epidemiological studies, case-cohort studies are susceptible to missing data. Multiple imputation (MI) has become increasingly popular for addressing missing data in epidemiological studies. It is currently unclear how best to incorporate the weights from a case-cohort analysis in MI procedures used to address missing covariate data.Method A simulation study was conducted with missingness in two covariates, motivated by a case study within the Barwon Infant Study. MI methods considered were: using the outcome, a proxy for weights in the simple case-cohort design considered, as a predictor in the imputation model, with and without exposure and covariate interactions; imputing separately within each weight category; and using a weighted imputation model. These methods were compared to a complete case analysis (CCA) within the context of a standard IPW analysis model estimating either the risk or odds ratio. The strength of associations, missing data mechanism, proportion of observations with incomplete covariate data, and subcohort selection probability varied across the simulation scenarios. Methods were also applied to the case study.Results There was similar performance in terms of relative bias and precision with all MI methods across the scenarios considered, with expected improvements compared with the CCA. Slight underestimation of the standard error was seen throughout but the nominal level of coverage (95%) was generally achieved. All MI methods showed a similar increase in precision as the subcohort selection probability increased, irrespective of the scenario. A similar pattern of results was seen in the case study.Conclusions How weights were incorporated into the imputation model had minimal effect on the performance of MI; this may be due to case-cohort studies only having two weight categories. In this context, inclusion of the outcome in the imputation model was sufficient to account for the unequal sampling probabilities in the analysis model.

Variation in analytic transparency in recent efficacy studies of antidepressant medication

2018 ◽  
Vol 23 (5) ◽  
pp. 177-182
Author(s):  
Steven Vannoy ◽  
Madeline Brodt ◽  
Lisa Cosgrove ◽  
Allen F Shaughnessy
Keyword(s):  
Missing Data ◽  
Antidepressant Treatment ◽  
Antidepressant Medication ◽  
Drug Efficacy ◽  
Mixed Effects ◽  
Repeated Measure ◽  
Study Results ◽  
Clinical Trial Results

The validity of clinical trial results is influenced by researchers’ decisions regarding the management of missing data. Inadequate management of missing data has been identified as a significant source of bias that can result in an overestimation of drug efficacy. Transparency related to the management of missing data is essential to assess the strength of evidence reported in publications. In a subset of 17 randomised clinical trials for two new antidepressant medications, we present a case study in which we examined investigators’ decisions regarding how to handle missing data and if their chosen method took into account, possible violations of analytic requirements that could affect results. The majority of trials (76%) concluded that there was a benefit of antidepressant treatment and in 94% the methodology for handling missing data was identifiable. Of these, 50% imputed data using the last observation carried forward and half used a mixed-effects model repeated measure approach. Most reports did not provide a rationale for the method used, and no trials described analyses regarding differences between completers and dropouts. Sensitivity analysis was inconsistently reported and correction for multiple comparisons was not uniformly applied. Lack of transparency for analytic choices related to handling of missing data testing was common in this subset of RCTs. Because management of missing data can directly influence the quality of study results, it is critical that journal editors develop and enforce standards for methodological transparency.

Interactive acculturation of Turkish-Belgian parents and children in Flanders: A case study of Beringen

2019 ◽  
Vol 16 (3) ◽  
pp. 417-428
Author(s):  
Özgün Ünver ◽  
Ides Nicaise
Keyword(s):  
Early Childhood ◽  
Maternal Employment ◽  
Domain Specificity ◽  
Specific Context ◽  
Childhood Education ◽  
Parents And Children ◽  
Education And Care ◽  
The Relationship ◽  
The Town

This article tackles the relationship between Turkish-Belgian families with the Flemish society, within the specific context of their experiences with early childhood education and care (ECEC) system in Flanders. Our findings are based on a focus group with mothers in the town of Beringen. The intercultural dimension of the relationships between these families and ECEC services is discussed using the Interactive Acculturation Model (IAM). The acculturation patterns are discussed under three main headlines: language acquisition, social interaction and maternal employment. Within the context of IAM, our findings point to some degree of separationism of Turkish-Belgian families, while they perceive the Flemish majority to have an assimilationist attitude. This combination suggests a conflictual type of interaction. However, both parties also display some traits of integrationism, which points to the domain-specificity of interactive acculturation.

PROCESSING AND IMAGING OF MARINE SEISMIC DATA FROM THE JEQUITINHONHA BASIN (BAHIA, BRAZIL)

2016 ◽  
Vol 33 (3) ◽  
Author(s):  
Lourenildo W.B. Leite ◽  
J. Mann ◽  
Wildney W.S. Vieira
Keyword(s):  
Seismic Data ◽  
Sedimentary Basins ◽  
Study Results ◽  
Marine Seismic ◽  
Present Case Study

ABSTRACT. The present case study results from a consistent processing and imaging of marine seismic data from a set collected over sedimentary basins of the East Brazilian Atlantic. Our general aim is... RESUMO. O presente artigo resulta de um processamento e imageamento consistentes de dados sísmicos marinhos de levantamento realizado em bacias sedimentares do Atlântico do Nordeste...

come_NET

2018 ◽  
Author(s):  
Konstantin Aal ◽  
Anne Weibert ◽  
Kai Schubert ◽  
Mary-Ann Sprenger ◽  
Thomas Von Rekowski
Keyword(s):  
Social Media ◽  
Design Process ◽  
Participatory Design ◽  
Controlled Environment ◽  
Design And Implementation ◽  
Neighborhood Contexts ◽  
Study Results ◽  
Computer Club ◽  
Close Cooperation

The case study presented in this chapter discusses the design and implementation of an online platform, “come_NET,” in the context of intercultural computer clubs in Germany. This tool was built in close cooperation with the children and adult computer club participants. It was designed to foster the sharing of ideas and experiences across distances, support collaboration, and make skills and expertise accessible to others in the local neighborhood contexts. In particular, the participatory-design process involving the children in the computer clubs fostered a profound understanding of the platform structure and functionalities. The study results show how younger children in particular were able to benefit, as the closed nature of the platform enabled them to gather experience as users of social media, but in a safe and controlled environment.

scholarly journals Optimizing the Environmental and Economic Sustainability of Remote Community Infrastructure

2020 ◽  
Vol 12 (6) ◽  
pp. 2208 ◽  
Author(s):  
Jamie E. Filer ◽  
Justin D. Delorit ◽  
Andrew J. Hoisington ◽  
Steven J. Schuldt
Keyword(s):  
Environmental Impacts ◽  
Sustainability Assessment ◽  
Model Performance ◽  
Assessment Model ◽  
Remote Communities ◽  
Remote Community ◽  
Rural Villages ◽  
Sustainable Technologies ◽  
Study Results

Remote communities such as rural villages, post-disaster housing camps, and military forward operating bases are often located in remote and hostile areas with limited or no access to established infrastructure grids. Operating these communities with conventional assets requires constant resupply, which yields a significant logistical burden, creates negative environmental impacts, and increases costs. For example, a 2000-member isolated village in northern Canada relying on diesel generators required 8.6 million USD of fuel per year and emitted 8500 tons of carbon dioxide. Remote community planners can mitigate these negative impacts by selecting sustainable technologies that minimize resource consumption and emissions. However, the alternatives often come at a higher procurement cost and mobilization requirement. To assist planners with this challenging task, this paper presents the development of a novel infrastructure sustainability assessment model capable of generating optimal tradeoffs between minimizing environmental impacts and minimizing life-cycle costs over the community’s anticipated lifespan. Model performance was evaluated using a case study of a hypothetical 500-person remote military base with 864 feasible infrastructure portfolios and 48 procedural portfolios. The case study results demonstrated the model’s novel capability to assist planners in identifying optimal combinations of infrastructure alternatives that minimize negative sustainability impacts, leading to remote communities that are more self-sufficient with reduced emissions and costs.

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