Using Cardinality Matching to Design Balanced and Representative Samples for Observational Studies

AbstractNumerous observational studies have attempted to identify risk factors for infection with SARS-CoV-2 and COVID-19 disease outcomes. Studies have used datasets sampled from patients admitted to hospital, people tested for active infection, or people who volunteered to participate. Here, we highlight the challenge of interpreting observational evidence from such non-representative samples. Collider bias can induce associations between two or more variables which affect the likelihood of an individual being sampled, distorting associations between these variables in the sample. Analysing UK Biobank data, compared to the wider cohort the participants tested for COVID-19 were highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the mechanisms inducing these problems, and approaches that could help mitigate them. While collider bias should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.

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Applying Generalizability Theory to Optimize Analysis of Spontaneous Teacher Talk in Elementary Classrooms

Journal of Speech Language and Hearing Research ◽

10.1044/2020_jslhr-19-00118 ◽

2020 ◽

Vol 63 (6) ◽

pp. 1947-1957

Author(s):

Alexandra Hollo ◽

Johanna L. Staubitz ◽

Jason C. Chow

Keyword(s):

Special Education Teachers ◽

Generalizability Theory ◽

Child Outcomes ◽

Elementary Classrooms ◽

Teacher Talk ◽

Group Instruction ◽

Data Set ◽

School Year ◽

Minimum Number ◽

Representative Samples

Purpose Although sampling teachers' child-directed speech in school settings is needed to understand the influence of linguistic input on child outcomes, empirical guidance for measurement procedures needed to obtain representative samples is lacking. To optimize resources needed to transcribe, code, and analyze classroom samples, this exploratory study assessed the minimum number and duration of samples needed for a reliable analysis of conventional and researcher-developed measures of teacher talk in elementary classrooms. Method This study applied fully crossed, Person (teacher) × Session (samples obtained on 3 separate occasions) generalizability studies to analyze an extant data set of three 10-min language samples provided by 28 general and special education teachers recorded during large-group instruction across the school year. Subsequently, a series of decision studies estimated of the number and duration of sessions needed to obtain the criterion g coefficient ( g > .70). Results The most stable variables were total number of words and mazes, requiring only a single 10-min sample, two 6-min samples, or three 3-min samples to reach criterion. No measured variables related to content or complexity were adequately stable regardless of number and duration of samples. Conclusions Generalizability studies confirmed that a large proportion of variance was attributable to individuals rather than the sampling occasion when analyzing the amount and fluency of spontaneous teacher talk. In general, conventionally reported outcomes were more stable than researcher-developed codes, which suggests some categories of teacher talk are more context dependent than others and thus require more intensive data collection to measure reliably.

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Current comprehensive management of cranial base chordomas: 10-year meta-analysis of observational studies

Yearbook of Neurology and Neurosurgery ◽

10.1016/j.yneu.2012.05.101 ◽

2012 ◽

Vol 2012 ◽

pp. 183-185

Author(s):

J.K. Liu

Keyword(s):

Observational Studies ◽

Meta Analysis ◽

Cranial Base ◽

Comprehensive Management

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The association between dietary inflammatory index and risk of central obesity in adults: An updated systematic review and meta-analysis

International Journal for Vitamin and Nutrition Research ◽

10.1024/0300-9831/a000648 ◽

2020 ◽

Vol 90 (5-6) ◽

pp. 535-552 ◽

Cited By ~ 2

Author(s):

Mahdieh Abbasalizad Farhangi ◽

Mahdi Vajdi

Keyword(s):

Systematic Review ◽

Observational Studies ◽

Central Obesity ◽

Assessment Tool ◽

Weighted Mean Difference ◽

Meta Analysis ◽

Dietary Factors ◽

Cross Sectional ◽

Inflammatory Status ◽

Obesity Indices

Abstract. Backgrounds: Central obesity, as a pivotal component of metabolic syndrome is associated with numerous co-morbidities. Dietary factors influence central obesity by increased inflammatory status. However, recent studies didn’t evaluate the association between central obesity and dietary inflammation index (DII®) that give score to dietary factors according to their inflammatory potential. In the current systematic review and meta-analysis, we summarized the studies that investigated the association between DII® with central obesity indices in the general populations. Methods: In a systematic search from PubMed, SCOPUS, Web of Sciences and Cochrane electronic databases, we collected relevant studies written in English and published until 30 October 2019. The population of included studies were apparently healthy subjects or individuals with obesity or obesity-related diseases. Observational studies that evaluated the association between DII® and indices of central obesity including WC or WHR were included. Results: Totally thirty-two studies were included; thirty studies were cross-sectional and two were cohort studies with 103071 participants. Meta-analysis of observational studies showed that higher DII® scores were associated with 1.81 cm increase in WC (Pooled weighted mean difference (WMD) = 1.813; CI: 0.785–2.841; p = 0.001). Also, a non-significant increase in the odds of having higher WC (OR = 1.162; CI: 0.95–1.43; p = 0.154) in the highest DII category was also observed. In subgroup analysis, the continent, dietary assessment tool and gender were the heterogeneity sources. Conclusion: The findings proposed that adherence to diets with high DII® scores was associated with increased WC. Further studies with interventional designs are necessary to elucidate the causality inference between DII® and central obesity indices.

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