On Simpson’s paradox: To remain or not to remain a population-based science

2021 ◽  
Vol 31 (3) ◽  
pp. 460-464
Author(s):  
Seth Oppong
Keyword(s):  
Psychological Research ◽  
Population Based ◽  
Simpson’S Paradox ◽  
Individual Level ◽  
Final Cause ◽  
Perceptual Control Theory ◽  
Perceptual Control ◽  
Standard Practices ◽  
The Individual ◽  
Simpson's Paradox

In this article, I critically reflect on J. F. Arocha’s (2021) contention that psychologists need to use methods and tools that are suitable for data analysis at the individual level. First, I discuss the beleaguered nature of the philosophical underpinnings of the standard practices in psychological research. Of the five assumptions he presented, the aggregate assumption results in Simpson’s paradox, a form of ecological fallacy. While the other assumptions need urgent attention, the proposals Arocha makes for addressing the aggregate assumption are still unsettled in many ways. I show that while perceptual control theory informed by the Aristotelian concept of final cause or telos allows for embracing variability as a psychological fact of human behaviour, one cannot say the same for his recommendation for the use of observation-oriented modelling (OOM) to address the aggregate assumption or to circumvent Simpson’s paradox.

scholarly journals Predicting the duration of sickness absence spells due to back pain: a population-based study from Sweden

2019 ◽  
Vol 77 (2) ◽  
pp. 115-121
Author(s):  
Annina Ropponen ◽  
Katalin Gémes ◽  
Paolo Frumento ◽  
Gino Almondo ◽  
Matteo Bottai ◽  
...  
Keyword(s):  
Back Pain ◽  
Sickness Absence ◽  
A Priori ◽  
Population Based ◽  
Final Model ◽  
Individual Level ◽  
Discriminatory Accuracy ◽  
C Statistic ◽  
International Statistical Classification ◽  
The Individual

ObjectivesWe aimed to develop and validate a prediction model for the duration of sickness absence (SA) spells due to back pain (International Statistical Classification of Diseases and Related Health Problems 10th Revision: M54), using Swedish nationwide register microdata.MethodsInformation on all new SA spells >14 days from 1 January 2010 to 30 June 2012 and on possible predictors were obtained. The duration of SA was predicted by using piecewise constant hazard models. Nine predictors were selected for the final model based on a priori decision and log-likelihood loss. The final model was estimated in a random sample of 70% of the SA spells and later validated in the remaining 30%.ResultsOverall, 64 048 SA spells due to back pain were identified during the 2.5 years; 74% lasted ≤90 days, and 9% >365 days. The predictors included in the final model were age, sex, geographical region, employment status, multimorbidity, SA extent at the start of the spell, initiation of SA spell in primary healthcare and number of SA days and specialised outpatient healthcare visits from the preceding year. The overall c-statistic (0.547, 95% CI 0.542 to 0.552) suggested a low discriminatory capacity at the individual level. The c-statistic was 0.643 (95% CI 0.634 to 0.652) to predict >90 days spells, 0.686 (95% CI 0.676 to 0.697) to predict >180 spells and 0.753 (95% CI 0.740 to 0.766) to predict >365 days spells.ConclusionsThe model discriminates SA spells >365 days from shorter SA spells with good discriminatory accuracy.

scholarly journals Cohort Profile: The China Multi-Ethnic Cohort (CMEC) Study

2020 ◽  
Author(s):  
Xing Zhao ◽  
Feng Hong ◽  
Jianzhong Yin ◽  
Wenge Tang ◽  
Gang Zhang ◽  
...  
Keyword(s):  
Clinical Laboratory ◽  
Disease Incidence ◽  
Data Access ◽  
Population Based ◽  
Baseline Survey ◽  
Individual Level ◽  
Long Term Storage ◽  
Level Data ◽  
The Individual ◽  
Study Participants

AbstractCohort purposeThe China Multi-Ethnic Cohort (CMEC) is a community population-based prospective observational study aiming to address the urgent need for understanding NCD prevalence, risk factors and associated conditions in resource-constrained settings for ethnic minorities in China.Cohort BasicsA total of 99 556 participants aged 30 to 79 years (Tibetan populations include those aged 18 to 30 years) from the Tibetan, Yi, Miao, Bai, Bouyei, and Dong ethnic groups in Southwest China were recruited between May 2018 and September 2019.Follow-up and attritionAll surviving study participants will be invited for re-interviews every 3-5 years with concise questionnaires to review risk exposures and disease incidence. Furthermore, the vital status of study participants will be followed up through linkage with established electronic disease registries annually.Design and MeasuresThe CMEC baseline survey collected data with an electronic questionnaire and face-to-face interviews, medical examinations and clinical laboratory tests. Furthermore, we collected biological specimens, including blood, saliva and stool, for long-term storage. In addition to the individual level data, we also collected regional level data for each investigation site.Collaboration and data accessCollaborations are welcome. Please send specific ideas to corresponding author at: [email protected].

scholarly journals Sickness absence among privately employed white-collar workers: A total population study in Sweden

2020 ◽  
pp. 140349482093427
Author(s):  
Kristin Farrants ◽  
Kristina Alexanderson
Keyword(s):  
Sickness Absence ◽  
Rural Areas ◽  
White Collar ◽  
Sociodemographic Factors ◽  
Population Based ◽  
Small Towns ◽  
Older Individuals ◽  
Individual Level ◽  
White Collar Workers ◽  
The Individual

Background: Knowledge about sickness absence (SA) and disability pension (DP) among privately employed white-collar workers is very limited. Aims: This study aimed to explore SA and DP among privately employed white-collar women and men using different measures of SA to investigate differences by branch of industry, and to analyse the association between sociodemographic factors and SA. Methods: This was a population-based study of all 1,283,516 (47% women) privately employed white-collar workers in Sweden in 2012, using register data linked at the individual level. Several different measures of SA and DP were used. Logistic regression was used to investigate associations of sociodemographic factors with SA. Results: More women than men had SA (10.9% women vs. 4.5% men) and DP (1.8% women vs. 0.6% men). While women had a higher risk of SA than men and had more SA days per employed person, they did not have more SA days per person with SA than men. The risk of SA was higher for women (odds ratio (OR)=2.54 (95% confidence interval (CI) 2.51–2.58)), older individuals (OR age 18–24 years=0.58 (95% CI 0.56–0.60); age 55–64 years OR=1.43 (95% CI 1.40–1.46) compared to age 45–54 years), living in medium-sized towns (OR=1.05 (95% CI 1.03–1.06)) or small towns/rural areas (OR=1.13 (95% CI 1.11–1.15)), with shorter education than college/university (OR compulsory only=1.64 (95% CI 1.59–1.69); OR high school=1.38 (95% CI 1.36–1.40)), born outside the EU25 (OR=1.23 (95% CI 1.20–1.27)) and singles with children at home (OR=1.33 (95% CI 1.30–1.36)). Conclusions: SA and DP among privately employed white-collar workers were lower than in the general population. SA prevalence, length and risk varied by branch of industry, sex and other sociodemographic factors, however, depending on the SA measure used.

scholarly journals Fair data for next-generation management of multiple sclerosis

2017 ◽  
Vol 24 (9) ◽  
pp. 1151-1156 ◽  
Author(s):  
Liesbet M Peeters
Keyword(s):  
Multiple Sclerosis ◽  
Multidisciplinary Treatment ◽  
Treatment Strategies ◽  
Population Based ◽  
Disease Modifying Drugs ◽  
Disease Epidemiology ◽  
Individual Level ◽  
Precision Level ◽  
The Central Nervous System ◽  
The Individual

Multiple sclerosis (MS) is a progressive demyelinating and degenerative disease of the central nervous system with symptoms depending on the disease type and the site of lesions and is featured by heterogeneity of clinical expressions and responses to treatment strategies. An individualized clinical follow-up and multidisciplinary treatment is required. Transforming the population-based management of today into an individualized, personalized and precision-level management is a major goal in research. Indeed, a complex and unique interplay between genetic background and environmental exposure in each case likely determines clinical heterogeneity. To reach insights at the individual level, extensive amount of data are required. Many databases have been developed over the last few decades, but access to them is limited, and data are acquired in different ways and differences in definitions and indexing and software platforms preclude direct integration. Most existing (inter)national registers and IT platforms are strictly observational or focus on disease epidemiology or access to new disease modifying drugs. Here, a method to revolutionize management of MS to a personalized, individualized and precision level is outlined. The key to achieve this next level is FAIR data.

scholarly journals Enriching Meta-Analytic Models of Summary Data: A Thought Experiment and Case Study

2019 ◽  
Vol 3 (1) ◽  
pp. 81-93 ◽  
Author(s):  
Blakeley B. McShane ◽  
Ulf Böckenholt
Keyword(s):  
Thought Experiment ◽  
Meta Analysis ◽  
Psychological Research ◽  
Individual Level ◽  
Level Data ◽  
The Common ◽  
The Individual ◽  
Analytic Models ◽  
Summary Data

Meta-analysis typically involves the analysis of summary data (e.g., means, standard deviations, and sample sizes) from a set of studies via a statistical model that is a special case of a hierarchical (or multilevel) model. Unfortunately, the common summary-data approach to meta-analysis used in psychological research is often employed in settings where the complexity of the data warrants alternative approaches. In this article, we propose a thought experiment that can lead meta-analysts to move away from the common summary-data approach to meta-analysis and toward richer and more appropriate summary-data approaches when the complexity of the data warrants it. Specifically, we propose that it can be extremely fruitful for meta-analysts to act as if they possess the individual-level data from the studies and consider what model specifications they might fit even when they possess only summary data. This thought experiment is justified because (a) the analysis of the individual-level data from the studies via a hierarchical model is considered the “gold standard” for meta-analysis and (b) for a wide variety of cases common in meta-analysis, the summary-data and individual-level-data approaches are, by a principle known as statistical sufficiency, equivalent when the underlying models are appropriately specified. We illustrate the value of our thought experiment via a case study that evolves across five parts that cover a wide variety of data settings common in meta-analysis.

scholarly journals Disagreement and Deliberation: Evidence from Three Deliberative Mini-Publics

2021 ◽  
Author(s):  
Kimmo Grönlund ◽  
Kaisa Herne ◽  
Kim Strandberg ◽  
Peter Söderlund
Keyword(s):  
Nuclear Power ◽  
Population Based ◽  
Experimental Manipulation ◽  
National Minority ◽  
Group Level ◽  
Individual Level ◽  
The Third ◽  
Before And After ◽  
The Individual ◽  
Citizen Deliberation

AbstractThis article is based on three experiments in citizen deliberation. We ask whether disagreement at group level as well as at individual level influence participants’ experiences of deliberation. In all three experiments, participants discussed in small groups and answered surveys before and after deliberations. The experiments were population-based with random selection. The topic of the first deliberation was nuclear power, the second dealt with immigration, and the third concerned policies for a language spoken by a national minority. The degree of group level disagreement was subject to experimental manipulation. In the first experiment, all the participants discussed in groups with mixed opinions. In the second experiment, participants were first categorized according to their baseline views, and then randomly allocated into either mixed or like-minded groups. In the third experiment, everyone discussed in like-minded groups. A trained facilitator moderated all small group discussions in the first two experiments. In the language experiment, the participants were randomly assigned into two treatments: groups with both moderation and deliberative norms, and ‘placebo’ groups. Our dependent variables consist of participants’ self-reported experiences of being heard in the discussion, and their feelings of mutual respect. The results show that all participants—regardless of group level disagreement—tend to be satisfied with deliberation. The only exception is the first experiment, where disagreement decreased process satisfaction slightly. At the individual level, participants’ deviation from the group mean had almost no effect.

scholarly journals A comparison of methods to generate adaptive reference ranges in longitudinal monitoring

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247338
Author(s):  
Davood Roshan ◽  
John Ferguson ◽  
Charles R. Pedlar ◽  
Andrew Simpkin ◽  
William Wyns ◽  
...  
Keyword(s):  
Bayesian Approach ◽  
Physiological State ◽  
Computational Cost ◽  
Biological Indicators ◽  
Population Based ◽  
Individual Variability ◽  
Reference Ranges ◽  
Individual Level ◽  
Expectation Maximisation ◽  
The Individual

In a clinical setting, biomarkers are typically measured and evaluated as biological indicators of a physiological state. Population based reference ranges, known as ‘static’ or ‘normal’ reference ranges, are often used as a tool to classify a biomarker value for an individual as typical or atypical. However, these ranges may not be informative to a particular individual when considering changes in a biomarker over time since each observation is assessed in isolation and against the same reference limits. To allow early detection of unusual physiological changes, adaptation of static reference ranges is required that incorporates within-individual variability of biomarkers arising from longitudinal monitoring in addition to between-individual variability. To overcome this issue, methods for generating individualised reference ranges are proposed within a Bayesian framework which adapts successively whenever a new measurement is recorded for the individual. This new Bayesian approach also allows the within-individual variability to differ for each individual, compared to other less flexible approaches. However, the Bayesian approach usually comes with a high computational cost, especially for individuals with a large number of observations, that diminishes its applicability. This difficulty suggests that a computational approximation may be required. Thus, methods for generating individualised adaptive ranges by the use of a time-efficient approximate Expectation-Maximisation (EM) algorithm will be presented which relies only on a few sufficient statistics at the individual level.

scholarly journals Use of medical care biases associations between Parkinson disease and other medical conditions

Neurology ◽  
2018 ◽  
Vol 90 (24) ◽  
pp. e2155-e2165 ◽  
Author(s):  
Anat Gross ◽  
Brad A. Racette ◽  
Alejandra Camacho-Soto ◽  
Umber Dube ◽  
Susan Searles Nielsen
Keyword(s):  
Parkinson Disease ◽  
Medical Care ◽  
Medical Condition ◽  
Population Based ◽  
Medical Conditions ◽  
Medicare Beneficiaries ◽  
Level Indicator ◽  
Individual Level ◽  
The Individual ◽  
Control Study

ObjectiveTo examine how use of medical care biases the well-established associations between Parkinson disease (PD) and smoking, smoking-related cancers, and selected positively associated comorbidities.MethodsWe conducted a population-based, case-control study of 89,790 incident PD cases and 118,095 randomly selected controls, all Medicare beneficiaries aged 66 to 90 years. We ascertained PD and other medical conditions using ICD-9-CM codes from comprehensive claims data for the 5 years before PD diagnosis/reference. We used logistic regression to estimate age-, sex-, and race-adjusted odds ratios (ORs) between PD and each other medical condition of interest. We then examined the effect of also adjusting for selected geographic- or individual-level indicators of use of care.ResultsModels without adjustment for use of care and those that adjusted for geographic-level indicators produced similar ORs. However, adjustment for individual-level indicators consistently decreased ORs: Relative to ORs without adjustment for use of care, all ORs were between 8% and 58% lower, depending on the medical condition and the individual-level indicator of use of care added to the model. ORs decreased regardless of whether the established association is known to be positive or inverse. Most notably, smoking and smoking-related cancers were positively associated with PD without adjustment for use of care, but appropriately became inversely associated with PD with adjustment for use of care.ConclusionUse of care should be considered when evaluating associations between PD and other medical conditions to ensure that positive associations are not attributable to bias and that inverse associations are not masked.

scholarly journals Master Narrative Methodology: A Primer for Conducting Structural-Psychological Research

2020 ◽  
Author(s):  
Moin Syed ◽  
Kate C. McLean
Keyword(s):  
Psychological Research ◽  
Structural Factors ◽  
Master Narrative ◽  
Individual Level ◽  
Script Analysis ◽  
Narrative Methodology ◽  
The Individual ◽  
Narrative Framework ◽  
Life Script

The emphasis placed on individual-level analysis throughout psychological science in general, and diversity science in particular, has left the role of structural factors under-theorized. Moreover, the field suffers from a lack of research methods that fully investigate structural-individual relations. This paper outlines one structural-psychological approach, the master narrative framework, and details various methods for taking social structures into account while still maintaining the focus on the individual. These methods, including event narratives, in-depth interviews, life-script analysis, focus groups, experiments, and conversation analysis, allow for understanding of both the nature and substance of the structures and how individuals interact with them.

scholarly journals Challenges and demands in the population-based work of public health nurses

2018 ◽  
Vol 46 (20_suppl) ◽  
pp. 53-58 ◽  
Author(s):  
Berit Misund Dahl
Keyword(s):  
Public Health ◽  
Nursing Education ◽  
Population Based ◽  
Public Health Nursing ◽  
Public Health Nurses ◽  
Individual Level ◽  
The Public ◽  
The Government ◽  
Health Nursing ◽  
The Individual

Aims: Government programs and the Norwegian Directorate of Health give public health nurses in Norway an explicit role in population-based health promotion and disease-prevention work. The aim of this paper is to explore Norwegian public health nurses’ experiences with population-based work. Methods: A phenomenological hermeneutic approach was adopted, involving face-to-face interviews with a purposeful sample of 23 public health nurses from urban and rural districts in two counties in Norway. Results: Three themes were identified: the predominance of work at the individual level, a lack of resources, and adherence to administrative directives. The interviews revealed that the public health nurses were mostly occupied with individual problem-solving activities. Population-based work was hardly prioritized, mostly because of a lack of resources and a lack of recognition of the population-based role of public health nurses. Conclusions:The study indicates contradictions between the public health nursing practice related to population-based work and the direction outlined by the government and the public health nursing curriculum, which may mean that the public health nursing role is not sufficiently clarified. The implementation of practice models and administrative directives and resources, as well as an explicit emphasis on population health in public health nursing education, can contribute to increased population-based interventions. Greater knowledge of and emphasis on population-based work in public health nursing are needed.

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