scholarly journals A Note on the Effect of Data Clustering on the Multiple-Imputation Variance Estimator: A Theoretical Addendum to the Lewis et al. article in JOS 2014

2016 ◽  
Vol 32 (1) ◽  
pp. 147-164 ◽  
Author(s):  
Yulei He ◽  
Iris Shimizu ◽  
Susan Schappert ◽  
Jianmin Xu ◽  
Vladislav Beresovsky ◽  
...  
Keyword(s):  
Multiple Imputation ◽  
Variance Estimators ◽  
Design Effects ◽  
Study Results ◽  
Analytic Expressions ◽  
Theoretical Reasoning ◽  
Care Survey ◽  
Variance Estimate ◽  
Nationally Representative ◽  
The Impact

Abstract Multiple imputation is a popular approach to handling missing data. Although it was originally motivated by survey nonresponse problems, it has been readily applied to other data settings. However, its general behavior still remains unclear when applied to survey data with complex sample designs, including clustering. Recently, Lewis et al. (2014) compared single- and multiple-imputation analyses for certain incomplete variables in the 2008 National Ambulatory Medicare Care Survey, which has a nationally representative, multistage, and clustered sampling design. Their study results suggested that the increase of the variance estimate due to multiple imputation compared with single imputation largely disappears for estimates with large design effects. We complement their empirical research by providing some theoretical reasoning. We consider data sampled from an equally weighted, single-stage cluster design and characterize the process using a balanced, one-way normal random-effects model. Assuming that the missingness is completely at random, we derive analytic expressions for the within- and between-multiple-imputation variance estimators for the mean estimator, and thus conveniently reveal the impact of design effects on these variance estimators. We propose approximations for the fraction of missing information in clustered samples, extending previous results for simple random samples. We discuss some generalizations of this research and its practical implications for data release by statistical agencies.

scholarly journals The Relative Impacts of Design Effects and Multiple Imputation on Variance Estimates: A Case Study with the 2008 National Ambulatory Medical Care Survey

2014 ◽  
Vol 30 (1) ◽  
pp. 147-161 ◽  
Author(s):  
Taylor Lewis ◽  
Elizabeth Goldberg ◽  
Nathaniel Schenker ◽  
Vladislav Beresovsky ◽  
Susan Schappert ◽  
...  
Keyword(s):  
Missing Data ◽  
Medical Care ◽  
Multiple Imputation ◽  
Data Uncertainty ◽  
Item Nonresponse ◽  
Single Imputation ◽  
Design Effects ◽  
Physician Care ◽  
Care Survey ◽  
Ambulatory Medical Care

Abstract The National Ambulatory Medical Care Survey collects data on office-based physician care from a nationally representative, multistage sampling scheme where the ultimate unit of analysis is a patient-doctor encounter. Patient race, a commonly analyzed demographic, has been subject to a steadily increasing item nonresponse rate. In 1999, race was missing for 17 percent of cases; by 2008, that figure had risen to 33 percent. Over this entire period, single imputation has been the compensation method employed. Recent research at the National Center for Health Statistics evaluated multiply imputing race to better represent the missing-data uncertainty. Given item nonresponse rates of 30 percent or greater, we were surprised to find many estimates’ ratios of multiple-imputation to single-imputation estimated standard errors close to 1. A likely explanation is that the design effects attributable to the complex sample design largely outweigh any increase in variance attributable to missing-data uncertainty.

scholarly journals 1078 Sleep Problems In Posttraumatic Stress Disorder (PTSD) In A Nationally Representative Us Sample Before And After Controlling For Comorbid Depression: Results From A Nationally Representative US Sample

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A410-A411
Author(s):  
M A Gupta ◽  
B Vujcic
Keyword(s):  
Medical Care ◽  
Sleep Disturbance ◽  
Sleep Problems ◽  
Comorbid Depression ◽  
Obstructive Sleep ◽  
Before And After ◽  
Care Survey ◽  
Nationally Representative ◽  
Ambulatory Medical Care ◽  
The Impact

Abstract Introduction The impact of psychiatric comorbidities on sleep disturbances in PTSD have been studied in the National Comorbidity Survey (NCS)(Leskin GA, 2002) and the NCS-replication (Lauterbach D, 2011) studies. We examined sleep problems in PTSD before and after controlling for comorbid depressive disease, in the National Ambulatory Medical Care Survey(NAMCS) and National Hospital Ambulatory Medical Care Survey (NHAMCS) which use a multi-stage probability design to collect nationally representative data on health care visits. Methods We examined patient visits(1995-2015) from NAMCS/NHAMCS with a PTSD diagnosis (ICD-9-CM 309.81). Both NAMCS/NHAMCS allow ≥3 reasons for visit(RFV) and ≥3 physician-assigned diagnoses (using ICD9-CM codes). The following variables were created: ‘Insomnia’: ICD9-CM codes 307.41,307.42,780.51,RFV 11351; ‘Sleep Disturbance’(SD): ICD-9CM codes 780.5, 780.50, 780.59; ‘Nightmares’: ICD9-CM code 307.4, RFV 11353; Obstructive sleep apnea (OSA): ICD-CM codes 327.23,780.57, RFV 11355, checklist; and ‘Depression’: ICD9-CM codes 296.2, 296.3, 296.82, 311, 296.20-296.36, 300.4. Results There were an estimated 37,262,245±3,203,047 (unweighted count or UWC=3,995; 66.4%±1.8% female;.mean±age: 40.39 ± 0.56 years; ‘Depression’ was comorbid with 37.7%±1.5% cases) PTSD patients visits. All sleep variables accounted for 11.2%±1.2% (UWC=303) of PTSD visits with their individual frequencies as follows: ‘Insomnia’6.5%±1.1%(UWC=153); ‘Nightmares’: 1.9%±0.4%(UWC=74); ‘SD’: 2.0%±0.3%(UWC=67); OSA: 1.3%±0.3%(UWC=27). Logistic regression analysis using PTSD versus all other patient visits as dependent variable revealed the following sleep predictors of PTSD after controlling for age and sex and: (i) before controlling for ‘Depression’: ‘Insomnia’: OR=7.16, (95%CI 4.78-10.73); ‘SD’: OR=4.43(95%CI 2.55-7.71); ‘Nightmares’:OR=104.29 (95%CI56.65-192.02); ‘OSA’: OR=1.71(95%CI 0.94-3.12); and (ii) after controlling for ‘Depression’: ‘Insomnia’: OR=2.88 (95%CI 1.87-4.42); ‘SD’: OR=2.84 (95%CI 1.73-4.67); and ‘Nightmares’: OR=58.33(95%CI 26.39-128.94); and ‘OSA’: OR=2.02(95%CI 1.14-3.57). Conclusion In a nationally representative sample, the association of PTSD with insomnia, sleep disturbance and nightmares remained significant, albeit decreased, after controlling for the confounding effect of comorbid depression; however the association of PTSD with OSA emerged only after the effect of depression was controlled for. Support None

scholarly journals Multiple-Imputation Variance Estimation in Studies With Missing or Misclassified Inclusion Criteria

2020 ◽  
Vol 189 (12) ◽  
pp. 1628-1632
Author(s):  
Mark J Giganti ◽  
Bryan E Shepherd
Keyword(s):  
Multiple Imputation ◽  
Variance Estimation ◽  
Variance Estimator ◽  
Inclusion Criteria ◽  
Variance Estimators ◽  
Coverage Probabilities ◽  
Log Odds ◽  
Variance Estimate ◽  
High Level ◽  
Analysis Models

Abstract In observational studies using routinely collected data, a variable with a high level of missingness or misclassification may determine whether an observation is included in the analysis. In settings where inclusion criteria are assessed after imputation, the popular multiple-imputation variance estimator proposed by Rubin (“Rubin’s rules” (RR)) is biased due to incompatibility between imputation and analysis models. While alternative approaches exist, most analysts are not familiar with them. Using partially validated data from a human immunodeficiency virus cohort, we illustrate the calculation of an imputation variance estimator proposed by Robins and Wang (RW) in a scenario where the study exclusion criteria are based on a variable that must be imputed. In this motivating example, the corresponding imputation variance estimate for the log odds was 29% smaller using the RW estimator than using the RR estimator. We further compared these 2 variance estimators with a simulation study which showed that coverage probabilities of 95% confidence intervals based on the RR estimator were too high and became worse as more observations were imputed and more subjects were excluded from the analysis. The RW imputation variance estimator performed much better and should be employed when there is incompatibility between imputation and analysis models. We provide analysis code to aid future analysts in implementing this method.

Income Diversification and Bank Efficiency in Vietnam

2017 ◽  
pp. 52-67
Author(s):  
Sang Nguyen Minh
Keyword(s):  
Regression Model ◽  
Commercial Banks ◽  
Banking System ◽  
Operational Efficiency ◽  
Commercial Banking ◽  
Tobit Regression ◽  
Income Diversification ◽  
Positive Effects ◽  
Study Results ◽  
The Impact

This study uses the DEA (Data Envelopment Analysis) method to estimate the technical efficiency index of 34 Vietnamese commercial banks in the period 2007-2015, and then it analyzes the impact of income diversification on the operational efficiency of Vietnamese commercial banks through a censored regression model - the Tobit regression model. Research results indicate that income diversification has positive effects on the operational efficiency of Vietnamese commercial banks in the research period. Based on study results, in this research some recommendations forpolicy are given to enhance the operational efficiency of Vietnam’s commercial banking system.

Some reflections on social structure: Theoretical and practical aspects

2020 ◽  
Vol 18 (6) ◽  
pp. 1063-1078
Author(s):  
T.N. Skorobogatova ◽  
I.Yu. Marakhovskaya
Keyword(s):  
Service Industry ◽  
External Factors ◽  
Infrastructure Development ◽  
Social Infrastructure ◽  
Social Sphere ◽  
The Social ◽  
Study Results ◽  
The Republic ◽  
Internal And External Factors ◽  
The Impact

Subject. This article discusses the role of social infrastructure in the national economy and analyzes the relationship between the notions of Infrastructure, Service Industry and Non-Productive Sphere. Objectives. The article aims to outline a methodology for development of the social infrastructure of Russia's regions. Methods. For the study, we used the methods of statistical and comparative analyses. The Republic of Crimea and Rostov Oblast's social infrastructure development was considered as a case study. Results. The article finds that the level of social infrastructure is determined by a number of internal and external factors. By analyzing and assessing such factors, it is possible to develop promising areas for the social sphere advancement. Conclusions. Assessment and analysis of internal factors largely determined by the region's characteristics, as well as a comprehensive consideration of the impact of external factors will help ensure the competitiveness of the region's economy.

The Impact of Bar Service Operation Practices on Organizational Performance in Indian Hotel Industry

2015 ◽  
Vol 8 (1) ◽  
Author(s):  
Mohinder C. Dhiman ◽  
Abhishek Ghai
Keyword(s):  
Organizational Performance ◽  
Hotel Industry ◽  
Demographic Variables ◽  
Study Results ◽  
Service Operation ◽  
Managerial Implications ◽  
Overall Performance ◽  
The Impact ◽  
The Relationship ◽  
Bivariate Test

The paper has a two fold purpose - examine the impact of bar service operation practices (BSOP) on organizational performance (OP) and study the relationship between organizational performance and demographic variables. Based on a survey of 362 bar managers perceptions on the impact of bar service operation practices on organizational performance were assessed by 59 practices and 6 demographic variables. Bivariate test and ANOVA were employed to test the working hypothesis in the study. Results indicated that there is a positive relationship between the bar service operation practices and organizational performance. Further, the results indicate some practical and managerial implications to improve organizational overall performance.

scholarly journals Advances of 2nd Life Applications for Lithium Ion Batteries from Electric Vehicles Based on Energy Demand

2021 ◽  
Vol 13 (10) ◽  
pp. 5726
Author(s):  
Aleksandra Wewer ◽  
Pinar Bilge ◽  
Franz Dietrich
Keyword(s):  
Life Cycle ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Energy Demand ◽  
Lithium Ion ◽  
Life Cycles ◽  
Future Research ◽  
Research Areas ◽  
Study Results ◽  
The Impact

Electromobility is a new approach to the reduction of CO2 emissions and the deceleration of global warming. Its environmental impacts are often compared to traditional mobility solutions based on gasoline or diesel engines. The comparison pertains mostly to the single life cycle of a battery. The impact of multiple life cycles remains an important, and yet unanswered, question. The aim of this paper is to demonstrate advances of 2nd life applications for lithium ion batteries from electric vehicles based on their energy demand. Therefore, it highlights the limitations of a conventional life cycle analysis (LCA) and presents a supplementary method of analysis by providing the design and results of a meta study on the environmental impact of lithium ion batteries. The study focuses on energy demand, and investigates its total impact for different cases considering 2nd life applications such as (C1) material recycling, (C2) repurposing and (C3) reuse. Required reprocessing methods such as remanufacturing of batteries lie at the basis of these 2nd life applications. Batteries are used in their 2nd lives for stationary energy storage (C2, repurpose) and electric vehicles (C3, reuse). The study results confirm that both of these 2nd life applications require less energy than the recycling of batteries at the end of their first life and the production of new batteries. The paper concludes by identifying future research areas in order to generate precise forecasts for 2nd life applications and their industrial dissemination.

scholarly journals Postsecondary Educational Attainment and Health among Younger U.S. Adults in the “College-for-All” Era

2021 ◽  
Vol 7 ◽  
pp. 237802312110211
Author(s):  
Anna Zajacova ◽  
Elizabeth Lawrence
Keyword(s):  
High School ◽  
Health Outcomes ◽  
High School Graduates ◽  
Early Adulthood ◽  
Associate Degrees ◽  
Bachelor's Degrees ◽  
Nationally Representative ◽  
The Impact ◽  
High School Diplomas ◽  
Postsecondary Attainment

Population-health research has neglected differentiation within postsecondary educational attainments. This gap is critical to understanding health inequality because college experience with no degree, vocational/technical certificates, and associate degrees may affect health differently. We examine health across detailed postsecondary attainment levels. We analyze data on 14,750 respondents in Waves I and IV of the nationally representative Add Health panel spanning adolescence to ages 26 to 34. Multivariate regression and counterfactual approaches to minimize the impact of confounders estimate multiple health outcomes across postsecondary attainment levels. Compared to high school diplomas, we find significant returns to bachelor’s degrees for most health outcomes and smaller but largely significant returns to associate degrees. In contrast, adults with some college but no degree or with vocational/technical certificates do not have better physical health than high school graduates. Our findings highlight the stark differentiation within higher education as reflected by the disparate health outcomes in early adulthood.

scholarly journals Determinants of Individual Support for Independence: Evidence from Montenegro

2021 ◽  
pp. 1-20
Author(s):  
Pëllumb Kelmendi ◽  
Christian Pedraza
Keyword(s):  
Individual Preference ◽  
Minority Rights ◽  
Relative Effect ◽  
Factors Associated ◽  
Eu Integration ◽  
Eu Membership ◽  
Individual Support ◽  
Nationally Representative ◽  
Democratic Principles ◽  
The Impact

Abstract This article investigates the determinants of individual support for independence in Montenegro. We outline five theoretically distinct groups of factors covered by the literature and evaluate their impact on individual preference for independence. Using observational data obtained from a nationally representative survey conducted in Montenegro in 2003–2004, we find support for several hypotheses, showing that identity, income, and partisanship significantly impact individual opinion about independence. We also investigate and discuss the relative effect size of different factors associated with preference for independence. Additionally, we test variables with hitherto unexplored implications for opinions on independence, including the impact of support for EU membership, as well as support for democratic principles. Our logistic regression analyses reveal that attitudes towards EU integration and minority rights are strongly associated with support for independence. By systematically analyzing existing and new hypotheses with data from an understudied case, our findings contribute to the nascent literature on individual preferences for independence.

scholarly journals Robust Step Detection from Different Waist-Worn Sensor Positions: Implications for Clinical Studies

2020 ◽  
Vol 4 (1) ◽  
pp. 50-58
Author(s):  
Matthias  Tietsch ◽  
Amir Muaremi ◽  
Ieuan Clay ◽  
Felix Kluge ◽  
Holger Hoefling ◽  
...  
Keyword(s):  
Clinical Study ◽  
Clinical Studies ◽  
Neurological Diseases ◽  
Human Gait ◽  
Robust Solution ◽  
Step Detection ◽  
Study Protocols ◽  
Study Results ◽  
Wide Range ◽  
The Impact

Analyzing human gait with inertial sensors provides valuable insights into a wide range of health impairments, including many musculoskeletal and neurological diseases. A representative and reliable assessment of gait requires continuous monitoring over long periods and ideally takes place in the subjects’ habitual environment (real-world). An inconsistent sensor wearing position can affect gait characterization and influence clinical study results, thus clinical study protocols are typically highly proscriptive, instructing all participants to wear the sensor in a uniform manner. This restrictive approach improves data quality but reduces overall adherence. In this work, we analyze the impact of altering the sensor wearing position around the waist on sensor signal and step detection. We demonstrate that an asymmetrically worn sensor leads to additional odd-harmonic frequency components in the frequency spectrum. We propose a robust solution for step detection based on autocorrelation to overcome sensor position variation (sensitivity = 0.99, precision = 0.99). The proposed solution reduces the impact of inconsistent sensor positioning on gait characterization in clinical studies, thus providing more flexibility to protocol implementation and more freedom to participants to wear the sensor in the position most comfortable to them. This work is a first step towards truly position-agnostic gait assessment in clinical settings.

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