Statistical data preparation: management of missing values and outliers

Kwak, Sang Gyu; Kim, Jong Hae

doi:10.4097/kjae.2017.70.4.407

Cited by 445 publications

(259 citation statements)

References 7 publications

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“…The normality in frequency of the differences was also evaluated using the Shapiro-Wilk test to confirm appropriateness of the paired t -test. Outliers in the airborne spore catches and daily infection rate data were excluded for the Shapiro-Wilk test because outliers could bias the results of paired t -test (Kwak and Kim, 2017). Excluding two outliers on both extremes of data sets, 16 and 11 pairs of data were used in the evaluation of ASM and IRM, respectively.…”

Section: Methodsmentioning

confidence: 99%

MBCAST: A Forecast Model for Marssonina Blotch of Apple in Korea

Kim

Kang

et al. 2019

Plant Pathol J

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Section: Methodsmentioning

confidence: 99%

MBCAST: A Forecast Model for Marssonina Blotch of Apple in Korea

Kim

Kang

et al. 2019

Plant Pathol J

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“…The dataset had negligible missing data 27 and was therefore handled by available case analysis 28 using the IBM Statistical Package for the Social Sciences (SPSS) version 25.0 (IBM Inc, Armonk, NY, USA). All data was sex-disaggregated.…”

Section: Resultsmentioning

confidence: 99%

Suicidal behaviour and clinical correlates in young adults in Rwanda: a population-based, cross-sectional study

Muwonge¹,

Umubyeyi²,

Rugema³

et al. 2019

Journal of Global Health Reports

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Back Background ground A significant proportion of persons who were contemplating suicide or completed suicide had met with a health care provider in the preceding months, however suicidal behaviour was not detected. To improve identification of persons with suicidal behaviour in need of care and support, this study aimed to investigate clinical symptoms that may identify young men and women (aged 20-35 years) with suicidal behaviour (ideation, plan and attempts) in Rwanda. Methods Methods This was a secondary analysis of data collected from a population based, cross-sectional study conducted in the Southern province of Rwanda in 2011/2012. After a response rate of 99.8%, the final sample was 917 people (477 women and 440 men). A validated "Mini-International Neuropsychiatric Interview" tool was used to measure suicidal behaviour. Bivariable and multivariable logistic regressions were performed to test the association between suicidal behaviour and clinical symptoms such as major depressive episode, physical pain symptoms and perceived overall health.

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“…Quality control (QC) of phenotypic data is described in detail elsewhere (29). Generally, QC included winsorizing 25(OH)D to minimize the influence of outliers and using a log transformation to improve normality of 25(OH)D distribution in each cohort (32).…”

Section: Methodsmentioning

confidence: 99%

Multi-ethnic analysis shows genetic risk and environmental predictors interact to influence 25(OH)D concentration and optimal vitamin D intake

Hatchell

Mares

et al. 2019

Preprint

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BackgroundVitamin D inadequacy affects almost 50% of adults in the United States and is associated with numerous adverse health effects. Vitamin D concentration [25(OH)D] is a complex trait with genetic and environmental predictors that work in tandem to influence 25(OH)D and may determine how much vitamin D intake is required to reach an optimal 25(OH)D concentration. To date, there has been little investigation into how genetics and environment interact to affect 25(OH)D.ObjectiveInteractions between continuous measures of a polygenic score (PGS) and vitamin D intake (PGS*intake) or available ultra-violet (UV) radiation (PGS*UV) were evaluated separately in individuals of African or European ancestry.MethodsMega-analyses were performed using three independent cohorts (N=9,668; African ancestry n=1,099; European ancestry n=8,569). Interaction terms and joint effects (main and interaction terms) were tested using one-degree of freedom (DF) and 2-DF models, respectively. All models controlled for age, sex, body mass index (BMI), cohort, and dietary intake/available UV. Additionally, in participants achieving Institute of Medicine (IOM) vitamin D intake recommendations, 25(OH)D was evaluated by level of genetic risk of 25(OH)D deficiency.ResultsThe 2-DF PGS*intake, 1-DF PGS*UV and 2-DF PGS*UV results were statistically significant in participants of European ancestry (p=3.3×10−18, 2.1×10−2, and 2.4×10−19, respectively), but not in those of African ancestry. In European-ancestry participants who reached IOM vitamin D intake guidelines, the percent of participants achieving adequate 25(OH)D (>20ng/ml) increased as genetic risk decreased (72% vs 89% in the highest vs lowest risk categories; p=0.018).ConclusionsAvailable UV radiation and vitamin D intake interact with genetics to influence 25(OH)D. Individuals with higher genetic risk of deficiency may require more vitamin D exposure to maintain optimal 25(OH)D concentrations. Overall, the results showcase the importance of incorporating both environmental and genetic factors into analyses, as well as the potential for gene-environment interactions to inform personalized dosing of vitamin D.Sources of SupportARICThe Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). The authors thank the staff and participants of the ARIC study for their important contributions.Funding for GENEVA was provided by National Human Genome Research Institute grant U01HG004402 (E. Boerwinkle).MESAMESA and the MESA SHARe project are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, UL1-TR-001881, and DK063491.The MESA CARe data used for the analyses described in this manuscript were obtained through Genetics (accession numbers). Funding for CARe genotyping was provided by NHLBI Contract N01-HC-65226.Funding support for the Vitamin D dataset was provided by grant HL096875WHIThe WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C. This manuscript was not prepared in collaboration with investigators of the WHI, has not been reviewed and/or approved by the Women’s Health Initiative (WHI), and does not necessarily reflect the opinions of the WHI investigators or the NHLBI.WHI PAGE is funded through the NHGRI Population Architecture Using Genomics and Epidemiology (PAGE) network (Grant Number U01 HG004790). Assistance with phenotype harmonization, SNP selection, data cleaning, meta-analyses, data management and dissemination, and general study coordination, was provided by the PAGE Coordinating Center (U01HG004801-01).Funding support for WHI GARNET was provided through the NHGRI Genomics and Randomized Trials Network (GARNET) (Grant Number U01 HG005152). Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the GARNET Coordinating Center (U01 HG005157). Assistance with data cleaning was provided by the National Center for Biotechnology Information. Funding support for genotyping, which was performed at the Broad Institute of MIT and Harvard, was provided by the NIH Genes, Environment and Health Initiative [GEI] (U01 HG004424). The datasets used for the analyses described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/sites/entrez?db=gap through dbGa Paccession phs000200.v11.p3.Funding for WHI SHARe genotyping was provided by NHLBI Contract N02-HL-64278.OtherKEH was supported by an NLM training grant to the Computation and Informatics in Biology and Medicine Training Program (NLM 5T15LM007359). Computational resources were supported by a core grant to the Center for Demography and Ecology at the University of Wisconsin-Madison (P2C HD047873).JM was supported by the Department of Ophthalmology and Visual Sciences, and by an unrestricted grant to the Department of Ophthalmology and Visual Sciences from the Research to Prevent Blindness, and by National Institutes of Health, National Eye Institute grants R01 EY016686, R01 EY025292.

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Statistical data preparation: management of missing values and outliers

Cited by 445 publications

References 7 publications

MBCAST: A Forecast Model for Marssonina Blotch of Apple in Korea

MBCAST: A Forecast Model for Marssonina Blotch of Apple in Korea

Suicidal behaviour and clinical correlates in young adults in Rwanda: a population-based, cross-sectional study

Multi-ethnic analysis shows genetic risk and environmental predictors interact to influence 25(OH)D concentration and optimal vitamin D intake

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