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James J. Annesi

obtained from previous research with different research goals. 19 , 21 After confirming that the 13% of missing cases met the criteria for being missing at random, 34 the expectation-maximization algorithm 35 was used for imputation within the intention-to-treat format. For analyses of score changes and

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Kara L. Gavin, Julian Wolfson, Mark Pereira, Nancy Sherwood and Jennifer A. Linde

data for effect estimation and missing data from participants narrowed the available sample size considerably. To address this issue, imputation models were considered to increase precision. However, these resulted in similar, nonsignificant findings. Thus, only the complete data models were reported

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Mariana Luciano de Almeida, Francine Golghetto Casemiro, Camila Tiome Baba, Diana Monteiro, Mariana Fornazieri, Natália Cerri, Daniele Frascá Martins Fernandes and Grace Angélica de Oliveira Gomes

analysis. Assuming unchanged conditions, this method considers the most recent measure of the individual prior to the final evaluation. It is common for postintervention losses to occur in follow-up studies; thus, when data are lost, intention-to-treat method acts as a method of imputation of data

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Jana Slaght, Martin Sénéchal and Danielle R. Bouchard

-based clinical trial . JAMA: The Journal of the American Medical Association , 266 ( 11 ), 1535 – 1542 . PubMed doi:10.1001/jama.1991.03470110081037 10.1001/jama.1991.03470110081037 Lee , P.H. ( 2013 ). Data imputation for accelerometer-measured physical activity: the combined approach . The American

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Sarah G. Sanders, Elizabeth Yakes Jimenez, Natalie H. Cole, Alena Kuhlemeier, Grace L. McCauley, M. Lee Van Horn and Alberta S. Kong

. Accelerometer files were read into R and summarized with package GGIR (version 1.5; open source software maintained by Vincent van Hees: https://cran.r-project.org/web/packages/GGIR/index.html ), using the Euclidean Norm Minus One metric and with no imputation. 15 – 17 The auto-calibration and detection of

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Melinda Forthofer, Marsha Dowda, Jennifer R. O’Neill, Cheryl L. Addy, Samantha McDonald, Lauren Reid and Russell R. Pate

days of ≥8 hours of wear each day (21% of children), missing values for the remaining times were estimated by multiple imputation using PROC MI in the SAS software program (Cary, NC). 19 There were no significant differences in the sociodemographic characteristics between the participants for whom PA

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Elske Stolte, Marijke Hopman-Rock, Marja J. Aartsen, Theo G. van Tilburg and Astrid Chorus

adjustment with theta parameterization, which is appropriate for categorical outcomes ( Muthén & Muthén, 1998–2007 ). Missing data were handled by means of multiple imputation in Mplus. To assess model fit we used the following fit statistics and criteria ( Hu & Bentler, 1999 ): comparative fit index (CFI

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Brian M. Wood, Herman Pontzer, Jacob A. Harris, Audax Z.P. Mabulla, Marc T. Hamilton, Theodore W. Zderic, Bret A. Beheim and David A. Raichlen

, O. ( 2019 ). mitml: Tools for Multiple Imputation in Multilevel Modeling. R package version 0.3-7 . Retrieved from https://CRAN.R-project.org/package=mitml Handy , S.L. , Boarnet , M.G. , Ewing , R. , & Killingsworth , R.E. ( 2002 ). How the built environment affects physical activity

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Katherine Reta Devonshire-Gill and Kevin Ian Norton

imputation for missing data. The following self-reported PA measures were derived for each respondent: (1) total PA minutes (sum of minutes walking, moderate LTPA, and vigorous LTPA weighted by a factor of 2) and (2) total sessions (sum of walking, moderate LTPA, and vigorous LTPA sessions). Based on these

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Mark A. Tully, Ilona I. McMullan, Nicole E. Blackburn, Jason J. Wilson, Laura Coll-Planas, Manuela Deidda, Paolo Caserotti, Dietrich Rothenbacher and on behalf of the SITLESS group

level of missing data of 5–6% in the variables of LSNS-6, DGLS-6, MVPA, SB, and LPA (Table  1 ), multi-imputation was applied using an expectation maximization approach in SPSS (version 25; IBM Corp., Armonk, NY). A multilevel linear regression analysis was carried out which addressed clustering by