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Kent A. Lorenz, Hans van der Mars, Pamela Hodges Kulinna, Barbara E. Ainsworth and Melbourne F. Hovell

imputation procedure in SAS 9.3, with 20 imputation cycles performed for each area. The imputed estimates were averaged to produce plausible replacement values that were merged with the observed data. This allowed us to analyze a complete dataset with a combination of observed data and plausible imputation

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David B. Creel, Leslie M. Schuh, Robert L. Newton Jr, Joseph J. Stote and Brenda M. Cacucci

variables to be entered into the analyses were estimated using multiple imputation. The dataset included complete data for 89.5% of participants and 98.4% of data points before imputation. To examine categorical relationships between exercise capacity, weight, and age, patients were divided into quartiles

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Anass Arrogi, Astrid Schotte, An Bogaerts, Filip Boen and Jan Seghers

analyses. Missing data exceeded the level at which results can be biased (>5% missing). 27 Therefore, imputations were performed to minimize bias and preserve power. 28 Multiple imputation by chained equations was used to replace missing data ( m  = 20 imputations). The procedure of chained equations is

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Richard Larouche, Joel D. Barnes, Sébastien Blanchette, Guy Faulkner, Negin A. Riazi, François Trudeau and Mark S. Tremblay

and replaced by the mean of valid weekdays or weekend days as appropriate. Then, daily step counts were averaged across the week. Mean imputation was only done if participants provided at least 3 valid days, including 1 weekend day, given the known variability in PA between weekdays and weekend days

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Koren L. Fisher, Elizabeth L. Harrison, Brenda G. Bruner, Joshua A. Lawson, Bruce A. Reeder, Nigel L. Ashworth, M. Suzanne Sheppard and Karen E. Chad

participants, resulting in a mean of four participants per neighborhood cluster. Finally, a sensitivity analysis was conducted to determine whether imputation for participants with differing amounts of missing data would have an effect on the results. The multivariate analysis was repeated using imputed values

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Sharon Hetherington, Paul Swinton, Tim Henwood, Justin Keogh, Paul Gardiner, Anthony Tuckett, Kevin Rouse and Tracy Comans

, whereas intention-to-treat analyses incorporated multiple imputation ( m  = 10) using the “mice” package ( Buuren & Groothuis-Oudshoorn, 2011 ) in the R programming language to replace missing data. Imputation models included age, sex, health care resource utilization, Geriatric Anxiety Index ( Pachana et

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Oleg Zaslavsky, Yan Su, Eileen Rillamas-Sun, Inthira Roopsawang and Andrea Z. LaCroix

strength was measured by a handgrip dynamometer and was rounded up to the nearest kilogram (in kg). BMI was calculated by dividing the weight (in kilograms) by the square of the height (in meters). Missing Data We conducted multiple imputations based on maximizing expectation method to account for the

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Tiago V. Barreira, Stephanie T. Broyles, Catrine Tudor-Locke, Jean-Philippe Chaput, Mikael Fogelholm, Gang Hu, Rebecca Kuriyan, Estelle V. Lambert, Carol A. Maher, José A. Maia, Timothy Olds, Vincent Onywera, Olga L. Sarmiento, Martyn Standage, Mark S. Tremblay, Peter T. Katzmarzyk and for the ISCOLE Research Group

chance of bias due to exclusion of these cases. Missing values were multiply imputed (5 imputations) using fully conditional specification methods, under missing at random assumptions 24 and using SAS software (version 9.4; PROC MI, SAS Institute Inc., Cary, NC). Country-specific models were used to

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Aubrey Newland, Rich Gitelson and W. Eric Legg

indicated that data were missing completely at random, χ 2  = 359.151, df  = 321, p  = .07, and <5% of the data were missing. Thus, hot-deck imputation ( Myers, 2011 ) was used to create the final data set. Hot-deck imputation is a method of replacing missing values of the nonrespondent with data from a

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David Geard, Amanda L. Rebar, Peter Reaburn and Rylee A. Dionigi

rate their life these days (0 =  the worst possible life to 10 =  the best possible life ) ( Pruchno & Wilson-Genderson, 2014 ). Data Analyses Model fit analyses Multiple imputation by chained equations was used to account for missing data in this study because it imputes complex multivariate data by