Data quality assurance, initial analyses, and missing data

FS Fred M. Ssewamala
OB Ozge Sensoy Bahar
PN Proscovia Nabunya
AT April D. Thames
TN Torsten B. Neilands
CD Christopher Damulira
BM Barbara Mukasa
RB Rachel Brathwaite
CM Claude Mellins
JS John Santelli
DB Derek Brown
SG Shenyang Guo
PN Phionah Namatovu
JK Joshua Kiyingi
FN Flavia Namuwonge
MM Mary M. McKay
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We will continue to use MIS IDA Q [75] to check for data-entry errors and missing values. Frequency tables for all variables and measures of central tendency and variability for continuous variables will characterize the sample overall and by randomization group. We will address incomplete data with direct maximum likelihood (ML) and multiple imputation (MI) [183] because they make the relatively mild assumption that incomplete data arise from a conditionally missing-at-random (MAR) mechanism [184]. Auxiliary variables will be included to help meet the MAR assumption [185, 186] and sensitivity analyses will be conducted with weighted MI [187] to assess the robustness of the MAR assumption [188]. SAS [189] and Mplus [190] will be used to perform the analyses.

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