Multiple imputations for missing data and intention-to-treat analysis will be used for statistical purpose.
We used as-treated analysis to process our observed data, and intention-to-treat analysis for our simulations.
We aimed to evaluate the extent, handling, and sensitivity analysis of missing data and intention-to-treat (ITT) analysis of randomized controlled trials (RCTs) in top tier medical journals, and compare our findings with previous reviews related to missing data and ITT in RCTs.
To minimise bias randomisation, concealed allocation, specific inclusion and exclusion criteria, blinded outcome assessment, patient blinding, blind data analysis, and intention to treat analysis have been used.
Only when the public is able to review the stops as their happen from the officer's perspective, can we truly know all the data, and the intentions behind the stops.
To assess the risk of bias, included articles were examined by two reviewers (MCE and MS) for: (1) generation of sequence allocation; (2) allocation concealment; (3) blinding; (4) incomplete outcome data; and (5) intention-to-treat (ITT) analysis.
Two approaches towards imputation of missing data are generally used to replace missing data and allow an intention to treat analysis: the last observation carried forward method and multiple imputation.
Twelve IRG screening indicators investigated the collection of weight and height data and information about intention to participate in adapted care management and the participation of students in group educational sessions.
To account for the difference of estimated effects between large and small trials, the qualities of study reporting including sequence generating, blinding, allocation concealment, incomplete follow-up data, sample size calculation and intention to treat were compared between large and small trials.
These models have the ability to handle missing data and therefore the intention-to-treat principle was adhered to [ 27].
