2015
DOI: 10.1016/j.jdent.2015.03.007
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Addressing missing participant outcome data in dental clinical trials

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Cited by 14 publications
(8 citation statements)
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References 50 publications
(154 reference statements)
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“…Some studies lost almost 50% of the originally randomized patients. Therefore, incomplete outcome data in these studies might be an important source of attrition bias that can possibly interfere with treatment estimates (Yao et al, ) and their precision given the lower than planned sample size (Spineli, Fleming, & Pandis, ). Again, it is important to emphasize that incomplete data may not lead to baseline imbalances in the evaluated primary outcome as this depends on the missingness mechanism (Hewitt, Kumaravel, Dumville, & Torgerson, ).…”
Section: Discussionmentioning
confidence: 99%
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“…Some studies lost almost 50% of the originally randomized patients. Therefore, incomplete outcome data in these studies might be an important source of attrition bias that can possibly interfere with treatment estimates (Yao et al, ) and their precision given the lower than planned sample size (Spineli, Fleming, & Pandis, ). Again, it is important to emphasize that incomplete data may not lead to baseline imbalances in the evaluated primary outcome as this depends on the missingness mechanism (Hewitt, Kumaravel, Dumville, & Torgerson, ).…”
Section: Discussionmentioning
confidence: 99%
“…As pointed out earlier, assumptions on the relationship between incomplete data due to dropouts and influence on the treatment effects based on the quantity and the pattern of dropouts may be misleading (Bell, Kenward, Fairclough, & Horton, ). The keys for evaluating attrition bias are the reasons for dropouts and the approach used to deal with missing data (Bell et al, ; Rubin, ; Spineli et al, ). When data are MCAR (Little et al, ; Spineli et al, ): missing data are completely unrelated to the study variables.…”
Section: Discussionmentioning
confidence: 99%
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“…An association of attrition bias and exaggerated treatment effects was recorded in the present study. Losses to follow up or incomplete accounting for dropouts may risk inflated effect sizes to either direction and erroneous study conclusions due to between-group discrepancies (25). This is of particular importance in the clinical context of orthodontics or other medical fields where treatment effectiveness is largely dependent on the cooperation/compliance of trial participants to the allocated intervention (19).…”
Section: Discussionmentioning
confidence: 99%
“…A modification of this tool was utilized for the included in-vitro/pre-clinical studies, as no pre-determined guidelines to assess the risk of bias exist and in order to incorporate specific important elements that would help identify the presence of potential bias. These include selection bias [17,18], performance bias [19], attrition bias [20] and reporting issues [21,22].…”
Section: Risk Of Bias Assessment Within Individual Studiesmentioning
confidence: 99%