Your English writing platform
Discover LudwigSimilar(60)
Very few systematic reviews (Cochrane: 7%; non-Cochrane: 2%) assessed blinding separately for more than one outcome measure or incomplete outcome data for more than one outcome (eg, where the outcome was measured at different time points) (Cochrane: 8%; non-Cochrane: 1%).
We have rated the risk of bias due to incomplete outcome data for each of these studies as high.
Sequence generation and incomplete outcome data for in vitro studies are not explained in most cases but just named.
That study found that 8% and 1% of Cochrane and non-Cochrane reviews, respectively, assessed incomplete outcome data for more than one outcome.
Another potential limitation is incomplete outcome ascertainment for physiological and quality of life measures due to withdrawals, which may have biased the findings.
4. Incomplete outcome data for each main outcome or class of outcomes: were incomplete outcome data adequately addressed? 5. Selective outcome reporting: are reports of the study free of suggestion of selective outcome reporting?
There was a high risk of bias due to incomplete outcome data for all included trials, which reflected a high rate of attrition in studies of this type of population rather than a methodological deficiency in the studies themselves.[6] [23][24][25][26][27][28][29][30] [23][24][25][26][27][28][29][30]
A large number of trials were considered to have unclear methodological quality: 94 trials for random sequence generation; 77 for allocation concealment; 46 for blinding of outcome assessment; 48 for incomplete outcome data; and 11 for selective reporting.
Fifty four of the included trials were rated as high quality in terms of random sequence generation; the corresponding numbers were 24 for allocation concealment; 70 for blinding of outcome assessment; 95 for incomplete outcome data; and 127 for selective reporting.
Eight trials had low risk of bias for reporting incomplete outcome data and for the remaining three trials this was unclear [ 16– 16].
For incomplete outcome data, we assigned a judgement for different outcomes (for example, loss to follow-up at different time points).
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com