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Use of the PLS tool for analysis determined the minimum study sample size.
The minimum study sample size was calculated for estimating the actual prevalence of diarrhea among areas under study.
The minimum study sample size was calculated using the power analysis, estimating the expected effects from the pilot data and assuming an alpha level of 0.10 and a power of 80% (minimum sample size for each group is 37 patients).
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Therefore, to keep handling artifacts at a minimum the study samples were all collected uniformly and with as short a handling time and as few thaw-freeze cycles as practically possible (cf. Materials and methods).
The data in this table show that, at a minimum, 50% of the study sample was performing at this level or above; however, when the results of whole class groups were reviewed, the findings were less favorable.
The LMR-LRT test provides a p value that can be used to determine if there is a statistical improvement in fit for the inclusion of one more class; (2) a minimum proportion of the study sample in a class: 5%; and (3) entropy, which indicates uncertainty in the classification of the model and is a measure of how well or how precisely study participants are classified into their most likely class.
For generalisability and to obtain a representative study sample it was decided to include a minimum of 60 participants.
Across studies, sample size varied from a minimum of 260 to a maximum of 80 904 (Table 1).
Based on the incidence of anemia and the dropout rate reported in previous studies, a minimum sample size of 427 patients was considered to estimate the anemia onset rate with a 95% precision.
Whilst formal sample size calculations for external validation studies are not necessary, there was little acknowledgement that the number of events is the effective sample size; 46% of datasets had fewer than 100 events, which is indicated, though from a single simulation study, as a minimum effective sample size for external validation [ 28].
Minimum sample sizes: The minimum sample sizes described above were imposed to avoid highly underpowered studies leading to 'uninformative' null findings and/or publication bias.
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