Sentence examples for deviation in the outcome from inspiring English sources

The original sample size calculation of 450 participants with valid outcome data at 3 months post-randomisation, was based on detecting a standardised effect size of 0.33 or a mean difference of one-third of a standard deviation in the outcome measures between the booster and control groups.

The original sample size calculation of 450 participants with valid outcome data at 3 months post-randomisation, was based on detecting a standardised effect size of 0.33 or a mean difference of one-third of a standard deviation in the outcome measures between the intervention and control groups.

A sample size of 18 participants will provide some guidance as to the likely size of the treatment effect and will allow nuisance parameters such as the variability (standard deviation) in the primary outcome to be estimated with some precision.

In order to detect a standardised difference of 0.5 standard deviation in the primary outcome measure (which can be considered as a clinically relevant difference), 2 × 63 patients are needed when two-sided testing and 80% power is assumed.

The sample size required adequate power (80%) to detect conceptually important differences (0.8 standard deviation) in the health outcomes of different respondent groups within a multivariate analysis with a significance level of 5% with the size of the smallest sub-group of respondents at 5% [ 11].

The sample size required adequate power (80%) to detect conceptually important differences (0.8 standard deviation) in the health outcomes of different respondent groups within a multivariate analysis with a significance level of 5% with the size of the 'rarest' sub-group of respondents at 5% [ 11].

To provide 80% power (5% level of significance) to detect a difference in means of 0.5 standard deviations in the primary outcome of speed of healing over six weeks, the target sample size was 140 participants, assuming a loss to follow-up of 10%.

All analyses were completed using standardized estimates, which are interpreted as the change in standard deviation of the outcome due to one standard deviation increase in the predictor, and are useful when variable scales are dissimilar as is the case in this study [ 39].

To estimate statistical power or required sample size in a study based on simple random sampling or allocation, one requires an estimate of the minimal important effect and (for continuous measures) the standard deviation of the outcome in the population studied.

If a study only reported the means and standard deviations for the baseline and follow-up measurements for each group, we needed to calculate the means and standard deviations for change in the outcome for these groups [ 31, 32].

Standardized regression weights correspond to effect size estimates and reflect the degree of change in the standard deviation of the outcome variable associated with a standard deviation change in the predictor.