The phrase "adjusted for prevalence" is correct and usable in written English.
It can be used in contexts where statistical data is being modified to account for the frequency of a particular condition or characteristic within a population.
Example: "The results of the study were adjusted for prevalence to ensure accurate comparisons across different demographic groups."
Alternatives: "corrected for frequency" or "modified for occurrence".
The corresponding CRR adjusted for prevalence of the risk factors included in the CHA2DS2-VASc score was 1.14 (95 % CI 0.93 1.40).
When adjusted for prevalence and bias, observed agreement varied by reminder from 0.33 (95% CI 0.24 – 0.42) to 0.99 (95% CI 0.97 – 1.00) and demonstrated almost perfect agreement for 7 of the 11 reminders.
Kappa statistics were further adjusted for prevalence and bias due to the potential effects of bias in the CDS logic and prevalence in the relative small sample of patients.
Simulations presented in this paper suggest that the number of level two groups and the number of individuals in each group should be adjusted for prevalence of the outcome.
To further compare the risk of ATE in patients with AF with that in non-AF patients, we calculated cumulative risk ratios (CRRs), both crude and adjusted for prevalence of the risk factors in the CHA2DS2-VASc score.
After adjusting for prevalence at each laboratory facility, pregnant women were less likely to have false-positive screening test results than non-pregnant persons [adjusted OR 0.65, 95% confidence interval (CI) (0.61, 0.70)] (Table 2).
Adjusting for prevalence and bias improved agreement for nearly all of the measures.
Although PABAK theoretically adjusts for prevalence, this statistic may be high to evaluate the agreements between two data sources, and may result in misleading conclusion.
The sample size of 1600 was estimated, based on a cluster design effect of 5.8, an ICC of 0.5 to adjust for prevalence estimation and 90%% power for analytical analyses.
Despite our ability to adjust for prevalence of certain diseases or poor perceived health at study enrolment, we cannot completely rule out the potential influence of co-morbidity on cancer survival according to smoking status.
Although there is no consensus on which specific statistics to report, there is consensus that statistics adjusting for prevalence must be reported in conjunction with kappa values [ 60, 62].
