The phrase "apply a prevalence" is not correct and does not convey a clear meaning in written English.
It is unclear what is intended by "apply a prevalence," as "prevalence" typically refers to the state of being widespread or common, and is not something that can be applied.
Example: "The study aimed to assess the prevalence of the disease in the population."
Alternatives: "implement a prevalence" or "establish a prevalence".
Applying an expected prevalence of headache of 35%%, based on review of previous studies [6], and with 95%% confidence and 10%% relative error margins, the minimum sample requirement was 1,000 biologically unrelated individuals from each (urban and rural) stratum.
As a previous paper noted, jail and prison populations have similarities in demographics related to HIV risk.[1] We applied an HIV prevalence of 1.7% to both jail and prison populations.
If these values of specificity and sensitivity are applied to a prevalence of 2.7% of detection of HCV antibodies found in the Italian population [26] the Negative Predictive Value (NPV according to Bayes theorem) is 0.99966, and the probability that 9 or more of the 276 negative results were false negatives is virtually zero (4.1−15, Exact Binomial Probability).
From the point of view of the −LR of 0.13, this permits the reduction of the probability of PC in almost 40% which when applied to a prevalence of approximately 50% significantly reduces the probability of cancer posttest to around 10%.
We estimated prevalence by applying an age-specific prevalence rate by 5-year age categories from the KNHANES 2005 or the Ko-EVE studies to the female populations in 2010 and summed up the totals to obtain standardized prevalence rates.
We included registries, databases or cohort studies which described the number of fatal food anaphylaxis cases in a defined population and time period and applied an assumed population prevalence rate of food allergy.
We calculated prevalence after applying a correction factor for 100% collection coverage.
When a study applied a longitudinal design, only the prevalence rate at the beginning of the study was recorded in our meta-analysis.
In order to obtain unbiased estimates of prevalence, we applied a first method that arithmetically adjusts the crude estimates, taking into account the false-positive and false-negative rates.
We first present how to define disease natural history models for Type 2 diabetes, i.e. a three-state Markov model and an illness-and-death Markov model, and then delineate how to apply Rothman prevalence pool concept and an E-M algorithm approach to estimate parameters.
