English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

between the presence of asthma and the risk of developing obstructive sleep apnea (OSA) in adults. Adults were randomly recruited from a population-based list of state employees and were followed for four years. Participants with asthma, when compared with those without, had a higher risk of developing OSA in four years (relative risk [RR] = 1.39; 95% CI: 1.06-1.82; p = 0.03) BACKGROUND When conducting clinical research, we usually recruit a subgroup of the population of interest in order to increase study efficiency (fewer costs and less time). This subgroup of individuals, the study population, are those individuals who meet the inclusion criteria and agree to participate in the study (Figure 1). We then complete the study and calculate an effect size (e.g., a mean difference or a relative risk) to answer our research question. This process (inference) involves using data collected from the study population to estimate the true effect size in the population of interest, i.e., the source population. In our example, investigators recruited a random sample of state employees (source population) who were eligible and agreed to participate in the study (study population) and reported that asthma increases the risk of developing OSA in the study population (RR = 1.39). To take into account a sampling error due to recruiting only a subgroup of the population of interest, they also calculated a 95% confidence interval (around the estimate) of 1.06-1.82, indicating a 95% probability that the true RR in the source population would be between 1.06 and 1.82.

Confidence intervals: a useful statistical tool to estimate effect sizes in the real world