Sample size determination is an essential step in planning a clinical study. It is critical to understand that different study designs need different methods of sample size estimation.Although there is a vast literature discussing sample size estimation, incorrect or improper formulas continue to be applied. This article reviews basic statistical concepts in sample size ABBREVIATIONS: LDL = low-density lipoprotein; RCT = randomized controlled trial
Risk calculators are online tools developed for use by physicians in clinical settings to predict the risk of a clinical event, and as an aid in personalizing medical decision-making. Cleveland Clinic prediction models are listed at http://rcalc.ccf.org. We illustrate how we used R to create a risk calculator, and demonstrate the ease of using R, RStudio, and a Shiny package.
To derive and internally validate a bronchiolitis-specific illness severity score (the Critical Bronchiolitis Score) that out-performs mortality-based illness severity scores (e.g., Pediatric Risk of Mortality) in measuring expected duration of respiratory support and PICU length of stay for critically ill children with bronchiolitis.DESIGN: Retrospective database study using the Virtual Pediatric Systems (VPS, LLC; Los Angeles, CA) database. SETTING:One-hundred twenty-eight North-American PICUs.PATIENTS: Fourteen-thousand four-hundred seven children less than 2 years old admitted to a contributing PICU with primary diagnosis of bronchiolitis and use of ICU-level respiratory support (defined as high-flow nasal cannula, noninvasive ventilation, invasive mechanical ventilation, or negative pressure ventilation) at 12 hours after PICU admission. INTERVENTIONS:Patient-level variables available at 12 hours from PICU admission, duration of ICU-level respiratory support, and PICU length of stay data were extracted for analysis. After randomly dividing the cohort into derivation and validation groups, patient-level variables that were significantly associated with the study outcomes were selected in a stepwise backward fashion for inclusion in the final score. Score performance in the validation cohort was assessed using root mean squared error and mean absolute error, and performance was compared with that of existing PICU illness severity scores. MEASUREMENTS AND MAIN RESULTS:Twelve commonly available patientlevel variables were included in the Critical Bronchiolitis Score. Outcomes calculated with the score were similar to actual outcomes in the validation cohort. The Critical Bronchiolitis Score demonstrated a statistically significantly stronger association with duration of ICU-level respiratory support and PICU length of stay than mortality-based scores as measured by root mean squared error and mean absolute error. CONCLUSIONS:The Critical Bronchiolitis Score performed better than PICU mortality-based scores in measuring expected duration of ICU-level respiratory support and ICU length of stay. This score may have utility to enrich interventional trials and adjust for illness severity in observational studies in this very common PICU condition.
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