title: Observational studies of patients in the emergency department: a comparison of 4 sampling methods.
abstract: OBJECTIVE
  We evaluate the ability of 4 sampling methods to generate representative samples of the emergency department (ED) population.
  
  
  METHODS
  We analyzed the electronic records of 21,662 consecutive patient visits at an urban, academic ED. From this population, we simulated different models of study recruitment in the ED by using 2 sample sizes (n=200 and n=400) and 4 sampling methods: true random, random 4-hour time blocks by exact sample size, random 4-hour time blocks by a predetermined number of blocks, and convenience or "business hours." For each method and sample size, we obtained 1,000 samples from the population. Using χ(2) tests, we measured the number of statistically significant differences between the sample and the population for 8 variables (age, sex, race/ethnicity, language, triage acuity, arrival mode, disposition, and payer source). Then, for each variable, method, and sample size, we compared the proportion of the 1,000 samples that differed from the overall ED population to the expected proportion (5%).
  
  
  RESULTS
  Only the true random samples represented the population with respect to sex, race/ethnicity, triage acuity, mode of arrival, language, and payer source in at least 95% of the samples. Patient samples obtained using random 4-hour time blocks and business hours sampling systematically differed from the overall ED patient population for several important demographic and clinical variables. However, the magnitude of these differences was not large.
  
  
  CONCLUSIONS
  Common sampling strategies selected for ED-based studies may affect parameter estimates for several representative population variables. However, the potential for bias for these variables appears small.
authors.affiliation: Department of Emergency Medicine, University of Colorado Denver School of Medicine, Aurora, CO, USA. morgan.valley@ucdenver.edu
authors.forename: Morgan A
authors.initials: MA
authors.lastname: Valley
authors.forename: Kennon J
authors.initials: KJ
authors.lastname: Heard
authors.forename: Adit A
authors.initials: AA
authors.lastname: Ginde
authors.forename: Dennis C
authors.initials: DC
authors.lastname: Lezotte
authors.forename: Steven R
authors.initials: SR
authors.lastname: Lowenstein
mesh_terms: D000046:Academic Medical Centers / Q000706:statistics & numerical data; D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D016009:Chi-Square Distribution; D002648:Child; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006786:Hospitals, Urban / Q000706:statistics & numerical data; D006801:Humans; D019458:Insurance Coverage / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D044469:Racial Groups / Q000706:statistics & numerical data; D012494:Sampling Studies; D012737:Sex Factors; D013997:Time Factors; D055815:Young Adult