title: Adaptive list sequential sampling method for population-based observational studies.
abstract: BACKGROUND
  In population-based observational studies, non-participation and delayed response to the invitation to participate are complications that often arise during the recruitment of a sample. When both are not properly dealt with, the composition of the sample can be different from the desired composition. Inviting too many individuals or too few individuals from a particular subgroup could lead to unnecessary costs or decreased precision. Another problem is that there is frequently no or only partial information available about the willingness to participate. In this situation, we cannot adjust the recruitment procedure for non-participation before the recruitment period starts.
  
  
  METHODS
  We have developed an adaptive list sequential sampling method that can deal with unknown participation probabilities and delayed responses to the invitation to participate in the study. In a sequential way, we evaluate whether we should invite a person from the population or not. During this evaluation, we correct for the fact that this person could decline to participate using an estimated participation probability. We use the information from all previously invited persons to estimate the participation probabilities for the non-evaluated individuals.
  
  
  RESULTS
  The simulations showed that the adaptive list sequential sampling method can be used to estimate the participation probability during the recruitment period, and that it can successfully recruit a sample with a specific composition.
  
  
  CONCLUSIONS
  The adaptive list sequential sampling method can successfully recruit a sample with a specific desired composition when we have partial or no information about the willingness to participate before we start the recruitment period and when individuals may have a delayed response to the invitation.
authors: Department of Clinical Epidemiology, Bioinformatics, and Biostatistics, Academic Medical Center - University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands. m.h.hof@amc.uva.nl.
authors: Michel H
authors: MH
authors: Hof
authors: Anita C J
authors: AC
authors: Ravelli
authors: Aeilko H
authors: AH
authors: Zwinderman
mesh_terms: D003198:Computer Simulation; D006801:Humans; D064887:Observational Studies as Topic / Q000379:methods*; D018579:Patient Selection; D011159:Population Surveillance / Q000379:methods*; D012107:Research Design; D012494:Sampling Studies