Aiming for a representative sample: Simulating random versus purposive strategies for hospital selection.
BACKGROUND
A ubiquitous issue in research is that of selecting a representative sample from the study population. While random sampling strategies are the gold standard, in practice, random sampling of participants is not always feasible nor necessarily the optimal choice. In our case, a selection must be made of 12 hospitals (out of 89 Dutch hospitals in total). With this selection of 12 hospitals, it should be possible to estimate blood use in the remaining hospitals as well. In this paper, we evaluate both random and purposive strategies for the case of estimating blood use in Dutch hospitals.


METHODS
Available population-wide data on hospital blood use and number of hospital beds are used to simulate five sampling strategies: (1) select only the largest hospitals, (2) select the largest and the smallest hospitals ('maximum variation'), (3) select hospitals randomly, (4) select hospitals from as many different geographic regions as possible, (5) select hospitals from only two regions. Simulations of each strategy result in different selections of hospitals, that are each used to estimate blood use in the remaining hospitals. The estimates are compared to the actual population values; the subsequent prediction errors are used to indicate the quality of the sampling strategy.


RESULTS
The strategy leading to the lowest prediction error in the case study was maximum variation sampling, followed by random, regional variation and two-region sampling, with sampling the largest hospitals resulting in the worst performance. Maximum variation sampling led to a hospital level prediction error of 15%, whereas random sampling led to a prediction error of 19% (95% CI 17%-26%). While lowering the sample size reduced the differences between maximum variation and the random strategies, increasing sample size to n = 18 did not change the ranking of the strategies and led to only slightly better predictions.


CONCLUSIONS
The optimal strategy for estimating blood use was maximum variation sampling. When proxy data are available, it is possible to evaluate random and purposive sampling strategies using simulations before the start of the study. The results enable researchers to make a more educated choice of an appropriate sampling strategy.
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands. L.R.vanHoeven-3@umcutrecht.nl.|Sanquin Blood Supply, Transfusion Technology Assessment Department, Sanquin Research, Amsterdam, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands. L.R.vanHoeven-3@umcutrecht.nl.
Loan R
LR
van Hoeven
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands. M.P.Janssen@umcutrecht.nl.|Sanquin Blood Supply, Transfusion Technology Assessment Department, Sanquin Research, Amsterdam, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands. M.P.Janssen@umcutrecht.nl.
Mart P
MP
Janssen
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands. K.C.B.Roes@umcutrecht.nl.
Kit C B
KC
Roes
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands. H.Koffijberg@umcutrecht.nl.|Department of Health Technology & Services Research, MIRA Institute for biomedical technology and technical medicine, University of Twente, Drienerlolaan 5, 7522, NB, Enschede, The Netherlands. H.Koffijberg@umcutrecht.nl.
Hendrik
H
Koffijberg
D001803:Blood Transfusion / Q000706:statistics & numerical data*; D003198:Computer Simulation; D003625:Data Collection; D003657:Decision Making; D006761:Hospitals; D006801:Humans; D018579:Patient Selection; D011897:Random Allocation; D018401:Sample Size; D015983:Selection Bias