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SUMMARY:Sample size\, statistical power and discrete choice experiments: H
 ow much is enough - Rose\, J (U of Technology\, Sydney)
DTSTART:20110817T100000Z
DTEND:20110817T104500Z
UID:TALK32393@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Discrete choice experiments (DCE) represent an important metho
 d for capturing data on the preferences held by both patients and health c
 are practitioners for various health care policies and/or products. Identi
 fying methods for reducing the number of respondents required for SC exper
 iments is important for many studies given increases in survey costs. Such
  reductions\, however\, must not come at the cost of a lessening in the re
 liability of the parameter estimates obtained from models of discrete choi
 ce. \n\nThe usual method of reducing the number of sampled respondents in 
 DCE experiments conducted in health studies appears to be using orthogonal
  fractional factorial experimental designs with respondents assigned to ch
 oice situations via either a blocking variable or via random assignment. T
 hrough the use of larger block sizes (i.e.\, each block has a larger numbe
 r of choice situations) or by the use of a greater number of choice situat
 ions being randomly assigned per respondent\, analysts may decrease the nu
 mber of respondents whilst retaining a fixed number of choice observations
  collected. It should be noted\, however\, that whilst such strategies red
 uce the number of respondents required for DCE experiments\, they also red
 uce the variability observed in other covariates collected over the sample
 . \n\nYet despite practical reasons to reduce survey costs\, particularly 
 through reductions in the sample sizes employed in DCE studies\, questions
  persist as to the minimum number of choice observations\, both in terms o
 f the number respondents as well as the number of questions asked of each 
 respondent\, that are required to obtain reliable parameter estimates for 
 discrete choice models estimated from DCE data. In this talk\, we address 
 both issues in the context of the main methods of generating experimental 
 designs for DCEs in health care studies. We demonstrate a method for calcu
 lating the minimum sample size required for a DCE that does not require ru
 les of thumb. \n\n
LOCATION:Seminar Room 1\, Newton Institute
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