{smcl} {* *! version 1.0 September 11, 2017 @ 08:34:54}{...} {vieweralsosee "wtdttt" "help wtdttt"}{...} {viewerjumpto "Syntax" "wtdtttpreddur##syntax"}{...} {viewerjumpto "Description" "wtdtttpreddur##description"}{...} {viewerjumpto "Options" "wtdtttpreddur##options"}{...} {viewerjumpto "Examples" "wtdtttpreddur##examples"}{...} {title:Title} {phang} {bf:wtdtttpreddur} {hline 2} Predict duration of observed prescription redemptions based on the estimated parametric Waiting Time Distribution (WTD). {marker syntax}{...} {title:Syntax} {p 8 40 2} {cmd:wtdtttpreddur} {help newvar} [{it:if}] [{it:in}] [{cmd:,} {opt iadp:ercentile(#)} {opt iadm:ean}] {marker description}{...} {title:Description} {pstd} {cmd:wtdtttpreddur} uses the last fitted reverse Waiting Time Distribution to predict the duration of a prescription redemption. Any covariates used in the reverse WTD should also be present in the dataset, where the prediction is to be calculated. Estimation of the WTD will typically take place in one dataset before another dataset is opened in which the prediction is then carried out. {marker options}{...} {title:Options} {phang} {opt iadp:ercentile(#)} Percentile to predict in the Inter-Arrival Distribution (IAD); default is 0.8 (80th percentile). This means that 80% of all users with similar covariate values are predicted to have a new prescription redemption within the predicted time value.{p_end} {phang} {opt iadm:ean} Predict the mean of the IAD instead of a percentile.{p_end} {marker examples}{...} {title:Examples} {pstd} In the following example we first fit a Log-Normal WTD model in one dataset before predicting the duration (90th percentile) based on observed prescription redemptions found in another dataset and the just obtained parameter values: {phang} {phang2}{cmd: . wtdttt last_rxtime, disttype(lnorm) reverse mucovar(i.packsize) logitpcovar(i.packsize)} {phang2}{cmd: . use lastRx_index, clear} {phang2}{cmd: . wtdtttpreddur probttt, iadpercentile(0.9)}{p_end} {pstd} To see this example in action run the example do-file {it:wtdttt_ex.do}, which contains analyses based on the datafiles {it:wtddat_covar.dta} and {it:lastRx_index} - both are simulated datasets and both are enclosed. {title:Author} {pstd}Katrine Bødkergaard Nielsen, Aarhus University, kani@clin.au.dk. {pstd}Henrik Støvring, Aarhus University, stovring@ph.au.dk.