*! version 1.0 May 17, 2017 @ 15:47:15

Title

wtdtttprob -- Predict probability of a
patient still being in treatment after a prescription
redemption based on the estimated parametric Waiting Time
Distribution (WTD).

Syntax

wtdtttprob
newvar [if] [in], distrx(varname)

Description

wtdtttprob uses the last fitted reverse Waiting Time Distribution
to estimate the probability of a user still being treated at a time
distrx after 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.

Options

distrx(varname) The specified variable should contain
the time after a prescription redemption for which the prediction
should be calculated. In some applications this will be the time
from a prescription until a subsequent event.

Examples

In the following example we first fit a Log-Normal WTD model
before predicting the probability of being treated based on observed
prescription redemptions found in a new dataset and the estimated
parameters:

. wtdttt last_rxtime, disttype(lnorm) reverse mucovar(i.packsize) logitpcovar(i.packsize)

. drop _all

. use rxdat

. wtdtttprob probttt, distrx(distlast))

Further examples are provided in the example do-file
wtdttt_ex.do, which contains analyses based on the datafile
wtddat.dta - a simulated dataset, which is also enclosed.

Author

Henrik Støvring, Aarhus University, stovring@ph.au.dk.