{smcl}
{* *!1.1.1 Brent Mcsharry brent@focused-light.net 11dec2015}{...}
{cmd:help rasprt}
{hline}
{title:Title}
{p2colset 5 18 20 2}{...}
{p2col :{hi:rasprt} {hline 2}}Plot a Risk adjusted sequential probability ratio test chart +/- Risk Adjusted Cumulative Sum chart (CUSUM){p_end}
{p2colreset}{...}
{title:Syntax}
{p 8 17 2}
{cmdab:rasprt}
{outcomevar sequencevar}
{ifin}
, {Predicted(varname)}
[{it:options}]
{synoptset 24 tabbed}{...}
{synopthdr}
{synoptline}
{syntab:Options}
{synopt:{opt OR(#)}} the odds ratio for the alternative hypothesis. Default is 2{p_end}
{synopt:{opt A1(#)}} alpha1 - upper outer threshold line. Default is 0.01{p_end}
{synopt:{opth A2(#)}} alpha2 - upper inner threshold line. Default is 0.05 {p_end}
{synopt:{opt B1(#)}} beta1 - lower outer threshold line. Default is 0.01 {p_end}
{synopt:{opth B2(#)}} beta2 - lower inner threshold line. Default is 0.05 {p_end}
{synopt:{opt noSPRT}} do not display the sequential probability ratio (displays only the cusum) {p_end}
{synopt:{opt noCUSUM}} do not display risk adjusted CUSUM. {p_end}
{synopt:{opt noRESET}} do not reset the plot to 0 when the outer threshold is crossed. {p_end}
{synopt:{opt HEADstart(#)}} point for CUSUM to reset to as a proportion of upper threshold. Default 0 {p_end}
{synopt:{opt XLABEL(rule_or_values)}} major ticks plus labels. see {help axis_label_options}{p_end}
{synopt:{opt TITLE(tinfo)}} overall title. see {help title_options }{p_end}
{synopt:{opt NOTE(tinfo)}} note about graph. see {help title_options }{p_end}
{synopt:{opt SUBtitle(tinfo)}} subtitle. see {help title_options }{p_end}
{synopt:{opt XTitle(tinfo)}} X axis title. see {help axis_options }{p_end}
{synopt:{opt XAXis(tinfo)}} which axis, 1 < # < 9. see {help axis_choice_options }{p_end}
{synoptline}
{p2colreset}{...}
{p 4 6 2}
{title:Description}
{pstd}
{cmd:rasprt} Plots the cumulative-log likelihood over sequential observations.
{p_end}
{pstd}
{cmd:outcomevar} The actual outcome being benchmarked. Must be binary.
{p_end}
{pstd}
{cmd:sequencevar} A variable denoting in what order each subject has entered the analysis.
{p_end}
{pstd}
{cmd:Predicted} The predicted value for the outcome under investigation.
{p_end}
{title:Options}
{dlgtab:Main}
{phang}
{opt noCUSUM} A Risk Adjusted Cumulative Sum imposes a lower absorbing barier at the zero line.
{p_end}
{phang}
{opt noRESET} continue the plot outside the outer threshold line, rather than reset to 0.
{p_end}
{phang}
{opt HEADstart(#)} point for CUSUM to reset to as a proportion of upper threshold. Default 0.
This is for creating fast initial response (FIR) CUSUM graphs. Must be a value between 0 and 1.{p_end}
{title:Authors}
{p 4 4 2}Brent McSharry, Starship Children's Hospital, Auckland New Zealand -
brentm@adhb.govt.nz
{p_end}
{title:Examples}
{hline}
{pstd}Setup{p_end}
{phang2}. {stata webuse cancer}{p_end}
{phang2}. {stata gen int study_entry_sequence=_n}{p_end}
{phang2} assuming co-efficients from a (fictional) validated benchmarking model - intercept -3.6, age 0.12 per year, coeficient for drug2 -3.5 and drug 3 -3.2 {p_end}
{phang2}. {stata gen double predicted_death=invlogit(-3.6+ 0.12*age -3.5*(drug==2) -3.2*(drug==3))}{p_end}
{phang2} Creating another fictional benchmarking model - in this case more outcomes are occuring than would be predicted {p_end}
{phang2}. {stata gen double xs_pred_death=invlogit(-5.6+ 0.12*age -5*(drug==2) -3.2*(drug==3))}{p_end}
{pstd}Plot{p_end}
{phang2}. {stata rasprt died study_entry_sequence, pr(predicted_death) }{p_end}
{phang2}. {stata rasprt died study_entry_sequence, pr(predicted_death) nocusum }{p_end}
{phang2}. {stata rasprt died study_entry_sequence, pr(predicted_death) nosprt }{p_end}
{phang2}. {stata rasprt died study_entry_sequence, pr(xs_pred_death) }{p_end}
{phang2}. {stata rasprt died study_entry_sequence, pr(xs_pred_death) noreset }{p_end}
{pstd}Working with dates - involves installing {cmd:mydatelabels}{p_end}
{phang2}. {stata gen study_entry_date = td(#1jan2010#) in 1}{p_end}
{phang2}. {stata replace study_entry_date = study_entry_date[_n-1] + (runiform()*20) in 2/l}{p_end}
{phang2} divide the year into quarters, and label the first case in each quarter {p_end}
{phang2}. {stata mydatelabels study_entry_sequence =ceil(month( study_entry_date )/3),local(mymac) start format(%tdMon_'YY)}{p_end}
{phang2}. {stata rasprt died study_entry_sequence, pr(p) xlabel(`mymac', axis(2)) xaxis(1 2)}{p_end}
{hline}
{title:Also see}
{psee} Stata: {stata findit tabcusum} command to graph O-E (VLAD) type CUSUM vertically aligned with tabular (SPRT) CUSUM{p_end}
{psee} Article: {it:International Journal for Quality in Health Care} 2003; Volume 15, Number 1: pp. 7–13
{browse "http://intqhc.oxfordjournals.org/content/15/1/7.full.pdf":Risk-adjusted sequential probability ratio tests and longitudinal surveillance methods}
{p_end}
{psee} Article: {it:Critical Care and Resuscitation} 2008; Volume 10, Number 3: pp. 239-251
{browse "http://cicm.org.au/journal/2008/september/ccr_10_3_010908_239_Cook.pdf":Review of the application of risk-adjusted charts to analyse mortality outcomes in critical care}{p_end}
{psee} Program: {browse "https://github.com/mcshaz/CusumARL/releases":CusumARL} is a free, open source (MIT license) console application for calculating average run lengths.
ARL0 is the expected number of observations taken from an in-control process until the control chart falsely signals.
ARL1 is the expected number of observations taken from an out-of-control process until the control chart correctly signals.
CusumARL is written by the author of RASPRT in C++ rather than Stata for reasons of performance over millions of Monte Carlo iterations.
Binaries have not been compiled for Mac or Linux - feel free to contact the author if you would like these binaries, or more features.
{p_end}
{hline}
{title:Change log}
{phang}
{opt 0.2.0}{p_end}
{phang}Graph commences at 0 rather than first cumulative observation. {p_end}
{phang}Graph line stops after exceeding threshold (instead of circle). {p_end}
{phang}headstart, note and title options introduced.{p_end}