{smcl}
{hline}
help for {hi:proptrim}
{hline}

{title:Propensity score trimming}

{p 4 8 2}
{cmdab:proptrim}
{it:treatvar}
{it:matchvar}
[{cmd:if} {it:exp}]
[{cmd:in} {it:range}]
[{cmd:,}
  {opt keep(varname)}
  {opt pctiles(numlist)}
  ]

{title:Description}

{pstd}
The command {cmd:proptrim} creates indicator variables to select which
subjects are included in a propensity analysis. By default, all
treated subjects ({it:treatvar} = 1) with propensity scores
({it:propvar}) higher than the highest propensity score in the
untreated, and all untreated subjects with a propensity score lower
than the lowest propensity score in the treated subjects. An {it:x}%
trimming will remove all subjects with propensity scores higher than
the (100-{it:x})th centile the {it:untreated}, and all subjects with a
propensity score lower than the {it:x}th centile in the {it:treated}
subjects. This may remove far more than 2{it:x}% of the sample, if
there is little overlap between treated and untreated subjects.

{title:Options}

{phang}
{opt pctiles(numlist)} A list of numbers representing the centiles at which
trimming will occur. For each {it:x} in the list, A variable called
{cmd:keep_}{it:x} will be created, containing 1 for those subjects with
propensity scores between the {it:x}th centile in the treated and the
(100-{it:x})th centile in the untreated.

{phang}
{opt keep(varname)} If this option is given, {cmd:proptrim} will generate
new variables called {it:varname_x}, rather than {cmd:keep_}{it:x}.

{title:Remarks}

{pstd}
Numbers containing decimal points can be passed to {opt pctiles}: the points
will be changed to underscores in the new variables created.

{title:References}

{p 4 8 2}

{title:Author}

{p 4 4 2}
Mark Lunt, Arthritis Research UK Epidemiology Unit

{p 4 4 2}
The University of Manchester

{p 4 4 2}
Please email {browse "mailto:mark.lunt@manchester.ac.uk":mark.lunt@manchester.ac.uk} if you encounter problems with this program

