{smcl} {hline} help for {hi:propwt} {hline} {title:Generating Weights for Propensity Analysis} {p 4 8 2} {cmdab:propwt} {it:treatvar} {it:propvar} [{cmd:if} {it:exp}] [{cmd:in} {it:range}] [{cmd:,} {opt ipt} {opt smr} {opt att} {opt atc} {opt alt} {opt gen(suffix)} {opt nosc:aled} ] {title:Description} {pstd} The command {cmd:propwt} produces one or more sets of weights to use in analysing data with propensity scores. The each subject's treatment status is given in {it:treatvar}: 0=untreated, 1=treated, and the propensity score is given in {it:propvar}. {title:Options} {phang} {opt ipt} The option {opt ipt} states that you wish to calculate Inverse Probability of Treatment Weights (IPTW), i.e. setting the distribution of covariates to be equal to that of the population (Robins et al 2000). This is the default, and only needs to be specified if you wish to calculate one of the other types of weights available in addition to the IPT weight. {phang} {opt att} The option {opt att} allows you to calculate Average Treatment effect on the Treated (ATT) weights, i.e. setting the distribution of covariates to be equal to that of the treated subjects. {phang} {opt smr} The ATT is also referred to as Standardized Mortality Ratio (SMR) by Sato & Matsuyama (2003): the option {opt smr} is provided as a synonym for {opt att}. {phang} {opt atc} The option {opt atc} allows you to calculate Average Treatment effect on the Controls (ATC) weights, i.e. setting the distribution of covariates to be equal to that of the control group. {phang} {opt alt} The option {opt alt} provides an alternative to the IPTW for calculating the Average Treatment Effect (ATE) weights, i.e. setting the distribution of covariates to be equal to that of the population - using the weighting method proposed by Austin Nichols (2008). {phang} {opt gen(suffix)} By default, {cmd:propwt} produces variables called {cmd:ipt_wt} and {cmd:smr_wt}. The option {opt gen(suffix)} will change the suffix {cmd:_wt} to {it:suffix}. {phang} {opt nosc:aled} By default, the weights are standardised by the marginal probability of treatment, which are referred to as "stabilized weights" by Robins et al (2000). Unstandardised weights can be generated by the option {cmd:noscaled}. {title:Remarks} {p 4 8 2} The weights can be generated manually as follows: gen ipt = cond(treatvar, 1/propvar, 1/(1-propvar)) gen att = cond(treatvar, 1, propvar/(1- propvar)) gen atc = cond(treatvar, (1-propvar)/propvar, 1) gen alt = cond(treatvar, (1-propvar), propvar) {title:References} {phang} James M. Robins, Miguel Angel Hern{c a'}n and Babette Brumback. {it:Marginal Structural Models and Causal Inference in Epidemiology.} Epidemiology (2000), vol 11 number 5, pp 550-560. {phang} Tosiya Sato and Yutake Matsuyama. {it:Marginal Structural Models as a Tool for Standardization.} Epidemiology (2003), vol 14 number 6, pp 680-686. {phang} Austin Nichols. {it:Erratum and discussion of propensity-score reweighting.} Stata Journal (2008), vol 8 number 4, pp 532-539. {title:Authors} {p 4 4 2} Mark Lunt, Arthritis Research UK Epidemiology Unit, The University of Manchester {p 4 4 2} Ariel Linden, Linden Consulting Group {p 4 4 2} Please email: {browse "mailto:mark.lunt@manchester.ac.uk":mark.lunt@manchester.ac.uk} or {browse "mailto:alinden@lindenconsulting.org":alinden@lindenconsulting.org} if you encounter problems with {cmd:propwt}