// Example of -rctmiss- // Follows help file // rctmiss.do 12jan2017 // UK500 data (quantitative outcome) use UK500, clear * Analysis assuming MAR, dropping missing baselines: reg sat96 rand sat94 i.centreid * Analysis assuming MAR, with mean imputation for missing baselines gen sat94fill = sat94 summ sat94 replace sat94fill = r(mean) if mi(sat94) reg sat96 rand sat94fill i.centreid * Same using rctmiss xi: rctmiss, pmmdelta(0): reg sat96 rand sat94 i.centreid * Single MNAR analysis, assuming missing values are 5 units lower than observed values in both arms: xi: rctmiss, pmmdelta(-5): reg sat96 rand sat94 i.centreid * Sensitivity analysis, assuming missing values are from 0 to 10 units * lower than observed values, in one arm or in both arms: xi: rctmiss, sens(rand) pmmdelta(-10/0): reg sat96 rand sat94 i.centreid * Improving appearance: xi: rctmiss, sens(rand, legend(rows(3)) title(Sensitivity analysis for UK500 data)) /// pmmdelta(-10/0) stagger(0.05) list(sepby(delta)): /// reg sat96 rand sat94 i.centreid // Smoking data (binary outcome) use smoke, clear tab rand quit, miss * Analysis assuming missing = smoking: gen quit2 = quit replace quit2 = 0 if missing(quit) logistic quit2 rand * Same analysis using rctmiss: rctmiss, pmmdelta(0, expdelta): logistic quit rand * Sensitivity analysis based around missing=smoking: rctmiss, sens(rand) pmmdelta(0(0.1)1, expdelta base(0)): logistic quit rand