covariates, cumulative days with abnormal platelet counts, cumulative days spent relapse-free, and cumulative days with GvHD. Step 3) From our original sample of N=137, we re-sampled with replacement M=137,000 pseudo-patients, retaining only baseline covariates The large sample reduces Monte Carlo V0. error, and should be as large as is practical. Resampling can be done, for example, using the SAS procedure SURVEYSELECT. Step 4) Using model coefficients generated in Step 2 and the baseline covariates from our 137,000 pseudo-patients, we generated follow-up data for each of the M pseudo-patients by imputing values for platelet levels, relapse, and graph-versus host disease. Time-varying covariates at baseline were set to and We also imputed the outcome variable A0=0, L0=(0,0). Y1, using observed baseline covariates and the imputed values for and Similar to the dataset A1 L1. created in step one, we retained a record for each of the 137,000 pseudo-patients for each day. For example, the value of (the indicator of GvHD on day for individuals who were k) previously GvHD-free and not yet censored or dead was generated from a binomial distribution with Step 2 can be performed in SAS with a single DATA step using DO loops to cycle through days 1 to 1825 (or until or ), and the GvHD values can be imputed for each person day by drawing a value from a Bernoulli distribution with the probability of GvHD onset () given above. As was observed in our example data, we set this probability to 1 if the pseudo-patient developed GvHD on a previous
covariates, cumulative days with abnormal platelet counts, cumulative days spent relapse-free, and cumulative days with GvHD. Step 3) From our original sample of N=137, we re-sampled with replacement M=137,000 pseudo-patients, retaining only baseline covariates V0. The large sample reduces Monte Carlo error, and should be as large as is practical. Resampling can be done, for example, using the SAS procedure SURVEYSELECT. Step 4) Using model coefficients generated in Step 2 and the baseline covariates from our 137,000 pseudo-patients, we generated follow-up data for each of the M pseudo-patients by imputing values for platelet levels, relapse, and graph-versus host disease. Time-varying covariates at baseline were set to A0=0, and L0=(0,0). We also imputed the outcome variable Y1, using observed baseline covariates and the imputed values for A1 and L1. Similar to the dataset created in step one, we retained a record for each of the 137,000 pseudo-patients for each For example, the value of (the indicator of GvHD on day k) for individuals who were previously GvHD-free and not yet censored or dead was generated from a binomial distribution with Step 2 can be performed in SAS with a single DATA step using DO loops to cycle through days 1 to 1825 (or until or ), and the GvHD values can be imputed for each person day by drawing a value from a Bernoulli distribution with the probability of GvHD onset () given above. As was observed in our example data, we set this probability to 1 if the pseudo-patient developed GvHD on a previous day.
EDE_25_6_2014_06_25_KEIL_EDE13-475_SDC1.docx