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06-29-2014 08:01 PM

I want to generate raw data for a study that has multiple treatment groups (2 treatment groups and 1 control group). Every participant in each of the groups are measured on two related outcomes ( r=.80). Then I need to repeat the same process to generate 10 studies, which I plan to meta-analyze. Each group has equal sample size (n=10). Any help with the simulation code will be appreciated. Outcomes are generated from a multivariate normal distribution.

Study | Groups | Outcomes |
---|---|---|

1 | Treatment Group 1 | Y1 |

Y2 | ||

Treatment Group 2 | Y1 | |

Y2 | ||

Control Group | Y1 | |

Y2 | ||

. | ||

. | ||

. | ||

10 | Treatment Group 1 | Y1 |

Y2 | ||

Treatment Group 2 | Y1 | |

Y2 | ||

Control Group | Y1 | |

Y2 |

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07-03-2014
03:17 PM

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07-03-2014 03:17 PM

*Simulating Data with SAS*. Because you want to simulate correlated responses, also read the section in Chapter 8 that deals with mixtures of MVN data. (Your example is a little simpler because your example does not contain correlations between treatment groups.)

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Solution

07-03-2014
03:17 PM

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07-03-2014 03:17 PM

*Simulating Data with SAS*. Because you want to simulate correlated responses, also read the section in Chapter 8 that deals with mixtures of MVN data. (Your example is a little simpler because your example does not contain correlations between treatment groups.)

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07-13-2014 05:38 PM

I will Rick. I really appreciate your help!

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02-19-2015 11:53 AM

Hi Rick! To extend my question, how then do you simulate correlated response between groups. Using the table above with slight modification, how do you simulate an outcome where each group (Control, Treat1 and Treat2) is only measured on one outcome (Y), but the outcome (Y) for each group is correlated. Again, thank you!

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02-19-2015 01:58 PM

Could you explain more or give an example? Each patient is an observation, right? Each patient only belongs to one group. So what do you mean that the outcomes are correlated?

Perhaps this will help: Sometimes researchers assume that

Trt1 ~ N( mu1, sd1)

Trt2 ~ N( mu2, sd2)

Control ~ N( mu3, sd3)

That makes each sample a draw from a different population, where the population parameters depend on the group. Mathematically, this is a draw from a finite mixture distribution. See p 120-121 of *Simulating Data with SAS*.

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02-25-2015 12:31 AM

Thank you for the response, Rick. I think I may have to re-think my question, but thanks for your suggestion.