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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: correlation for longitudinal data |

Date |
Tue, 21 Jul 2009 14:56:30 +0100 |

To expand on Austin's advice, there are then lots of ways of defining correlation, ranging from collapsing each panel to a single data point to something more elaborate. Loss of independence may not be crucial unless you really want P-values or confidence intervals (and in either case you need to worry about distributions too). By Cauchy-Schwarz correlations are bounded by [-1,1] regardless of generating process. The bigger issue is disentangling within-panel and between-panel variation, which implies that you need an appropriate model. In short, I think you need to signal more of what you want to get better advice. Nick n.j.cox@durham.ac.uk Ricardo Ovaldia replied to Austin Nichols: >Sure: >corr y x The problem is that I have non-independent observations. Longitudinal (panel) data. Multiple measurements per patient over time. Pearson's correlation assumes independence, therefore -corr- is not appropiate. Ricardo Ovaldia<ovaldia@yahoo.com> : Sure: corr y x but I guess you have something else in mind--perhaps you could clarify. Maybe you want to allow mean and SD to change over time, and standardize by time e.g. webuse psidextract, clear corr lwage ed ren lwage y ren ed x egen my=mean(y), by(t) egen sy=sd(y), by(t) egen mx=mean(x), by(t) egen sx=sd(x), by(t) g zy=(y-my)/sy g zx=(x-mx)/sx reg zy zx, nocons corr zy zx On Mon, Jul 20, 2009 at 9:51 AM, Ricardo Ovaldia<ovaldia@yahoo.com> wrote: > Is there a way to estimate a correlation coefficient of two continuous measurements each collected over time on a sample of patients? Not sure if I can do this with -xtreg- for example. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: correlation for longitudinal data***From:*Ricardo Ovaldia <ovaldia@yahoo.com>

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