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From |
"Clive Nicholas" <Clive.Nicholas@newcastle.ac.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Longitudinal data using Stata |

Date |
Sat, 6 Aug 2005 04:03:54 +0100 (BST) |

George Konstantinou wrote: > I have 150 (daily) repeated measurements (scores) of 68 individuals where > each score corresponds to the severity of the symptoms (0=no symptoms to > 5=severe symptoms, continuous variable). For all these days I have > recorded the pollutants' load in the atmosphere (continuous variables). For the suggestions that follow, please bear in mind that these are _only_ suggestions and nothing else. Nobody knows your data better than you do. :) > I would like to figure out if there is an association between the severity > of the symptoms with all the polutants. But there is a logical thought > that perhaps the symptoms are getting more severe after some days of e.g. > heavy loads of pollutants and not necessarily the same day. So how can I > check if there is such a pattern? > > Which you think is the appropriate statistical approach to deal with these > data and how can I apply this to Stata? Keeping it simple and tractable, I would start from how the dependent variable is structured: only six values running from 0 to 5. Given this, one approach would be to try -ologit- (or -oprobit- if you prefer) and then modelling the time variables as, say, fortnights (of which, there would be ten - plus ten days - in a 150-day period). I would hesitate to model more specific temporal variables than this, since N=68 and you might be consuming too many degrees of freedom, especially if you have other variables that you wanted to put into the model. An alternative approach would be to 'pool' your N units across T time-points. This would you give you (N * T) = NT observations. In your case: 68 * 150 = 10200 observations. This easily sorts out any df problems. At a stroke, your options multiply, and you can use any number of pooled regression methods. Perhaps the two most common and one newcomer are relevant here: (a) fixed-effect OLS models (-xtreg, fe-), or better still -areg- if you wished to control for 'clustering' effects. Such models are best where the 'fixed-error' component u[i] (i.e., variables that do not vary over time, such as race and gender) is known to be correlated with the regressors Xb; (b) random-effect GLS models (-xtreg- or -xtreg, re-), which allows you to model any time-invariant variables, but can be used _only_ if corr(u[i], Xb) = 0; and (c) a mixture model of (a) and (b) in form of -xtmixed-, which is new to Stata 9. But, unfortunately, there's a catch. None of these pooled models may really be appropriate, since the dependent variable has a heavily bounded range: these models are technically only 'legal' to use if the dependent variable is unbounded, lest it predicts values outside the range. Also, if you wanted to model the effect of each day specifically, you could with these models quite easily given your large df: but I wouldn't like to be the one sifting through regression output containing 150+ variables! Thus, there's a trade-off: use -ologit- which is more appropriate for your DV but run the risk of consuming lots of df if your model is large, or use an -xt- model which sorts out any df problems but which may not be appropriate for your DV. I hope all the above helps, and good luck. CLIVE NICHOLAS |t: 0(044)7903 397793 Politics |e: clive.nicholas@ncl.ac.uk Newcastle University |http://www.ncl.ac.uk/geps Whereever you go and whatever you do, just remember this. No matter how many like you, admire you, love you or adore you, the number of people turning up to your funeral will be largely determined by local weather conditions. * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Longitudinal data using Stata***From:*"George Konstantinou" <georgenk@med.auth.gr>

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