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From |
"Seyi Soremekun" <Seyi.Soremekun@lshtm.ac.uk> |

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

Subject |
st: Re: interpret dydx as probabilities after logistic or xtlogit |

Date |
Mon, 11 Jul 2011 12:25:42 +0100 |

Hi All, I have a model with a categorical (binary) by continuous interaction, which I estimate using logistic regression or RE logistic regression thus: logistic infacility i.intvn##c.quintile xtlogit infacility i.intvn##c.quintile, re i(zone) or I would like to estimate the change in probability (not odds) of my outcome "infacility" for a unit change in quintile, at intvn=0 and intvn=1, i.e. explain the interaction by looking at the actual estimated slopes of quintile, separately for intvn=0 and 1 Are the slopes obtained via a simple post-estimation margins dydx calculation?: margins, dydx(quintile) over(intvn) continuous post or margins, dydx(quintile) over(intvn) predict(pu0) continuous post ...where one then exponentiates the results to show the risk ratio for the unit change? The reason I ask is that from graphing the raw proportions of infacility for each level of quintile separately for intvn=0 and 1 gives a very different picture to the results obtained from the above methods, even without the adjustment for clustering (i.e. via logistic model): 1. Logistic model odds ratio for quintile (when intvn==0) = 0.4446739 dydx for quintile (over intvn when =0) = -0.1383194, exponentiated to 0.87 2. Random effects model odds ratio for quintile (when intvn==0) = 0.6044537 dydx for quintile (over intvn when =0) = -.0948134, exponentiated to .91 3. raw data when intvn=0 change in probability (% reduction) of infacility at: quintile 2/1 = 0.73 quintile 3/2 = 0.68 quintile 4/3 = 0.59 quintile 5/4 = 0.38 One can see that the average pecentage reduction (~0.60) in the probability of infacility from the raw data (3.) is much higher than either logistic model. So: 1. Am I interpreting the dydx results from the logistic models in the correct fashion - as a percentage reduction in probability for a unit change in quintile (i.e. averaged risk ratio)? 2. If so any ideas why the model results look so different to the raw data - even in the simple logistic model without clustering adjustment? thanks, Seyi Seyi Soremekun Faculty of Epidemiology and Public Health London School of Hygiene and Tropical Medicine London WC1E 7HT +442079272464 * * 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/

**Follow-Ups**:**Re: st: Re: interpret dydx as probabilities after logistic or xtlogit***From:*Maarten Buis <maartenlbuis@gmail.com>

**References**:**st: marginal effects and std. coefficients after xtlogit***From:*Jörg Eulenberger <j.eulenberger@web.de>

**Re: st: marginal effects and std. coefficients after xtlogit***From:*Ulrich Kohler <kohler@wzb.eu>

**Re: st: marginal effects and std. coefficients after xtlogit***From:*Jörg Eulenberger <j.eulenberger@web.de>

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