[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
"G Livesey" <glivesey@inlogic.co.uk> |

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

Subject |
Re: st: Question on a meta-regression problem |

Date |
Mon, 27 Apr 2009 14:24:09 +0100 |

Firstly I would like to thank those who responded to my prior statlisters email, and for the valuable references therein. I was advised to try GLST, but for me this raises an important issue. Previously I mentioned I was using metareg not glst to look at dose-response data in observational studies, and this raised some query as to what problem I had with glst, while at the same time it appeared that I was being firmly recommended to use glst in preference to metareg. I have had difficulty in application of the glst method to the analysis of the slopes of observational studies I am working on. One of the problems I have reproduced using "classical" data re-reported to illustrate the use of glst in the Stata glst help file (from Greenland.1992 ) and the Stata book Methods of Meta-analysis (from Wolk . 1999) For me the results raise a central question, but first lets look at the results I get from Wolk's data (Table 1). I can see that glst is far superior to wls, but it is not clear to me that this is owing largely to an account being made of covariance gained from the case and none case data. Rather it seems to me to be mostly an intercept problem that affects the assessed slope reported by wls. Table 1 using Wolk..1999's data: Coef lci uci P Slope from wls -0.017 -0.046 0.012 0.246 Slope from glst -0.023 -0.048 0.001 0.063 Slope from metareg -0.022 -0.046 0.001 0.065 By contrast, the glst and metareg approach give virtually identical results. A common explanation is that the difference between wls and glst is an account by the latter for raw case and non case data to gain access to a pseudo-covariance, while wls does not. With the data from Wolk .1999 I find metareg to give the same result as glst. For metareg I first re-associate the standard error value with the referent, an se that is universally combined with se values and reported with the confidence intervals for quantiles >1. A similar level of identity between metareg and glst as shown in Table 1 is obtained for the majority of studies I am analysing as part of a current project, leading me to believe that for these studies it is not necessary to apply glst. Such would be useful because not all studies report full data, especially ones tending to find lesser or unclear effects. Now a different example. The Greenland.1999 data obtained via the glst help file better represent the problem I see with a minority of studies I am analyzing. Again the wls procedure has an intercept problem that affects the reported slope. But is there still a residual intercept problem affecting the reported slope in the glst procedure or is this difference truly a refection of the covariance effect alluded to? (There is certainly a problem with the output from predict xb after glst, as xb does not passing among the observations). Table 2 Greenland 1992 Coef lci uci P Slope from wls 0.033 -0.003 0.070 0.074 Slope from glst 0.045 0.005 0.086 0.028 Slope from metareg 0.050 0.010 0.090 0.013 A characteristic of the minority of studies (like that analysed in Table 2) seems to be that the data are scattered or at least apparently non-linear near the intercept (when joining data points). Such may be real or more often than not appears could be random. This leaves me believing that we may still have an intercept problem affecting the slope in the glst approach. If the latter is true we might expect meta-analysis of the slopes to be less heterogeneous for slopes obtained by metareg than when obtained by glst, which holds true at least for the studies I am analysing. So is the glst method as reliable as one may be wanting to believe? Can anyone explain these data as robustly not due to an intercept problem? The theory behind wanting to account for the 'lost covariance' when analysing single studies may be sound, but I am worried that implementation of the theoretical improvement has given rise to or leaves open another important problem yet to be resolved. The question I raise is highly important because results from glst analyses are often passed to national and international organisations. With many thanks for your help. Geoff L. Please consider the environment. Geoffrey Livesey B.Sc., Ph.D., R.P.H.Nutr. Registered Public Health Nutritionist Intelligent Nutrition and Health Research and Review Commissioned by Industry, Government, and Academia Member of SENSE, Professional Nutrition Consultants www.sense-nutrition.org.uk Member of VCG, Professional Bioscience Consultants www.vcgllp.com INDEPENDENT NUTRITION LOGIC (INLogic) Ltd NUTRITION RESEARCH MANAGEMENT AND CONSULTANCY Limited Company registered in England and Wales Number 4991400 VAT Registration Number GB 731 9065 38 Tel: +44-1953-606689 Mobile: +44-7990-964609 Fax: +44-1953-600218 E: glivesey@inlogic.co.uk A: Pealerswell House 21 Bellrope Lane Wymondham Norfolk NR18 OQX United Kingdom W: www.inlogic.co.uk * * 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/

- Prev by Date:
**st: AW: RE: bar graph with 2 y-axes** - Next by Date:
**st: comparing loglikelihoods of ols dummy regression model vs random intercept model** - Previous by thread:
**st: bar graph with 2 y-axes** - Next by thread:
**st: comparing loglikelihoods of ols dummy regression model vs random intercept model** - Index(es):

© Copyright 1996–2015 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |