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From |
"Austin Nichols" <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Trend test in meta analysis |

Date |
Fri, 21 Dec 2007 21:23:09 -0500 |

Vijay, Tom, and Ben: I have read neither of the references cited by Ben and Tom, but it seems to me a more fundamental problem is that the meta-analysis includes observational studies (I assume). The principle of meta-analysis is to combine several low-precision unbiased estimates to get a high-precision unbiased estimate; if every study exhibits a positive bias, can one even consider a meta-analysis? In that case, some kind of sensitivity testing à la Rosenbaum's _Observational Studies_ seems in order. That said, I am wondering if some kind of sign test (see -help signtest-) might not be a good way forward, e.g. count the number of studies with s1>0 (where s1 is the coef on category 1, low dose smoker, with the reference category nonsmoker represented by the zero--could also omit comparisons with zero) and s1<0, those with s2>s1 and with s2<s1, those with s2>0 and s2<0, etc. Under the null of no trend, all of these events have probability one half. A similar approach would be to use -nptrend oddsrat, by(category)-, I think. Does that make sense in this context? This is very far afield from my own field... On Dec 21, 2007 4:59 PM, Tom Trikalinos <ttrikalin@gmail.com> wrote: > Hi vijay > > I'm not clear what form your data have. > > Let me say right off the bat that such a question (dose-response) > could be evaluated on a qualitative basis (with a plot) without giving > a p-value. This should not be discarded as an option, given the strong > assumptions that are inherent in the data-abstraction process during > meta-analysis. > > That being said see if the paper by Jesse Berlin et al. > "Meta-analysis of epidemiologic dose-response data." Epidemiology. > 1993 May;4(3):218-28. PMID: 8512986 helps. > > Two general comments > A. first summing up the ORs 1 to 10 and then seeing for a trend (e.g. > with a meta-regression) is subject to Simpson's paradox. This is why > you need an approach like the on described in the cited paper. > > B. I'm not sure how metap could help you. -metap- does a > meta-analysis of significance levels, a whole family of non-parametric > meta-analysis methods (metap implements 3 of numerous approaches). > This is essentially an omnibus test and therefore does not have the > interpretation you would wish. A meta-analysis of p-values asks is > there evidence of significant deviation from the null in AT LEAST ONE > of the studies? A typical misinterpretaion of metap results is that > this is the p-value for the overall summary effect. > > > hope these thoughts help > > tom > > > > On Dec 21, 2007 3:52 PM, Jayaprakash, Vijay > > <Vijay.Jayaprakash@roswellpark.org> wrote: > > Hi Stata users, > > I'm trying to do a meta analysis of data from 10 different studies. The smoking variable in each study is stratified into categories (Category 1:1-10 cigarettes, Category 2: 10-20 cigs, Category3: 20-30 cigs etc.). I calculated the OR for each category (of smoking) by study. I then did a meta analysis and calculated the OR for each category by random effects model using the meta command: > > meta or1 lcl1 ucl1, ci eform * * 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: Trend test in meta analysis***From:*"Jayaprakash, Vijay" <Vijay.Jayaprakash@roswellpark.org>

**Re: st: Trend test in meta analysis***From:*"Tom Trikalinos" <ttrikalin@gmail.com>

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