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From |
tiago.pereira@incor.usp.br |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: How to reproduce a very specific text book example of logistic regression using Stata |

Date |
Wed, 7 Nov 2007 18:17:57 -0200 (BRT) |

Dear all, The following question is not entirely related to Stata, but I will be really grateful if you could suggest any tip to answer it using Stata. I am trying to figure out which analysis was done in a text book of application of logistic regression in genetic studies. The aim is to know which model was applied to be able to learn it. According to the book, the model was fit by logistic regression using the GLIM program. The GOF of each model was assessed by scaled deviance and AIC. I could only reproduce succesfully only the final result, since it is a simple logistic regression model: . xi: logistic status i.exposure [freq=count] It reproduces exactly what is presented in the book: Logistic regression Number of obs = 2112 LR chi2(2) = 6.77 Prob > chi2 = 0.0338 Log likelihood = -1460.1621 Pseudo R2 = 0.0023 ---------------------------------------------------------------------- status | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+-------------------------------------------------------- _Iexposure_1 | 1.099477 .1666565 0.63 0.532 .8168884 1.4798 _Iexposure_2 | 1.347291 .2037921 1.97 0.049 1.001632 1.8122 ---------------------------------------------------------------------- The objective of such analysis is to know which coding (genetic model) fits better the data (coding cases_1\constrols_1 cases_2\constrols_2 and cases_3\constrols_3 as 1, 0.5, and 0 or 1,0, and 1). According to the book, the best model will be that that uses the coding 1, 0.5, and 0 (or 2,1 and 0), which produces the following results: Scaled deviance df AIC Constant 52.209 32 -11.791 Cons+coding 1,0.5,0 45.731 31 -16.269 The data is shown below. Thank you for any help. All the best, Tiago *----------- begin data ------------- input cases_1 cases_2 cases_3 controls_1 controls_2 controls_3 strata 74 44 23 51 75 13 1 26 23 4 23 30 8 2 59 42 10 49 42 9 3 34 36 6 27 22 4 4 37 41 7 28 35 15 5 35 33 8 37 40 9 6 33 23 10 38 46 13 7 48 35 8 48 35 7 8 54 45 8 50 40 8 9 62 66 9 59 57 9 10 57 69 7 33 56 20 11 end rename cases_1 a0 rename cases_2 b0 rename cases_3 c0 rename controls_1 a1 rename controls_2 b1 rename controls_3 c1 reshape long a b c, i(strata) j(status) gen id = _n rename a count2 rename b count1 rename c count0 reshape long count, i(id) j(exposure) replace status= cond(status==1,0,1) *----------- end data -------------- -- Tiago V. Pereira Heart Institute - InCor Laboratory of Genetics and Molecular Cardiology, Department of Biochemistry and Molecular Biology, Federal University of São Paulo. Av. Dr. Eneas de Carvalho Aguiar, 44; Cerqueira César - CEP 05403-000 Sao Paulo, SP Brazil. Tel./fax: +55 11 3069 5068 email: tiago.pereira@incor.usp.br * * 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/

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