Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down on April 23, and its replacement, **statalist.org** is already up and running.

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

From |
Joseph Hilbe <j.m.hilbe@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: RE: risk ratio |

Date |
Sat, 20 Mar 2010 13:01:47 -0700 |

I have an article coming out in the next Stata Journal that details how to create synthetic models for a wide variety of discrete response regression models. For your problem though, I think that the best approach is to create a synthetic binary logistic model with a single predictor - as you specified. Then model the otherwise logistic data as Poisson with a robust variance estimator. And the coefficient must be exponentiated. It can be interpreted as a relative risk ratio. Below is code to create a simple binary logistic model. Then model as mentioned above. You asked for a continuous pseudo-random variate, so I generated it from a normal distribution. I normally like to use pseudo-random uniform variates rather normal variates when creating these types of models, but it usually makes little difference. Recall that without a seed the model results will differ each time run. If you want the same results, pick a seed. I used my birthday. I hope that this is what you were looking for. Joseph Hilbe clear set obs 50000 set seed 1230 gen x1 = invnorm(runiform()) gen xb = 2 + 0.75*x1 gen exb = 1/(1+exp(-xb)) gen by = rbinomial(1, exb) glm by x1, nolog fam(bin 1) glm by x1, nolog fam(poi) eform robust . glm by x1, nolog fam(bin 1) Generalized linear models No. of obs = 50000 Optimization : ML Residual df = 49998 Scale parameter = 1 Deviance = 37672.75548 (1/df) Deviance = .7534852 Pearson = 49970.46961 (1/df) Pearson = .9994494 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = ln(u/(1-u)) [Logit] AIC = .7535351 Log likelihood = -18836.37774 BIC = -503294.5 ------------------------------------------------------------------------------ | OIM by | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .7534291 .0143134 52.64 0.000 .7253754 .7814828 _cons | 1.993125 .0149177 133.61 0.000 1.963887 2.022363 ------------------------------------------------------------------------------ . glm by x1, nolog fam(poi) eform robust Generalized linear models No. of obs = 50000 Optimization : ML Residual df = 49998 Scale parameter = 1 Deviance = 12673.60491 (1/df) Deviance = .2534822 Pearson = 7059.65518 (1/df) Pearson = .1411988 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] AIC = 1.970592 Log pseudolikelihood = -49262.80246 BIC = -528293.7 ------------------------------------------------------------------------------ | Robust by | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | 1.104476 .0021613 50.78 0.000 1.100248 1.10872 ------------------------------------------------------------------------------ . Tomas Lind wrote: Does anyone know how to generate fake data for a dichotomous outcome (0, 1) that is dependent on a continuous exposure variable in an epidemiological relative risk context. I know how to use the logit transformation but in that case exposure is proportional to log(ods) and not to risk. * * 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**:**st: RE: RE: risk ratio***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

- Prev by Date:
**Re: st: RE: [Mata] naming matrices in a loop** - Next by Date:
**st: ssm with hierarchical data** - Previous by thread:
**st: How do I impose the constraint that rho is a given number (eg 0.2) using biprobit?** - Next by thread:
**st: RE: RE: risk ratio** - Index(es):