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From |
Justin Schaffer <justin.schaffer@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Cox regression using a shared frailty model in multiply imputed data |

Date |
Tue, 11 Feb 2014 05:53:18 -0800 |

Hello, first time poster here (please excuse my ignorance). Stata (using Stata 13) will allow me to create a Cox regression model with shared frailty on a multiply imputed dataset. However, it does not give me an estimate of theta after running the command as it does when I run the regression model with shared frailty on non-imputed data. Can someone explain why this is, and whether I am violating some obscure law of statistics when I create a Cox regression model with shared frailty on my imputed dataset? I assume that estimating the standard error of theta is not statistically valid on an imputed dataset which is why the theta value is not shown, but to be honest, I am out of my league here. One option I'm considering is running the regression on each of my imputed data sets after creating a separate file for each imputed dataset (using mi set flongsep) and then averaging the theta values and estimating the standard error using Rubin's rules (although I have no clue if this is statistically valid). Notably, the variable that I regress on as well as the "random" variable of my shared frailty model need not be imputed (i.e. in the example below I run a Cox regression with shared frailty on the variable "age" with the variable "hosp_id" as my random effect variable, although neither "age" nor "hosp_id" were actually imputed in my dataset). Example: stset daysAlive, fail(dead) sts generate nelsonAalen = na mi set wide model01 mi register regular age hosp_id nelsonAalen dead mi register impute imputed_variable mi impute chained (pmm) imputed_variable = age hosp_id dead nelsonAalen, add(20) augment chaindots burnin(10) rseed(1234) mi stset daysAlive, fail(dead==1) //Cox model with shared frailty on imputed dataset mi estimate, hr dots: stcox age, shared(hosp_id) > Imputations (20): > .........10.........20 done > > Multiple-imputation estimates Imputations = 20 > Cox regression: Breslow method for ties Number of obs = 3174 > Average RVI = 0.0000 > Largest FMI = 0.0000 > DF adjustment: Large sample DF: min = . > avg = . > max = . > Model F test: Equal FMI F( 1, .) = 31.39 > Within VCE type: OIM Prob > F = 0.0000 > > ------------------------------ ------------------------------------------------ > _t | Haz. Ratio Std. Err. t P>|t| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > age | 1.030194 .0054693 5.60 0.000 1.01953 1.040969 > ------------------------------------------------------------------------------ //Cox model with shared frailty on non-imputed dataset stcox age, shared(hosp_id) > failure _d: dead == 1 > analysis time _t: daysAlive > > Fitting comparison Cox model: > > Estimating frailty variance: > > Iteration 0: log profile likelihood = -8334.2065 > Iteration 1: log profile likelihood = -8334.2065 (backed up) > Iteration 2: log profile likelihood = -8333.3181 > Iteration 3: log profile likelihood = -8333.2928 > Iteration 4: log profile likelihood = -8333.2927 > > Fitting final Cox model: > > Iteration 0: log likelihood = -8369.2282 > Iteration 1: log likelihood = -8333.5078 > Iteration 2: log likelihood = -8333.2927 > Iteration 3: log likelihood = -8333.2927 > Refining estimates: > Iteration 0: log likelihood = -8333.2927 > > Cox regression -- > Breslow method for ties Number of obs = 3174 > Gamma shared frailty Number of groups= 60 > Group variable: hosp_id > > No. of subjects = 3174 Obs per group: min= 1 > No. of failures = 1138 avg = 52.9 > Time at risk = 2951413 max= 283 > > Wald chi2(1) =31.39 > Log likelihood = -8333.2927 Prob > chi2 =0.0000 > > ------------------------------------------------------------------------------ > _t | Haz. Ratio Std. Err. z P>|z| [95% Conf.Interval] > -------------+---------------------------------------------------------------- > age | 1.030194 .0054693 5.60 0.000 1.01953 1.040969 > -------------+---------------------------------------------------------------- > theta | .0391666 .0190069 > ------------------------------------------------------------------------------ > Likelihood-ratio test of theta=0: chibar2(01) = 11.50 Prob>=chibar2 =0.000 > > Note: standard errors of hazard ratios are conditional on theta. Many thanks in advance for any statistical advice you gurus have to offer. Sincerely, JMS * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Cox regression using a shared frailty model in multiply imputed data***From:*Stas Kolenikov <skolenik@gmail.com>

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