topic a binomial model with dummy variabes in New SAS User

a binomial model with dummy variabes

farzad14000fr — Fri, 21 Jun 2019 13:28:01 GMT

Hello to every one,
I do my master thesis about " what is the long term effect of month of the Birth in a child's life ?
and I would like to explain it with the sas table which is in attach file ? I dont know how can I progamme it in Sas . because I would like to have 11 month and my variables are often dummies .
and I would like to know which modele use reg or logistic to show the probality of death among the children with using the variables wich i Have
I use this code
proc logistic data=user.afbr70fl;
> class b1 ;
> model b5(child is alive) =b1(month bineaire) /link=glogit ;
> run;
and i would like to try insere the other varibles ou is it better to use a proc reg ?
the variables are

chid is alive 1 or 2

the month of Birth 1 to 12 , Current age of child 1 to 20 , Highest year of education of mom 1 to 7 , Wealth index combined 1 to 5 ,

and another variables

Re: a binomial model with dummy variabes

alexgouv — Fri, 21 Jun 2019 14:09:34 GMT

Is this code not working or are you just asking which proc to use? You should use proc logistic over proc reg since your response variable is categorical.

@farzad14000fr

Re: a binomial model with dummy variabes

ballardw — Fri, 21 Jun 2019 18:10:51 GMT

SAS provides several procedures for survival analysis (flip side of death)

Procs LIFEREG, LIFETEST, ICLIFETEST, ICPHREG, QUANTLIFE, PHREG and SURVEYPHREG.

Which to use depends on data and assumptions involved.

ICLIFETEST

computes nonparametric estimates of survivor functions for interval-censored data. You can use this procedure to compare the underlying survival distributions of two or more samples of interval-censored data.

ICPHREG

fits proportional hazards regression models to interval-censored data. You can select a piecewise constant function as the baseline hazard function, or you can model the cumulative baseline hazard function by cubic splines.

LIFEREG

fits parametric models to failure time data that can be left-censored, right-censored, or interval-censored. The log of the survival time is modeled as a linear effect of covariates and a random disturbance term, the distribution of which includes the Weibull, log-normal, and log-logistic distributions.

LIFETEST

computes the Kaplan-Meier estimate of a survivor function and provides the log-rank test to compare the underlying hazards of two or more samples of right-censored data. You can also use this procedure to study the association between the failure time and a number of concomitant variables.

PHREG

fits the Cox proportional hazards model and its extensions, which include the multiplicative intensity model, the shared frailty model, and the Fine-Gray model for competing-risks data.

QUANTLIFE

performs quantile regression for survival data by modeling the quantiles of the lifetime variable as a function of the covariates. Because lifetime distributions are usually more skewed, the quantiles of the lifetime are more informative than the mean for summarizing the lifetime distribution.

SURVEYPHREG

is a Cox modeling procedure similar to PROC PHREG, appropriate for analyzing data that are collected from a survey sample.

The SEVERITY procedure in SAS/ETS software is also a survival analysis procedure.

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