Dear Colleagues, I am running a logistic model where the dependent variable is the logit of the probability of a patient being diagnosed with COVID-19. The dataset comprises 4,908 patients, of whom 1,729 tested positive for COVID-19. The dataset includes demographic and clinical information about the patients and information on the predominant variant (original, Gamma, Delta, and Omicron) at the time these patients sought care. In the final model, we identified three statistically significant interactions between the symptoms (runny nose, sore throat, and loss of smell) and the variant type. I would like to know how to calculate these interactions' power. Thank you in advance. Best regards, Iuri ... View more

Dear all, I am attempting to obtain the combined parameters from 25 logistic regressions using imputed datasets. At this stage, I am not concerned about the imputation method. Initially, I followed an example from the SAS manual, which worked fine. However, when applying the same approach to my dataset, I successfully obtained the parameter estimates and standard errors, but the other statistics appear as missing values. Could you please help me identify what might be wrong with my code? Thank you in advance. Best regards,   CODES:     PROC LOGISTIC descending DATA=TOT1; CLASS PAIS (REF='BR')/ param=glm;; MODEL IVA=PAIS; BY _IMPUTATION_; ODS OUTPUT PARAMETERESTIMATES=lgsparms; RUN;   PROC MIANALYZE PARMS(classvar=classval)=lgparms; class pais; MODELEFFECTS intercept pais;   RESULTS:   The MIANALYZE Procedure   Model InformationPARMS Data SetNumber of Imputations WORK.LGPARMS 25 Variance Information (25 Imputations)Parameter pais Variance DF RelativeIncreasein Variance FractionMissingInformation RelativeEfficiencyBetween Within Totalintercept  pais MEpais PE 0 0.023737 0.023737 . 0 . . 0 0.049479 0.049479 . 0 . . 0 0.049514 0.049514 . 0 . . Parameter Estimates (25 Imputations)Parameter pais Estimate Std Error 95% Confidence Limits DF Minimum Maximum Theta0 t for H0:Parameter=Theta0 Pr > |t|intercept  pais MEpais PE 5.382674 0.154068 . . . 5.382674 5.382674 0 . . 0.150375 0.222438 . . . 0.150375 0.150375 0 . . -0.210373 0.222518 . . . -0.210373 -0.210373 0 . RUN; ... View more

Dear All, We are analyzing data from a cohort of participants with a high risk of HIV infection. At this moment, our focus is on sexually transmitted disease infection. Given that patients in our cohort may have more than one sexually transmitted disease, we are considering using a frailty model. We are currently thinking of using the COX Regression model, but I would like to check if the data structure I am creating is correct.   The structure of our database would incorporate time-dependent variables. Here we have the data for three participants. The observation of the participant 1 (ID=1) is censored at the first visit, then he experiences the event at the second visit, and does not return. The second participant experiences several events while the third one is censored at all visits. ID  TSTART TSTOP STATUS  1         0           91         0 1        91        187        1 2         0          115       0 2         115       210      1 2         210       321      0 2        321        430      1 2        430        542      1 3        0            122      0 3        122       312       0 3       312       431        0   The other issue I wanted to address is how to decide on the distribution of the random parameter associated with frailty. Is there a specific test for this? Thank you.. Best regards, Iuri leite   ... View more

Dear Colleagues, I am inquiring to know if it is possible to execute a survival random forest analysis using SAS. I have been examining the PROC HPFOREST procedure, but I have not encountered any features related to survival analysis within this context. I have come across a paper wherein survival was considered using a discrete-time logistic regression model. Thank you in advance. Best regards, Iuri Leite ... View more

  Dear all, I am analyzing a dataset from a cohort of participants in which we observe if they develop specific infection at each quarterly visit. In this dataset a significant number of participants developed the disease more than once. Initially, we used a hazard model considering the time until the first occurrence of the infection. However, we are now interested in identifying factors associated with more than one occurrence of the disease. Specifically, in the case of two infections, the individual is identified with an infection at a specific visit, receives treatment and then in another visit, the disease is identified again. Since time is an important element, is it possible to conduct a survival analysis in this case? Is it possible to conduct a survival considering an ordinal response?  Thank you in advance. Best regards, Iuri Leite ... View more