12-19-2024
iuri_leite
Fluorite | Level 6
Member since
03-10-2018
- 47 Posts
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Latest posts by iuri_leite
Subject Views Posted 851 10-02-2024 08:14 AM 950 09-30-2024 01:07 PM 1355 08-17-2024 05:47 PM 1420 08-17-2024 03:34 PM 1449 08-17-2024 02:12 PM 661 11-06-2023 03:29 PM 2197 10-02-2023 12:08 PM 2217 10-02-2023 08:10 AM 2314 09-28-2023 05:57 PM 881 09-13-2023 10:45 AM -
Activity Feed for iuri_leite
- Posted Re: Power interaction on Statistical Procedures. 10-02-2024 08:14 AM
- Posted Power interaction on Statistical Procedures. 09-30-2024 01:07 PM
- Posted Re: Imputation on Statistical Procedures. 08-17-2024 05:47 PM
- Posted Re: Imputation on Statistical Procedures. 08-17-2024 03:34 PM
- Posted Imputation on Statistical Procedures. 08-17-2024 02:12 PM
- Posted Fraity Model with PhReg on Statistical Procedures. 11-06-2023 03:29 PM
- Posted Re: Survival Random Forest on Statistical Procedures. 10-02-2023 12:08 PM
- Posted Re: Survival Random Forest on Statistical Procedures. 10-02-2023 08:10 AM
- Posted Survival Random Forest on Statistical Procedures. 09-28-2023 05:57 PM
- Posted Re: Comparing two ROC curves on Statistical Procedures. 09-13-2023 10:45 AM
- Posted Comparing two ROC curves on Statistical Procedures. 09-12-2023 03:54 PM
- Posted Test for median difference on Statistical Procedures. 08-22-2023 06:43 PM
- Posted Re: longitudinal data on Statistical Procedures. 06-08-2023 08:19 AM
- Posted longitudinal data on Statistical Procedures. 06-03-2023 04:58 PM
- Posted Re: Wald test Logistic regression on Statistical Procedures. 04-17-2023 09:45 AM
- Posted Wald test Logistic regression on Statistical Procedures. 04-13-2023 08:58 AM
- Posted Re: Exact GEE logistic regression on Statistical Procedures. 10-31-2022 07:35 PM
- Tagged Re: Exact GEE logistic regression on Statistical Procedures. 10-31-2022 07:35 PM
- Posted Exact GEE logistic regression on Statistical Procedures. 10-25-2022 07:30 AM
- Posted Exponential Family on Statistical Procedures. 10-17-2022 08:04 AM
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Posts I Liked
Subject Likes Author Latest Post 1
10-02-2024
08:14 AM
Dear Dave, I submitted a paper, and one of the reviewers raised a concern about the lack of a reported power calculation for the sample size: “No formal power calculation/sample size is reported. Given the number of predictors and interactions, it would be helpful to know if the study was adequately powered.” I would like to discuss the best approach to calculate the power. In fact, we did not select a sample; we used data from all patients who sought assistance at a Primary Care Unit. Therefore, I would like to find the best way to solve this problem. Regard
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09-30-2024
01:07 PM
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
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08-17-2024
05:47 PM
Dear Page, I believe I figured out what is happening. I will post here the results for a variable that has no missing value (DST): Parameter Estimates (25 Imputations) Parameter Estimate Std Error 95% Confidence Limits DF Minimum Maximum Theta0 t for H0: Parameter=Theta0 Pr > |t| intercept -5.780452 0.219929 -6.21156 -5.34935 10234 -5.868025 -5.683721 0 -26.28 <.0001 dst 1.030766 0.322012 0.39934 1.66219 2637.5 0.823595 1.204587 0 3.20 0.0014 As you can see, we have the estimate and its respective standard error, its 95% confidence limits, and the p-values. For the variable pais (country) we had only the values for the parameters and their respective confidence interval. But the country is a variable without any missing. Thanks a lot. Regards
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08-17-2024
03:34 PM
Dear Paige, yes. When I posted the results the platform changed the tables. The variable pais has three categories and one is the reference. So, I have the results for two categories. However in the results, I have only the parameter estimates and their respective standard errors. The p-value is missing and all the other statistics are missing. Best regards
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08-17-2024
02:12 PM
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;
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11-06-2023
03:29 PM
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
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10-02-2023
12:08 PM
Dear @sbxkoenk, thanks for sending me an email. I used the discrete-time hazards model few times (with logistic and log-log link), but I am not interested in using it. I am interested in using the random forest survival model for variable selection. In the paper I posted, It seems to me that it was used a survival forest for a data structured for the implementation of a discrete-time logistic regression. I will read the papers again. Thanks a lot. Regards, Iuri Leite
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10-02-2023
08:10 AM
Dear @ballardw , here is the link of the application of random survival forest using the discrete-time hazard model. Claim Risk Scoring Using Survival Analysis Framework and Machine Learning with Random Forest (sas.com) There is also this paper with an application using R. https://www.sciencedirect.com/science/article/pii/S2589537021003138 Best regards, Iuri Leite
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09-28-2023
05:57 PM
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
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09-12-2023
03:54 PM
Dear Colleagues, I have two measures, MPR and Relato30, that I wish to compare against a gold standard: TFV_CAT. Given that I'm working with longitudinal data, I am employing PROC GEE for a dichotomous response. Below is the code I am using: proc gee data=dbs_jovens; class copid; model tfv_cat(event="1")=mpr/ dist=bin; repeated subject=copid; output out=model_mpr p=p_mpr; run; proc logistic data=model_mpr; model tfv_cat(event="1")= / nofit outroc=roc_mpr; roc pred=p_mpr; run; proc gee data=dbs_jovens; class copid; model tfv_cat(event="1")=relato30/ dist=bin; repeated subject=copid; output out=model_relato30 p=p_relato30; run; proc logistic data=model_relato30; model tfv_cat(event="1")= / nofit outroc=roc_relato30; roc pred=p_relato30; run; I would like to test the differences between the curves. Do I need to create a unique dataset that includes the ROC curves from both models (roc_mpr and roc_relato30)? Thank you in advance. Regards, Iuri
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08-22-2023
06:43 PM
Dear All, I am running a test for median differences among three countries: ODS GRAPHICS ON; PROC NPAR1WAY WILCOXON MEDIAN; CLASS Country; VAR time; RUN; In the results I do not get the results for Median two-way analysis, but for Median one-way analysis. Median One-Way AnalysisChi-Square DF Pr > ChiSq Could anyone tel me how a can obtain the Median Two-way results? Best regards; Iuri
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06-03-2023
04:58 PM
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
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04-17-2023
09:45 AM
Dear @StatDave, thanks a lot. It worked just fine. Best regards, Iuri
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