The data in this study fell into three types: 1) continuous measures where only one observation was made on the experimental unit; 2) continuous measures where there were repeated observations, usually over time, where each experimental unit had multiple observations under different conditions; and, 3) time to survival after tumor transplant. The data in type 1 were analyzed using the Analysis of Variance (ANOVA) and Multiple Linear and Non-Linear Regression. Sub-group comparisons were made after the overall analyses using the Student t-test in single degree of freedom contrasts. Analyses were performed using SAS PROC GLM and SAS PROC REG statistical programs. The data from type 2 observations were analyzed using Mixed Model ANOVAs suitable for repeated measures. Single degree of freedom contrasts were performed between and within conditions, at specific times, and across time using the methods proposed by (46 (link)). The survival data were analyzed using Kaplan-Meir stratified analyses in SAS PROC LIFETEST and the Cox Proportional Hazards model in SAS PROC PHREG. Statistical significance was set at p = 0.05 with adjustments for multiple comparisons when appropriate.
Sas proc lifetest
Manufactured by SAS Institute
SAS PROC LIFETEST is a statistical procedure within the SAS software suite that provides nonparametric analysis of time-to-event data, commonly referred to as survival analysis. It calculates estimates of survival functions, compares survival curves across groups, and tests for differences in survival distributions.
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Lab products found in correlation
3 protocols using sas proc lifetest
1
Multivariate Analysis of Experimental Data
2
Retention and Mental Health Trajectories in Perinatal Care
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We provide descriptive statistics of the participants presenting for MC3 Perinatal Care arm services, including their presenting concerns, and levels of mental health symptoms from the time of recruitment into services. Survival analysis, using Cox regression (SAS PROC PHREG), was used to examine retention in services; we report hazard ratios for this analysis. We examined Black race as a predictor of retention; we did not have a large enough sample of participants identifying as other races or Latino/a to include as predictors. SAS PROC LIFETEST was used to plot the survival trajectories. We used logistic regression to examine demographic predictors of enrolling for those who were referred.
To examine the change in mental health symptoms over time, we first examined selective attrition by using logistic regression to predict missingness. For each time point, we tested anxiety and depression symptoms at the prior time point as predictors, using logistic regression. Next, trajectories of mental health symptoms were modeled using latent growth modeling in Mplus, using full information maximum likelihood to handle missing data. Data analyses were performed using SAS 9.4 and Mplus v. 8.8
To examine the change in mental health symptoms over time, we first examined selective attrition by using logistic regression to predict missingness. For each time point, we tested anxiety and depression symptoms at the prior time point as predictors, using logistic regression. Next, trajectories of mental health symptoms were modeled using latent growth modeling in Mplus, using full information maximum likelihood to handle missing data. Data analyses were performed using SAS 9.4 and Mplus v. 8.8
3
Multivariate Analysis of Experimental Data
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The data in this study fell into three types: 1) continuous measures where only one observation was made on the experimental unit; 2) continuous measures where there were repeated observations, usually over time, where each experimental unit had multiple observations under different conditions; and, 3) time to survival after tumor transplant. The data in type 1 were analyzed using the Analysis of Variance (ANOVA) and Multiple Linear and Non-Linear Regression. Sub-group comparisons were made after the overall analyses using the Student t-test in single degree of freedom contrasts. Analyses were performed using SAS PROC GLM and SAS PROC REG statistical programs. The data from type 2 observations were analyzed using Mixed Model ANOVAs suitable for repeated measures. Single degree of freedom contrasts were performed between and within conditions, at specific times, and across time using the methods proposed by (46 (link)). The survival data were analyzed using Kaplan-Meir stratified analyses in SAS PROC LIFETEST and the Cox Proportional Hazards model in SAS PROC PHREG. Statistical significance was set at p = 0.05 with adjustments for multiple comparisons when appropriate.
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