What is the rationale behind the necessity for this particular scenario? On occasion, I am required to generate dummy variables when conducting PROC NLMIXED operations. This post offers an effective solution to this issue. For reference, the original post is reproduced below.
Implement the delta method to report the means and confidence intervals of difference scores from log-transformed longitudinal data, so that results can be reported on the original scale of the outcome.
These measures are the Fraction of Missing information (FMI), the relative increase in variance due to nonresponse (RIV), and the Relative Efficiency (RE). They are derived from values of the between, and within imputation variance and the total variance. There exist two versions of the FMI, which are referred to as lambda and FMI.
We usually report these measures of Missing data information in TLGs of sensitivity analyses.
This paper recommends using the UN structure with a new approach to analyze longitudinal data from randomized clinical trials. The approach proposed in this paper can assist in the convergence when the UN structure is used.
Exact statistics can be useful in situations where the asymptotic assumptions are not met, and so the asymptotic p-values are not close approximations for the true p-values.
Study ID: D4191C00004 [ClinicalTrials.gov Identifier: NCT02352948; 一项评估 MEDI4736 (Durvalumab) 作为单一疗法或与 Tremelimumab 联合治疗的影响的全球研究，该研究通过 PD-L1 表达与护理标准对比确定，用于治疗局部晚期或转移性非小细胞肺癌 (ARCTIC) 患者]
Sub-study B has 1 treatment comparison of interest that is considered primary as follows:
Yining Ye, et al. (2013) proposed a group sequential Holm procedure when there are multiple primary endpoints. This method addresses multiplicities arising from multiple primary endpoints and from multiple analyses in a group sequential design.
There are three ways to find a solution for linear model parameters when the design matrix is of less than full rank. 这篇是Linear Model in SAS (2)中Estimable Functions的先决知识点.本文为辅助材料，整理自JH的讲义。
Let's have a look at some algebra of linear model in SAS. We'll skip the simple knowledge points commonly found in textbooks in this post.
PROC REG can compute two types of sums of squares associated with the estimated coefficients in the model. These are referred to as Type I and Type II sums of squares and are computed by specifying SS1 or SS2, or both, as MODEL statement options.