Mixed models for repeated measures (MMRM) is widely used for analyzing longitdinal continuous outcomes in randomized clinical trials. Repeated measures refer to multiple measures taken from the same experimental unit, such as a couple of tests over time on the same subject. And the advantage of this model is that it can avoid model misspcification and provide unbiased estimation for data that is missing completely at random (MCAR) or missing at random (MAR).
I'm tickled pink to announce the release of
mcradds (version 1.0.1) helps with designing, analyzing and visualization in In Vitro Diagnostic trials.
Recently, I've been developing my R package - mcradds, which will be my first package released to CRAN. To be honest, finishing coding is just the first step for R package development, whereas I feel like the submission to CRAN is the most challenging for me. This blog is to keep track of something I came across during the submission process to help giving me a reminder when I would develop other packages in next steps. If you are a beginner like me, this blog will be beneficial to you as well.
In the previous article (Understanding Multiple Imputation in SAS), we talked about how to implement multiple imputation in the SAS procedure to compare the difference between the treatment and placebo groups. Let's look at how to do it in non-inferiority and superiority trials, which differ from common use.
There are plenty of methods that could be applied to the missing data, depending on the goal of the clinical trial. The most common and recommended is multiple imputation (MI), and other methods such as last observation carried forward (LOCF), observed case (OC) and mixed model for repeated measurement (MMRM) are also available for sensitivity analysis.
This is a brief note about confidence interval of Hazard Ratio.
网上看到一个学习资料，推荐下。参考资料：Chapter 7 Simulation-based Inference 来自于Book STAT160 R/RStudio Companion / 2021。
- One-Proportion Inference
- One-Mean Inference
- Two-proportion inference
- Two-mean inference
Missing data is inevitable for several reasons during the clinical trials. As we know, missing data can be classified into one of three categories, like MCAR(Missing Completely At Random), MAR(Missing At Random) and MNAR(Missing Not At Random).
As indicated in the title, this article will discuss how to solve this problem in
Here is just a trick note to demonstrate how to split the column when you use the
mutate function from the
dplyr package in R.
Survival analysis is often used in tumor clinical trials, and there are usually two estimations that appear in the report: the median survival time and the median follow-up time.
Partial dates are very common in clinical trials, such as AE that allow some parts of the date or time to be missing. However, when you create the ADaM dataset for AE, some variables like ASTDT (Analysis Start Date) or AENDT (Analysis End Date) are numeric, so they can be derived only when the date is complete and then you can calculate the durations.
I'm a R-lover and believe that anything SAS can do, R can do better. As R is such a powerful language for statistical analysis in clinical trials. Once, I posted an article that said how to insert blank rows, so I looked up how to do that in R.
This casual note is to record how to use R to replace the NA with 0 or any string. Generally, NA can be generated for different reasons, like unclean data, data transformation, or missing values. Otherwise, we have to convert NA to zero or other stings in order to present them in tables or listings.
The box plot is used to demonstrate the data distribution in common and to look for outliers. We can also see where the 25% and 75% quarters are, as well as the median value from the box. As a result, it's a very helpful visual chart.
Recently, I'm a little confused how to create or save PNG graphs in SAS. Normally, we would have been to create RTF or PDF instead but there was sometimes a specific requestment to save as PNG directly. So we need to know how to complete it in SAS when I have a graph generated by SGPLOT or GTL procedure.
This article aims to learn the basic calculation process of least-squares means (LS means).