# KeepNotes blog

Stay hungry, Stay Foolish.

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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.

#### Introduction

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.

• 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 `ggplot2`.

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).

This article is to illustate how to conduct an (Analysis of Covariance) ANCOVA model to determine whether or not the change from baseline glucose is affected by treatment significantly. In other words, using ANCOVA to compare the adjusted means of two or more independent groups while acounting for one or more covariates.

As a new SAS user, a common question that is always asked and searched on google is: how can I get the statistic into a table?

This post is just a note referred from one article as shown below that I think would be beneficial for anyone who is as new as I am, as this requirement is fairly common in pharamaceutical programming.