**To My Precious Abstract Algebra Days**

1000 Thanks to Dr. David Hemmer!

My favorite professor all time!

**To My Precious Abstract Algebra Days**

1000 Thanks to Dr. David Hemmer!

My favorite professor all time!

Book 2 NJ, April 2021 很多年前展览管开书市的时候我买了这本书，粉色封面的。但是我翻了几页就没有读过了。 今年读完，这哪里是儿童读物？这明明就是人生箴言啊。日本的这些儿童读物真的是哀伤，明亮又温暖。 书里有要我们好好吃饭的山的味道，海的味道。有做事的习惯，用完就放回去。要锻炼身体的韵律操。有学海无涯的多多读书。有友情，帮助明爬树。有朦胧的crush,明天还要给阿泰修铅笔. 有对梦想承诺的一言为定。有对自己热爱的事情的不妥协，我不想用我的提琴奏军曲。有关键对话的，可不可麻烦你不要带蝴蝶结来上学，因为美代会要。有洛基和明的离别，那是求而不得没有办法的事。有懂得欣赏别人，也回来爱自己的天鹅舞虽然好，但也可以试着喜欢自己创造的舞蹈！还有小林校长包涵了一颗原子弹的温柔，小豆豆，你真的是个好孩子！

Background story: Last time, when we used R to calculate the sample size, we specified Type I error α and Type II error β, but what does the meaning behind α and β? Review: We define the “best” sample size that has less variation of the sample mean from sample to sample.

Background Story: One day, my boss asked me to check if the data has a certain number of events to perform an efficacy analysis. I was curious how did he come up with the number, later I know he must have done the Sample Size Calculation. Today we will go over the basics and R applications for sample size calculation.

Factorial Design allows the investigation of sets of categorical predictors, and the interaction between them. Today we will go over some basics of One Fixed Factor and One Random Factor Design. Keywords: Factor: categorical predictors Fixed Factors: Estimate the difference in means between groups defined by specified categorical predictors. e.x. ANOVA model, one measurement per subject. Random Factors: Estimate the variance…

Background Movie: Based on my taste, 我不是药神 Drug Deal/Dying to Survive is one of the best movies in 2018. It was based on a true story that some cancer patients use a kind of cheap Indian medicine (Veenat 100) as a substitution of the authentic Novartis Glivec 400 to prolong their lives. Besides the touching story, Veenat 100 is a…

Background Story: Once, we need to do an Interim Analysis, I didn’t understand why we need to do it. Later I learned in Clinical Trial studies, our ultimate goal is trying to get approval for FDA submission at the end of studies. While exhausting and long progress, sometimes we perform Interim Analysis before the completed trial to access Safety, Futility,…

Background Stroy: Last time we emphasized the importance of Randomization because it will provide a balanced measurement for treated and placebo groups, so the treatment is exchangeable. Today we will introduce 3 common randomization methods for different clinical trial purposes and the R code for implementing them: Simple Randomization, Block Randomization, and Stratified Randomization.

Data Science Day 26 When I was cleaning my home, I found a brand new book of Fundamentals of Machine Learning for Predictive Data Analytics. Therefore I decided to read the book and share some exercise problems.

Background Story: We often use different populations (Safety, Efficacy, ITT, Per-Protocol) to generate Clinical Trial Reports, but what are the differences between the populations? Today we will go over the basics of Intention to Treat and Per-protocol Analysis.

Background Story: Once my boss asked me to review an Open CDISC report for SDTM dataset packages. I wasn’t sure what to do at first. With the help of my colleagues, I gradually develop a sense of how to review the CDISC report. I’d like to share some review processes and common Open CDISC Summary from PINNACLE21.