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As we know, the objective response rate (ORR) is used as a key endpoint to demonstrate the efficacy of a treatment in oncology and is also valuable for clinical decision making in phase I-II trials, especially in single-arm trials.

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趁着最近阿里云双11的优惠活动,我计划更换下博客所在的ECS服务器(其实为了响应消费降级~),咨询了下售前和售后,最终顺利完成迁移,记录一下迁移过程以备后续所需。

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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).

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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.

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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.

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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.

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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).

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OpenAI于3月1日发布了ChatGPT API,但其只提供了Python中如何调用此API的文档说明。尽管没提到如何使用R来调用,但是毫无疑问R肯定是可以的,所以我用google搜了下。以下是基于网上资料而整理的简短介绍,如何在R中用ChatGPT。

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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.

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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.

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