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Continue with the question in the previous article (Multiple Imputaton - Linear Regression in R), where we just discussed how to compute the pooled coefficients of ANCOVA using mice package but left out the Ls-means and hypothesis test. Luckly I find out that emmeans package have wrapped this process inside so we can use it to obtain the pooled Ls-means estimation and p-value straightforward wihout pool function of mice.

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在临床试验中,通常使用SAS来完成统计分析和生成图表,但我们不应该只局限于一种编程方法,而且这个所用的编程语言SAS并不是开源的。毫无疑问SAS能完成的事情,R和Python同样能做;但有些R和Python能做的,SAS却很难完成,我想这就是开源和不开源的区别。

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Time-to-event endpoints are widely used in oncology trials, such as OS and PFS. And survival analysis is a common method for estimating time-to-event endpoints. In this blog, I’d like to make a note of how to summarize the essential results for survival analysis in oncology trials in R and also compare them with SAS.

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The Best Overall Response (BOR) is a very common evaluation of efficacy in oncology trials. Usually, it is defined as the best response among all time-point responses from the treatment start until the first disease progression, in the order of CR, PR, SD, PD, and NE per RECIST 1.1. For non-randomized trials, BOR is not only the best among all responses but also requires confirmation for CR and PR to ensure the result is not a measurement error. More details can be found in the RECIST 1.1 document, which I will not expand on here.

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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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