# KeepNotes blog

Stay hungry, Stay Foolish.

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When I learned the Miettinen and Nurminen Test algorithm from the `rate-compare` article (Unstratified and Stratified Miettinen and Nurminen Test), I found that its CI is given by the roots of an equation. In order to reproduce its algorithm, I'd like to learn more about the Bisection method first.

This is a continuation of the previous article, Tipping Point Analysis in Multiple Imputation Using SAS. In the last post, we talked about the tipping point analysis in monotone imputation, but how to implement TPA in MCMC imputation since the `MNAR` statement can only support the shift adjustment in monotone and FCS.

The tipping point analysis has been a useful sensitivity analysis for multiple imputation to assess the robustness of the deviations from the MCAR or MAR assumptions. It aims to find out how severe departures from MAR will overturn the conclusions from primary analysis. If the departures are considered unlikely, this can give strong evidence supporting the treatment effect found in the primary analysis under the MAR assumptions.

The logrank test is the most commonly used statistical test in clinical trials to compare the survival distributions in different treatment groups. We usually just use the logrank results to test whether there is a difference between two survival curves. But what does this difference mean?

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`.

We have discussed the multiple imputation in non-monotone pattern of missingness in the article of Understanding Multiple Imputation in SAS, and sort out how to implement it in SAS. While here, I would like to learn how to use linear regression in multiple imputation to deal with monotone pattern data in R.

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.

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.

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.

Originally, I created an issue in `CAMIS` github asking how to do the hypothesis testing of MMRM in R, especially in non-inferiority or superiority trials. And then I received a reminder that I can get the manual from `mmrm` package document.

In the article Definition of least-squares means (LS means), we have known how to compute the LS mean step by step and how to implement it in the `emmeans` package that will calculate the estimated mean value for different factor variables and assume the mean value for continuous variables.

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.

#### 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.