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rbmi: Frequently Asked Questions

Alessandro Noci, Craig Gower-Page and Marcel Wolbers

Source: vignettes/FAQ.Rmd
FAQ.Rmd

1 Introduction

This document provides answers to common questions about the rbmi package. It is intended to be read after the rbmi: Quickstart vignette.


1.1 Is rbmi validated?

With regards to software in the pharmaceutical industry, validation is the act of ensuring that the software meets the needs and requirements of users given the conditions of actual use. The FDA provides general principles and guidance for validation but leaves it to individual sponsors to define their specific validation processes. Therefore, no individual R package can claim to be ‘validated’ independently, as validation depends on the entire software stack and the specific processes of each company.

That being said, some of the core components of any validation process are the design specification (what is the software supposed to do) as well as the testing / test results that demonstrate that the design specification has been met. For rbmi, the design specification is documented extensively, both at a macro level in vignettes and literature publications, and at a micro level in detailed function manuals. This is supported by our extensive suite of unit and integration tests, which ensure the software consistently produces correct output across a wide range of input scenarios.

This documentation and test coverage enable rbmi to be easily installed and integrated into any R system, in alignment with the system’s broader validation process.


1.2 How do the methods in rbmi compare to the mixed model for repeated measures (MMRM) implemented in the mmrm package?

rbmi was designed to complement and, occasionally, replace standard MMRM analyses for clinical trials with longitudinal endpoints.

Strengths of rbmi compared to the standard MMRM model are:

  • rbmi was designed to allow for analyses which are fully aligned with the the estimand definition. To facilitate this, it implements methods under a range of different missing data assumptions including standard missing-at-random (MAR), extended MAR (via inclusion of time-varying covariates), reference-based missingness, and not missing-at-random at random (NMAR; via \(\delta\)-adjustments). In contrast, the standard MMRM model is only valid under a standard MAR assumption which is not always plausible. For example, the standard MAR assumption is rather implausible for implementing a treatment policy strategy for the intercurrent event “treatment discontinuation” if a substantial proportion of subjects are lost-to-follow-up after discontinuation.
  • The \(\delta\)-adjustment methods implemented in rbmi can be used for sensitivity analyses of a primary MMRM- or rbmi-type analysis.

Weaknesses of rbmi compared to the standard MMRM model are:

  • MMRM models have been the de-facto standard analysis method for more than a decade. rbmi is currently less established.
  • rbmi is computationally more intensive and using it requires more careful planning.


1.3 How does rbmi compare to general-purpose software for multiple imputation (MI) such as mice?

rbmi covers only “MMRM-type” settings, i.e. settings with a single longitudinal continuous outcome which may be missing at some visits and hence require imputation.

For these settings, it has several advantages over general-purpose MI software:

  • rbmi supports imputation under a range of different missing data assumptions whereas general-purpose MI software is mostly focused on MAR-based imputation. In particular, it is unclear how to implement jump to reference (JR) or copy increments in reference (CIR) methods with such software.
  • The rbmi interface is fully streamlined to this setting which arguably makes the implementation more straightforward than for general-purpose MI software.
  • The MICE algorithm is stochastic and inference is always based on Rubin’s rules. In contrast, method “conditional mean imputation plus jackknifing” (method="method_condmean(type = "jackknife")") in rbmi does not require any tuning parameters, is fully deterministic, and provides frequentist-consistent inference also for reference-based imputations (where Rubin’s rule is very conservative leading to actual type I error rates which can be far below their nominal values).

However, rbmi is much more limited in its functionality than general-purpose MI software.


1.4 How to handle missing data in baseline covariates in rbmi?

rbmi does not support imputation of missing baseline covariates. Therefore, missing baseline covariates need to be handled outside of rbmi. The best approach for handling missing baseline covariates needs to be made on a case-by-case basis but in the context of randomized trials, relatively simple approach are often sufficient (White and Thompson (2005)).


1.5 Why does rbmi by default use an ANCOVA analysis model and not an MMRM analysis model?

The theoretical justification for the conditional mean imputation method requires that the analysis model leads to a point estimator which is a linear function of the outcome vector (Wolbers et al. (2022)). This is the case for ANCOVA but not for general MMRM models. For the other imputation methods, both ANCOVA and MMRM are valid analysis methods. An MMRM analysis model could be implemented by providing a custom analysis function to the analyse() function.

For further expalanations, we also cite the end of section 2.4 of the conditional mean imputation paper (Wolbers et al. (2022)):

The proof relies on the fact that the ANCOVA estimator is a linear function of the outcome vector. For complete data, the ANCOVA estimator leads to identical parameter estimates as an MMRM model of all longitudinal outcomes with an arbitrary common covariance structure across treatment groups if treatment-by-visit interactions as well as covariate-by-visit-interactions are included in the analysis model for all covariates,17 (p. 197). Hence, the same proof also applies to such MMRM models. We expect that conditional mean imputation is also valid if a general MMRM model is used for the analysis but more involved argument would be required to formally justify this.


1.6 How can I analyse the change-from-baseline in the analysis model when imputation was done on the original outcomes?

This can be achieved using custom analysis functions as outlined in Section 7 of the Advanced Vignette. e.g.

ancova_modified <- function(data, ...) {
    data2 <- data %>% mutate(ENDPOINT = ENDPOINT - BASELINE)
    rbmi::ancova(data2, ...)
}

anaObj <- rbmi::analyse(
    imputeObj,
    ancova_modified,
    vars = vars
 )


White, Ian R, and Simon G Thompson. 2005. “Adjusting for Partially Missing Baseline Measurements in Randomized Trials.” Statistics in Medicine 24 (7): 993–1007.
Wolbers, Marcel, Alessandro Noci, Paul Delmar, Craig Gower-Page, Sean Yiu, and Jonathan W Bartlett. 2022. “Standard and Reference-Based Conditional Mean Imputation.” Pharmaceutical Statistics 21 (6): 1246–57.