Monday, Aug 5: 2:00 PM - 3:50 PM
5067
Contributed Papers
Oregon Convention Center
Room: CC-G131
Multiple imputation (MI) is a popular experimental procedure for handling missing data, while robust regression implements novel empirical evidence for the decision regarding non-normal or normal variables. By down-weighting the influence of outliers, Robust regression minimize residual impact on the coefficient estimates. This macro combines two methods together to accurately capture this relationship in continuous efficacy laboratory data and protect against potential non‐normality/outliers in the original or
imputed dataset. Another speaker will focus on Patient reported outcomes, such as quality of life (QOL), that are commonly collected in oncology studies, and are increasingly common in meta-analyses. Work is based on a meta-analysis data set where studies of QOL among cancer patients receiving radiation therapy have reported been reported longitudinally as correlated continuous
repeated measures. Results based on a simulation study will demonstrate that bias and coverage problems tend to arise as the proportion of studies reporting medians increases and as the underlying distributions become more skewed. Next speaker will focus on a functional programming style approach to Monte Carlo sample size determination analysis in R and
SAS. An example from Ophthalmology with a SAS code for analysis done at Alcon would be shared. Final Speakers will share knowledge of certain mixed effect models, Longitudinal meta-analysis and multimodel display for clinical trial to screen Dimentia patients will also be shared.
Main Sponsor
Section for Statistical Programmers and Analysts
Presentations
Multiple imputation (MI) is a popular experimental procedure for handling missing data, while robust regression implements novel empirical evidence for the decision regarding non-normal or normal variables. By down-weighting the influence of outliers, Robust regression minimize residual impact on the coefficient estimates. A macro developed by the Fortrea Company, combines two methods together to accurately capture this relationship in continuous efficacy laboratory data and protect against potential non‐normality/outliers in the original or imputed dataset. This paper provides an example programming procedure and suggests possible improvements in the macro based on the author's experience.
Keywords
Multiple imputation
missing data
non-normal
Robust regression
This contribution reflects on framework and tools by the R Validation Hub for risk-based assessment of R packages within validated infrastructure.
The R Validation Hub is a cross-industry initiative, led by approximately 10 organizations with frequent involvement from health authorities. The R validation Hub is funded by the R Consortium and has the mission to support the adoption of R within regulated industries, with an emphasis on biopharmaceuticals.
We will discuss the framework for the risk-based assessment of R packages, that has been utilized by key pharma companies across the industry. We will also showcase the {riskmetric} R package, that evaluates the risk of an R package using a specified set of metrics and validation criteria, and the {riskassessment} app, that augments the utility of the {riskmetric} package with a Shiny app front end. Lastly, we will outline a prototype of a technical framework for a 'repository' of R packages with accompanying evidence of their quality and the assessment criteria.
Keywords
R Package
Risk assessment
Open-source software
Regulated environment
validation
We propose a functional programming style approach to Monte Carlo sample size determination analysis in R and SAS. Our proposed workflow centers around the development of a study-specific R package used to conduct the analysis, exporting functions for simulating data, modeling data, and summarizing results. Doing so has numerous advantages–R packages have a predictable structure, come with powerful documentation and unit testing tools, are portable, and are easy to collaborate on. In lieu of more standard functional tools such as the lapply() family or the {purrr} library we recommend the use of the exported functions with parallelizable rowwise operations on nested tibbles from the {tidyr} package, extending the notion of "tidy" data to the "tidy" organization of simulation data. We also discuss a functional style approach to modeling data in SAS via macros for designs involving the use of SAS-specific tools such as PROC MIXED, demonstrating a methodology for using SAS and R in tandem. We conclude with an example from ophthalmology, showcasing the development and use of an R package and SAS code for such an analysis at Alcon.
Keywords
Functional Programming
Clinical Trial Design
Monte Carlo Simulation
R
SAS
This presentation talks about asymptotic results for the maximum likelihood and restricted maximum likelihood (REML) estimators within a two-way crossed mixed effect model as the sizes of the rows, columns, and cells tend to infinity. Under very mild conditions which do not require the assumption of normality, the estimators are proven to be asymptotically normal, possessing a structured covariance matrix. The growth rate for the number of rows, columns, and cells is unrestricted, whether considered pairwise or collectively.
Keywords
Crossed random effect
Maximum likelihood estimator
Kronecker product
Variance components
Asymptotic independence
Co-Author(s)
Scott Sisson, University of New South Wales, Sydney
Alan Welsh, Australian National University
First Author
Ziyang Lyu, University of New South Wales
Presenting Author
Ziyang Lyu, University of New South Wales
Patient reported outcomes, such as quality of life (QOL), are commonly collected in oncology studies, and are increasingly common in meta-analyses. We currently have a meta-analysis data set where studies of QOL among cancer patients receiving radiation therapy have reported been reported longitudinally as correlated continuous repeated measures. While most studies of QOL have reported means and standard deviations, some studies have reported medians with ranges, interquartile ranges (IQR), or both. It is unknown how existing methods for mean from median estimation may affect results of a meta-analysis when data are from longitudinal studies reporting correlated repeated measures. In a simulation study, we varied the underlying distributions, numbers of studies and subjects within studies, data reported (medians with range, IQR or both), and proportion of studies reporting medians. Results show that bias and coverage problems tend to arise as the proportion of studies reporting medians increases and as the underlying distributions become more skewed.
Keywords
Meta-Analysis
Simulation
Quality of Life
Cancer
Longitudinal
Dementia is a complex disease due to various etiologies. New multimodal deep learning algorithms were developed to improve the diagnosis of dementia into different categories of normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD).
One of the core difficulties in implementing Dementia clinical trials, especially the AD trials lies in the diagnostic ambiguity of Alzheimer's, where symptomatic overlap with other cognitive disorders often leads to misdiagnosis. Dementia clinical trials usually have high screen failure rates and burden for the sponsor for the manual inclusion screening verification.
In our work, we explore the use of this multimodal deep learning algorithm as a tool for the clinical trial patient disease screening verification to reduce the cost of the clinical study while improving the quality. We will present the accuracy assessment of the deep learning algorithm compared to the neurologist assessment based on the sensitivity, specificity, PPV and NPV in the real-world clinical trial setting. We will explore the optimal set of input variables used for the algorithm to balance the accuracy and cost and time of the medical exams.
Keywords
multimodal deep learning algorithm
Dementia clinical trial
disease screening
sensitivity, specificity, PPV and NPV
real world
Co-Author(s)
Ying Liu, Princeton Pharmatech LLC
Polina Vyniavska, Princeton Pharmatech LLC
First Author
William Jin, West Windsor - Plainboro High School North
Presenting Author
William Jin, West Windsor - Plainboro High School North