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A reference interval, or an interval in which a prespecified proportion of measurements from a healthy population are expected to fall, is used to determine whether a person's measurement is typical of a healthy individual. For a specific biomarker, multiple published studies may provide data collected from healthy participants. A reference interval estimated by combining the data across these studies is typically more generalizable than a reference interval based on a single study. Methods for estimating reference intervals from random effects meta-analysis and fixed-effects meta-analysis have been recently proposed and implemented using R software. We present an R Shiny tool, RIMeta, implementing these methods, which allows users not proficient in R to estimate a reference interval from a meta-analysis using aggregate data (mean, standard deviation, and sample size) from each study. RIMeta (https://cers.shinyapps.io/RIMeta/) provides users a convenient way to estimate a reference interval from a meta-analysis and to generate the reference interval plot to visualize the results. The use of this web-based R Shiny tool does not require the installation of R or any background knowledge of programming. We explain all functions of the R Shiny tool and illustrate how to use it with a real data example.

Population‐adjusted indirect comparison (PAIC) is an increasingly used technique for estimating the comparative effectiveness of different treatments for the health technology assessments when head‐to‐head trials are unavailable. Three commonly used PAIC methods include matching‐adjusted indirect comparison (MAIC), simulated treatment comparison (STC), and multilevel network meta‐regression (ML‐NMR). MAIC enables researchers to achieve balanced covariate distribution across two independent trials when individual participant data are only available in one trial. In this article, we provide a comprehensive review of the MAIC methods, including their theoretical derivation, implicit assumptions, and connection to calibration estimation in survey sampling. We discuss the nuances between anchored and unanchored MAIC, as well as their required assumptions. Furthermore, we implement various MAIC methods in a user‐friendly R Shiny application Shiny‐MAIC. To our knowledge, it is the first Shiny application that implements various MAIC methods. The Shiny‐MAIC application offers choice between anchored or unanchored MAIC, choice among different types of covariates and outcomes, and two variance estimators including bootstrap and robust standard errors. An example with simulated data is provided to demonstrate the utility of the Shiny‐MAIC application, enabling a user‐friendly approach conducting MAIC for healthcare decision‐making. The Shiny‐MAIC is freely available through the link: https://ziren.shinyapps.io/Shiny_MAIC/. [ABSTRACT FROM AUTHOR]

Abstract Adaptive platform trials (APTs) offer an innovative approach to studying multiple therapeutic interventions more efficiently through flexible features such as adding and dropping interventions as evidence emerges, creating a seamless process that avoids enrollment disruption. The benefits and practical challenges of implementing APTs have been widely discussed in the literature; however, less consideration has been given to how to use the non-concurrent control (NCC) data (i.e., the data generated by patients recruited in the control arm before a new treatment is added) when the outcome of interest is a time to event endpoint. Including the NCC can increase the power of the trial. However, due to the omnipresent change of standard care over time, complete borrowing of the NCC survival data may lead to some bias in the estimation. In this paper, we propose an alternative approach to borrow the concurrent observation part of the NCC data by left truncation using a simple decision-making flowchart, which can reduce the bias due to the change of standard care under certain assumptions. Then, the restricted mean survival time (RMST), estimated by the Kaplan-Meier method, is used to compare the treatment versus the pooled control group. We present two simulation studies to illustrate the performance of the decision-making flowchart method under different scenarios. We advocate researchers and drug developers to apply and validate this simple approach in practice. [ABSTRACT FROM AUTHOR]

Data increasingly take the form of a multi-way array, or tensor, in several biomedical domains. Such tensors are often incompletely observed. For example, we are motivated by longitudinal microbiome studies in which several timepoints are missing for several subjects. There is a growing literature on missing data imputation for tensors. However, existing methods give a point estimate for missing values without capturing uncertainty. We propose a multiple imputation approach for tensors in a flexible Bayesian framework, that yields realistic simulated values for missing entries and can propagate uncertainty through subsequent analyses. Our model uses efficient and widely applicable conjugate priors for a CANDECOMP/PARAFAC (CP) factorization, with a separable residual covariance structure. This approach is shown to perform well with respect to both imputation accuracy and uncertainty calibration, for scenarios in which either single entries or entire fibers of the tensor are missing. For two microbiome applications, it is shown to accurately capture uncertainty in the full microbiome profile at missing timepoints and used to infer trends in species diversity for the population. Documented R code to perform our multiple imputation approach is available at https://github.com/lockEF/MultiwayImputation.