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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/., (© 2024 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)

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., (© 2023 John Wiley & Sons Ltd.)