Background Right-and left-sided colon cancer are increasingly regarded as two
independent disease entities based on different gene expression profiles as well as …

In nonrandomised studies, inferring causal effects requires appropriate methods for
addressing confounding bias. Although it is common to adopt propensity score analysis to …

Use of disease risk score (DRS)–based confounding adjustment when estimating treatment
effects on multiple outcomes is not well studied. We designed an empirical cohort study to …

Background Renoprotective effects of sodium glucose transporter 2 (SGLT2) inhibitors,
including dapagliflozin, were observed in randomized controlled trials (RCTs). The …

The propensity score has become a standard tool to control for large numbers of variables in
healthcare database studies. However, little has been written on the challenge of comparing …

Objective To further the precision medicine goal of tailoring medical treatment to individual
patient characteristics by providing a method of analysis of the effect of test treatment, T …

As real world evidence on drug efficacy involves nonrandomized studies, statistical methods
adjusting for confounding are needed. In this context, prognostic score (PGS) analysis has …

Suppose that an investigator wants to estimate an association between a continuous
exposure variable and an outcome, adjusting for a set of confounders. If the exposure …

Introduction For a new drug to be developed, the desired properties are described in a target
product profile. Objective We propose a framework for using real-world data to measure the …

The disease risk score (DRS) is a summary score that is a function of a potentially large set
of covariates. The DRS can be used to control for confounding by the covariates that went …