Summary and discussion of “The central role of the propensity score in observational studies for causal effects”
نویسندگان
چکیده
Observational studies draw inferences about the possible effect of a treatment on subjects, where the assignment of subjects into a treated group versus a control group is outside the control of the investigator. Let r1(x) be the response when a unit with covariates x receives the treatment (z = 1), and r0(x) be the response when that unit does not receive the treatment (z = 0). Then one is interested in inferring the effect size:
منابع مشابه
Causal Inference in Anesthesia and Perioperative Observational Studies
Purpose of Review Observational studies are of great importance to anesthesia and perioperative care research, as they reflect routine clinical practice. However, because observational data are nonexperimental, assigning causality to identified relationships has a significant risk of bias. After describing the pros and cons of observational studies, we provide an overview of the different metho...
متن کاملاستفاده از Propensity Score برای همسان سازی نمونه ها در یک مطالعه مورد شاهدی
Background and Aim: Case-Control studies provide evidence in the area of health. Validity and accuracy of such studies depend to a large extent on the similarity (similar distributions) of the case and control groups according to confounding variables. Matching is a method for controlling or eliminating the effects of important confounders. Matching using propensity score has recently been intr...
متن کاملAn Application of Non-response Bias Reduction Using Propensity Score Methods
In many statistical studies some units do not respond to a number or all of the questions. This situation causes a problem called non-response. Bias and variance inflation are two important consequences of non-response in surveys. Although increasing the sample size can prevented variance inflation, but cannot necessary adjust for the non-response bias. Therefore a number of methods ...
متن کاملMatching and Propensity Scores
The popularity of matching techniques has increased considerably during the last decades. They are mainly used for matching treatment and control units in order to estimate causal treatment effects from observational studies or for integrating two or more data sets that share a common subset of covariates. In focusing on causal inference with observational studies, we discuss multivariate match...
متن کاملMatching to estimate the causal effects from multiple treatments
The propensity score is a common tool for estimating the causal effect of a binary treatment using observational data. In this setting, matched methods, defined as either individual matching, subclassifying, or using inverse probability weighting on the propensity score, can reduce the initial covariate bias between the treatment and control groups. With more than two treatment options, however...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2014