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The applied literature on propensity scores has often cited the c-statistic as a measure of the ability of the propensity score to control confounding. However, a high c-statistic in the propensity model is neither necessary nor sufficient for control of confounding. Moreover, use of the c-statistic as a guide in constructing propensity scores may result in less overlap in propensity scores between treated and untreated subjects; this may require the analyst to restrict populations for inference. Such restrictions may reduce precision of estimates and change the population to which the estimate applies. Variable selection based on prior subject matter knowledge, empirical observation, and sensitivity analysis is preferable and avoids many of these problems. Copyright © 2010 John Wiley & Sons, Ltd.
This article was published in the following journal.
Name: Pharmacoepidemiology and drug safety
Propensity score (PS) and instrumental variable (IV) are analytical techniques used to adjust for confounding in observational research. More and more, they seem to be used simultaneously in studies e...
The performance of inverse probability of treatment weighting and full matching on the propensity score in the presence of model misspecification when estimating the effect of treatment on survival outcomes.
There is increasing interest in estimating the causal effects of treatments using observational data. Propensity-score matching methods are frequently used to adjust for differences in observed charac...
Depressive state has been reported to be significantly associated with higher-level functional capacity among community-dwelling elderly. However, few studies have investigated the associations among ...
Variable (wavelength or feature) selection techniques have become a critical step for the analysis of datasets with high number of variables and relatively few samples. In this study, a novel variable...
The aim of this study is to identify biomarkers of disease recurrence and prognosis to optimize patient selection for treatment with cytoreductive surgery (CRS) with hyperthermic intraperi...
Coordinated multi-center project with several complementary goals in each one of the sub-projects included. Objectives: 1.To determine risk factors of death, or major complications in the ...
Background: Recent observational studies have reported possible arrhythmogenic effects with long-acting beta-agonists (LABA), while the long-acting anticholinergic tiotropium has been asso...
BBR 2778 is a novel aza-anthracenedione that has activity in experimental tumors and shows reduced potential for cardiotoxicity in animal models. This cytotoxic agent has structural simila...
Patients with acute abdominal pain and suspicion of appendicitis are common. The management of these patients is controversial with large variations between hospitals. The clinical diagnos...
Conditional probability of exposure to a treatment given observed covariates.
Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.
Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor.
A distribution in which a variable is distributed like the sum of the squares of any given independent random variable, each of which has a normal distribution with mean of zero and variance of one. The chi-square test is a statistical test based on comparison of a test statistic to a chi-square distribution. The oldest of these tests are used to detect whether two or more population distributions differ from one another.
A method, developed by Dr. Virginia Apgar, to evaluate a newborn's adjustment to extrauterine life. Five items - heart rate, respiratory effort, muscle tone, reflex irritability, and color - are evaluated 60 seconds after birth and again five minutes later on a scale from 0-2, 0 being the lowest, 2 being normal. The five numbers are added for the Apgar score. A score of 0-3 represents severe distress, 4-7 indicates moderate distress, and a score of 7-10 predicts an absence of difficulty in adjusting to extrauterine life.
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