RATIONALE, AIMS AND OBJECTIVES When a randomized controlled trial is not feasible, health researchers typically use observational data and rely on statistical methods to adjust for confounding when estimating treatment effects. These methods generally fall into 3 categories: (1) estimators based on a model for the outcome using conventional regression adjustment; (2) weighted estimators based o...

This retrospective cross-sectional paper examines the relationship between maternal smoking during pregnancy and children's behavioural problems at 9 years of age independent of a wide range of possible confounders. The final sample comprised 7,505 nine-year-old school children participating in the first wave of the Growing Up in Ireland study. The children were selected through the Irish natio...

TO THE EDITOR—We appreciate the comments from Decker et al regarding our study [1]. There are now 3 studies reporting comparative effectiveness results for high-dose vs standard-dose influenza vaccine. Decker et al's Sanofi Pasteur ran-domized trial [2] necessarily focused on symptomatic influenza infection as hospitalization with complications of influenza is less common, and comparisons based...

This study uses the Propensity Score Matching to examine the income impact of different credit sources on accessed households in the Northern Mountains of Vietnam. Results show that overall rural credit serves an important role in improving household income with respect to total income, per capita income and nonfarm income. However, different credit affects recipients heterogeneously. Whereas a...

Suppose we are interested in estimating the average causal effect from an observational study. A doubly robust estimator, which is a hybrid of the outcome regression and propensity score weighting, is more robust than estimators obtained by either of them in the sense that, if at least one of the two models holds, the doubly robust estimator is consistent. However, a doubly robust estimator may...

Propensity scores are widely used in cohort studies to improve performance of regression models when considering large numbers of covariates. Another type of summary score, the disease risk score (DRS), which estimates disease probability conditional on nonexposure, has also been suggested. However, little is known about how it compares with propensity scores. Monte Carlo simulations were condu...

OBJECTIVES The propensity score (PS) method is increasingly used to assess treatment effects in nonrandomized trials. Although there are several methods to use the PS for analysis, matching treated and untreated patients by the PS is recommended by most researchers among other reasons because this allows assessing covariate balance before and after matching. Although the standardized difference...

Abstract The Food for Education (FFE) program was introduced to Bangladesh in 1993. This paper evaluates the effect of this program on school participation and duration of schooling using household survey data collected in 2000. Using propensity score matching combined with differencein-differences methodologies, we find that the program is successful in that eligible children on average have 1...

1 Health Protection and Influenza Research Group, Division of Epidemiology and Public Health, University of Nottingham, Nottingham, UK 2 Department of Anaesthesia, National Hospital for Neurology and Neurosurgery, & Surgical Outcomes Research Centre, University College London Hospital, London, UK 3 Wellcome Department of Imaging Neuroscience, University College London, London, UK 4 Department o...

Abstract Propensity scores represent the probability that an individual is assigned to a treatment group given the individual’s scores on a set of covariates. Propensity scores are used in nonrandomized studies to equate treatment and control groups on a large number of covariates measured at baseline. Assumptions necessary for propensity score analysis to yield a valid estimate of t...