1,720,988 research outputs found
Machine learning for the estimation of the propensity score: a simulation study = Il machine learning nella stima del propensity score: uno studio di simulazione
No full textAn evaluation of the variability of episiotomy rates across hospitals: the case of Sardinia
No full textThe aim of this paper is to assess the extent of variation in the use of epi-
siotomy across hospitals and to evaluate how much of this variation can be explained by case-mix factors. Using o cial hospital abstracts on deliveries occurred in the hospitals of the Italian region of Sardinia during 2009, we implement a multilevel logistic model in order to predict the likelihood of an
episiotomy from a set of covariates which includes both socio-demographic
and clinical indices. Results suggest that, with the exception of education,
socio-demographic variables were not signi cant in determining episiotomy while several clinical predictors were signi cant. Our main nding is that almost half of the variation in episiotomy rates remains unexplained after conditioning on clinical indicators and socio-demographic factors
CMatching: Matching Algorithms for Causal Inference with Clustered Data
No full textProvides functions to perform matching algorithms for causal inference with clustered data, as described in B. Arpino and M. Cannas (2016) . Pure within-cluster and preferential within-cluster matching are implemented. Both algorithms provide causal estimates with cluster-adjusted estimates of standard errors
Template matching for hospital comparison: an application to birth event data in Italy
No full textQuality evaluation in healthcare has obtained a growing attention in the statistical literature. In order to evaluate hospital performances by comparing hospital outcomes it is necessary to remove the bias due to the different case-mix in each hospital, which is usually done using statistical modeling as a risk adjustment tool. Template matching is a new matching approach allowing to remove bias selection in a multi-treatment setting, resulting in a clean and easily interpretable evaluation. We adopted this method to compare caesarean section rates across the hospitals of two Italian regions: Sardinia and Lombardy. We found 5 (out of 79) hospitals with abnormal performance on the same template of expectant mothers, and all these hospitals are located in Lombardy, whereas we do not observe a relationship with the number of deliveries per year and the ownership. Despite this, fairness of the comparing procedure makes easier for the policy makers the identification of potential outliers with respect to both patients’ selection and outcomes
Optimal Matching with Matching Priority
Full text linkMatching algorithms are commonly used to build comparable subsets (matchings) in observational studies. When a complete matching is not possible, some units must necessarily be excluded from the final matching. This may bias the final estimates comparing the two populations, and thus it is important to reduce the number of drops to avoid unsatisfactory results. Greedy matching algorithms may not reach the maximum matching size, thus dropping more units than necessary. Optimal matching algorithms do ensure a maximum matching size, but they implicitly assume that all units have the same matching priority. In this paper, we propose a matching strategy which is order optimal in the sense that it finds a maximum matching size which is consistent with a given matching priority. The strategy is based on an order-optimal matching algorithm originally proposed in connection with assignment problems by D. Gale. When a matching priority is given, the algorithm ensures that the discarded units have the lowest possible matching priority. We discuss the algorithm’s complexity and its relation with classic optimal matching. We illustrate its use with a problem in a case study concerning a comparison of female and male executives and a simulation
Propensity score matching with clustered data: an application to birth register data
No full textThis article focuses on the implementation of propensity score matching for clustered data. Different approaches to reduce bias due to cluster-level confounders are considered and compared using Monte Carlo simulations. We investigated methods that exploit the clustered structure of the data in two ways: in the estimation of the propensity score model (through the inclusion of fixed or random effects) or in the implementation of the matching algorithm. In addition to a pure within-cluster matching, we also assessed the performance of a new approach, 'preferential' within-cluster matching. This approach first searches for control units to be matched to treated units within the same cluster. If matching is not possible within-cluster, then the algorithm searches in other clusters. All considered approaches successfully reduced the bias due to the omission of a cluster-level confounder. The preferential within-cluster matching approach, combining the advantages of within-cluster and between-cluster matching, showed a relatively good performance both in the presence of big and small clusters, and it was often the best method. An important advantage of this approach is that it reduces the number of unmatched units as compared with a pure within-cluster matching. We applied these methods to the estimation of the effect of caesarean section on the Apgar score using birth register data
On the support of matching algorithms
Full text linkIn causal inference a matching algorithm assigns a subset of control units to each treated unit. Using combinatorial techniques we explore the support of matching algorithms to provide counting results and investigate the role of the dimension of the covariates’ space
Estimating the effect of prenatal care on birth outcomes
No full textUsing data from official hospital abstracts on deliveries occurred in Sardinia during years 2010 and 2011, we implemented an
Augmented Inverse Probability Weighted (AIPW) model in order to study the effect of increased prenatal care during pregnancy on
birth outcomes. Results showed that moderate levels of prenatal care, as measured by the number of sonograms, increase the
Apgar score of the infant, while a higher number of sonograms do not have any additional marginal effect on the outcome
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