Propensity score matching

Observational studies In 741 patients with coronary bypass and/or valve operations transfusion of more than 5.5 units of blood products was associated with a higher incidence of re-operation, neurological, infectious, and cardiac complications, multisystem organ failure, and deaths [10 ]. However, these results were distorted by confounding by indication, because patients who receive more than 5.5 units of blood are more severely ill than those who receive fewer transfusions or none at all. Studies of the potential harmful effects of blood transfusions should be carefully corrected for disease severity, for example by propensity score matching analyses [ll ]. Among propensity-matched patients undergoing... 

Ngaage DL, Cale AR, Cowen ME, Griffin S, Guvendik L. Aprotinin in primary cardiac surgery operative outcome of propensity score-matched study. Ann Thorac Surg 2008 86(4) 1195-202. 

All methods used to account for households behavioral responses to transfers are data intensive. Most of the CCT evaluations from World Bank (forthcoming) are randomized evaluations, with information collected from treatment and control groups. Jalan and Ravallion (2003) use propensity score matching to evaluate the distributional outcomes of Argentina s Trabajar program. Ravallion, van de Walle, and Gautam (1995) and van de Walle (2003) use panel data and instrumental variable models to estimate a reduced form equation of household consumption on transfer incomes. 

Musculoskeletal A study with propensity score matching showed that statin use was associated with an increased odd of diagnosis of musculoskeletal conditions, arthropathies and injury [74] (JAMA Intern Med 2013,173 1318-1326). 

Once variables are identified, one can confrol for them through direct matching or stratification, whereby fhe fargef and comparator groups are logically divided by the attributes of fhe covariafes. However, in a multivariable context, the data may be too sparse to provide adequate sample size to allow matching on all covariates or to provide subpopulations within each covariate-defined stratum (i.e., there may be empty cells defined by combinations of covariates). A popular tool to overcome this limitation is propensity score analysis (Rosenbaum 2002 Rubin 1997). 

An amiodarone interaction was also indicated in a retrospective cohort study of patients over 65 years of age on at least six continuous months of warfarin therapy. The two cohorts, patients receiving amiodarone or not, were matched based on age, gender, year of cohort entry and a high-dimensional propensity score. The initiation of amiodarone among older patients taking warfarin was associated with a more than twofold increase in the risk of hospitalization for haemorrhage with a relatively high fatality rate [12 ]. 

PPI group was matched 1 1 with the clopidogrel alone group using propensity scores. This resulted in 2674 patient pairs. The mean age of patients was 61.30 years, with a mean follow-up of 268 days and 70% were male. The use of clopidogrel with PPIs significantly increased the risk of adverse cardiovascular evenfs (hazard ratio = 1.438 95% confidence interval, 1.237-1.671), compared with the use of clopidogrel alone PO -]. 

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