Outpatient calcium‐channel blockers and the risk of postpartum haemorrhage: a cohort study
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To determine whether outpatient exposure to calcium-channel blockers (CCBs) at the time of delivery is associated with an increased risk for postpartum haemorrhage (PPH).Cohort study.United States of America.Medicaid beneficiaries.We identified a cohort of 9750 patients with outpatient prescriptions for CCBs, methyldopa, or labetalol for pre-existing or gestational hypertension whose days of supply overlapped with delivery; 1226 were exposed to CCBs. The risk of PPH was compared in those exposed to CCBs to those exposed to methyldopa or labetalol. Propensity score matching and stratification were used to address potential confounding.The occurrence of PPH during the delivery hospitalisation.There were 27 patients exposed to CCBs (2.2%) and 232 patients exposed to methyldopa or labetalol (2.7%) who experienced PPH. After accounting for confounders, there was no meaningful association between CCB exposure and PPH in the propensity score matched (odds ratio 0.77, 95% CI 0.50-1.18) or stratified (odds ratio 0.79, 95% CI 0.53-1.19) analyses. Similar results were obtained across multiple sensitivity analyses.The outpatient use of CCBs in late pregnancy for the treatment of hypertension does not increase the risk of PPH.Keywords:
Labetalol
Gestational hypertension
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This chapter explores the important issue of confounding in observational studies. The potential imbalances that result for not controlling assignment of treatment or exposure may lead to imbalance of variables that are associated with both treatment and intervention (or exposure) thus confounding results. Therefore, in this context, a potential relationship between an intervention and an outcome could be invalid. This chapter therefore explains basic definitions of confounding and presents some methods to control for confounders, highlighting the use of the propensity score, which is considered a robust method for this purpose. Different techniques of adjustment using propensity score (matching, stratification, regression, and weighting) are also discussed. This chapter concludes with a case discussion about confounding and how to address it.
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Propensity score matching is a common tool for adjusting for observed confounding in observational studies, but is known to have limitations in the presence of unmeasured confounding. In many settings, researchers are confronted with spatially-indexed data where the relative locations of the observational units may serve as a useful proxy for unmeasured confounding that varies according to a spatial pattern. We develop a new method, termed Distance Adjusted Propensity Score Matching (DAPSm) that incorporates information on units' spatial proximity into a propensity score matching procedure. We show that DAPSm can adjust for both observed and some forms of unobserved confounding and evaluate its performance relative to several other reasonable alternatives for incorporating spatial information into propensity score adjustment. The method is motivated by and applied to a comparative effectiveness investigation of power plant emission reduction technologies designed to reduce population exposure to ambient ozone pollution. Ultimately, DAPSm provides a framework for augmenting a "standard" propensity score analysis with information on spatial proximity and provides a transparent and principled way to assess the relative trade offs of prioritizing observed confounding adjustment versus spatial proximity adjustment.
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A propensity score is the probability of being treated or exposed, given measured confounders such as age. Propensity score methods can control for measured confounders in observational research, but not for unmeasured confounders. Study participants with the same propensity score will, on average, have a similar distribution of measured confounders. The propensity score can be used to control for confounding by means of stratification, matching, including the propensity score as a covariate in a multivariable regression model, or by weighting the study population using the propensity score. Propensity score methods can often control for more confounders than other methods, particularly in the case of a rare outcome. Reports on propensity score methods should mention which confounders were included in the propensity score and to what extent confounders were balanced across study groups, after stratification based on the propensity score.
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Abstract Confounding variables can affect the results from studies of children with Down syndrome and their families. Traditional methods for addressing confounders are often limited, providing control for only a few confounding variables. This study introduces propensity score matching to control for multiple confounding variables. Using Tennessee birth data as an example, newborns with Down syndrome were compared with a group of typically developing infants on birthweight. Three approaches to matching on confounders—nonmatched, covariate matched, and propensity matched—were compared using 8 potential confounders. Fewer than half of the newborns with Down syndrome were matched using covariate matching, and the matched group was differed from the unmatched newborns. Using propensity scores, 100% of newborns with Down syndrome could be matched to a group of comparison newborns, a decreased effect size was found on newborn birthweight, and group differences were not statistically significant.
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Purpose Propensity scores are used in observational studies to adjust for confounding, although they do not provide control for confounders omitted from the propensity score model. We sought to determine if tests used to evaluate logistic model fit and discrimination would be helpful in detecting the omission of an important confounder in the propensity score. Methods Using simulated data, we estimated propensity scores under two scenarios: (1) including all confounders and (2) omitting the binary confounder. We compared the propensity score model fit and discrimination under each scenario, using the Hosmer–Lemeshow goodness-of-fit (GOF) test and the c-statistic. We measured residual confounding in treatment effect estimates adjusted by the propensity score omitting the confounder. Results The GOF statistic and discrimination of propensity score models were the same for models excluding an important predictor of treatment compared to the full propensity score model. The GOF test failed to detect poor model fit for the propensity score model omitting the confounder. C-statistics under both scenarios were similar. Residual confounding was observed from using the propensity score excluding the confounder (range: 1–30%). Conclusions Omission of important confounders from the propensity score leads to residual confounding in estimates of treatment effect. However, tests of GOF and discrimination do not provide information to detect missing confounders in propensity score models. Our findings suggest that it may not be necessary to compute GOF statistics or model discrimination when developing propensity score models. Copyright © 2004 John Wiley & Sons, Ltd.
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Goodness of fit
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In this article, we presented the rationale and calculation procedures of the propcnsity score matching (PSM), and its application in the designing stage of an cpidcrniological study. Based on existing observational data, PSM can be used to select one or more comparable controls for each subject in 'treatment' group according to the propensity scores estimated by 'treatment' variable and main covariates. The results of an example analysis showed that the bias for main confounders between the treated and control samples declined more than 55% after PMS. Conclusion: PSM can reduce most of the confounding bias of the observational study, and can obtain approximate study effect to the randomized controlled trials when used in the designing of thc cpidcmiological study.
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Propensity score matching; Confounding bias; Epidemiology
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