The number of strata in propensity score stratification for a binary outcome

Dealing with confounding in observational studies: A scoping review of methods evaluated in simulation studies with single-point exposure

The aim of this article was to perform a scoping review of methods available for dealing with confounding when analyzing the effect of health care treatments with single-point exposure in observational data. We aim to provide an overview of methods and their performance assessed by simulation studies indexed in PubMed. We searched PubMed for simulation studies published until January 2021. Our search was restricted to studies evaluating binary treatments and binary and/or continuous outcomes. Information was extracted on the methods' assumptions, performance, and technical properties. Of 28,548 identified references, 127 studies were eligible for inclusion. Of them, 84 assessed 14 different methods (ie, groups of estimators that share assumptions and implementation) for dealing with measured confounding, and 43 assessed 10 different methods for dealing with unmeasured confounding. Results suggest that there are large differences in performance between methods and that the performance of a specific method is highly dependent on the estimator. Furthermore, the methods' assumptions regarding the specific data features also substantially influence the methods' performance. Finally, the methods result in different estimands (ie, target of inference), which can even vary within methods. In conclusion, when choosing a method to adjust for measured or unmeasured confounding it is important to choose the most appropriate estimand, while considering the population of interest, data structure, and whether the plausibility of the methods' required assumptions hold.

Propensity score methods for observational studies with clustered data: A review

Propensity score methods are a popular approach to mitigating confounding bias when estimating causal effects in observational studies. When study units are clustered (eg, patients nested within health systems), additional challenges arise such as accounting for unmeasured confounding at multiple levels and dependence between units within the same cluster. While clustered observational data are widely used to draw causal inferences in many fields, including medicine and healthcare, extensions of propensity score methods to clustered settings are still a relatively new area of research. This article presents a framework for estimating causal effects using propensity scores when study units are nested within clusters and are nonrandomly assigned to treatment conditions. We emphasize the need for investigators to examine the nature of the clustering, among other properties, of the observational data at hand in order to guide their choice of causal estimands and the corresponding propensity score approach.

Research and scholarly methods: Propensity scores

Propensity score methods are increasingly used as a tool to control for confounding bias in pharmacoepidemiologic studies. The propensity score is a dimension reducing balancing score, creating treatment and reference groups that have comparable distributions of measured covariates. The purpose of this methods review is to provide an overview of the use of propensity score methods, including a summary of important data assumptions, various applications of the propensity score, and how to evaluate covariate balance. This article is intended for pharmacists and researchers who wish to receive an introduction to propensity score methods and be able to engage in high-level discussions on application and reporting.

Variation in pediatric cervical spine imaging across trauma centers-A cause for concern?

BACKGROUND

Traumatic pediatric cervical spine injury can be challenging to diagnose, and the clinical algorithms meant to aid physicians differ from adult trauma protocols. Despite the existence of standardized guidelines, imaging decisions may vary according to physician education, subjective assessment, and experience with pediatric trauma patients. Our study investigates the rates of pediatric posttraumatic cervical spine imaging across trauma centers, hypothesizing that more specialized centers will have lower rates of advanced cervical spine imaging.

METHODS

The 2015 to 2016 Trauma Quality Improvement Program database was reviewed for patients younger than 18 years- to assess rates of cervical spine imaging on presentation across different trauma centers. Propensity stratification logistic regression was performed controlling for patient- and center-specific variables. p Values less than 0.05 were considered significant.

RESULTS

Of 110,769 pediatric trauma patients, 35.2% were female, and the average age was 9.6 years. Overall, 3.6% had cervical spine computed tomography (CT) and less than 1% had cervical spine MRI or X-ray. Compared with all others, Level I trauma centers were significantly less likely to use cervical spine CT for the initial evaluation of younger (≤14 years) but not older trauma patients (adjusted odds ratio [AOR], 0.89; 95% confidence interval [CI], 0.80-0.99; AOR, 0.97; 95% CI, 0.87-1.09); Level I centers had higher odds of cervical spine MRI use, but only for patients 14 years or younger (AOR, 1.63; 95% CI, 1.09-2.44). Pediatric-designated trauma centers had significantly lower odds of cervical spine CT (≤14 years: AOR, 0.70; 95% CI, 0.63-0.78; >14 years: AOR, 0.67; 95% CI, 0.67-0.75) and higher odds of cervical spine X-ray (≤14 years: AOR, 4.75; 95% CI, 3.55-6.36; >14 years: AOR, 4.50; 95% CI, 2.72-7.45) for all ages, but higher odds of cervical spine MRI for younger patients only (≤14 years: AOR, 2.10; 95% CI, 1.38-3.21).

CONCLUSION

Level I and pediatric designations were associated with lower rates of cervical spine CT. Pediatric centers were also more likely to use cervical spine X-ray. This variability of imaging use further supports the need to disseminate and educate providers on pediatric-specific cervical spine evaluation guidelines.

LEVEL OF EVIDENCE

Prognostic and epidemiological, level III.

Influence of race and sociodemographic factors on declining resection for gastric cancer: A national study

BACKGROUND

The purpose of this study was to determine whether racial or other demographic characteristics were associated with declining surgery for early stage gastric cancer.

METHODS

Patients with clinical stage I-II gastric adenocarcinoma were identified from the NCDB. Multivariable logistic models identified predictors for declining resection. Patients were stratified based on propensity scores, which were modeled on the probability of declining. Overall survival was evaluated using the Kaplan-Meier method.

RESULTS

Of 11,326 patients, 3.68% (n = 417) declined resection. Patients were more likely to refuse if they were black (p < 0.001), had Medicaid or no insurance (p < 0.001), had shorter travel distance to the hospital (p < 0.001) or were treated at a non-academic center (p = 0.001). After stratification, patients who declined surgery had worse overall survival (all strata, p < 0.001).

CONCLUSIONS

Racial and sociodemographic disparities exist in the treatment of potentially curable gastric cancer, with patients who decline recommended surgery suffering worse overall survival.