The Role of Distributional Overlap on the Precision Gain of Bounds for Generalization

Over the past ten years, propensity score methods have made an important contribution to improving generalizations from studies that do not select samples randomly from a population of inference. However, these methods require assumptions and recent work has considered the role of bounding approaches that provide a range of treatment impact estimates that are consistent with the observable data. An important limitation to bound estimates is that they can be uninformatively wide. This has motivated research on the use of propensity score stratification to narrow bounds. This article assesses the role of distributional overlap in propensity scores on the effectiveness of stratification to tighten bounds. Using the results of two simulation studies and two case studies, I evaluate the relationship between distributional overlap and precision gain and discuss the implications when propensity score stratification is used as a method to improve precision in the bounding framework.

Effect of Clinical Inertia on Diabetes Complications among Individuals with Type 2 Diabetes: A Retrospective Cohort Study

Background and Objectives: Clinical inertia is a key obstacle that leads to suboptimal care in patients with type 2 diabetes mellitus (T2DM). It can occur at any stage of T2DM treatment. However, the effect of clinical inertia on diabetes complications has not been studied sufficiently. This study aimed to evaluate the effect of clinical inertia on the risk of diabetes complications among patients with T2DM. Materials and Methods: A retrospective cohort study was conducted at a tertiary teaching hospital in Thailand between 2011 and 2017. Outpatients with T2DM, aged 40–65 years, presenting an HbA1c greater than 7% were included in this study. Clinical inertia was identified when patients did not get treatment intensification at the index date and a subsequent prescription. The association between clinical inertia and diabetes complications, including a composite of macrovascular complications and a composite of microvascular complications, was determined using a Cox proportional hazard model. Propensity score methods were applied, to control confounding by indication. Results: Of 686 patients with T2DM, 165 (24.0%) experienced clinical inertia. Baseline low-density lipoprotein cholesterol, blood pressure, body mass index, the estimated glomerular filtration rate, and medication between the two groups did not differ significantly. Our study found that clinical inertia was associated with a significantly increased risk of diabetic nephropathy (adjusted HR 1.51, 95% CI 1.01–2.27). The results remained the same as when using propensity score methods. According to the post hoc analysis, lowering the HbA1c levels by 1% results in a significant decrease in the rate of diabetic complications (adjusted HR 0.92, 95% CI 0.86–0.99), the composite of microvascular complications (adjusted HR 0.91, 95%CI 0.84–0.98) and diabetic nephropathy (adjusted HR 0.89, 95% CI 0.80–0.98). Conclusions: Our results demonstrated a significant effect of clinical inertia on diabetic nephropathy. Patients with an HbA1c level over the target range should have their medication intensified to reduce the risk of diabetic nephropathy.

Association of Prior to Intensive Care Unit Statin Use With Outcomes on Patients With Acute Kidney Injury

Purpose: To evaluate the association of prior to intensive care unit (ICU) statin use with the clinical outcomes in critically ill patients with acute kidney injury (AKI).Materials and Methods: Patients with AKI were selected from the Medical Information Mart for Intensive Care IV (version 1.0) database for this retrospective observational study. The primary outcome was 30-day intensive care unit (ICU) mortality. A 30-day in-hospital mortality and ICU length of stay (LOS) were considered as secondary outcomes. Comparison of mortality between pre-ICU statin users with non-users was conducted by the multivariate Cox proportional hazards model. Comparison of ICU LOS between two groups was implemented by multivariate linear model. Three propensity score methods were used to verify the results as sensitivity analyses. Stratification analyses were conducted to explore whether the association between pre-ICU statin use and mortality differed across various subgroups classified by sex and different AKI stages.Results: We identified 3,821 pre-ICU statin users and 9,690 non-users. In multivariate model, pre-ICU statin use was associated with reduced 30-day ICU mortality rate [hazard ratio (HR) 0.68 (0.59, 0.79); p < 0.001], 30-day in-hospital mortality rate [HR 0.64 (0.57, 0.72); p < 0.001] and ICU LOS [mean difference −0.51(−0.79, −0.24); p < 0.001]. The results were consistent in three propensity score methods. In subgroup analyses, pre-ICU statin use was associated with decreased 30-day ICU mortality and 30-day in-hospital mortality in both sexes and AKI stages, except for 30-day ICU mortality in AKI stage 1.Conclusion: Patients with AKI who were administered statins prior to ICU admission might have lower mortality during ICU and hospital stay and shorter ICU LOS.

Exploratory Compatibility Regularity of Traditional Chinese Medicine on Osteoarthritis Treatment: A Data Mining and Random Walk-Based Identification

Osteoarthritis (OA) is a degressive and complex disease which is a growing public health problem on a global scale. On basis of an in-house database consisting of clinical records of 13,083 OA patients, the Traditional Chinese Medicine (TCM) was divided into 4 categories of medicines on the basis of the curative properties of herbs. Due to the lack of depth and internal relationship in the calculation results of TCM compatibility law data mining methods such as statistics and frequency analysis, we use a variety of multidimensional complex network methods that can efficaciously find the compatibility law of TCM, including similarity measure, graphical visualization of network diagram, random walking, and propensity score methods. We summarize common couplet medicines utilized for the treatment of osteoarthritis. The similarity measure method was used to investigate the commonly used drugs for the treatment of osteoarthritis. The method of association rule analysis is used to recognize the compatibility between the components. On basis of the propensity score methods, the evaluation displayed that, compared with single drug, the drug group increased ESR, CRP, C3, C4, IgG, and IgA more efficiently. Concluding, a random walk model was constructed to assess drug efficacy. After applying a random walk model, while revealing the compatibility among different components of TCM, their therapeutic efficacy against OA is analyzed. We obtained four groups of drug combination clusters by similarity measure and 11 pairs of highly connected drugs by association rules, which are cardinal drug combinations in the prescription for the treatment of OA. We also found that different traditional drug pairs were associated with different laboratory indexes, and drug combinations could better optimize laboratory indexes. This study presented that the TCM constituents complement one another. Besides, the therapeutic effects resulting from a variety of combinations of these constituents are quite different.

The Effects of the 1930s HOLC “Redlining” Maps

This study uses a boundary design and propensity score methods to study the effects of the 1930s-era Home Owners Loan Corporation (HOLC) “redlining” maps on the long-run trajectories of urban neighborhoods. The maps led to reduced home ownership rates, house values, and rents and increased racial segregation in later decades. A comparison on either side of a city-level population cutoff that determined whether maps were drawn finds broadly similar conclusions. These results suggest the HOLC maps had meaningful and lasting effects on the development of urban neighborhoods through reduced credit access and subsequent disinvestment. (JEL G21, J15, N32, N42, N92, R23, R31)

The Role of Sample Size to Attain Statistically Comparable Groups – A Required Data Preprocessing Step to Estimate Causal Effects With Observational Data

Propensity score methods provide data preprocessing tools to remove selection bias and attain statistically comparable groups – the first requirement when attempting to estimate causal effects with observational data. Although guidelines exist on how to remove selection bias when groups in comparison are large, not much is known on how to proceed when one of the groups in comparison, for example, a treated group, is particularly small, or when the study also includes lots of observed covariates (relative to the treated group’s sample size). This article investigates whether propensity score methods can help us to remove selection bias in studies with small treated groups and large amount of observed covariates. We perform a series of simulation studies to study factors such as sample size ratio of control to treated units, number of observed covariates and initial imbalances in observed covariates between the groups of units in comparison, that is, selection bias. The results demonstrate that selection bias can be removed with small treated samples, but under different conditions than in studies with large treated samples. For example, a study design with 10 observed covariates and eight treated units will require the control group to be at least 10 times larger than the treated group, whereas a study with 500 treated units will require at least, only, two times bigger control group. To confirm the usefulness of simulation study results for practice, we carry out an empirical evaluation with real data. The study provides insights for practice and directions for future research.

Applying Propensity Score Methods in Clinical Research in Neurology

Propensity score-based analysis is increasingly being used in observational studies to estimate the effects of treatments, interventions, and exposures. We introduce the concept of the propensity score and how it can be used in observational research. We describe four different ways of using the propensity score: matching on the propensity score, inverse probability of treatment weighting using the propensity score, stratification on the propensity score, and covariate adjustment on the propensity score (with a focus on the first two). We provide recommendations for the use and reporting of propensity score methods for the conduct of observational studies in neurological research.