Using Matching Methods From Both Fisher\u27s Experimental Design and Rubin\u27s Causal Model to Compare Between Two Medical Facilities with Extremely Skewed Number of Subjects

The present study deals with the problem of comparison between a two medical facilities\u27 with extremely skewed sample sizes from non-experimental study. The data came from a study of rehabilitation interventions with patients diagnosed with cardiac and pulmonary issues who received treatment either in inpatient rehabilitation facilities (IRFs) or in skilled nursing facilities (SNFs). Due to inclusion and exclusion criteria, however, the study had failed to recruit sufficient number of participants between two comparison groups: 319 from IRFs and 27 from SNFs. As a result, the main hypothesis of the study was not tested due to the disparity of the participants between the two comparison groups, which could not be analyzed as a study with an unbalanced design because of lack of power in the analysis (Beacham, 2008). In medical research, this kind of problem occurs often not only because of inclusion and exclusion criteria in recruiting patients for a study but also because of dropout patients due to many reasons, such as technical changes (certain insurance and/or Medicare policies eliminate possible participants), medical changes, or personal circumstances change in the middle of the study. By extracting matching methods from both Fisher\u27s experimental design and Rubin\u27s Causal Model (RCM) the present study attempts to offer ways to draw the causal inference in a non-experimental study with sample size disparity between two comparison groups, especially when collected data disable a researcher to analyze. The matched datasets were analyzed in two ways: multivariate of covariance (MANCOVA) first and two analysis of covariance (ANCOVA) models when there was a significant main effect in the previous MANCOVA model. No significant different effectiveness was found between IRFs and SNFs in the 1:1 Matched Data, but IRFs took better care than SNFs in the Caliper Matched Data, rehabilitating the patients diagnosed with cardiac and pulmonary diseases on the functional independent measure (FIM). In comparison methodology, the results suggested that datasets created by both matching methods provided similar results, but that Fisher\u27s design fits better for small dataset while RCM, for larger dataset by using propensity scores to balance the matching sets

Blood Pressure Variability and Risk of Heart Failure in ACCORD and the VADT

In ACCORD, CV and ARV of SBP and DBP were associated with increased risk of HF, even after adjusting for other risk factors and mean blood pressure (e.g., CV-SBP: hazard ratio [HR] 1.15, P = 0.01; CV-DBP: HR 1.18, P = 0.003). In the VADT, DBP variability was associated with increased risk of HF (ARV-DBP: HR 1.16, P = 0.001; CV-DBP: HR 1.09, P = 0.04). Further, in ACCORD, those with progressively lower baseline blood pressure demonstrated a stepwise increase in risk of HF with higher CV-SBP, ARV-SBP, and CV-DBP. Effects of blood pressure variability were related to dips, not elevations, in blood pressure.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Blood Pressure and Pulse Pressure Effects on Renal Outcomes in the Veterans Affairs Diabetes Trial (VADT)

OBJECTIVE: Blood pressure (BP) control for renal protection is essential for patients with type 2 diabetes. Our objective in this analysis of Veterans Affairs Diabetes Trial (VADT) data was to learn whether on-study systolic BP (SBP), diastolic BP (DBP), and pulse pressure (PP) affected renal outcomes measured as albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR). RESEARCH DESIGN AND METHODS: The VADT was a prospective, randomized study of 1,791 veterans with type 2 diabetes to determine whether intensive glucose control prevented major cardiovascular events. In this post hoc study, time-varying covariate survival analyses and hazard ratios (HR) were used to determine worsening of renal outcomes. RESULTS: Compared with SBP 105–129 mmHg, the risk of ACR worsening increased significantly for SBP 130–139 mmHg (HR 1.88 [95% CI 1.28–2.77]; P = 0.001) and for SBP ≥140 mmHg (2.51 [1.66–3.78]; P < 0.0001). Compared with a PP range of 40–49 mmHg, PP <40 was associated with significantly lowered risk of worsening ACR (0.36 [0.15–0.87]; P = 0.022) and PP ≥60 with significantly increased risk (2.38 [1.58–3.59]; P < 0.0001). Analyses of BP ranges associated with eGFR worsening showed significantly increased risk with rising baseline SBP and an interaction effect between SBP ≥140 mmHg and on-study A1C. These patients were 15% more likely than those with SBP <140 mmHg to experience eGFR worsening (1.15 [1.00–1.32]; P = 0.045) for each 1% (10.9 mmol/mol) A1C increase. CONCLUSIONS: SBP ≥130 mmHg and PP >60 mmHg were associated with worsening ACR. The results suggest that treatment of SBP to <130 mmHg may lessen ACR worsening. The interaction between SBP ≥140 mmHg and A1C suggests that the effect of glycemic control on reducing progression of renal disease may be greater in hypertensive patients