Unadjusted Coefficients Research Articles

PC.03 Relative intakes of energy and protein in the first week after birth are associated with neurodevelopmental outcome at 24 months in extremely preterm infants

BackgroundGiven the rapid brain growth that occurs during the third trimester, optimal nutrition offers a potential way to improve neurodevelopmental outcome in preterm infants. This pilot study aimed to explore...

A Comparison of Data Driven-based Measures of Adherence to Oral Hypoglycemic Agents in Medicaid Patients

Objective: Using observational clinical data from our local operational health information exchange, we evaluated different methods for measuring adherence to Oral Antihyperglycemic Agents (OHA) in patients with Type 2 diabetes. The primary objective is to compare different OHA adherence measures based on their associations with glycated hemoglobin A1c (HbA1c) levels. The secondary objective is to examine the relationship between patient demographic and clinical characteristics and HbA1c level. Methods: An observational sample of 831 Medicaid patients with Type 2 diabetes who had HbA1c test results recorded between January 1, 2001 and December 31, 2005 was identified in the Indiana Network of Patient Care (INPC). OHA adherence was measured by medication possession ratio (MPR), proportion of days covered (PDC), and the number of gaps (GAP) for 3, 6, and 12-month intervals prior to the HbA1c test date. The associations between these nine OHA adherence measures and HbA1c levels were examined and compared using mixed effects generalized linear models. Patient age, gender, race, duration of OHA treatment, number of concurrent OHAs, and OHA class were used to control the possible confounders in the analyses. Results: All three OHA adherence definitions showed consistent and significant association with HbA1c control. Unadjusted coefficients ranged -0.98 to -1.07 for PDC, -0.90 to -0.92 for MPR, and 0.25 to 0.19 for GAP. The 6-month PDC showed the strongest association with HbA1c levels in both unadjusted (-1.07, p<0.0001) and adjusted (-1.12, p<0.0001) models. Conclusion: Better OHA adherence is significantly associated with lower HbA1c level in Medicaid patients with Type 2 diabetes. The 6-month PDC is more highly correlated with the outcome than other OHA adherence measurements.

Accounting for bias in regression coefficients with example from feed efficiency

Estimates of regression coefficients are biased if the independent (or ‘ x’) variables contain errors (for example, measurement errors). Equations are derived for the amount of bias in bivariate regression where one independent variable contains significant error, but errors in the other are negligible. Results are tabulated for differing amounts of error and a range of correlations (from 0.1 to 0.9) between the two independent variables. The process of estimating residual feed intake (RFI) is used to illustrate biases present in real-life data. RFI is defined as the amount of feed eaten by an animal less what would be expected from the animal's metabolic weight and weight gain. Measurement errors of metabolic weight, especially if calculated from the mean of several weighings, are relatively small. In contrast, errors in weight gain may be substantial. Regression coefficients from fitting the RFI equation using two different estimates of weight gain are compared with equations derived from genotypic regression and feed standards tables. The unadjusted coefficients differ substantially, but are shown to be much more consistent after adjusting for bias using equations derived in this paper.

Adjusted Coefficients of Determination for Logistic Regression

This article proposes two adjusted coefficients of determination for logistic regression for measuring the strength of association between the outcome variable and the predictors as an extension of a similar concept in linear regression. The proposed adjusted coefficients correct the overestimation problem of the unadjusted coefficients and are especially useful when the sample size is small or the number of predictors is large. An R program is provided on the Internet.

On Small Sample Properties of R2 in a Linear Regression Model with Multivariate t Errors and Proxy Variables

In this paper we consider the small sample properties of the coefficient of determination in a linear regression model with multivariate t errors when proxy variables are used instead of unobservable regressors. The results show that if the unobservable variable is an important variable, the adjusted coefficient of determination can be more unreliable in small samples than the unadjusted coefficient of determination from both viewpoints of the bias and the MSE.