Jeffrey N. Jonkman

Disordered Gambling among College Students: A Meta-Analytic Synthesis

The purpose of this study was to use a meta-analytic procedure to synthesize the rates of disordered gambling for college students that have been reported in the research literature. In order to identify all possible studies that met stringent inclusion criteria, Medline, PsychINFO, and SocioIndex databases were searched with the terms “gambling,” and “college student”. This process resulted in 15 studies concerning gambling among college students that were published through July 2005. To synthesize the 15 studies, a random effects model for meta-analysis was applied. The estimated proportion of disordered gamblers among college students was 7.89%. This estimate is noteworthy because it is higher than that reported for adolescents, college students or adults in a previous study using meta-analytic procedures with studies conducted prior to 1997.

A Note on Variance Estimation in Random Effects Meta-Regression

ABSTRACT For random effects meta-regression inference, variance estimation for the parameter estimates is discussed. Because estimated weights are used for meta-regression analysis in practice, the assumed or estimated covariance matrix used in meta-regression is not strictly correct, due to possible errors in estimating the weights. Therefore, this note investigates the use of a robust variance estimation approach for obtaining variances of the parameter estimates in random effects meta-regression inference. This method treats the assumed covariance matrix of the effect measure variables as a working covariance matrix. Using an example of meta-analysis data from clinical trials of a vaccine, the robust variance estimation approach is illustrated in comparison with two other methods of variance estimation. A simulation study is presented, comparing the three methods of variance estimation in terms of bias and coverage probability. We find that, despite the seeming suitability of the robust estimator for random effects meta-regression, the improved variance estimator of Knapp and Hartung (2003) yields the best performance among the three estimators, and thus may provide the best protection against errors in the estimated weights.

Improving Perinatal Regionalization for Preterm Deliveries in a Medicaid Covered Population: Initial Impact of the Arkansas ANGELS Intervention

OBJECTIVE To examine the factors associated with delivery of preterm infants at neonatal intensive care unit (NICU) hospitals in Arkansas during the period 2001-2006, with a focus on the impact of a Medicaid supported intervention, Antenatal and Neonatal Guidelines, Education, and Learning System (ANGELS), that expanded the consulting capacity of the academic medical centers maternal fetal medicine practice. DATA SOURCES A dataset of linked Medicaid claims and birth certificates for the time period by clustering Medicaid claims by pregnancy episode. Pregnancy episodes were linked to residential county-level demographic and medical resource characteristics. Deliveries occurring before 35 weeks gestation (n=5,150) were used for analysis. STUDY DESIGN Logistic regression analysis was used to examine time trends and individual, county, and intervention characteristics associated with delivery at hospitals with NICU, and delivery at the academic medical center. PRINCIPAL FINDINGS Perceived risk, age, education, and prenatal care characteristics of women affected the likelihood of use of the NICU. The perceived availability of local expertise was associated with a lower likelihood that preterm infants would deliver at the NICU. ANGELS did not increase the overall use of NICU, but it did shift some deliveries to the academic setting. CONCLUSION Perinatal regionalization is the consequence of a complex set of provider and patient decisions, and it is difficult to alter with a voluntary program.

Equivalence testing for parallelism in the four-parameter logistic model.

For assay or dose-response data in drug discovery, it is often important to test for parallelism of the response curves for two preparations, such as a test drug and a standard drug, in order to determine the potency of the test preparation relative to the standard preparation. A typical approach is to perform a three-degree of freedom approximate F test of the null hypothesis that the relevant parameters are equal for the two preparations. We argue that this problem may be more appropriately viewed as a practical equivalence testing problem, and present an alternative method for testing parallelism in the four-parameter logistic response curve, based on the theory of intersection–union tests. The approach is intuitively appealing and simple to implement using commonly available software, and may provide more appropriate inference for the problem of interest. Two examples are discussed to illustrate the testing approach outlined in this article, and to compare it with the typical approach to testing parallelism. A simulation study is also presented to compare the empirical properties of the two different testing approaches for a set of cases based approximately on one of the examples.

Use of Specialty OB Consults During High-Risk Pregnancies in a Medicaid-Covered Population: Initial Impact of the Arkansas ANGELS Intervention

This study examines the impact of a Medicaid-supported intervention (Antenatal and Neonatal Guidelines, Education and Learning System) to expand a high-risk obstetrics consulting service on the use of specialty consults between 2001 and 2006. Using a Medicaid claims-birth certificate data set, we find a decline over time in use of specialty consults for lower risk diagnoses and a shift to remote modalities for contact. Local physician participation in grand rounds via teleconference was associated both with specialty contact and use of remote modalities. Local physician use of a Call Center service was also associated with patient specialty contact. Expansion of telemedicine remote sites did not increase the likelihood of contact but was associated with the shift toward remote modalities. Specialty consult use and modality were influenced by the care context of the patient, particularly level of pregnancy risk, the specialty of the primary prenatal care provider, the timing of her prenatal care, and her ethnicity and education level.

Identifying a cohort of patients with early-stage breast cancer: a comparison of hospital discharge and primary data.

Background.Hospital discharge data are a potential source of information for quality of care; however, they lack detailed clinical data. Objectives.To assess the usefulness of hospital discharge data for describing patterns of care. Research Design. Cohort study comparing hospital discharge data with data collected from medical records and patients. Patients.Women diagnosed with early-stage breast cancer in Massachusetts and Minnesota (1993–1995). Measures.The percentage of patients in the primary data set who did not match a record in the discharge data set, and the percentage of patients in the discharge data set who did not match a record in the primary data set. Odds ratios for appearing in one data set, but not the other according to patient and hospital characteristics. Results.For patients in the primary data set, 26.9% from Massachusetts and 13.2% from Minnesota did not match a record in the discharge data set. In both states, factors associated with failure to match to the discharge data included receipt of breast conserving surgery, shorter length of stay, and treatment hospital. For patients in the discharge data set, 43.4% in Massachusetts and 30.3% in Minnesota did not match a patient in the primary data set. In both states, factors associated with failure to match to the primary data included treatment hospital and the presence of positive lymph nodes. Conclusions.Hospital discharge data were fairly sensitive when linked to patients with early-stage breast cancer who were identified through hospital records. The discharge data lacked specificity, however. If discharge data are used to characterize patterns care for inpatients with early stage disease, estimates are likely to be inaccurate due to the inclusion of unsuitable patients in the denominator used to calculate procedure rates.

A comparison of the variance estimation methods for heteroscedastic nonlinear models

Heteroscedasticity is commonly encountered when fitting nonlinear regression models in practice. We discuss eight different variance estimation methods for nonlinear regression models with heterogeneous response variances, and present a simulation study to compare the performance of the eight methods in terms of estimating the standard errors of the fitted model parameters. The simulation study suggests that when the true variance is a function of the mean model, the power of the mean variance function estimation method and the transform-both-sides method are the best choices for estimating the standard errors of the estimated model parameters. In general, the wild bootstrap estimator and two modified versions of the standard sandwich variance estimator are reasonably accurate with relatively small bias, especially when the heterogeneity is nonsystematic across values of the covariate. Furthermore, we note that the two modified sandwich estimators are appealing choices in practice, considering the computational advantage of these two estimation methods relative to the variance function estimation method and the transform-both-sides approach. Copyright

Testing for parallelism in the heteroscedastic four-parameter logistic model

ABSTRACT For bioassay data in drug discovery and development, it is often important to test for parallelism of the mean response curves for two preparations, such as a test sample and a reference sample in determining the potency of the test preparation relative to the reference standard. For assessing parallelism under a four-parameter logistic model, tests of the parallelism hypothesis may be conducted based on the equivalence t-test or the traditional F-test. However, bioassay data often have heterogeneous variance across dose levels. Specifically, the variance of the response may be a function of the mean, frequently modeled as a power of the mean. Therefore, in this article we discuss estimation and tests for parallelism under the power variance function. Two examples are considered to illustrate the estimation and testing approaches described. A simulation study is also presented to compare the empirical properties of the tests under the power variance function in comparison to the results from ordinary least squares fits, which ignore the non-constant variance pattern.