Biomedical subjects

Kurt K Lohman

Publications and source records attributed to Kurt K Lohman.

5 recordsLinked to original sources

PubMed2006

Modeling adolescent drug-use patterns in cluster-unit trials with multiple sources of correlation using robust latent class regressions.

PURPOSE: The purpose of the study is to examine variation in adolescent drug-use patterns by using latent class regression analysis and evaluate the properties of an estimating-equations approach under different cluster-unit trial designs. METHODS: A set of second-order estimating equations for latent class models under the cluster-unit trial design are proposed. This approach models the correlation within subclusters (drug-use behaviors), but ignores the correlation within clusters (communities). A robust covariance estimator is proposed that accounts for within-cluster correlation. Performance of this approach is addressed through a Monte Carlo simulation study, and practical implications are illustrated by using data from the National Evaluation of the Enforcing Underage Drinking Laws Randomized Community Trial. RESULTS: The example shows that the proposed method provides useful information about the heterogeneous nature of drug use by identifying two subtypes of adolescent problem drinkers. A Monte Carlo simulation study supports the proposed estimation method by suggesting that the latent class model parameters were unbiased for 30 or more clusters. Consistent with other studies of generalized estimating equation (GEE) estimators, the robust covariance estimator tended to underestimate the true variance of regression parameters, but the degree of inflation in the test size was relatively small for 70 clusters and only slightly inflated for 30 clusters. CONCLUSIONS: The proposed model for studying adolescent drug use provides an alternative to standard diagnostic criteria, focusing on the nature of the drug-use profile, rather than relying on univariate symptom counts. The second-order GEE-type estimation procedure provided a computationally feasible approach that performed well for a moderate number of clusters and was consistent with prior studies of GEE under the generalized linear model framework.

Adolescent↗

PubMed2005

A latent class analysis of underage problem drinking: evidence from a community sample of 16-20 year olds.

The aim of this paper is to shed light on the nature of underage problem drinking by using an empirically based method to characterize the variation in patterns of drinking in a community sample of underage drinkers. A total of 4056 16-20-year-old current drinkers from 212 communities in the US were surveyed by telephone as part of the National Evaluation of the Enforcing Underage Drinking Laws (EUDL) Program. Latent class models were used to create homogenous groups of drinkers with similar drinking patterns defined by multiple indicators of drinking behaviors and alcohol-related problems. Two types of underage problem drinkers were identified; risky drinkers (30%) and regular drinkers (27%). The most prominent behaviors among both types of underage problem drinkers were binge drinking and getting drunk. Being male, other drug use, early onset drinking and beliefs about friends drinking and getting drunk were all associated with an increased risk of being a problem drinker after adjustment for other factors. Beliefs that most friends drink and current marijuana use were the strongest predictors of both risky problem drinking (OR=4.0; 95% CI=3.1, 5.1 and OR=4.0; 95% CI=2.8, 5.6, respectively) and regular problem drinking (OR=10.8; 95% CI=7.0, 16.7 and OR=10.2; 95% CI=6.9, 15.2). Young adulthood (ages 18-20) was significantly associated with regular problem drinking but not risky problem drinking. The belief that most friends get drunk weekly was the strongest discriminator of risky and regular problem drinking patterns (OR=5.3; 95% CI=3.9, 7.1). These findings suggest that underage problem drinking is most strongly characterized by heavy drinking behaviors which can emerge in late adolescence and underscores its association with perceptions regarding friends drinking behaviors and illicit drug use.

Adolescent↗

PubMed2005

Relationship between albuminuria and cardiovascular disease in Type 2 diabetes.

Impaired renal function and albuminuria, common among people with type 2 diabetes, are strong predictors of atherosclerotic cardiovascular events. However, the relationships among albuminuria and measures of calcified atherosclerotic plaque are unknown. Coronary and carotid artery calcified plaque were measured using fast-gated helical computed tomography, and B-mode ultrasonography measured common carotid artery intima-medial thickness (IMT) in 588 white participants with type 2 diabetes from 325 families ascertained for the presence of multiple siblings with type 2 diabetes. Measured risk factors included age, gender, BP, body mass index, GFR, glycosylated hemoglobin, LDL cholesterol, HDL cholesterol, smoking, and medications that affect urine albumin:creatinine ratio (ACR). Generalized estimating equations with exchangeable correlation and the sandwich estimator of the variance were used to test for an association among coronary artery calcified plaque, carotid artery calcified plaque, carotid IMT, and ACR while adjusting for measured risk factors. Participants had a mean +/- SD (median) age of 61.2 +/- 9.2 yr (61.0 yr), ACR of 106.2 +/- 590 mg/g (12.9 mg/g), GFR of 93.3 +/- 33.2 ml/min (87.4 ml/min), coronary artery calcium mass score of 1394 +/- 2685 (323), carotid artery calcium mass score of 295 +/- 652 (51), and IMT of 0.66 +/- 0.12 mm (0.65 mm). Adjusting for the measured covariates, ACR was strongly and positively associated with coronary artery calcium (P = 0.004) and carotid artery calcium (P = 0.0004). Albuminuria is strongly associated with calcified plaque in the coronary and carotid arteries in white individuals with type 2 diabetes and relatively preserved renal function.

Aged↗

PubMed2003

DNA damage and breast cancer risk.

To evaluate whether deficient DNA repair contributes to elevated DNA damage and breast carcinogenesis, we used the comet assay (single-cell alkaline gel electrophoresis) to measure the levels of DNA damage in peripheral lymphocytes from 70 breast cancer cases and 70 controls. DNA damage, measured as the comet tail moment, was not influenced by age, family history (FH), age at menarche, age at first birth or parity. The results showed that cancer cases had significantly higher DNA damage compared with controls; the comet tail moments (mean +/- SD) for cases and controls were: 10.78 +/- 3.63 and 6.86 +/- 2.76 (P < 0.001) for DNA damage at baseline (DB), 21.24 +/- 4.88 and 14.97 +/- 4.18 (P < 0.001) for DNA damage after exposure to 6 Gy of ionizing radiation (DIR), and 14.76 +/- 5.35 and 9.75 +/- 3.35 (P < 0.001) for DNA damage remaining after 10 min repair following exposure to 6 Gy of IR (DRP), respectively. Body mass index (BMI) affected DNA damage differently for cases and controls. Damage decreased with increasing BMI for controls, while damage increased with increasing BMI for cases. Above-median DNA damage was significantly associated with breast cancer risk; the age-adjusted odds ratio (OR) = 13.44 [95% confidence interval (CI) = 5.97-30.24] for DB, 13.65 (6.07-30.71) for DIR and 6.54 (3.11-13.79) for DRP, respectively. This association was stronger in women with above-median BMI. Our results, although based on a relatively small group of subjects, indicate that elevated DNA damage is significantly associated with breast cancer risk and warrant larger studies to further define the molecular mechanisms of DNA damage/repair in breast cancer susceptibility.

Age Distribution↗

PubMed2002

Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random.

The generalized estimating equations (GEE) approach is commonly used to model incomplete longitudinal binary data. When drop-outs are missing at random through dependence on observed responses (MAR), GEE may give biased parameter estimates in the model for the marginal means. A weighted estimating equations approach gives consistent estimation under MAR when the drop-out mechanism is correctly specified. In this approach, observations or person-visits are weighted inversely proportional to their probability of being observed. Using a simulation study, we compare the performance of unweighted and weighted GEE in models for time-specific means of a repeated binary response with MAR drop-outs. Weighted GEE resulted in smaller finite sample bias than GEE. However, when the drop-out model was misspecified, weighted GEE sometimes performed worse than GEE. Weighted GEE with observation-level weights gave more efficient estimates than a weighted GEE procedure with cluster-level weights.

Computer Simulation↗