Biomedical subjects

D Hedeker

Publications and source records attributed to D Hedeker.

At least 19 recordsLinked to original sources

PubMed2006

Obesity prevention in low socioeconomic status urban African-american adolescents: study design and preliminary findings of the HEALTH-KIDS Study.

OBJECTIVES: Obesity prevention among children and adolescents is a public health priority; however, limited school-based intervention trials targeting obesity have been conducted. This article provides an overview of the study design and baseline preliminary findings of our ongoing school-based intervention study. DESIGN: Randomized intervention trial to test a school-based, environmental obesity prevention program in urban low socioeconomic status (SES) African-American adolescents. The intervention program was developed based on several behavioral theories and was guided by preliminary findings based on focus group discussion and baseline data. SETTING: Four Chicago public schools in the US. SUBJECTS: Over 450 5-7th graders and their families and schools were involved. RESULTS: Our baseline data indicate a high prevalence of overweight (43% in boys and 41% in girls) and a number of problems in these children's physical activity and eating patterns. Only 26% reported spending > or = 20 min engaged in vigorous-moderate exercise in > or = 5 days over the past 7 days; 29% reported spending > or = 5 h each day watching TV, playing video games, or using computer. They also consumed too many fried foods and soft drinks. On average, 55% consumed fried foods > or = 2 times/day over the past 7 days; regarding soft drinks, 70% reported consuming > or = 2 times/day. CONCLUSION: School-based obesity prevention programs are urgently needed in the target US urban, low SES, minority communities. These data can be used to inform intervention activities.

Adolescent↗

PubMed2001

A comparative evaluation of three self-rating scales for acute mania.

This study compared the performance of three self-rating mania scales, The Internal State Scale (ISS), the Self-Report Manic Inventory (SRMI), and the Altman Self-Rating Mania Scale (ASRM), in a group of patients with acute mania. Forty-four adult inpatients with bipolar disorder, manic or mixed, completed all scales shortly after admission, and 31 patients completed them again after 4-6 weeks of pharmacotherapy. Patients also were rated by clinicians on the Clinician-Administered Rating Scale for Mania (CARS-M). At baseline, scores on the ASRM and the ISS well-being subscale were significantly correlated with CARS-M scores. Posttreatment scores were significantly decreased for the ASRM, SRMI, and the ISS activation subscale. The sensitivities for each scale to correctly identify patients with acute symptoms was 45% for the ISS, 86% for the SRMI, and 93% for the ASRM. Specificities were 73%, 46.6%, and 33%, respectively. The ASRM and SRMI were more sensitive than the ISS in screening patients with acute mania. All three measures were sensitive to treatment effects; however, the item content of the SRMI and the poor sensitivity of the ISS may limit their utility in inpatient settings.

Acute Disease↗

PubMed2001

An application of the thresholds of change model to the analysis of mental health data.

The threshold of change model (TCM) is a statistical technique for analyzing ordered stages of change variables. TCM focuses on the thresholds that separate the ordered stages, and the effects of explanatory variables are evaluated in terms of raising or lowering the thresholds. TCM also allows the explanatory variables to exert differential influence on each threshold. In this paper, we use TCM to analyze the data from a clinical trial that compared assertive community treatment (ACT) with standard case management (SCM) for patients with co-occurring severe mental illness and substance use disorder. Endpoint data (36-month follow up) were used for this analysis. The response variable is the recoded Substance Abuse Treatment Scale with three ordered levels (engagement/persuasion, active treatment, and recovery/relapse prevention), and hence two thresholds. The explanatory variables are gender and group (ACT vs. SCM). The results indicate that gender exerts constant and significant effects on both thresholds. The group effect is somewhat mixed: ACT lowers the first threshold (active treatment), but raises the second threshold (recovery/relapse prevention).

Adolescent↗

PubMed2001

Validity of the Test of Infant Motor Performance for discriminating among infants with varying risk for poor motor outcome.

OBJECTIVE: The objective was to assess the ability of the Test of Infant Motor Performance (TIMP) to discriminate among infants with varying degrees of risk for motor developmental morbidity on the basis of perinatal medical complications. STUDY DESIGN: Ninety-eight infants were tested weekly with the TIMP until 4 months of age. Comparisons were made among 5 groups of infants: (1) term infants without significant medical problems (low risk); (2) infants born prematurely with no significant medical problems (medium risk); (3) infants born at <30 weeks' gestational age or with birth weight <1500 g (high risk); (4) infants with chronic lung disease (high risk); and (5) infants with brain insults (high risk). A random-effects growth curve analysis assessed differences between the groups in slope and level of development across time. RESULTS: Infants in the low- and medium-risk groups did not differ from each other but were significantly better performers than infants in the high-risk groups. Infants with brain insults performed significantly less well than all other infants, both in absolute level of performance and in developmental slope across time. Performance by black infants averaged 2 points higher than that of other infants. CONCLUSIONS: The TIMP can discriminate among infants with differing risks for motor developmental delay.

Brain Injuries↗

PubMed2001

Statistical analysis of randomized trials in tobacco treatment: longitudinal designs with dichotomous outcome.

This article considers two important issues in the statistical treatment of data from tobacco-treatment clinical trials: (1) data analysis strategies for longitudinal studies and (2) treatment of missing data. With respect to data analysis strategies, methods are classified as 'time-naïve' or longitudinal. Time-naïve methods include tests of proportions and logistic regression. Longitudinal methods include Generalized Estimating Equations and Generalized Linear Mixed Models. It is concluded that, despite some advantages accruing to 'time-naïve' methods, in most situations, longitudinal methods are preferable. Longitudinal methods allow direct effects of the tests of time and the interaction of treatment with time, and allow model estimates based on all available data. The discussion of missing data strategies examines problems accruing to complete-case analysis, last observation carried forward, mean substitution approaches, and coding participants with missing data as using tobacco. Distinctions between different cases of missing data are reviewed. It is concluded that optimal missing data analysis strategies include a careful description of reasons for data being missing, along with use of either pattern mixture or selection modeling. A standardized method for reporting missing data is proposed. Reference and software programs for both data analysis strategies and handling of missing data are presented.

Humans↗

PubMed2001

A random-effects mixture model for classifying treatment response in longitudinal clinical trials.

A random-effects regression model that allows the random coefficients to have a multivariate normal mixture distribution is described for classifying treatment response in longitudinal clinical trials. The proposed model is capable of dealing with longitudinal data from unknown heterogeneous populations. As applied to longitudinal clinical trials, for example, the model can distinguish subgroups of treatment response. Use of the proposed model is illustrated by analyzing data from two psychiatric clinical trials. The first includes depressed patients assigned to drug treatment who are repeatedly measured in terms of their level of depression. The second trial examined schizophrenic patients longitudinally who were assigned to either a drug or placebo condition. For both, the random-effects mixture model allows an assessment of whether patients comprise distinct populations in terms of their treatment response. Based on parameter estimates of the mixture model, ample evidence for a mixture of response to treatment is observed for both datasets.

Algorithms↗

PubMed2000

Analysis of longitudinal substance use outcomes using ordinal random-effects regression models.

In this paper we describe analysis of longitudinal substance use outcomes using random-effects regression models (RRM). Some of the advantages of this approach is that these models allow for incomplete data across time, time-invariant and time-varying covariates, and can estimate individual change across time. Because substance use outcomes are often measured in terms of dichotomous or ordinal categories, our presentation focuses on categorical versions of RRM. Specifically, we present and describe an ordinal RRM that includes the possibility that covariate effects vary across the cutpoints of the ordinal outcome. This latter feature is particularly useful because a treatment can have varying effects on full versus partial abstinence, for example. Data from a smoking cessation study are used to illustrate application of this model for analysis of longitudinal substance use data.

Humans↗

PubMed2000

Random-effects regression analysis of correlated grouped-time survival data.

Random-effects regression modelling is proposed for analysis of correlated grouped-time survival data. Two analysis approaches are considered. The first treats survival time as an ordinal outcome, which is either right-censored or not. The second approach treats survival time as a set of dichotomous indicators of whether the event occurred for time periods up to the period of the event or censor. For either approach both proportional hazards and proportional odds versions of the random-effects model are developed, while partial proportional hazards and odds generalizations are described for the latter approach. For estimation, a full-information maximum marginal likelihood solution is implemented using numerical quadrature to integrate over the distribution of multiple random effects. The quadrature solution allows some flexibility in the choice of distributions for the random effects; both normal and rectangular distributions are considered in this article. An analysis of a dataset where students are clustered within schools is used to illustrate features of random-effects analysis of clustered grouped-time survival data.

Biometry↗

PubMed1998

Modelling ordinal responses from co-twin control studies.

The co-twin control design has been widely used in studying the effects of environmental factors on the development of diseases. For binary outcomes that arise from co-twin control studies, the conditional likelihood method is commonly used. This approach, however, does not readily extend to ordinal response data because the standard conditional likelihood does not exist for cumulative logit or proportional odds models. In this paper, we investigate the applicability of the random-effects and GEE approaches in analysing ordinal response data from co-twin control studies. Using both approaches, we re-analyse data from a co-twin control study of the impact of military services during the Vietnam era on post-traumatic stress disorders (PTSD). The ordinal models have considerably increased power in detecting the effects of exposure when compared to the analyses using a dichotomized response. We discuss the interpretation of the estimates from GEE and random-effect models in the context of the twin data.

Cohort Studies↗

PubMed1998

Comparison of population-averaged and subject-specific approaches for analyzing repeated binary outcomes.

Several approaches have been proposed to model binary outcomes that arise from longitudinal studies. Most of the approaches can be grouped into two classes: the population-averaged and subject-specific approaches. The generalized estimating equations (GEE) method is commonly used to estimate population-averaged effects, while random-effects logistic models can be used to estimate subject-specific effects. However, it is not clear to many epidemiologists how these two methods relate to one another or how these methods relate to more traditional stratified analysis and standard logistic models. The authors address these issues in the context of a longitudinal smoking prevention trial, the Midwestern Prevention Project. In particular, the authors compare results from stratified analysis, standard logistic models, conditional logistic models, the GEE models, and random-effects models by analyzing a binary outcome from two and seven repeated measurements, respectively. In the comparison, the authors focus on the interpretation of both time-varying and time-invariant covariates under different models. Implications of these methods for epidemiologic research are discussed.

Epidemiologic Methods↗

PubMed1997

The Altman Self-Rating Mania Scale.

We report on the development, reliability, and validity of the Altman Self-Rating Mania Scale (ASRM). The ASRM was completed during medication washout and after treatment by 22 schizophrenic, 13 schizoaffective, 36 depressed, and 34 manic patients. The Clinician-Administered Rating Scale for Mania (CARS-M) and Mania Rating Scale (MRS) were completed at the same time to measure concurrent validity. Test-retest reliability was assessed separately on 20 depressed and 10 manic patients who completed the ASRM twice during washout. Principal components analysis of ASRM items revealed three factors: mania, psychotic symptoms, and irritability. Baseline mania subscale scores were significantly higher for manic patients compared to all other diagnostic groups. Manic patients had significantly decreased posttreatment scores for all three subscales. ASRM mania subscale scores were significantly correlated with MRS total scores (r = .718) and CARS-M mania subscale scores (r = .766). Test-retest reliability for the ASRM was significant for all three subscales. Significant differences in severity levels were found for some symptoms between patient ratings on the ASRM and clinician ratings on the CARS-M. Mania subscale scores of greater than 5 on the ASRM resulted in values of 85.5% for sensitivity and 87.3% for specificity. Advantages of the ASRM over other self-rating mania scales are discussed.

Adult↗

PubMed1997

Psychotic exacerbations and enhanced vasopressin secretion in schizophrenic patients with hyponatremia and polydipsia.

BACKGROUND: For unclear reasons, life-threatening water intoxication often coincides with acute psychosis in polydipsic schizophrenic patients with chronic hyponatremia. In contrast, most polydipsic schizophrenic patients are normonatremic and never manifest hyponatremia. To explore whether the effect of acute psychosis on water balance differs in these 2 schizophrenic subgroups, we compared their responses to drug-induced psychotic exacerbations. METHODS: Matched polydipsic schizophrenic patients with (n = 6) and without (n = 8) hyponatremia were identified based on past and current indexes of fluid intake and hydration. A transient psychotic exacerbation was induced with an infusion of the psychotomimetic methylphenidate hydrochloride (0.5 mg/kg of body weight over a 60-second period). Antidiuretic hormone levels, subjective desire for water, and factors known to influence water balance were measured at 15-minute intervals for 2 hours. RESULTS: Except for the expected differences in plasma osmolality and sodium, basal measures were similar in the 2 groups. Following methylphenidate administration, antidiuretic hormone levels increased more in the hyponatremic patients (P < .02), despite their consistently lower plasma osmolality (P < .007). No known or putative antidiuretic hormone stimulus could account for this finding. Only basal positive psychotic symptoms (P < .09) and plasma sodium (P < .18) were even marginally associated with the peak antidiuretic hormone responses, but neither factor could explain the difference in the response by the 2 groups. CONCLUSION: Psychotic exacerbations are associated with enhanced antidiuretic hormone secretion, for unknown reasons, in schizophrenic patients with hyponatremia and polydipsia, thereby placing them at increased risk of life-threatening water intoxication.

Acute Disease↗

PubMed1997

A worksite smoking intervention: a 2 year assessment of groups, incentives and self-help.

Sixty-three companies in the Chicago area were recruited to participate in a worksite smoking cessation program. Participants in each worksite received a television program and newspaper supplement (part of a community-wide media campaign), and one of three conditions: (1) self-help manuals alone (M), (2) self-help manuals and incentives for 6 months (IM) or (3) maintenance manuals, incentives and cognitive-behavioral support groups for 6 months (GIM). Results at the 2 year assessment are examined using a random-effects regression model. In addition, various definitions of quit-rate commonly used in smoking cessation research are explored and the advantages of using a public health approach in the worksite are examined.

Humans↗

PubMed1997

Random effects probit and logistic regression models for three-level data.

In analysis of binary data from clustered and longitudinal studies, random effect models have been recently developed to accommodate two-level problems such as subjects nested within clusters or repeated classifications within subjects. Unfortunately, these models cannot be applied to three-level problems that occur frequently in practice. For example, multicenter longitudinal clinical trials involve repeated assessments within individuals and individuals are nested within study centers. This combination of clustered and longitudinal data represents the classic three-level problem in biometry. Similarly, in prevention studies, various educational programs designed to minimize risk taking behavior (e.g., smoking prevention and cessation) may be compared where randomization to various design conditions is at the level of the school and the intervention is performed at the level of the classroom. Previous statistical approaches to the three-level problem for binary response data have either ignored one level of nesting, treated it as a fixed effect, or used first- and second-order Taylor series expansions of the logarithm of the conditional likelihood to linearize these models and estimate model parameters using more conventional procedures for measurement data. Recent studies indicate that these approximate solutions exhibit considerable bias and provide little advantage over use of traditional logistic regression analysis ignoring the hierarchical structure. In this paper, we generalize earlier results for two-level random effects probit and logistic regression models to the three-level case. Parameter estimation is based on full-information maximum marginal likelihood estimation (MMLE) using numerical quadrature to approximate the multiple random effects. The model is illustrated using data from 135 classrooms from 28 schools on the effects of two smoking cessation interventions.

Clinical Trials as Topic↗

PubMed1996

Intraclass correlation estimates in a school-based smoking prevention study. Outcome and mediating variables, by sex and ethnicity.

Most school-based smoking prevention studies employ designs in which schools or classrooms are assigned to different treatment conditions while observations are made on individual students. This design requires that the treatment effect be assessed against the between-school variance. However, the between-school variance is usually larger than the variance that would be obtained if students were individually randomized to different conditions. Consequently, the power of the test for a treatment effect is reduced, and it becomes difficult to detect important treatment effects. To assess the potential loss of power or to calculate appropriate sample sizes, investigators need good estimates of the intraclass correlations for the variables of interest. The authors calculated intraclass correlations for some common outcome variables in a school-based smoking prevention study, using a three-level model-i.e., students nested within classrooms and classrooms nested within schools. The authors present the intraclass correlation estimates for the entire data set, as well as separately by sex and ethnicity. They also illustrate the use of these estimates in the planning of future studies.

Adolescent↗

PubMed1996

MIXOR: a computer program for mixed-effects ordinal regression analysis.

MIXOR provides maximum marginal likelihood estimates for mixed-effects ordinal probit, logistic, and complementary log-log regression models. These models can be used for analysis of dichotomous and ordinal outcomes from either a clustered or longitudinal design. For clustered data, the mixed-effects model assumes that data within clusters are dependent. The degree of dependency is jointly estimated with the usual model parameters, thus adjusting for dependence resulting from clustering of the data. Similarly, for longitudinal data, the mixed-effects approach can allow for individual-varying intercepts and slopes across time, and can estimate the degree to which these time-related effects vary in the population of individuals. MIXOR uses marginal maximum likelihood estimation, utilizing a Fisher-scoring solution. For the scoring solution, the Cholesky factor of the random-effects variance-covariance matrix is estimated, along with the effects of model covariates. Examples illustrating usage and features of MIXOR are provided.

Antipsychotic Agents↗

PubMed1996

MIXREG: a computer program for mixed-effects regression analysis with autocorrelated errors.

MIXREG is a program that provides estimates for a mixed-effects regression model (MRM) for normally-distributed response data including autocorrelated errors. This model can be used for analysis of unbalanced longitudinal data, where individuals may be measured at a different number of timepoints, or even at different timepoints. Autocorrelated errors of a general form or following an AR(1), MA(1), or ARMA(1,1) form are allowable. This model can also be used for analysis of clustered data, where the mixed-effects model assumes data within clusters are dependent. The degree of dependency is estimated jointly with estimates of the usual model parameters, thus adjusting for clustering. MIXREG uses maximum marginal likelihood estimation, utilizing both the EM algorithm and a Fisher-scoring solution. For the scoring solution, the covariance matrix of the random effects is expressed in its Gaussian decomposition, and the diagonal matrix reparameterized using the exponential transformation. Estimation of the individual random effects is accomplished using an empirical Bayes approach. Examples illustrating usage and features of MIXREG are provided.

Adolescent↗

PubMed1996

Impacts of foot orthoses on pain and disability in rheumatoid arthritics.

Rheumatoid arthritis (RA) frequently causes foot pain and swelling that affect ambulation. Pharmaceutical management of pain and disability is standard in clinical practice. The use of functional posted foot orthoses, as an adjunct to pharmaceutical treatment, is a promising treatment for managing foot pain and disability in RA. Its effectiveness, however, has not been rigorously evaluated. We performed a double-blind clinical trial using foot orthoses vs. placebo orthoses in the management of the rheumatoid arthritic foot, while subjects continued customary treatment. On the basis of findings of no effect on disability and pain measures, this study indicates no benefit of functional posted foot orthoses over placebos.

Adolescent↗