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R D Gibbons

Publications and source records attributed to R D Gibbons.

At least 37 records · Page 2Linked to original sources
PubMed1994

Application of random-effects probit regression models.

A random-effects probit model is developed for the case in which the outcome of interest is a series of correlated binary responses. These responses can be obtained as the product of a longitudinal response process where an individual is repeatedly classified on a binary outcome variable (e.g., sick or well on occasion t), or in "multilevel" or "clustered" problems in which individuals within groups (e.g., firms, classes, families, or clinics) are considered to share characteristics that produce similar responses. Both examples produce potentially correlated binary responses and modeling these person- or cluster-specific effects is required. The general model permits analysis at both the level of the individual and cluster and at the level at which experimental manipulations are applied (e.g., treatment group). The model provides maximum likelihood estimates for time-varying and time-invariant covariates in the longitudinal case and covariates which vary at the level of the individual and at the cluster level for multilevel problems. A similar number of individuals within clusters or number of measurement occasions within individuals is not required. Empirical Bayesian estimates of person-specific trends or cluster-specific effects are provided. Models are illustrated with data from mental health research.

Cluster Analysis↗
PubMed1994

Random-effects regression models for clustered data with an example from smoking prevention research.

A random-effects regression model is proposed for analysis of clustered data. Unlike ordinary regression analysis of clustered data, random-effects regression models do not assume that each observation is independent but do assume that data within clusters are dependent to some degree. The degree of this dependency is estimated along with estimates of the usual model parameters, thus adjusting these effects for the dependency resulting from the clustering of the data. A maximum marginal likelihood solution is described, and available statistical software for the model is discussed. An analysis of a dataset in which students are clustered within classrooms and schools is used to illustrate features of random-effects regression analysis, relative to both individual-level analysis that ignores the clustering of the data, and classroom-level analysis that aggregates the individual data.

Attitude to Health↗
PubMed1994

A computerized image analysis system for estimating Tanner-Whitehouse 2 bone age.

A method for assigning Tanner-Whitehouse 2 skeletal maturity scores (or bone ages) to hand-wrist X-rays by an image analysis computer system is described. An operator positions the relevant area of the X-ray on a light box beneath a video camera. Correct positioning is assured by computer templates of each bone stage. Thereafter the process is automatic; the computer, not the operator, rates the bones. The system produces continuous stage scores, not discrete ones such as B, C or D. Data are given which show that the computer-assisted skeletal age score is more repeatable than the usual manual (or unassisted) rating. The absolute difference between duplicates averaged 0.25 stages; differences of as much as 1.0 stage occurred in only 3% of duplicates compared with 15% obtained in manual ratings.

Age Determination by Skeleton↗
PubMed1994

A random-effects ordinal regression model for multilevel analysis.

A random-effects ordinal regression model is proposed for analysis of clustered or longitudinal ordinal response data. This model is developed for both the probit and logistic response functions. The threshold concept is used, in which it is assumed that the observed ordered category is determined by the value of a latent unobservable continuous response that follows a linear regression model incorporating random effects. A maximum marginal likelihood (MML) solution is described using Gauss-Hermite quadrature to numerically integrate over the distribution of random effects. An analysis of a dataset where students are clustered or nested within classrooms is used to illustrate features of random-effects analysis of clustered ordinal data, while an analysis of a longitudinal dataset where psychiatric patients are repeatedly rated as to their severity is used to illustrate features of the random-effects approach for longitudinal ordinal data.

Adolescent↗
PubMed1993

Some conceptual and statistical issues in analysis of longitudinal psychiatric data. Application to the NIMH treatment of Depression Collaborative Research Program dataset.

Longitudinal studies have a prominent role in psychiatric research; however, statistical methods for analyzing these data are rarely commensurate with the effort involved in their acquisition. Frequently the majority of data are discarded and a simple end-point analysis is performed. In other cases, so called repeated-measures analysis of variance procedures are used with little regard to their restrictive and often unrealistic assumptions and the effect of missing data on the statistical properties of their estimates. We explored the unique features of longitudinal psychiatric data from both statistical and conceptual perspectives. We used a family of statistical models termed random regression models that provide a more realistic approach to analysis of longitudinal psychiatric data. Random regression models provide solutions to commonly observed problems of missing data, serial correlation, time-varying covariates, and irregular measurement occasions, and they accommodate systematic person-specific deviations from the average time trend. Properties of these models were compared with traditional approaches at a conceptual level. The approach was then illustrated in a new analysis of the National Institute of Mental Health Treatment of Depression Collaborative Research Program dataset, which investigated two forms of psychotherapy, pharmacotherapy with clinical management, and a placebo with clinical management control. Results indicated that both person-specific effects and serial correlation play major roles in the longitudinal psychiatric response process. Ignoring either of these effects produces misleading estimates of uncertainty that form the basis of statistical tests of hypotheses.

Analysis of Variance↗
PubMed1993

Length of stay and recidivism in schizophrenia: a study of public psychiatric hospital patients.

OBJECTIVE: Psychiatric beds in public hospitals have decreased 80% since 1955, but admissions have risen correspondingly, largely because of high recidivism rates. Decreases in numbers of beds have been partly achieved by shortening the length of stay, which lessened by half between 1970 and 1980. This study was undertaken to determine whether duration of hospital treatment affects the rate and rapidity of relapse among schizophrenic patients. METHOD: Data on 1,500 patients from 10 state hospitals were gathered for 18 months after initial discharge. Predictor variables included age, sex, marital status, race, number of previous admissions, location of the facility, and length of stay. Data were analyzed by survival analysis with a Cox regression model for two times to initial relapse: 30 days and 18 months (outcome). RESULTS: Length of stay was significantly related to each time to relapse after the effects of number of previous admissions and age were partialed out. Facility location was not predictive, but intrahospital effects were tested by examining the data on the largest facility; again, length of stay significantly predicted relapse. CONCLUSIONS: Although the magnitude of the effect was small, the clinical significance of the findings is the greater likelihood that brief-stay patients will be rehospitalized within 30 days after discharge than will patients treated for longer periods. Brief hospitalization seems generally applicable to psychiatric populations, but there may be a small but important group of seriously mentally ill patients for whom other alternatives are possibly more appropriate and should be explored.

Adult↗
PubMed1993

Assessing the severity of depressive states in recently detoxified alcoholics.

The problem of how to assess "unbiased" symptoms of depression meaningfully in the context of alcoholism bedevils studies of concomitant alcoholism and affective disorder. This study summarizes this controversy and discusses its implications for developing improved measures of depression severity among alcoholics. The Beck Depression Inventory (BDI) responses of 130 alcoholic applicants applying for inpatient care were evaluated using a two-parameter normal item response model. This study demonstrates that a single dimension of depression severity accounts for patient responses well, but that seven BDI items were relatively poor markers of syndrome severity for these alcoholics. The study documents growing consensus among investigators as to which BDI items constitute a fair scale of depression severity among alcoholic patients The availability of "unbiased criteria" for assessing the severity of depression among alcoholics applying for inpatient treatment will enable investigators and clinicians to recognize patients with concomitant alcoholism and affective disorder for special attention and/or treatment. The modified BDI scale proposed here is a candidate for a clinical definition of depression severity among alcoholic patients by virtue of this study's demonstration that it defines a distinct syndrome in this patient sample; but the modified scale must undergo independent tests of validity before its clinical utility can be established.

Adult↗
PubMed1992

An application of item response theory to alexithymia assessment among abstinent alcoholics.

An item response theory (IRT) model identified three dimensions assessed by the Toronto Alexithymia Scale (TAS) in a sample of 130 male applicants for inpatient care at a Veterans Administration (VA) medical center alcoholism treatment program. A unidimensional solution did not capture all of alexithymia's theoretical features. Subjects with lower alexithymia scores gave positive responses to items tapping emotional awareness deficits; only those with higher alexithymia scores gave positive responses to items tapping external, operative cognitive style. Thus, a total TAS score may not represent alexithymia accurately in substance-abusing patient populations.

Adult↗
PubMed1992

Predicting risk for medical malpractice claims using quality-of-care characteristics.

The current fault-based tort system assumes that claims made against physicians are inversely related to the quality of care they provide. In this study we identified physician characteristics associated with elements of medical care that make physicians vulnerable to malpractice claims. A sample of physicians (n = 248) thought to be at high or low risk for claims was surveyed on various personal and professional characteristics. Statistical analysis showed that 9 characteristics predicted risk group. High risk was associated with increased age, surgical specialty, emergency department coverage, increased days away from practice, and the feeling that the litigation climate was "unfair." Low risk was associated with scheduling enough time to talk with patients, answering patients' telephone calls directly, feeling "satisfied" with practice arrangements, and acknowledging greater emotional distress. Prediction was more accurate for physicians in practice 15 years or less. We conclude that a relationship exists between a history of malpractice claims and selected physician characteristics.

Clinical Medicine↗
PubMed1992

The efficacy of psychotropic drugs: implications for power analysis.

The estimation of the correct sample size to successfully test a hypothesis has become critical. A common approach to this problem is for the investigating team to complete a pilot study of a few patients to establish the "active drug-placebo" difference, using this "effect size" to perform the power analysis for sample size estimation. Given the variability evident in the effect size from completed and published studies, the pilot study approach may not be entirely dependable. The authors propose a method to obtain this initial "active drug-placebo" difference, in the field of psychotropic drug research. They apply meta-analysis to statistically summarize effect sizes obtained from an exhaustive review of the literature for a specific psychotropic drug in a given clinical condition. All double-blind, random assignment studies are used to calculate the effect size; therefore, no selection bias exists. These literature-based effect sizes are then used to perform the traditional power analysis for sample size estimation. The authors propose these estimations as a convenient reference source for future clinical investigators.

Humans↗
PubMed1991

Lack of a bimodal distribution of ventricular size in schizophrenia: a Gaussian mixture analysis of 1056 cases and controls.

The finding of clinical and laboratory differences between schizophrenic patients with large and small cerebral ventricles has led to the widespread assumption that large ventricles are a marker that characterizes a subgroup of patients with schizophrenia. We reviewed all published English language ventricle-to-brain ratio (VBR) studies in which individual data points were available (schizophrenics: n = 691, medical controls; n = 205, normal volunteers: n = 160). Using a univariate normal mixture model to examine the distribution of ventricular size in each group, we found no evidence of a mixture of Gaussian distributions (i.e., "bimodality") within any of the three groups. The same analysis was then performed on the combined sample of schizophrenic patients and normal and medical controls, respectively. In each case the improvement in fit of a mixture of normal distributions compared to a single component normal distribution was significant. The data do not support the notion that ventricular enlargement is a discontinuous marker of a subtype of schizophrenia.

Analysis of Variance↗
PubMed1991

Random regression models for multicenter clinical trials data.

A random-effects regression model is proposed for the analysis of data arising from multicenter clinical trials. Advantages of the random regression model (RRM) in this context include that it allows for varying numbers of subjects within the different centers, it can assess the influence of variables measured both at the level of the subject and at the level of the center on the subject's clinical outcome, and it controls for and estimates the amount of intracenter variation that is present in the data. An example utilizing data collected in the National Institute of Mental Health schizophrenia collaborative study, where subjects were clustered within nine centers, illustrates the usefulness of the statistical model. Other applications and extensions of RRM within a psychiatric framework are discussed.

Humans↗
PubMed1990

A statistical method for evaluating suicide clusters and implementing cluster surveillance.

The absence of any standard definition of suicide cluster events hinders understanding of the prevalence of the problem, hinders the development of appropriate public health responses to observed clusters, and ultimately hinders investigation of the mechanisms underlying contagious communication of suicidal behavior. The authors introduce a Poisson mixture model for assessing potential clusters of adolescent suicide, apply that model to the monthly incidence rates of adolescent suicide for one populous US county over the last 11 years, and generate 99% tolerance limits with 95% confidence for the number of suicides which may occur by chance within specific intervals of time in that county. The suicide incidence data showed a remarkable fit to a single Poisson distribution, suggesting it is not unreasonable to consider the cases as randomly-distributed and independent events. The authors conclude that there is no evidence that adolescent suicides occurred in clusters in the place and in the time frame under study, and recommend the Poisson mixture model for ascertaining clusters as well as implementing cluster surveillance.

Adolescent↗
PubMed1989

What is the mechanism by which suicide attempts predispose to later suicide attempts? A mathematical model.

A state dependence model of serial behavior suggests that each occurrence increases the subsequent likelihood of that behavior being repeated. A heterogeneity model, by contrast, suggests that the likelihood of a behavior occurring is predetermined, and uninfluenced by intervening occurrences. We have applied the random-effects probit model of Gibbons and Bock (1987) to examine the fit of the state dependence and heterogeneity models to longitudinal data on suicide attempts by 928 patients with affective disorder. Heterogeneity but not state dependence was required to model these data. The findings suggest that when considering patients with moderate to severe major affective disorder, the clinician should not interpret the absence of any recent suicide attempts to mean that the patient is at relatively low risk for attempting suicide in the future. An implication of the heterogeneity model is that suicide attempts made many years ago may have equal value to recent attempts when estimating an individual's "predisposition" to nonlethal attempts in the future.

Adolescent↗
PubMed1989

Assessment of the debrisoquin and dextromethorphan phenotyping tests by gaussian mixture distributions analysis.

From a sample of 149 unrelated Spaniards, individuals were phenotyped for their ability to hydroxylate debrisoquin and O-demethylate dextromethorphan. The distribution of urinary metabolic ratios for each test was analyzed by univariate gaussian mixture distributions analysis to determine the number of populations, the mean and standard deviation of the metabolic ratios for each population, and the proportion belonging to each population. For the 124 subjects phenotyped with both the debrisoquin and dextromethorphan tests a bivariate analysis was performed. The results demonstrate that both tests similarly separated this sample into two populations, with 10% belonging to poor metabolizer phenotypes. In addition, the correlation between the metabolic ratios from each test is significant, indicating that they are measuring the same biologic trait and the certainty of correctly identifying the debrisoquin oxidation phenotype of an individual is improved by using the results of both tests.

Debrisoquin↗
PubMed1989

Investigating drug plasma levels and clinical response using random regression models.

The reanalysis of the Riesby dataset using a random regression model indicates a significant effect of DMI plasma measurements on HAM-D scores across the four timepoints of the study. This effect is especially strong when the HAM-D change from baseline score is used in place of the actual HAM-D score at the four timepoints. A significant effect of IMI was not found for either the actual HAM-D score or the HAM-D change from baseline score. An endogenous effect was marginally significant when the actual HAM-D score was used; however, this effect was not observed when the HAM-D change score was used as the dependent measure. There also was evidence of a marginally significant effect due to autocorrelation of the residuals, indicating that the residuals at a given timepoint were related to the residuals from previous timepoints according to a first-order autoregressive process. In contrast, when the repeated measures MANOVA was used to analyze these data, a significant effect of DMI was not observed, since subjects without complete data at all timepoints had to be dropped from the analysis. In longitudinal psychiatric studies where missing data are the rule (rather than the exception), the random regression approach provides an attractive alternative to the traditional methods of analyzing longitudinal data.

Humans↗
PubMed1988

Cerebrospinal fluid and urinary biogenic amines in depressed patients and healthy controls.

The National Institute of Mental Health-Clinical Research Branch Collaborative Study investigated 132 drug-free, severely depressed patients and 80 healthy controls. Forty-five percent of the depressed patients excreted markedly elevated levels of urinary epinephrine (E) and metanephrine (MET), while only 5% of healthy controls did so. Using gaussian mixture distributions, we identified two subgroups of depressed patients: one excreting normal levels and the other excreting high levels of urinary E, MET, norepinephrine, and normetanephrine. Cerebrospinal fluid homovanillic acid levels were low in a subgroup of depressed patients. When analyzed by subgroup, the elevated E + MET group had markedly lower cerebrospinal fluid homovanillic acid levels than controls, whereas depressed patients with normal catecholamine levels did not. Since it has been postulated that there are two subgroups of depressed patients, those with low 3-methoxy-4-hydroxyphenylglycol (MHPG) levels and normal 5-hydroxyindoleacetic acid (5-HIAA) levels and those with normal MHPG levels and low 5-HIAA levels, several analyses were performed to see if such a group could be identified. Our analysis failed to find evidence of a subgroup of depressives with low MHPG and normal 5-HIAA levels or normal MHPG and low 5-HIAA levels.

Biogenic Amines↗