Biomedical subjects

Thomas R Ten Have

Publications and source records attributed to Thomas R Ten Have.

13 recordsLinked to original sources

PubMed2006

Longitudinal and repeated cross-sectional cluster-randomization designs using mixed effects regression for binary outcomes: bias and coverage of frequentist and Bayesian methods.

As medical applications for cluster randomization designs become more common, investigators look for guidance on optimal methods for estimating the effect of group-based interventions over time. This study examines two distinct cluster randomization designs: (1) the repeated cross-sectional design in which centres are followed over time but patients change, and (2) the longitudinal design in which individual patients are followed over time within treatment clusters. Simulations of each study design stipulated a multiplicative treatment effect (on the log odds scale), between 5 and 15 clusters in each of two treatment arms, and followed over two time periods. Estimation options included linear mixed effects models using restricted maximum likelihood (REML), generalized estimating equations (GEE), mixed effects logistic regression using both penalized quasi likelihood (PQL) and numerical integration, and Bayesian Monte Carlo analysis. For the repeated cross-sectional designs, most methods performed well in terms of bias and coverage when clusters were numerous (30) and variability across clusters of baseline risk and treatment effect was modest. With few clusters (two groups of five) and higher variability, only the Bayesian methods maintained coverage. In the longitudinal designs, the common methods of REML, GEE, or PQL performed poorly when compared to numerical integration, while Bayesian methods demonstrated less bias and better coverage for estimates of both log odds ratios and risk differences. The performance of common statistical tools for the analysis of cluster randomization designs depends heavily on the precise design, the number of clusters, and the variability of baseline outcomes and treatment effects across centres.

Bayes Theorem↗

PubMed2006

A mixed effects Markov model for repeated binary outcomes with non-ignorable dropout.

In many areas of research, repeated binary measures often represent a two-state stochastic process, where individuals can transition among two states. In a behavioural or physical disability setting, individuals can flow from susceptible or subthreshold state, to an infectious or symptomatic state, and back to a subthreshold state. Quite often the transition among the states happens in continuous time but is observed at discrete, irregularly spaced timepoints which may be unique to each individual. Methods for analyses of such data are typically based on the Markov assumption. Cook (Biometrics 1999; 55:915-920) introduced a conditional Markov model that accommodates the subject-to-subject variation in the model parameters with random effects. We extend this model by adding a non-ignorable dropout component to the model. Specification of the distribution of the random effects is made to guarantee a closed form expression of the marginal likelihood. This methodology is illustrated by applications to a data set from a parasitic field infection survey, a data set from a cocaine treatment study, and a data set from an aging study. Simulations suggest that the shared parameter model is robust with respect to at least one alternative non-ignorable model.

Activities of Daily Living↗

PubMed2006

Random effects logistic models for analysing efficacy of a longitudinal randomized treatment with non-adherence.

We present a random effects logistic approach for estimating the efficacy of treatment for compliers in a randomized trial with treatment non-adherence and longitudinal binary outcomes. We use our approach to analyse a primary care depression intervention trial. The use of a random effects model to estimate efficacy supplements intent-to-treat longitudinal analyses based on random effects logistic models that are commonly used in primary care depression research. Our estimation approach is an extension of Nagelkerke et al.'s instrumental variables approximation for cross-sectional binary outcomes. Our approach is easily implementable with standard random effects logistic regression software. We show through a simulation study that our approach provides reasonably accurate inferences for the setting of the depression trial under model assumptions. We also evaluate the sensitivity of our approach to model assumptions for the depression trial.

Humans↗

PubMed2005

Using a Bayesian latent growth curve model to identify trajectories of positive affect and negative events following myocardial infarction.

Positive and negative affect data are often collected over time in psychiatric care settings, yet no generally accepted means are available to relate these data to useful diagnoses or treatments. Latent class analysis attempts data reduction by classifying subjects into one of K unobserved classes based on observed data. Latent class models have recently been extended to accommodate longitudinally observed data. We extend these approaches in a Bayesian framework to accommodate trajectories of both continuous and discrete data. We consider whether latent class models might be used to distinguish patients on the basis of trajectories of observed affect scores, reported events, and presence or absence of clinical depression.

Bayes Theorem↗

PubMed2004

Reducing suicidal ideation and depressive symptoms in depressed older primary care patients: a randomized controlled trial.

CONTEXT: Suicide rates are highest in late life; the majority of older adults who die by suicide have seen a primary care physician in preceding months. Depression is the strongest risk factor for late-life suicide and for suicide's precursor, suicidal ideation. OBJECTIVE: To determine the effect of a primary care intervention on suicidal ideation and depression in older patients. DESIGN AND SETTING: Randomized controlled trial known as PROSPECT (Prevention of Suicide in Primary Care Elderly: Collaborative Trial) with patient recruitment from 20 primary care practices in New York City, Philadelphia, and Pittsburgh regions, May 1999 through August 2001. PARTICIPANTS: Two-stage, age-stratified (60-74, > or =75 years) depression screening of randomly sampled patients; enrollment included patients who screened positive and a random sample of screened negative patients. This analysis included patients with a depression diagnosis (N = 598). INTERVENTION: Treatment guidelines tailored for the elderly with care management compared with usual care. MAIN OUTCOME MEASURES: Assessment of suicidal ideation and depression severity at baseline, 4 months, 8 months, and 12 months. RESULTS: Rates of suicidal ideation declined faster (P =.01) in intervention patients compared with usual care patients; at 4 months, in the intervention group, raw rates of suicidal ideation declined 12.9% points (29.4% to 16.5%) compared with 3.0% points (20.1% to 17.1% in usual care [P =.01]). Among patients reporting suicidal ideation, resolution of ideation was faster among intervention patients (P =.03); differences peaked at 8 months (70.7% vs 43.9% resolution; P =.005). Intervention patients had a more favorable course of depression in both degree and speed of symptom reduction; group difference peaked at 4 months. The effects on depression were not significant among patients with minor depression unless suicidal ideation was present. CONCLUSIONS: Evidence of the intervention's effectiveness in community-based primary care with a heterogeneous sample of depressed patients introduces new challenges related to its sustainability and dissemination. The intervention's effectiveness in reducing suicidal ideation, regardless of depression severity, reinforces its role as a prevention strategy to reduce risk factors for suicide in late life.

Aged↗

PubMed2004

Joint modeling of longitudinal and survival data via a common frailty.

We develop a joint model for the analysis of longitudinal and survival data in the presence of data clustering. We use a mixed effects model for the repeated measures that incorporates both subject- and cluster-level random effects, with subjects nested within clusters. A Cox frailty model is used for the survival model in order to accommodate the clustering. We then link the two responses via the common cluster-level random effects, or frailties. This model allows us to simultaneously evaluate the effect of covariates on the two types of responses, while accounting for both the relationship between the responses and data clustering. The model was motivated by a study of end-stage renal disease patients undergoing hemodialysis, where we wished to evaluate the effect of iron treatment on both the patients' hemoglobin levels and survival times, with the patients clustered by enrollment site.

Algorithms↗

PubMed2004

Deviations from the population-averaged versus cluster-specific relationship for clustered binary data.

There has been much debate about the relative merits of mixed effects and population-averaged logistic models. We present a different perspective on this issue by noting that the investigation of the relationship between these models for a given dataset offers a type of sensitivity analysis that may reveal problems with assumptions of the mixed effects and/or population-averaged models for clustered binary response data in general and longitudinal binary outcomes in particular. We present several datasets in which the following violations of assumptions are associated with departures from the expected theoretical relationship between these two models: 1) negative intra-cluster correlations; 2) confounding of the response-covariate relationship by cluster effects; and 3) confounding of autoregressive relationships by the link between baseline outcomes and subject effects. Under each of these conditions, the expected theoretical attenuation of the population-averaged odds ratio relative to the cluster-specific odds ratio does not necessarily occur. In all cases, the naive fitting of a random intercept logistic model appears to lead to bias. In response, the random intercept model is modified to accommodate negative intra-cluster correlations, confounding due to clusters, or baseline correlations with random effects. Comparisons are made with GEE estimation of population-averaged models and conditional likelihood estimation of cluster-specific models. Several examples, including a cross-over trial, a multicentre nonrandomized treatment study, and a longitudinal observational study are used to illustrate these modifications.

Animals↗

PubMed2003

Causal logistic models for non-compliance under randomized treatment with univariate binary response.

We propose a method for estimating the marginal causal log-odds ratio for binary outcomes under treatment non-compliance in placebo-randomized trials. This estimation method is a marginal alternative to the causal logistic approach by Nagelkerke et al. (2000) that conditions on partially unknown compliance (that is, adherence to treatment) status, and also differs from previous approaches that estimate risk differences or ratios in subgroups defined by compliance status. The marginal causal method proposed in this paper is based on an extension of Robins' G-estimation approach for fitting linear or log-linear structural nested models to a logistic model. Comparing the marginal and conditional causal log-odds ratio estimates provides a way of assessing the magnitude of unmeasured confounding of the treatment effect due to treatment non-adherence. More specifically, we show through simulations that under weak confounding, the conditional and marginal procedures yield similar estimates, whereas under stronger confounding, they behave differently in terms of bias and confidence interval coverage. The parametric structures that represent such confounding are not identifiable. Hence, the proof of consistency of causal estimators and corresponding simulations are based on two different models that fully identify the causal effects being estimated. These models differ in the way that compliance is related to potential outcomes, and thus differ in the way that the causal effect is identified. The simulations also show that the proposed marginal causal estimation approach performs well in terms of bias under the different levels of confounding due to non-adherence and under different causal logistic models. We also provide results from the analyses of two data sets further showing how a comparison of the marginal and conditional estimators can help evaluate the magnitude of confounding due to non-adherence.

Confounding Factors, Epidemiologic↗

PubMed2003

Depression and HIV infection: impact on immune function and disease progression.

Can psychological factors, such as depression, affect human immunodeficiency virus progression? HIV infection is viewed as a chronic illness in which those infected often confront a number of emotional challenges and physical health and disease-related issues. Over the past 20 years, there has been increasing evidence that depression and other mood-related disturbances are commonly observed among HIV-positive individuals. There is also mounting data showing that depressive symptoms might further impact upon specific elements of immune system functioning and influence quality of life and health status. This paper will highlight studies examining the prevalence of depression during HIV infection and review some of the evidence examining the impact of depressive symptoms on immune function and HIV disease progression.

CD4 Lymphocyte Count↗

PubMed2003

The compliance score as a regressor in randomized trials.

The compliance score in randomized trials is a measure of the effect of randomization on treatment received. It is in principle a group-level pretreatment variable and so can be used where individual-level measures of treatment received can produce misleading inferences. The interpretation of models with the compliance score as a regressor of interest depends on the link function. Using the identity link can lead to valid inference about the effects of treatment received even in the presence of nonrandom noncompliance; such inference is more problematic for nonlinear links. We illustrate these points with data from two randomized trials.

Bias↗

PubMed2003

Probing the safety of medications in the frail elderly: evidence from a randomized clinical trial of sertraline and venlafaxine in depressed nursing home residents.

BACKGROUND: In nursing home residents and other frail elderly patients, old age and potential drug-drug and drug-disease interactions may affect the relative safety and efficacy of medications. The purpose of this study was to examine the efficacy and tolerability of venlafaxine and sertraline for the treatment of depression among nursing home residents. METHOD: The study was a 10-week randomized, double-blind, controlled trial of venlafaxine (doses up to 150 mg/day) versus sertraline (doses up to 100 mg/day) among 52 elderly nursing home residents with a DSM-IV depressive disorder and, at most, moderate dementia. The primary measure of outcome was the Hamilton Rating Scale for Depression (HAM-D). Adverse events were monitored and recorded systematically during the trial. RESULTS: Twelve subjects were discontinued due to serious adverse events (SAE), 5 were discontinued due to other significant side effects, and 2 withdrew consent. Tolerability estimated by the time to termination was lower for venlafaxine than sertraline for serious adverse events (log rank statistic = 5.28, p =.022), for serious adverse events or side effects (log rank statistic = 8.08, p =.005), or for serious adverse events, side effects, or withdrawal of consent (log rank statistic = 10.04, p =.002). Mean (SD) HAM-D scores at baseline were 20.2 (3.4) for sertraline and 20.3 (3.7) for venlafaxine; intent-to-treat endpoint HAM-D scores were 12.2 (5.1) and 15.7 (6.2) (F = 3.45; p =.069). There were no differences in categorical responses for the intent-to-treat sample or completers. CONCLUSION: In this frail elderly population, venlafaxine was less well tolerated and, possibly, less safe than sertraline without evidence for an increase in efficacy. This unexpected finding demonstrates the need for systematic research on the safety of drugs in the frail elderly.

Aged↗

PubMed2002

An assessment of non-randomized medical treatment of long-term schizophrenia relapse using bivariate binary-response transition models.

The analyses of observational longitudinal studies involving concurrent changes in treatment and medical conditions present difficulties because of the multitude of directions of potential relationships: past medication influences current symptoms; past symptoms influence current medication; and current medication is associated with current symptoms. In the context of a long-term study of non-randomized pharmacological treatment of schizophrenic relapse, we present an analysis of bivariate discrete-time transitional data with binary responses in an attempt to understand the transitional and concurrent relationships between schizophrenia relapse and medication use. A naive analysis does not show any association between previous medication and current relapse. However, we provide evidence suggesting that current treatment may impact current relapse for those who have previously taken medication, but not for those who haven't taken medication in the past. When univariate models are specified to assess these associations, the bivariate nature of the problem requires a choice of which response, relapse or medication, should be the dependent variable. In this case, the choice of relapse or medication as a dependent variable does matter. Hence, our results derive from models where both relapse and medication are treated as dependent variables. Specifically, we specify a bivariate log odds ratio for current relapse and current medication use and a separate univariate logit component for each of these outcomes. Each of these components contains transitional associations with previous relapse and medication. Such models represent extensions of univariate transitional association models (e.g. Diggle et al. (1994)) and correspond to bivariate transitional models (e.g. Zeger and Liang (1991)). We incorporate changes in transitional associations into the full-data parametric model for final inference, and investigate if these temporal changes are due to learning effects or the impact of drop-out. We also perform residual analyses and sensitivity analyses in the context of missing data patterns.

Journal Article↗

PubMed2002

Association of depression with viral load, CD8 T lymphocytes, and natural killer cells in women with HIV infection.

OBJECTIVE: Clinical and epidemiology studies have implicated depression as a risk factor in the morbidity and mortality of many human diseases. This study sought to determine if depression was associated with alterations in cellular immunity variables-specifically, natural killer (NK) cells and CD8 T lymphocytes-in women with HIV infection. METHOD: Ninety-three women (63 HIV-seropositive, 30 HIV-seronegative) were studied as part of an ongoing longitudinal study conducted at two sites. Subjects underwent extensive clinical, psychiatric, and immunological evaluations. CBC counts and flow cytometry panels were conducted and NK cell activity assayed for all subjects; viral load was determined for HIV-seropositive subjects. RESULTS: The overall rate of major depression in the HIV-seropositive and HIV-seronegative women was 15.87% (N=10 of 63) and 10.00% (N=3 of 30), respectively. HIV-seropositive women had higher depressive symptom scores than did the comparison subjects (Hamilton depression scale: mean=8.62 [SD=7.26] versus mean=5.67 [SD=7.33], respectively). Both groups had similar anxiety scores. Depressive and anxiety symptoms were significantly associated with higher activated CD8 T lymphocyte counts and higher viral load levels. Major depression was associated with significantly lower natural killer cell activity, and depressive and anxiety symptom scores showed a similar correlation. CONCLUSIONS: Our findings provide the first evidence that depression may alter the function of killer lymphocytes in HIV-infected women and suggest that depression may decrease natural killer cell activity and lead to an increase in activated CD8 T lymphocytes and viral load. The rate of current major depression in these HIV-seropositive women (none of whom had current substance abuse) is approximately twice that reported for HIV-seropositive men. The rate is also consistent with studies of women with other medical illnesses and with a recent epidemiology study that associated depression with mortality in HIV-infected women with chronic depressive symptoms. Depression may have a negative impact on innate immunity. Examination of killer lymphocytes may prove useful in assessing the potential relationship between depression, immunity, and HIV disease progression in women.

Adult↗