Biomedical subjects

J G Ibrahim

Publications and source records attributed to J G Ibrahim.

At least 19 recordsLinked to original sources

PubMed2001

High-dose interferon alfa-2b significantly prolongs relapse-free and overall survival compared with the GM2-KLH/QS-21 vaccine in patients with resected stage IIB-III melanoma: results of intergroup trial E1694/S9512/C509801.

PURPOSE: Vaccine alternatives to high-dose interferon alfa-2b therapy (HDI), the current standard adjuvant therapy for high-risk melanoma, are of interest because of toxicity associated with HDI. The GM2 ganglioside is a well-defined melanoma antigen, and anti-GM2 antibodies have been associated with improved prognosis. We conducted a prospective, randomized, intergroup trial to evaluate the efficacy of HDI for 1 year versus vaccination with GM2 conjugated to keyhole limpet hemocyanin and administered with QS-21 (GMK) for 96 weeks (weekly x 4 then every 12 weeks x 8). PATIENTS AND METHODS: Eligible patients had resected stage IIB/III melanoma. Patients were stratified by sex and number of positive nodes. Primary end points were relapse-free survival (RFS) and overall survival (OS). RESULTS: Eight hundred eighty patients were randomized (440 per treatment group); 774 patients were eligible for efficacy analysis. The trial was closed after interim analysis indicated inferiority of GMK compared with HDI. For eligible patients, HDI provided a statistically significant RFS benefit (hazard ratio [HR] = 1.47, P = .0015) and OS benefit (HR = 1.52, P = .009) for GMK versus HDI. Similar benefit was observed in the intent-to-treat analysis (RFS HR = 1.49; OS HR = 1.38). HDI was associated with a treatment benefit in all subsets of patients with zero to > or = four positive nodes, but the greatest benefit was observed in the node-negative subset (RFS HR = 2.07; OS HR = 2.71 [eligible population]). Antibody responses to GM2 (ie, titers > or = 1:80) at days 29, 85, 365, and 720 were associated with a trend toward improved RFS and OS (P2 = .068 at day 29). CONCLUSION: This trial demonstrated a significant treatment benefit of HDI versus GMK in terms of RFS and OS in melanoma patients at high risk of recurrence.

Adult↗

PubMed2001

Heterogeneity in phase I clinical trials: prior elicitation and computation using the continual reassessment method.

Heterogeneity in a phase I clinical trial patient population may lead to distinctly different dose-response relationships along covariate values. For a given target probability of toxicity, this implies different maximum tolerated doses (MTDs) for each distinct subpopulation. Within the framework of O'Quigley, Pepe and Fisher's (1990) continual reassessment method, we propose the notion of average and patient-specific MTDs by augmenting the dose--response model with other covariates to account for such differences. A method to elicit prior distributions on the dose and other covariate parameters are proposed, based on the predictive approach of Ibrahim and Laud (1994), Laud and Ibrahim (1995), and Ibrahim, Ryan and Chen (1998). This approach relies on prior predictions for the response vector y(0) and a quantity a(0) specifying uncertainty in y(0). Then, y(0) and a(0) are used to specify a prior for the regression coefficients in a semi-automatic fashion. The elicitation scheme for y(0) uses results from previous phase I cancer clinical trials. The average and patient-specific MTDs and an elicitation method are demonstrated in logistic regression examples.

Algorithms↗

PubMed2001

Bias in estimating association parameters for longitudinal binary responses with drop-outs.

This paper considers the impact of bias in the estimation of the association parameters for longitudinal binary responses when there are drop-outs. A number of different estimating equation approaches are considered for the case where drop-out cannot be assumed to be a completely random process. In particular, standard generalized estimating equations (GEE), GEE based on conditional residuals, GEE based on multivariate normal estimating equations for the covariance matrix, and second-order estimating equations (GEE2) are examined. These different GEE estimators are compared in terms of finite sample and asymptotic bias under a variety of drop-out processes. Finally, the relationship between bias in the estimation of the association parameters and bias in the estimation of the mean parameters is explored.

Algorithms↗

PubMed2001

Maximum likelihood methods for cure rate models with missing covariates.

We propose maximum likelihood methods for parameter estimation for a novel class of semiparametric survival models with a cure fraction, in which the covariates are allowed to be missing. We allow the covariates to be either categorical or continuous and specify a parametric distribution for the covariates that is written as a sequence of one-dimensional conditional distributions. We propose a novel EM algorithm for maximum likelihood estimation and derive standard errors by using Louis's formula (Louis, 1982, Journal of the Royal Statistical Society, Series B 44, 226-233). Computational techniques using the Monte Carlo EM algorithm are discussed and implemented. A real data set involving a melanoma cancer clinical trial is examined in detail to demonstrate the methodology.

Algorithms↗

PubMed2001

Bayesian semiparametric models for survival data with a cure fraction.

We propose methods for Bayesian inference for a new class of semiparametric survival models with a cure fraction. Specifically, we propose a semiparametric cure rate model with a smoothing parameter that controls the degree of parametricity in the right tail of the survival distribution. We show that such a parameter is crucial for these kinds of models and can have an impact on the posterior estimates. Several novel properties of the proposed model are derived. In addition, we propose a class of improper noninformative priors based on this model and examine the properties of the implied posterior. Also, a class of informative priors based on historical data is proposed and its theoretical properties are investigated. A case study involving a melanoma clinical trial is discussed in detail to demonstrate the proposed methodology.

Bayes Theorem↗

PubMed2000

Group sequential designs for cure rate models with early stopping in favour of the null hypothesis.

Ewell and Ibrahim derived the large sample distribution of the logrank statistic under general local alternatives. Their asymptotic results enable us to extend several group sequential designs which allow for early stopping in favour of the null hypothesis to the setting in which the cure rate model is appropriate. In particular, we derive stopping rules for the cure rate model using conditional power, predictive power and repeated confidence intervals methods. We illustrate the methods proposed using a hypothetical phase III clinical trial which is typical for melanoma studies.

Clinical Trials, Phase III as Topic↗

PubMed2000

Longitudinal design for phase I clinical trials using the continual reassessment method.

The goal of phase I clinical trials is to estimate the maximum tolerated dose (MTD), the highest dose at which a specified probability of toxic response is not exceeded. However, this is not the only piece of information that is useful for the design of phase II and phase III clinical trials. Information on cumulative toxicity is important as well. To study the effect of cumulative toxicity in patients, it is necessary to examine how patients respond to multiple-dose administrations. To this end, we propose a longitudinal dose-response model that accommodates this concern, which is motivated from clearance-rate considerations and from a model proposed by Simon et al. To appropriately titrate an individual's dosage at each time period, we also propose an updating mechanism based on the Bayesian paradigm. We select individual and group MTDs using Legedza and Ibrahim's extension of O'Quigley et al. 's Continual Reassessment Method. Simulations are described to demonstrate the usefulness of our proposal. Control Clin Trials 2000;21:574-588

Bayes Theorem↗

PubMed2000

The treatment and outcome of cancer patients with thromboses on central venous catheters.

Thromboses are a common complication of central venous catheters in cancer patients. This study was performed to analyze retrospectively the treatment and outcome of all patients with venous thromboses related to central venous catheters at a major cancer center. From 1992 through 1995, 319 oncology patients with central venous catheters underwent radionuclide venography (RNV) at the Dana-Farber Cancer Institute for suspected catheter or venous thrombosis. The treatment and outcome of patients found to have venous thromboses were evaluated. Of the 319 patients, 112 were found to have evidence of venous thrombosis. The median age and platelet counts were not significantly different between the patients with and without thromboses. The most common indication for obtaining RNV was difficulty in aspirating or infusing material through the catheter. Patients who had pain or edema, or both, of the neck or upper extremity were more likely to have a venous thrombosis. Regardless of therapeutic intervention, including anticoagulation with heparin or coumadin, or both; line removal or replacement; or a combination thereof, no patients had a major adverse outcome such as pulmonary embolism, compromise of limb, or death. Only 4 patients did not have resolution of their presenting symptoms, all of whom were treated with line replacement. The overall survival of patients with and without thromboses was not significantly different. Either anticoagulation or removal of the central venous catheter (or both) appears to be adequate treatment for catheter-related thrombosis. A prospective trial to evaluate these approaches may be worthwhile so that the use of unnecessary procedures may be avoided in this patient population.

Actuarial Analysis↗

PubMed2000

On Bayesian inference for proportional hazards models using noninformative priors.

In this article, we investigate the properties of the posterior distribution under the uniform improper prior for two commonly used proportional hazards models; the Weibull regression model and the extreme value regression model. We allow the observations to be right censored. We obtain sufficient conditions for the existence of the posterior moment generating function of the regression coefficients. A dataset involving a lung cancer clinical trial and a simulation are presented to illustrate our results.

Bayes Theorem↗

PubMed2000

Estimation with correlated censored survival data with missing covariates.

Incomplete covariate data are a common occurrence in studies in which the outcome is survival time. Further, studies in the health sciences often give rise to correlated, possibly censored, survival data. With no missing covariate data, if the marginal distributions of the correlated survival times follow a given parametric model, then the estimates using the maximum likelihood estimating equations, naively treating the correlated survival times as independent, give consistent estimates of the relative risk parameters Lipsitz et al. 1994 50, 842-846. Now, suppose that some observations within a cluster have some missing covariates. We show in this paper that if one naively treats observations within a cluster as independent, that one can still use the maximum likelihood estimating equations to obtain consistent estimates of the relative risk parameters. This method requires the estimation of the parameters of the distribution of the covariates. We present results from a clinical trial Lipsitz and Ibrahim (1996b) 2, 5-14 with five covariates, four of which have some missing values. In the trial, the clusters are the hospitals in which the patients were treated.

Journal Article↗

PubMed2000

Bayesian predictive inference for time series count data.

Correlated count data arise often in practice, especially in repeated measures situations or instances in which observations are collected over time. In this paper, we consider a parametric model for a time series of counts by constructing a likelihood-based version of a model similar to that of Zeger (1988, Biometrika 75, 621-629). The model has the advantage of incorporating both overdispersion and autocorrelation. We consider a Bayesian approach and propose a class of informative prior distributions for the model parameters that are useful for prediction. The prior specification is motivated from the notion of the existence of data from similar previous studies, called historical data, which is then quantified into a prior distribution for the current study. We derive the Bayesian predictive distribution and use a Bayesian criterion, called the predictive L measure, for assessing the predictions for a given time series model. The distribution of the predictive L measure is also derived, which will enable us to compare the predictive ability for each model under consideration. Our methodology is motivated by a real data set involving yearly pollen counts, which is examined in some detail.

Bayes Theorem↗

PubMed2000

High- and low-dose interferon alfa-2b in high-risk melanoma: first analysis of intergroup trial E1690/S9111/C9190.

PURPOSE: Pivotal trial E1684 of adjuvant high-dose interferon alfa-2b (IFNalpha2b) therapy in high-risk melanoma patients demonstrated a significant relapse-free and overall survival (RFS and OS) benefit compared with observation (Obs). PATIENTS AND METHODS: A prospective, randomized, three-arm, intergroup trial evaluated the efficacy of high-dose IFNalpha2b (HDI) for 1 year and low-dose IFNalpha2b (LDI) for 2 years versus Obs in high-risk (stage IIB and III) melanoma with RFS and OS end points. RESULTS: A total of 642 patients were enrolled (608 patients eligible), of whom a majority (75%) had nodal metastasis (50% had nodal recurrence). Unlike E1684, E1690 allowed entry of patients with T4 (> 4 mm) deep primary tumors, regardless of nodal dissection, and 25% of the patients entered onto this trial had deep primary tumors (compared with 11% in E1684). At 52 months' median follow-up, HDI demonstrated an RFS benefit exceeding that of LDI compared with Obs. The 5-year estimated RFS rates for the HDI, LDI, and Obs arms were 44%, 40%, and 35%, respectively. The hazards ratio for the intent-to-treat analysis of HDI versus Obs was 1.28 (P(2) =.05); for LDI versus Obs, it was 1.19 (P(2) =.17). By Cox analysis, the impact of HDI on RFS achieved significance (P(2) =.03). The RFS benefit was equivalent for node-negative and node-positive patients. Neither HDI nor LDI has demonstrated an OS benefit compared with Obs at this time. A major improvement in the median OS of patients in the E1690 Obs arm was noted in comparison with E1684 (6 years v 2.8 years). An analysis of salvage therapy for patients who relapsed on E1690 demonstrated that a significantly larger proportion of patients in the Obs arm received IFNalpha-containing salvage therapy compared with the HDI arm; this therapy was unavailable to patients during E1684, and patients with undissected regional nodes were not included in E1684. This study did not specify therapy at recurrence. Analysis of treatments received at recurrence demonstrated significantly more frequent use of IFNalpha2b at relapse from Obs than from HDI, which may have confounded interpretation of the survival benefit of assigned treatments in E1690. CONCLUSION: The results of the intergroup E1690 trial demonstrate an RFS benefit of IFNalpha2b that is dose-dependent and significant for HDI by Cox multivariable analysis.

Aged↗

PubMed1999

Using missing data methods in genetic studies with missing mutation status.

Because of current techniques of determining gene mutation, investigators are now interested in estimating the odds ratio between genetic status (mutation, no mutation) and an outcome variable such as disease cell type (A, B). In this paper we consider the mutation of the RAS genetic family. To determine if the genes have mutated, investigators look at five specific locations on the RAS gene. RAS mutated is a mutation in at least one of the five gene locations and RAS non-mutated is no mutation in any of the five locations. Owing to limited time and financial resources, one cannot obtain a complete genetic evaluation of all five locations on the gene for all patients. We propose the use of maximum likelihood (ML) with a 2(6) multinomial distribution formed by cross-classifying the binary mutation status at five locations by binary disease cell type. This ML method includes all patients regardless of completeness of data, treats the locations not evaluated as missing data, and uses the EM algorithm to estimate the odds ratio between genetic mutation status and the disease type. We compare the ML method to complete case estimates, and a method used by clinical investigators, which excludes patients with data on less than five locations who have no mutations on these sites.

Biometry↗

PubMed1999

Likelihood methods for incomplete longitudinal binary responses with incomplete categorical covariates.

We consider longitudinal studies in which the outcome observed over time is binary and the covariates of interest are categorical. With no missing responses or covariates, one specifies a multinomial model for the responses given the covariates and uses maximum likelihood to estimate the parameters. Unfortunately, incomplete data in the responses and covariates are a common occurrence in longitudinal studies. Here we assume the missing data are missing at random (Rubin, 1976, Biometrika 63, 581-592). Since all of the missing data (responses and covariates) are categorical, a useful technique for obtaining maximum likelihood parameter estimates is the EM algorithm by the method of weights proposed in Ibrahim (1990, Journal of the American Statistical Association 85, 765-769). In using the EM algorithm with missing responses and covariates, one specifies the joint distribution of the responses and covariates. Here we consider the parameters of the covariate distribution as a nuisance. In data sets where the percentage of missing data is high, the estimates of the nuisance parameters can lead to highly unstable estimates of the parameters of interest. We propose a conditional model for the covariate distribution that has several modeling advantages for the EM algorithm and provides a reduction in the number of nuisance parameters, thus providing more stable estimates in finite samples.

Affect↗

PubMed1999

Monte Carlo EM for missing covariates in parametric regression models.

We propose a method for estimating parameters for general parametric regression models with an arbitrary number of missing covariates. We allow any pattern of missing data and assume that the missing data mechanism is ignorable throughout. When the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM algorithm by the method of weights proposed in Ibrahim (1990, Journal of the American Statistical Association 85, 765-769). We extend this method to continuous or mixed categorical and continuous covariates, and for arbitrary parametric regression models, by adapting a Monte Carlo version of the EM algorithm as discussed by Wei and Tanner (1990, Journal of the American Statistical Association 85, 699-704). In addition, we discuss the Gibbs sampler for sampling from the conditional distribution of the missing covariates given the observed data and show that the appropriate complete conditionals are log-concave. The log-concavity property of the conditional distributions will facilitate a straightforward implementation of the Gibbs sampler via the adaptive rejection algorithm of Gilks and Wild (1992, Applied Statistics 41, 337-348). We assume the model for the response given the covariates is an arbitrary parametric regression model, such as a generalized linear model, a parametric survival model, or a nonlinear model. We model the marginal distribution of the covariates as a product of one-dimensional conditional distributions. This allows us a great deal of flexibility in modeling the distribution of the covariates and reduces the number of nuisance parameters that are introduced in the E-step. We present examples involving both simulated and real data.

Algorithms↗

PubMed1998

A semi-parametric Bayesian approach to generalized linear mixed models.

The linear mixed effects model with normal errors is a popular model for the analysis of repeated measures and longitudinal data. The generalized linear model is useful for data that have non-normal errors but where the errors are uncorrelated. A descendant of these two models generates a model for correlated data with non-normal errors, called the generalized linear mixed model (GLMM). Frequentist attempts to fit these models generally rely on approximate results and inference relies on asymptotic assumptions. Recent advances in computing technology have made Bayesian approaches to this class of models computationally feasible. Markov chain Monte Carlo methods can be used to obtain 'exact' inference for these models, as demonstrated by Zeger and Karim. In the linear or generalized linear mixed model, the random effects are typically taken to have a fully parametric distribution, such as the normal distribution. In this paper, we extend the GLMM by allowing the random effects to have a non-parametric prior distribution. We do this using a Dirichlet process prior for the general distribution of the random effects. The approach easily extends to more general population models. We perform computations for the models using the Gibbs sampler.

Bayes Theorem↗

PubMed1998

A Bayesian framework for intent-to-treat analysis with missing data.

In longitudinal clinical trials, one analysis of interest is an intention-to-treat analysis, which groups subjects according to the randomized treatment regardless of whether they stayed on that treatment or not. When in addition to going off the randomized treatment subjects may also drop out of the study and be lost to follow-up, it is unclear what an intention-to-treat analysis should be. If measurements are made after treatment drop-out on a random sample of subjects who drop the treatment, then Hogan and Laird (1996, Biometrics 52, 1002-1017) present a random effects model, well suited to this type of analysis, which fits a two-piece linear spline to the data with the knot at the time the assigned treatment is dropped. This article presents a Bayesian approach to fitting a similar two-piece linear spline model and shows how the model can be applied to data that have no off-treatment observations.

Acquired Immunodeficiency Syndrome↗

PubMed1998

A semiparametric Bayesian approach to the random effects model.

In longitudinal random effects models, the random effects are typically assumed to have a normal distribution in both Bayesian and classical models. We provide a Bayesian model that allows the random effects to have a nonparametric prior distribution. We propose a Dirichlet process prior for the distribution of the random effects; computation is made possible by the Gibbs sampler. An example using marker data from an AIDS study is given to illustrate the methodology.

Acquired Immunodeficiency Syndrome↗