Search PubMed⌕ Search

Biomedical subjects

R M Simon

Publications and source records attributed to R M Simon.

At least 19 recordsLinked to original sources
PubMed2001

Clinical trial designs for the early clinical development of therapeutic cancer vaccines.

There are major differences between therapeutic tumor vaccines and chemotherapeutic agents that have important implications for the design of early clinical trials. Many vaccines are inherently safe and do not require phase I dose finding trials. Patients with advanced cancers and compromised immune systems are not good candidates for assessing either the toxicity or efficacy of therapeutic cancer vaccines. The rapid pace of development of new vaccine candidates and the variety of possible adjuvants and modifications in method of administration makes it important to use efficient designs for clinical screening and evaluation of vaccine regimens. We review the potential advantages of a wide range of clinical trial designs for the development of tumor vaccines. We address the role of immunological endpoints in early clinical trials of tumor vaccines, investigate the design implications of attempting to use disease stabilization as an end point and discuss the difficulties of reliably utilizing historical control data. Several conclusions for expediting the clinical development of effective cancer vaccines are proposed.

Adjuvants, Immunologic↗
PubMed2000

Approximate Bayesian evaluation of multiple treatment effects.

We propose an approximate Bayesian method for comparing an experimental treatment to a control based on a randomized clinical trial with multivariate patient outcomes. Overall treatment effect is characterized by a vector of parameters corresponding to effects on the individual patient outcomes. We partition the parameter space into four sets where, respectively, the experimental treatment is superior to the control, the control is superior to the experimental, the two treatments are equivalent, and the treatment effects are discordant. We compute posterior probabilities of the parameter sets by treating an estimator of the parameter vector like a random variable in the Bayesian paradigm. The approximation may be used in any setting where a consistent, asymptotically normal estimator of the parameter vector is available. The method is illustrated by application to a breast cancer data set consisting of multiple time-to-event outcomes with covariates and to count data arising from a cross-classification of response, infection, and treatment in an acute leukemia trial.

Antineoplastic Agents↗
PubMed1998

Clustering of non-major histocompatibility complex susceptibility candidate loci in human autoimmune diseases.

Human autoimmune diseases are thought to develop through a complex combination of genetic and environmental factors. Genome-wide linkage searches of autoimmune and inflammatory/immune disorders have identified a large number of non-major histocompatibility complex loci that collectively contribute to disease susceptibility. A comparison was made of the linkage results from 23 published autoimmune or immune-mediated disease genome-wide scans. Human diseases included multiple sclerosis, Crohn's disease, familial psoriasis, asthma, and type-I diabetes (IDDM). Experimental animal disease studies included murine experimental autoimmune encephalomyelitis, rat inflammatory arthritis, rat and murine IDDM, histamine sensitization, immunity to exogenous antigens, and murine lupus (systemic lupus erythematosus; SLE). A majority (approximately 65%) of the human positive linkages map nonrandomly into 18 distinct clusters. Overlapping of susceptibility loci occurs between different human immune diseases and by comparing conserved regions with experimental autoimmune/immune disease models. This nonrandom clustering supports a hypothesis that, in some cases, clinically distinct autoimmune diseases may be controlled by a common set of susceptibility genes.

Animals↗
PubMed1997

Evaluating treatments when a gender by treatment interaction may exist.

We propose a two-stage procedure for investigating whether males and females respond differently to treatment. The size of the first stage is based on the assumption of homogeneity of treatment effects across genders. Using stage I, we test for a gender by treatment interaction. If non-significant, we compute an overall average treatment effect and terminate the study. If we find an apparent interaction at the end of the first stage, we consider each gender separately. Because we now need to estimate treatment effects separately for each gender, we may have a need to collect additional information in a second stage. We consider the performance of our procedure for a normally distributed endpoint as well as for a survival model.

Colonic Neoplasms↗
PubMed1996

Oncogene alterations in primary, recurrent, and metastatic human bone tumors.

We investigated the structure and the expression of various oncogenes in three of the most common human bone tumors-osteosarcoma (36 samples from 34 patients), giant cell tumor (10 patients), and chondrosarcoma (18 patients)-in an attempt to identify the genetic alterations associated with these malignancies. Alterations of RB and p53 were detected only in osteosarcomas. Alterations of c-myc, N-myc, and c-fos were detected in osteosarcomas and giant cell tumors. Ras alterations (H-ras, Ki-ras, N-ras) were rare. Chondrosarcomas did not contain any detectable genetic alterations. Our results suggest that alterations of c-myc, N-myc, and c-fos oncogenes occur in osteosarcomas, in addition to those previously described for the tumor suppressor genes RB and p53. Moreover, statistical analyses indicate that c-fos alterations occur more frequently in osteosarcoma patients with recurrent or metastatic disease.

Adolescent↗
PubMed1996

New statistical strategy for monitoring safety and efficacy in single-arm clinical trials.

PURPOSE: Efficacy and toxicity are both important outcomes in cancer clinical trials. Nonetheless, most statistical designs for phase II trials only provide rules for evaluating treatment efficacy, and moreover only allow early stopping after fixed cohorts of patients have been treated. We illustrate a new statistical design strategy for monitoring both adverse and efficacy outcomes on a patient-by-patient basis in phase II and other single-arm clinical trials. DESIGN: The new strategy is used to design a phase II trial of the experimental regimen idarubicin plus cytarabine (ara-C) plus cyclosporine for treatment of patients with intermediate-prognosis acute myelogenous leukemia (AML). The design requires a maximum of 56 patients and provides continuous monitoring boundaries to terminate the trial if the toxicity rate is unacceptably high or the complete remission (CR) rate is unacceptably low compared with the rates of these events with the standard regimen of anthracycline plus ara-C. RESULTS: The design has an 88% to 91% probability of stopping the trial early with a median of 15 to 18 patients if the toxicity rate of the experimental regimen is .05 to .10 above that of the standard and there is no improvement in the CR rate. If there is a .15 improvement in the CR rate and the toxicity rate is no more than .05 above that of the standard, then there is at least an 83% probability that the trial will run to completion. CONCLUSION: The proposed monitoring strategy provides a flexible, practical means to continuously monitor both safety and efficacy in single-arm cancer clinical trials. The design strategy can be implemented easily using a freely available menu-driven computer program, and provides a scientifically sound alternative to the use of ad hoc safety monitoring rules.

Anthracyclines↗
PubMed1995

Bayesian sequential monitoring designs for single-arm clinical trials with multiple outcomes.

We present a Bayesian approach for monitoring multiple outcomes in single-arm clinical trials. Each patient's response may include both adverse events and efficacy outcomes, possibly occurring at different study times. We use a Dirichlet-multinomial model to accommodate general discrete multivariate responses. We present Bayesian decision criteria and monitoring boundaries for early termination of studies with unacceptably high rates of adverse outcomes or with low rates of desirable outcomes. Each stopping rule is constructed either to maintain equivalence or to achieve a specified level of improvement of a particular event rate for the experimental treatment, compared with that of standard therapy. We avoid explicit specification of costs and a loss function. We evaluate the joint behaviour of the multiple decision rules using frequentist criteria. One chooses a design by considering several parameterizations under relevant fixed values of the multiple outcome probability vector. Applications include trials where response is the cross-product of multiple simultaneous binary outcomes, and hierarchical structures that reflect successive stages of treatment response, disease progression and survival. We illustrate the approach with a variety of single-arm cancer trials, including bio-chemotherapy acute leukaemia trials, bone marrow transplantation trials, and an anti-infection trial. The number of elementary patient outcomes in each of these trials varies from three to seven, with as many as four monitoring boundaries running simultaneously. We provide general guidelines for eliciting and parameterizing Dirichlet priors and for specifying design parameters.

Bayes Theorem↗
PubMed1994

A comparison of two phase I trial designs.

Phase I cancer chemotherapy trials are designed to determine rapidly the maximum tolerated dose of a new agent for further study. A recently proposed Bayesian method, the continual reassessment method, has been suggested to offer an improvement over the standard design of such trials. We find the previous comparisons did not completely address the relative performance of the designs as they would be used in practice. Our results indicate that with the continual reassessment method, more patients will be treated at very high doses and the trials will take longer to complete. We offer some suggested improvements to both the standard design and the Bayesian method.

Bayes Theorem↗
PubMed1994

Selecting the best dose when a monotonic dose-response relation exists.

We propose a method for selecting the best treatment when a monotonic dose-response relationship exists. Because of side effects associated with higher doses, the highest dose may not be the optimum, particularly when a lower dose gives a similar response. Rather than assume a particular functional relationship of dose to response, we use isotonic regression techniques. We consider the case of three treatment levels, which is applicable to many clinical trials. The lowest treatment level may represent a placebo or no treatment control. While we focus primarily on Bernoulli response variables, we also discuss a model for normally distributed data. We suggest a two-stage procedure that we have investigated via simulation.

Clinical Trials, Phase II as Topic↗
PubMed1994

Extension of one-sided test to multiple treatment trials.

In a two-treatment clinical trial, a one-sided test is sometimes used in reaching a decision. Usually we are interested in doing a one-sided test because of the existence of an unequal preference between the two treatments. When a standard control is just as good or better than the new experimental treatment (which has more toxicity or cost), we will stay with the standard control. In this paper, we extend the concept of a one-sided test to the multiple treatment trial where three or more treatments are involved. We assume that there is an order of strictly decreasing preference among the treatments. We propose two multiple-step decision procedures that are similar to the bubble sorting algorithm and will guarantee a high probability of finally selecting the correct treatment. We also provide methods to calculate the sample size required to detect a specific difference. The derivation is based on normal data, and the extension to binomial or exponential data with random censoring is through large sample approximation.

Algorithms↗
PubMed1993

Qualitative interactions in multifactor studies.

In clinical trials qualitative interaction or crossover interaction is said to occur when one treatment is superior for some sets of patients and the alternative treatment is superior for other subsets. Here we propose a definition of no qualitative interaction with respect to a single continuous covariate which implies that one treatment is superior to the other treatment over a prespecified range of the covariate. Further, in studies involving patients cross-classified by two or more prognostic factors, we define a marginal qualitative interaction with respect to a treatment factor and a prognostic factor by averaging in some sense over the remaining prognostic factors. In some situations, the methodology of Gail and Simon (1985, Biometrics 41, 361-372) is shown to be appropriate. Their procedure assumes independent estimates of treatment effect for each subset of patients and this may not be appropriate for the generalizations we propose. Therefore, we generalize the procedure of Gail and Simon to the case of two correlated estimates of treatment effect, providing a table of critical values. Results for one-sided tests for the case of J correlated estimates are also obtained. We also present an example illustrating our procedure.

Clinical Trials as Topic↗
PubMed1990

Comparison of in vitro anticancer-drug-screening data generated with a tetrazolium assay versus a protein assay against a diverse panel of human tumor cell lines.

The National Cancer Institute (NCI) is implementing a large-scale in vitro drug-screening program that requires a very efficient automated assay of drug effects on tumor cell viability or growth. Many laboratories worldwide have adopted a microculture assay based on metabolic reduction of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT). However, because of certain technical advantages to use of the protein-binding dye sulforhodamine B (SRB) in a large-scale screening application, a detailed comparison of data generated by each type of assay was undertaken. The MTT and SRB assays were each used to test 197 compounds, on simultaneous days, against up to 38 human tumor cell lines representing seven major tumor categories. On subsequent days, 38 compounds were retested with the SRB assay and 25 compounds were retested with the MTT assay. For each of these three comparisons, we tabulated the differences between the two assays in the ratios of test group values to control values (T/C) for cell survival; calculated correlation coefficients for various T/C ratios; and estimated the bivariate distribution of the values for IC50 (concentration of drug resulting in T/C values of 50%, or 50% growth inhibition) for the two assays. The results indicate that under the experimental conditions used and within the limits of the data analyses, the assays perform similarly. Because the SRB assay has practical advantages for large-scale screening, however, it has been adopted for routine use in the NCI in vitro antitumor screen.

Antineoplastic Agents↗
PubMed1989

A method for the evaluation of dose-toxicity relationships in clinical trials.

This paper considers a proportional hazards model that describes the relationship between time-dependent cumulative dose of drug and development of toxicity. We estimate probabilities of developing toxicity in both the presence and the absence of competing risks and provide variances for the latter case. Mitoxantrone data collected in Southwest Oncology Group studies illustrate the methods.

Adult↗
PubMed1987

Cardiotoxicity in patients treated with mitoxantrone: Southwest Oncology Group phase II studies.

A model describing the development of cardiotoxicity as a function of cumulative dose of a drug and other covariates is presented. Methods are given for testing and illustrating a cumulative dose effect. Also, equations for estimation of the probability of developing cardiotoxicity in the presence or absence of competing risks are given. The methods are illustrated for mitoxantrone by means of data obtained from Southwest Oncology Group studies.

Dose-Response Relationship, Drug↗