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Biomedical subjects

P F Thall

Publications and source records attributed to P F Thall.

8 recordsLinked to original sources

Treatment of newly diagnosed acute myelogenous leukemia with granulocyte-macrophage colony-stimulating factor (GM-CSF) before and during continuous-infusion high-dose ara-C + daunorubicin: comparison to patients treated without GM-CSF.

We gave 56 patients with newly diagnosed acute myelogenous leukemia (AML) granulocyte-macrophage colony-stimulating factor (GM-CSF) 20 or 125 micrograms/m2 once daily subcutaneously before (for up to 8 days or until GM-CSF-related complications developed) and during, or only during (patients presenting with blast counts greater than 50,000 or other leukemia-related complications) ara-C (1.5 g/m2 daily x 4 by continuous infusion) and daunorubicin (45 mg/m2 daily x 3) chemotherapy. Because results seemed independent of GM-CSF schedule, we compared results in these 56 patients with results in 176 patients with newly diagnosed AML given the same dose and schedule of ara-C without GM-CSF (110 patients ara-C alone, 66 patients ara-C + amsacrine or mitoxantrone). Comparison involved fitting a logistic regression model predicting probability of complete remission (CR) and a Cox regression model to predict survival (most patients in all three studies were dead) with treatment included as a covariate in both analyses. After adjusting for other prognostically significant covariates [presence of an antecedent hematologic disorder, an Inv (16), t(8;21), or abnormalities of chromosomes 5 and/or 7, performance status, age, bilirubin], treatment with ara-C + daunorubicin + GM-CSF was predictive of both a lower CR rate and a lower survival probability. There were no treatment-covariate interactions, suggesting that the negative effect of this GM-CSF treatment regime was not an artifact of some imbalance in patient characteristics. The unadjusted Kaplan-Meier hazard rate of the ara-C + daunorubicin + GM-CSF group was not uniquely high during the initial 4 weeks after start of therapy, but was highest among the three treatment groups throughout weeks 5 to 16, suggesting that the negative effect of this treatment was not caused by acute toxicity. Patients who did not enter CR with this treatment tended to have persistent leukemia rather than prolonged marrow aplasia, suggesting that this treatment and, in particular, GM-CSF may increase resistance of myeloid leukemia cells to chemotherapy. To date, relapse rates are similar in all three groups (P = .43) (as are survival rates once patients are in CR) but much of the remission duration data is heavily censored, unlike the survival data. Our results suggest caution in the use of GM-CSF to sensitize myeloid leukemia cells to daunorubicin + ara-C chemotherapy.

Antineoplastic Combined Chemotherapy Protocols

Incorporating historical control data in planning phase II clinical trials.

Phase II studies of new medical treatments often use historical data on the standard treatment for comparative evaluation. Incorrectly disregarding inherent variability in the historical data may lead to erroneous conclusions regarding the efficacy of the experimental treatment. We propose an approach to phase II trial design which accounts for both inter-study and intra-study variation. Our results indicate that it is sometimes best to randomize a proportion of the patients to a control arm. We choose this proportion to maximize the precision of the estimated experimental treatment effect. We evaluate operating characteristics of the design numerically, and provide illustrations based on historical data from cancer chemotherapy trials.

Data Interpretation, Statistical

Some covariance models for longitudinal count data with overdispersion.

A family of covariance models for longitudinal counts with predictive covariates is presented. These models account for overdispersion, heteroscedasticity, and dependence among repeated observations. The approach is a quasi-likelihood regression similar to the formulation given by Liang and Zeger (1986, Biometrika 73, 13-22). Generalized estimating equations for both the covariate parameters and the variance-covariance parameters are presented. Large-sample properties of the parameter estimates are derived. The proposed methods are illustrated by an analysis of epileptic seizure count data arising from a study of progabide as an adjuvant therapy for partial seizures.

Analysis of Variance

Distribution-free confidence intervals for a parameter of Wilcoxon-Mann-Whitney type for ordered categories and progressive censoring.

Halperin, Gilbert, and Lachin (1987, Biometrics 43, 71-80) obtain confidence intervals for Pr(X less than Y) based on the two-sample Wilcoxon statistic for continuous data. Their approach is applied here to ordered categorical data and right-censored continuous data, using the generalization zeta = Pr(X less than Y) + 1/2Pr(X = Y) to account for ties. Deviations from nominal coverage probability for various sample sizes and values of zeta are obtained via simulation of either three or six ordered categories based on underlying Poisson or exponential distributions. The simulation results indicate that the proposed method performs quite well, and it is apparently superior to the approach of Hochberg (1981, Communications in Statistics--Theory and Methods A10, 1719-1732) for values of zeta far from 1/2.

Biometry

A two-stage design for choosing among several experimental treatments and a control in clinical trials.

In clinical trials where several experimental treatments are of interest, the goal may be viewed as identification of the best of these and comparison of that treatment to a standard control therapy. However, it is undesirable to commit patients to a large-scale comparative trial of a new regimen without evidence that its therapeutic success rate is acceptably high. We propose a two-stage design in which patients are first randomized among the experimental treatments, and the single treatment having the highest observed success rate is identified. If this highest rate falls below a fixed cutoff then the trial is terminated. Otherwise, the "best" new treatment is compared to the control at a second stage. Locally optimal values of the cutoff and the stage-1 and stage-2 sample sizes are derived by minimizing expected total sample size. The design has both high power and high probability of terminating early when no experimental treatment is superior to the control. Numerical results for implementing the design are presented, and comparison to Dunnett's (1984, in Design of Experiments: Ranking and Selection, T. J. Santner and A. C. Tamhane (eds), 47-66; New York: Marcel Dekker) optimal one-stage procedure is made.

Biometry

Optimal two-stage designs for clinical trials with binary response.

We present a simple two-stage design for a randomized clinical trial with dichotomous outcomes. It is based on a design of Ellenberg and Eisenberger which permits early acceptance of the null hypothesis after the first stage. We have optimized the design by minimizing the average expected sample size subject to constraints on the type 1 and type 2 errors. The resulting designs have maximum sample size only slightly larger than that of single stage trial, with 40-45 per cent of this maximum allocated to the first stage, and null hypothesis probability of early acceptance in excess of 0.60.

Clinical Trials as Topic

Mixed Poisson likelihood regression models for longitudinal interval count data.

In many longitudinal studies it is desired to estimate and test the rate over time of a particular recurrent event. Often only the event counts corresponding to the elapsed time intervals between each subject's successive observation times, and baseline covariate data, are available. The intervals may vary substantially in length and number between subjects, so that the corresponding vectors of counts are not directly comparable. A family of Poisson likelihood regression models incorporating a mixed random multiplicative component in the rate function of each subject is proposed for this longitudinal data structure. A related empirical Bayes estimate of random-effect parameters is also described. These methods are illustrated by an analysis of dyspepsia data from the National Cooperative Gallstone Study.

Adult

Assessment of stratum-covariate interactions in Cox's proportional hazards regression model.

We assess stratum (e.g. treatment) interactions with covariates and with the baseline hazard function in the proportional hazards (PH) regression model for lifetime data. We consider models incorporating stratum interactions both with and without stratification of the risk sets in the likelihood function, and describe likelihood ratio statistics for tests of the presence of these interactions. We also present step-down methods for building reduced models which include stratum-specific parameters corresponding to covariates which interact with treatment. We apply PH models with such interactions to a clinical trial of DES in the treatment of prostate cancer to determine optimal treatment conditional on each patient's covariates.

Analysis of Variance