Biomedical subjects

J J Hanfelt

Publications and source records attributed to J J Hanfelt.

5 recordsLinked to original sources

PubMed1999

Optimal multi-stage designs for a phase II trial that permits one dose escalation.

Simon's optimal two-stage design is widely used to conduct single-dose phase II clinical trials. We extend this basic methodology to the situation where the researcher desires to test an experimental drug for activity at a low dose level, but is willing to increase the dose part-way through the trial if the early results suggest that the low dose is ineffective. Interest is confined to at most one dose escalation, and no consideration is given to escalating the dose within a patient. Optimal multi-stage designs are presented that are more efficient than the naive approach of merely conducting two consecutive Simon optimal trials, one at the low dose and the second (if deemed necessary) at the high dose. As in Simon's original design, toxicity is not considered here as a primary endpoint. Hence, the designs presented in this paper are appropriate only when the toxicity of the drug is well understood at both dose levels.

Algorithms↗

PubMed1999

A modification of Simon's optimal design for phase II trials when the criterion is median sample size.

We present a modification to Simon's optimal design for phase II trials in which the objective is to minimize the median sample size rather than the expected sample size when the true response rate is poor (p = p0). We argue that the modified design may be preferred in smaller institutions when the focus is on a single or small number of phase II trials rather than a large program of phase II trials.

Algorithms↗

PubMed1998

Inference for odds ratio regression models with sparse dependent data.

Suppose the number of 2 x 2 tables is large relative to the average table size, and the observations within a given table are dependent, as occurs in longitudinal or family-based case-control studies. We consider fitting regression models to the odds ratios using table-level covariates. The focus is on methods to obtain valid inferences for the regression parameters beta when the dependence structure is unknown. In this setting, Liang (1985, Biometrika 72, 678-682) has shown that inference based on the noncentral hypergeometric likelihood is sensitive to misspecification of the dependence structure. In contrast, estimating functions based on the Mantel-Haenszel method yield consistent estimators of beta. We show here that, under the estimating function approach, Wald's confidence interval for beta performs well in multiplicative regression models but unfortunately has poor coverage probabilities when an additive regression model is adopted. As an alternative to Wald inference, we present a Mantel-Haenszel quasi-likelihood function based on integrating the Mantel-Haenszel estimating function. A simulation study demonstrates that, in medium-sized samples, the Mantel-Haenszel quasi-likelihood approach yields better inferences than other methods under an additive regression model and inferences comparable to Wald's method under a multiplicative model. We illustrate the use of this quasi-likelihood method in a study of the familial risk of schizophrenia.

Biometry↗

PubMed1997

Radiation therapy for rhabdomyosarcoma: local failure risk for Clinical Group III patients on Intergroup Rhabdomyosarcoma Study II.

PURPOSE: A subset of 362 pediatric patients with rhabdomyosarcoma was selected from a total of 532 eligible IRS-II patients in Clinical Group III to assess the local and regional failure rates following radiotherapy and to determine patient, tumor, and treatment factors contributing to the risk for local and regional failure. METHODS AND MATERIALS: The study population was selected from all eligible IRS-II Clinical Group III patients. Excluded patients were those with "special pelvic" primary sites whose protocol management restricted radiotherapy (n = 123), and those who were removed from the study before radiotherapy was to begin, or because it was omitted (n = 47). A binary recursive partitioning model was used to identify subgroups of the remaining 362 patients at risk of local or regional failure. RESULTS: The local (only) failure rate was 17% (95% confidence interval, 13-21%), and the local (all) failure rate was 20% (95% confidence interval, 16-24%). The 5-year actuarial risk of local (all) failure was 22% (95% confidence interval, 18-27%). The risk of regional (nodal) failure was between 2% and 23%. Increasing tumor size predicted an increased local failure risk. Primary tumors located above the clavicle had a reduced risk of local failure. The binary recursive partitioning model identified a subset of patients at high risk of local failure. Those patients had primary tumors in the chest, pelvic region, extremity, or trunk, or tumors > 10 cm in diameter. Their local failure rate was 35% (compared to 15% for the remaining patients). The subset of patients at high risk for regional (nodal) failure had node involvement at diagnosis and a primary tumor originating at a site other than orbit, parameningeal, or trunk. Compliance with radiation treatment guidelines approached but did not achieve statistical significance as a predictive factor for local failure. By univariate analysis, factors not influencing local failure risk were age, race, gender, adenopathy, and histology. CONCLUSION: Radiation therapy and chemotherapy administered to Clinical Group III patients entered into the IRS-II protocol produced sustained local control in most cases. Knowledge of the factors which predict an increased risk of local or regional failure will facilitate the design of new treatment strategies.

Adult↗

PubMedDate not supplied

Statistical approaches to experimental design and data analysis of in vivo studies.

The objective of any experiment is to obtain an unbiased and precise estimate of a treatment effect in an efficient manner. Statistical aspects of the design, conduct, and analysis of the experiment play a major role in determining whether this goal is met. We highlight some of the more important statistical issues that pertain to in vivo studies. Particular emphasis is placed on the role of randomization, the number of animals, the utilization of repeated measures data, adjustments for missing data, and dealing with multiple causes of death or treatment failure. The discussion is not intended to be a comprehensive guide to all the statistical issues that can occur in animal experiments. Rather, the objective is to acquaint researchers with components of the experiment that will require careful statistical thought.

Animals↗