William F. Rosenberger

Comparison of open and laparoscopic live donor nephrectomy

OBJECTIVE This study compares an initial group of patients undergoing laparoscopic live donor nephrectomy to a group of patients undergoing open donor nephrectomy to assess the efficacy, morbidity, and patient recovery after the laparoscopic technique. SUMMARY BACKGROUND DATA Recent data have shown the technical feasibility of harvesting live renal allografts using a laparoscopic approach. However, comparison of donor recovery, morbidity, and short-term graft function to open donor nephrectomy has not been performed previously. METHODS An initial series of patients undergoing laparoscopic live donor nephrectomy were compared to historic control subjects undergoing open donor nephrectomy. The groups were matched for age, gender, race, and comorbidity. Graft function, intraoperative variables, and clinical outcome of the two groups were compared. RESULTS Laparoscopic donor nephrectomy was attempted in 70 patients and completed successfully in 94% of cases. Graft survival was 97% versus 98% (p = 0.6191), and immediate graft function occurred in 97% versus 100% in the laparoscopic and open groups, respectively (p = 0.4961). Blood loss, length of stay, parenteral narcotic requirements, resumption of diet, and return to normal activity were significantly less in the laparoscopic group. Mean warm ischemia time was 3 minutes after laparoscopic harvest. Morbidity was 14% in the laparoscopic group and 35% in the open group. There was no mortality in either group. CONCLUSIONS Laparoscopic live donor nephrectomy can be performed with morbidity and mortality comparable to open donor nephrectomy, with substantial improvements in patient recovery after the laparoscopic approach. Initial graft survival and function rates are equal to those of open donor nephrectomy, but longer follow-up is necessary to confirm these observations.

Randomization in clinical trials : theory and practice

Preface. Randomization and the Clinical Trial. Issues in the Design of Clinical Trials. Randomization for Balancing Treatment Assignments. Balancing on Known Covariates. The Effects of Unobserved Covariates. Selection Bias. Randomization as a Basis for Inference. Inference for Stratified, Blocked, and Covariate-Adjusted Analyses. Randomization in Practice. Response-Adaptive Randomization. Inference for Response-Adaptive Rondomization. Response-Adaptive Randomization in Practice. Some Useful results in Large Sample Theory. Large Sample Inference for Complete and Restricted Randomization. Large sample Inference for Response-Adaptive Randomization. Author Index. Subject Index.

Optimality, Variability, Power: Evaluating Response-Adaptive Randomization Procedures for Treatment Comparisons

We provide a theoretical template for the comparison of response-adaptive randomization procedures for clinical trials. Using a Taylor expansion of the noncentrality parameter of the usual chi-squared test for binary responses, we show explicitly the relationship among the target allocation proportion, the bias of the randomization procedure from that target, and the variability induced by the randomization procedure. We also generalize this relationship for more than two treatments under various multivariate alternatives. This formulation allows us to directly evaluate and compare different response-adaptive randomization procedures and different target allocations in terms of power and expected treatment failure rate without relying on simulation. For K = 2 treatments, we compare four response-adaptive randomization procedures and three target allocations based on multiple objective optimality criteria. We conclude that the drop-the-loser rule and the doubly adaptive biased coin design are clearly superior to sequential maximum likelihood estimation or the randomized play-the-winner rule in terms of decreased variability, but the latter is preferable because it can target any desired allocation. We discuss how the template developed in this article is useful in the design and evaluation of clinical trials using response-adaptive randomization.

A Random Walk Rule for Phase I Clinical Trials

We describe a family of random walk rules for the sequential allocation of dose levels to patients in a dose-response study, or phase I clinical trial. Patients are sequentially assigned the next higher, same, or next lower dose level according to some probability distribution, which may be determined by ethical considerations as well as the patients response. It is shown that one can choose these probabilities in order to center dose level assignments unimodally around any target quantile of interest. Estimation of the quantile is discussed; the maximum likelihood estimator and its variance are derived under a two-parameter logistic distribution, and the maximum likelihood estimator is compared with other nonparametric estimators. Random walk rules have clear advantages: they are simple to implement, and finite and asymptotic distribution theory is completely worked out. For a specific random walk rule, we compute finite and asymptotic properties and give examples of its use in planning studies. Having the finite distribution theory available and tractable obviates the need for elaborate simulation studies to analyze the properties of the design. The small sample properties of our rule, as determined by exact theory, compare favorably to those of the continual reassessment method, determined by simulation.

Bayesian optimal designs for Phase I clinical trials.

A broad approach to the design of Phase I clinical trials for the efficient estimation of the maximum tolerated dose is presented. The method is rooted in formal optimal design theory and involves the construction of constrained Bayesian c- and D-optimal designs. The imposed constraint incorporates the optimal design points and their weights and ensures that the probability that an administered dose exceeds the maximum acceptable dose is low. Results relating to these constrained designs for log doses on the real line are described and the associated equivalence theorem is given. The ideas are extended to more practical situations, specifically to those involving discrete doses. In particular, a Bayesian sequential optimal design scheme comprising a pilot study on a small number of patients followed by the allocation of patients to doses one at a time is developed and its properties explored by simulation.

The use of response-adaptive designs in clinical trials.

Response-adaptive designs in clinical trials are schemes for patient assignment to treatment, the goal of which is to place more patients on the better treatment based on patient responses already accrued in the trial. While ethically attractive at first glance, these designs have had very little use in practice; yet the statistical literature is rich on this subject. We discuss procedures and properties of these designs. Particular focus is given to the randomized play-the-winner rule of Wei and Durham, which was used in the ECMO trial. We also discuss reasons for the lack of use of these models, and areas of current and future research to address the weaknesses of these methods. We conclude that these designs may be applicable in some situations and describe conditions under which such a trial may be feasible.

Handling Covariates in the Design of Clinical Trials

There has been a split in the statistics community about the need for taking covariates into account in the design phase of a clinical trial. There are many advocates of using stratification and covariate-adaptive random ization to promote balance on certain known covariates. However, balance does not always promote efficiency or ensure more patients are assigned to the better treatment. We describe these procedures, including model-based procedures, for incorporating covariates into the design of clinical trials, and give examples where balance, efficiency and ethical considerations may be in conflict. We advocate a new class of procedures, covariate-adjusted response adaptive (CARA) randomization procedures that attempt to optimize both efficiency and ethical considerations, while maintaining randomization. We review all these procedures, present a few new simulation studies, and con clude with our philosophy.

Implementing optimal allocation in sequential binary response experiments

For sequential experiments with K treatments, we establish two formal optimization criteria to find optimal allocation strategies. Both criteria involve the sample sizes on each treatment and a concave noncentrality parameter from a multivariate test. We show that these two criteria are equivalent. We apply this result to specific questions: (1) How do we maximize power of a multivariate test of homogeneity with binary response?, and (2) for fixed power, how do we minimize expected treatment failures? Because the solutions depend on unknown parameters, we describe a response-adaptive randomization procedure that “targets” the optimal allocation and provides increases in power along the lines of 2–4% over complete randomization for equal allocation. The increase in power contradicts the conclusions of other authors who have explored other randomization procedures for K = 2 and have found that the variability induced by randomization negates any benefit of targeting an optimal allocation.

Asymptotic normality of maximum likelihood estimators from multiparameter response-driven designs

Abstract Estimation and inference for dependent trials are important issues in response-adaptive allocation designs; maximum likelihood estimation is one route of interest. We present three noval response-driven designs and derive their maximum likelihood estimators. We also provide convenient regularity conditions that ensure the maximum likelihood estimator from a multiparameter stochastic process exists and is asymptotically multivariate normal. While these conditions may not be the most general, they are easily verified for our applications.

Bayesian Methods and Ethics in a Clinical Trial Design.

Partial table of contents: MAJOR ISSUES Ethically Optimizing Clinical Trials (K. Schaffner) Admissibility of Treatment (N. Sedransk) TEST CASE: VERAPAMIL/NITROPRUSSIDE The Mechanics of Conducting a Clinical Trial (E. Heitmiller & T. Blanck) Issues of Statistical Design (N. Sedransk) Operational History and Procedural Feasibility (J. Kadane) Verapamil versus Nitroprusside: Results of the Clinical Trial I (J. Kadane & N. Sedransk) Verapamil versus Nitroprusside: Results of the Clinical Trial II (E. Heitmiller, et al.) OTHER ISSUES Authors Response to Commentaries I and II (D. Kairys) EPILOGUE Epilogue (J. Kadane) Indexes.