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N Stallard

Publications and source records attributed to N Stallard.

At least 19 recordsLinked to original sources
PubMed2001

Stopping rules for phase II studies.

This paper, the second in a series of three papers concerned with the statistical aspects of interim analyses in clinical trials, is concerned with stopping rules in phase II clinical trials. Phase II trials are generally small-scale studies, and may include one or more experimental treatments with or without a control. A common feature is that the results primarily determine the course of further clinical evaluation of a treatment rather than providing definitive evidence of treatment efficacy. This means that there is more flexibility available in the design and analysis of such studies than in phase III trials. This has led to a range of different approaches being taken to the statistical design of stopping rules for such trials. This paper briefly describes and compares the different approaches. In most cases the stopping rules can be described and implemented easily without knowledge of the detailed statistical and computational methods used to obtain the rules.

Antineoplastic Agents↗
PubMed2001

Learning from previous responses in phase I dose-escalation studies.

Dose escalation in phase I studies is generally performed on the basis of clinical experience and judgement. In this paper some of the statistical approaches that have been proposed for the formalization of the procedure are described. Apart from the use of the Continual Reassessment Method in oncology studies, such formal methods have received little implementation. The purpose of presenting them here is to promote their further exploration and appropriate implementation. Certain limitations are discussed, which will be best overcome by collaboration between clinical pharmacologists and statisticians.

Antineoplastic Agents↗
PubMed2001

Interim analyses and sequential designs in phase III studies.

Recruitment of patients to a clinical trial usually occurs over a period of time, resulting in the steady accumulation of data throughout the trial's duration. Yet, according to traditional statistical methods, the sample size of the trial should be determined in advance, and data collected on all subjects before analysis proceeds. For ethical and economic reasons, the technique of sequential testing has been developed to enable the examination of data at a series of interim analyses. The aim is to stop recruitment to the study as soon as there is sufficient evidence to reach a firm conclusion. In this paper we present the advantages and disadvantages of conducting interim analyses in phase III clinical trials, together with the key steps to enable the successful implementation of sequential methods in this setting. Examples are given of completed trials, which have been carried out sequentially, and references to relevant literature and software are provided.

Clinical Trials, Phase III as Topic↗
PubMed2001

Severe infections after bone marrow transplantation.

Bone marrow transplantation and stem cell transplantation have become standard therapies offering potential cures for a number of hematologic malignancies and immunologic disorders. Severe infection remains a life threatening complication after transplantation, contributes significantly to morbidity, and may necessitate admission to the ICU. It is estimated that between 20 and 40% of patients receiving bone marrow transplant will require ICU admission in the initial posttransplantation phase. Historically, survival rates after admission to the ICU are dismal, particularly if mechanical ventilation is required for respiratory failure. Other organ involvement worsens the prognosis still further and has led to proposals for rationing or restricting access to critical care units and supportive measures. Recent studies have reported small but significant improvements in outcome after critical illness. Whether this improvement is a result of changes in levels of supportive care or a more defined patient selection is uncertain. Moreover, risk factors identifying patients who will benefit most from intensive support are poorly defined. However, it is generally accepted that respiratory failure requiring invasive mechanical ventilation is associated with a poor prognosis in this patient group. Early involvement of intensivists in the management of critical illness in transplant recipients is likely to continue to improve survival in this group of patients.

Bone Marrow Transplantation↗
PubMed2001

Optimal adaptive designs for binary response trials.

We derive the optimal allocation between two treatments in a clinical trial based on the following optimality criterion: for fixed variance of the test statistic, what allocation minimizes the expected number of treatment failures? A sequential design is described that leads asymptotically to the optimal allocation and is compared with the randomized play-the-winner rule, sequential Neyman allocation, and equal allocation at similar power levels. We find that the sequential procedure generally results in fewer treatment failures than the other procedures, particularly when the success probabilities of treatments are smaller.

Biometry↗
PubMed2001

Decision-theoretic designs for pre-phase II screening trials in oncology.

A Bayesian decision-theoretic method is proposed for conducting small, randomized pre-phase II selection trials. The aim is to improve on the design of Thall and Estey (1993, Statistics in Medicine 12, 1197-1211). Designs are derived that optimize a gain function accounting for current and future patient gains, per-patient cost, and future treatment development cost. To reduce the computational burden associated with backward induction, myopic versions of the design that consider only one, two, or three future decisions at a time are also considered. The designs are compared in the context of a screening trial in acute myelogenous leukemia.

Bayes Theorem↗
PubMed2000

Exact sequential tests for single samples of discrete responses using spending functions.

Sequential tests are increasingly used to reduce the expected sample size of trials in medical research. The majority of such methods are based on the assumption of normality for test statistics. In clinical trials yielding a single sample of discrete data, that assumption is often poorly satisfied. In this paper we show how a novel application of the spending function approach of Lan and DeMets can be used together with exact calculation methods to design sequential procedures for a single sample of discrete random variables without the assumption of normality. A special case is that of binomial data and the paper is illustrated by the design of a cytogenetic study which motivated this work.

Binomial Distribution↗
PubMed1999

Decision theoretic designs for phase II clinical trials with multiple outcomes.

In many phase II clinical trials, it is essential to assess both efficacy and safety. Although several phase II designs that accommodate multiple outcomes have been proposed recently, none are derived using decision theory. This paper describes a Bayesian decision theoretic strategy for constructing phase II designs based on both efficacy and adverse events. The gain function includes utilities assigned to patient outcomes, a reward for declaring the new treatment promising, and costs associated with the conduct of the phase II trial and future phase III testing. A method for eliciting gain function parameters from medical collaborators and for evaluating the design's frequentist operating characteristics is described. The strategy is illustrated by application to a clinical trial of peripheral blood stem cell transplantation for multiple myeloma.

Bayes Theorem↗
PubMed1998

Approximately optimal designs for phase II clinical studies.

There is no consensus on determination of sample size in phase II clinical trials. The use of Bayesian decision theory has been proposed by Stallard (1), among others. In this article, optimal three-stage designs are obtained using decision theory. These are compared with procedures proposed by Schoenfeld (2), Ensign et al. (3), and Chen et al. (4) and the sequential probability ratio test of Wald (5) and Barnard (6). The three-stage procedures are shown to be close to the true optimal test; the sequential probability ratio test is easier to obtain and only marginally inferior. Because optimality of the decision-theory approach depends on accurate specification of costs and profits, an assessment is made of the sensitivity of the proposed procedures to a range of gain function parameter values.

Bayes Theorem↗
PubMed1998

Sample size determination for phase II clinical trials based on Bayesian decision theory.

This paper describes an application of Bayesian decision theory to the determination of sample size for phase II clinical studies. The approach uses the method of backward induction to obtain group sequential designs that are optimal with respect to some specified gain function. A gain function is proposed focussing on the financial costs of, and potential profits from, the drug development programme. On the basis of this gain function, the optimal procedure is also compared with an alternative Bayesian procedure proposed by Thall and Simon. The latter method, which tightly controls type I error rate, is shown to lead to an expected gain considerably smaller than that from the optimal test. Gain functions with respect to which Thall and Simon's boundary is optimal are sought and it is shown that these can only be of the form considered, that is, with constant cost for phase III study and cost of the phase II study proportional to the sample size, if potential profit increases over time.

Bayes Theorem↗
PubMed1997

Splenic rupture following cardiopulmonary resuscitation.

Cardiopulmonary resuscitation has improved outcome from cardiac arrest. However complications may occur secondary to the resuscitation efforts. We present a case of intraabdominal haemorrhage, due to traumatic rupture of the spleen and discuss the problems of diagnosing intraabdominal haemorrhage in the post cardiac arrest patient, whose hypotension may be ascribed to myocardial dysfunction.

Aged↗
PubMed1996

An alternative approach to the analysis of animal carcinogenicity studies.

Long-term animal carcinogenicity studies are an important part of the risk analysis process assessing the carcinogenic potential of products to humans. Results from the statistical analysis of the data from such studies are generally presented as a series of hypothesis tests indicating whether there was a significant rise in the number of tumors at given sites. The conclusion from such an analysis depends on the size of the experiment. In particular, the number of false-negative results can be high when tumors are rare. In this paper, a test for equivalence fixing the proportion of false negatives is proposed. The effect on the required sample size is also discussed.

Animal Testing Alternatives↗
PubMed1996

Comparison of the spending function method and the Christmas tree correction for group sequential trials.

Sequential designs for continuous monitoring can be derived from the theory of a Brownian motion process. In practice, infrequent analyses lead to a discrete monitoring process. In this paper, two methods proposed to correct for discrete monitoring are compared. The methods are used to create procedures similar to both the O'Brien and Fleming test and the triangular test and are compared in terms of the error rates. For the O'Brien and Fleming test, the spending function method is found to achieve the required error rates more accurately than the Christmas tree correction, while for the triangular test, both methods perform as planned.

Clinical Trials as Topic↗
PubMed1995

A parametric multistate model for the analysis of carcinogenicity experiments.

A fully parametric multistate model is explored for the analysis of animal carcinogenicity experiments in which the time of tumour onset is not known. This model does not require assumptions about tumour lethality or cause of death judgements and can be fitted in the absence of sacrifice data. The model is constructed as a three-state model with simple parametric forms for the transition rates. Maximum likelihood methods are used to estimate the transition rates and different treatment groups are compared using likelihood ratio tests. Selection of an appropriate model and methods to assess the fit of the model are illustrated with data from animal experiments. Comparisons with standard methods are made.

Animals↗
PubMed1995

Reducing animal numbers in the fixed-dose procedure.

The fixed-dose procedure (FDP) was proposed by the British Toxicology Society (1984) as an alternative to assessment of acute oral toxicity via estimation of the LD50. The procedure is incorporated in OECD guidelines on acute oral toxicity testing. Whitehead and Curnow (1992) used a mathematical model to describe the statistical properties of the FDP. This paper uses a simplified model to investigate further the procedure. In particular the effects of altering the number of animals included at each stage in the procedure are evaluated. It is shown that a reduction in the number of animals tested makes little difference to the toxic classification of a substance with a steep dose-response curve, but has increasing effect as the dose-response curve becomes shallower. The simplified model also shows that in the proposed procedure the most likely classification depends on the LD of the substance tested. Changing the number of animals tested results in the most likely classification depending on other LD values. The effect of additional variation is also considered. Such variation might arise from within-laboratory differences. Although this increases the range of substances for which misclassification is likely, the increase is not much affected by the number of animals tested.

Animal Welfare↗