Biomedical subjects

John O'Quigley

Publications and source records attributed to John O'Quigley.

13 recordsLinked to original sources

PubMed2006

Optimal designs for estimating the most successful dose.

There has been much recent interest in phase I/II dose finding designs in which information on both toxicity and efficacy is used. Unlike the classic phase I dose finding design in which the aim is to identify the MTD (maximum tolerated dose corresponding to some percentile of acceptable toxicity), a phase I/II dose finding study aims to locate the most successful dose (MSD), i.e. the dose which maximizes the product of the probability of seeing no toxicity together with the probability of seeing a therapeutic response). In this work we present an abstract theoretical design for this purpose. We call this design a 'virtual' design. The virtual design, conceptually similar to that developed for phase I designs alone, is based on a bivariate response. The design has optimal properties in that the current estimates of both probability of toxicity and those for response achieve the Cramer-Rao bound for every dose level. Unhappily, the virtual design is not available for practical use but its use can be exploited in theoretical investigations in much the same way as one uses the Cramer-Rao bound for unbiased estimators, i.e. a tool which enables us to see how much room for improvement may exist for any given real design. Via examples taken from the literature on phase I/II dose finding we illustrate how this technique can provide us with further insight on the relative performance of competing designs.

Antineoplastic Agents↗

PubMed2006

Quantification of the prentice criteria for surrogate endpoints.

In a recent discussion on the Prentice criteria for surrogate endpoints (1989) and the role of meta analyses in this context, Alonso et al. expand on a number of points. Among these is a suggestion to base a measure of the degree at which an endpoint meets the Prentice criteria on a likelihood reduction factor. Although new in this context, the suggestion has been studied elsewhere and the measure of Alonso et al., as presented, requires some modification if it is to be useful in general situations. As it stands it depends upon the censoring mechanism, even when independent of the failure mechanism. This is, however, easily fixed. The measure suggested by Alonso et al. can be seen to fit in with a well-established theory so that, once corrected for the presence of censoring, it can be employed with confidence. Of particular interest is the wide availability of a standard SAS program producing a coefficient which can be immediately transformed to provide an estimate of the population quantity behind the Alonso et al. proposal.

Biomarkers↗

PubMed2005

Explained randomness in proportional hazards models.

A coefficient of explained randomness, analogous to explained variation but for non-linear models, was presented by Kent. The construct hinges upon the notion of Kullback-Leibler information gain. Kent and O'Quigley developed these ideas, obtaining simple, multiple and partial coefficients for the situation of proportional hazards regression. Their approach was based upon the idea of transforming a general proportional hazards model to a specific one of Weibull form. Xu and O'Quigley developed a more direct approach, more in harmony with the semi-parametric nature of the proportional hazards model thereby simplifying inference and allowing, for instance, the use of time dependent covariates. A potential drawback to the coefficient of Xu and O'Quigley is its interpretation as explained randomness in the covariate given time. An investigator might feel that the interpretation of the Kent and O'Quigley coefficient, as a proportion of explained randomness of time given the covariate, is preferable. One purpose of this note is to indicate that, under an independent censoring assumption, the two population coefficients coincide. Thus the simpler inferential setting for Xu and O'Quigley can also be applied to the coefficient of Kent and O'Quigley. Our second purpose is to point out that a sample-based coefficient in common use in the SAS statistical package can be interpreted as an estimate of explained randomness when there is no censoring. When there is censoring the SAS coefficient would not seem satisfactory in that its population counterpart depends on an independent censoring mechanism. However there is a quick fix and we argue in favour of its use.

Antimetabolites, Antineoplastic↗

PubMed2005

Retrospective analysis of sequential dose-finding designs.

The continual reassessment method (CRM) is a dose-finding design using a dynamic sequential updating scheme. In common with other dynamic schemes the method estimates a current dose level corresponding to some target percentile for experimentation. The estimate is based on all included subjects. This continual reevaluation is made possible by the use of a simple model. As it stands, neither the CRM, nor any of the other dynamic schemes, allow for the correct estimation of some target percentile, based on retrospective data apart from the exceptional situation in which the simplified model exactly generates the observations. In this article we focus on the very specific issue of retrospective analysis of data generated by some arbitrary mechanism and subsequently analyzed via the continual reassessment method. We show how this can be done consistently. The proposed methodology is not restricted to that particular design and is applicable to any sequential updating scheme in which dose levels are associated with percentiles via model inversion.

Clinical Trials, Phase I as Topic↗

PubMed2004

Erosion of regression effect in a survival study.

Lack of persistence, or erosion, of the regression effect is an alternative to proportional hazards of particular interest in many medical applications. Such a departure from proportional hazards is often the most likely direction in which the model may be inadequate. Questions such as, is the effect of treatment only transitory or to what extent does an initially measured prognostic variable maintain its impact, frequently arise. In the context of a simple changepoint model, we propose a test of the null hypothesis of proportional hazards against the specific alternative of erosion of the regression effect. The particular changepoint model used can be viewed as a first approximation to a more complex reality, an approximation that enables us to avoid specifically modeling the functional form that any erosion might take. Practical guidelines for carrying out the test are provided. The approach is illustrated in the context of a study on risk factors for breast cancer survival.

Biometry↗

PubMed2003

A phase I study of irinotecan as a 3-week schedule in children with refractory or recurrent solid tumors.

PURPOSE: A phase I study was performed to determine the maximum-tolerated dose (MTD) and safety profile of irinotecan (CPT-11) administered as a single intravenous infusion every 3 weeks in children with recurrent or refractory solid tumors. PATIENTS AND METHODS: Eighty-one patients were enrolled, including 48 less heavily, and 33 heavily pretreated patients (cranial irradiation and/or high-dose chemotherapy). Children received CPT-11 as a 120-minute infusion at doses ranging from 200 to 720 mg/m2. The dose-limiting toxicities (DLT) on first cycle were determined in both cohorts. RESULTS: One hundred twenty-two cycles and 81 cycles were administered in less heavily, and heavily pretreated patients, respectively. The primary DLT was delayed diarrhea in less heavily pretreated patients, and neutropenia in heavily pretreated patients. MTD was 600 mg/m2 in both cohorts. Grade 3 to 4 neutropenia occurred in 33% and 38% of cycles in less heavily, and heavily pretreated patients, respectively. Grade 3 to 4 nonhematologic toxicities included nausea/vomiting (7% and 4% of cycles in less heavily, and heavily pretreated patients, respectively), asthenia (7% and 4% of cycles, respectively), and delayed diarrhea (6% and 2.5% of cycles, respectively). Four partial responses at 600 mg/m2 (high-grade glioma, neuroblastoma, medulloblastoma, and rhabdomyosarcoma) and 21 minor responses and stable diseases were observed. Pharmacokinetic analysis of CPT-11 and SN-38 was performed in 77 patients. The mean +/- standard deviation (SD) CPT-11 plasma clearance was 20.7 +/- 9.5 L/h/m2 (range, 5 to 54). The mean +/- SD SN-38 metabolic ratio was 1.5% +/- 1.1% (range, 0.15% to 5.55%). CONCLUSION: The recommended phase II dose of CPT-11 in a 3-week schedule is 600 mg/m2 in less heavily, and heavily pretreated children with solid tumors.

Adolescent↗

PubMed2003

Interval estimates of the probability of toxicity at the maximum tolerated dose for small samples.

Following on from the work of O'Quigley et al., we investigate the performance of interval estimates of the probability of toxicity following completion of a phase I clinical trial. Our particular focus is on very small sample sizes, not uncommon in phase I studies. Specifically, we study the situations for which the sample sizes are 12 or 16. Simulations are used to show that the coverage of the confidence intervals, even for very modest sample sizes, are close to nominal in most cases. Averaged over the range of situations considered, coverage rates are accurate for both sample sizes of 12 and 16. As for the larger sample size of 20, studied previously, it is possible to obtain further, albeit modest, improvements via the use of the Cornish-Fisher inversion.

Antineoplastic Agents↗

PubMed2003

Continual reassessment method for ordered groups.

We investigate the two-group continual reassessment method for a dose-finding study in which we anticipate some ordering between the groups. This is a situation in which, for either group, we have little or almost no knowledge about which of the available dose levels will correspond to the maximum tolerated dose (MTD), but we may have quite strong knowledge concerning which of the two groups will have the higher level of MTD, if indeed they do not have the same MTD. The motivation for studying this problem came from an investigation into a new therapy for acute leukemia in children. The background to this study is discussed. There were two groups of patients: one group already received heavy prior therapy while the second group had received relatively much lighter prior therapy. It was therefore anticipated that the second group would have an MTD higher or at least as high as the first. Generally, likelihood methods or, equivalently, the use of noninformative Bayes priors, can be used to model the main aspects of the study, i.e., the MTD for one of the groups, reserving more informative Bayes modeling to be applied to the secondary features of the study. These secondary features may simply be the direction of the difference between the MTD levels for the two groups or, possibly, information on the potential gap between the two MTDs.

Antineoplastic Agents↗

PubMed2002

Proportional hazards models with frailties and random effects.

We discuss some of the fundamental concepts underlying the development of frailty and random effects models in survival. One of these fundamental concepts was the idea of a frailty model where each subject has his or her own disposition to failure, their so-called frailty, additional to any effects we wish to quantify via regression. Although the concept of individual frailty can be of value when thinking about how data arise or when interpreting parameter estimates in the context of a fitted model, we argue that the concept is of limited practical value. Individual random effects (frailties), whenever detected, can be made to disappear by elementary model transformation. In consequence, unless we are to take some model form as unassailable, beyond challenge and carved in stone, and if we are to understand the term 'frailty' as referring to individual random effects, then frailty models have no value. Random effects models on the other hand, in which groups of individuals share some common effect, can be used to advantage. Even in this case however, if we are prepared to sacrifice some efficiency, we can avoid complex modelling by using the considerable power already provided by the stratified proportional hazards model. Stratified models and random effects models can both be seen to be particular cases of partially proportional hazards models, a view that gives further insight. The added structure of a random effects model, viewed as a stratified proportional hazards model with some added distributional constraints, will, for group sizes of five or more, provide no more than modest efficiency gains, even when the additional assumptions are exactly true. On the other hand, for moderate to large numbers of very small groups, of sizes two or three, the study of twins being a well known example, the efficiency gains of the random effects model can be far from negligible. For such applications, the case for using random effects models rather than the stratified model is strong. This is especially so in view of the good robustness properties of random effects models. Nonetheless, the simpler analysis, based upon the stratified model, remains valid, albeit making a less efficient use of resources.

Humans↗

PubMed2002

Non-parametric optimal design in dose finding studies.

We describe a non-parametric optimal design as a theoretical gold standard for dose finding studies. Its purpose is analogous to the Cramer-Rao bound for unbiased estimators, i.e. it provides a bound beyond which improvements are not generally possible. The bound applies to the class of non-parametric designs where the data are not assumed to be generated by any known parametric model. Whenever parametric assumptions really hold it may be possible to do better than the optimal non-parametric design. The goal is to be able to compare any potential dose finding scheme with the optimal non-parametric benchmark. This paper makes precise what is meant by optimal in this context and also why the procedure is described as non-parametric.

Journal Article↗

PubMed2002

Continual reassessment designs with early termination.

The continual reassessment method (CRM) is an increasingly popular approach for estimating the maximum tolerated dose (MTD) in phase I dose finding studies. In its original formulation, the scheme is based on a fixed sample size. Many experimenters feel that, whenever possible, it may be advantageous to bring these trials to an early halt and thus reduce average sample size required to complete the study. To address this issue a stopping rule has been proposed (O'Quigley and Reiner, 1998) based on the idea that continuing the study would not lead to a change in recommendation with high probability. The rule, based on precise probabilistic calculation, is quite involved and not straightforward to implement. A much simpler rule can be constructed based on the idea of having settled at some level. In this work we investigate more deeply the essential ingredients behind these rules and consider more closely their operating characteristics.

Journal Article↗

PubMed2002

Curve-free and model-based continual reassessment method designs.

Gasparini and Eisele (2000, Biometrics 56, 609 615) present a development of the continual reassessment method of O'Quigley, Pepe, and Fisher (1990, Biometrics 46, 33-48). They call their development a curve-free method for Phase I clinical trials. However, unless we are dealing with informative prior information, then the curve-free method coincides with the usual model-based continual reassessment method. Both methods are subject to arbitrary specification parameters, and we provide some discussion on this. Whatever choices are made for one method, there exists equivalent choices for the other method, where " equivalent" means that the operating characteristics (sequential dose allocation and final recommendation) are the same. The insightful development of Gasparini and Eisele provides clarification on some of the basic ideas behind the continual reassessment method, particularly when viewed from a Bayesian perspective. But their development does not lead to a new class of designs and the comparative results in their article, indicating some preference for curve-free designs over model-based designs, are simply reflecting a more fortunate choice of arbitrary specification parameters. Other choices could equally well have inversed their conclusion. A correct conclusion should be one of operational equivalence. The story is different for the case of informative priors, a situation that is inherently much more difficult. We discuss this. We also mention the important idea of two-stage designs (Moller, 1995, Statistics in Medicine 14, 911-922; O'Quigley and Shen, 1996, Biometrics 52, 163-174), arguing, via a simple comparison with the results of Gasparini and Eisele (2000), that there is room for notable gains here. Two-stage designs also have an advantage of avoiding the issue of prior specification altogether.

Bayes Theorem↗

PubMedDate not supplied

Identifying the most successful dose (MSD) in dose-finding studies in cancer.

For a dose finding study in cancer, the most successful dose (MSD), among a group of available doses, is that dose at which the overall success rate is the highest. This rate is the product of the rate of seeing non-toxicities together with the rate of tumor response. A successful dose finding trial in this context is one where we manage to identify the MSD in an efficient manner. In practice we may also need to consider algorithms for identifying the MSD which can incorporate certain restrictions, the most common restriction maintaining the estimated toxicity rate alone below some maximum rate. In this case the MSD may correspond to a different level than that for the unconstrained MSD and, in providing a final recommendation, it is important to underline that it is subject to the given constraint. We work with the approach described in O'Quigley et al. [Biometrics 2001; 57(4):1018-1029]. The focus of that work was dose finding in HIV where both information on toxicity and efficacy were almost immediately available. Recent cancer studies are beginning to fall under this same heading where, as before, toxicity can be quickly evaluated and, in addition, we can rely on biological markers or other measures of tumor response. Mindful of the particular context of cancer, our purpose here is to consider the methodology developed by O'Quigley et al. and its practical implementation. We also carry out a study on the doubly under-parameterized model, developed by O'Quigley et al. but not

Antineoplastic Agents↗