Biomedical subjects

J O'Quigley

Publications and source records attributed to J O'Quigley.

At least 19 recordsLinked to original sources

PubMed1999

Two-sample continual reassessment method.

We discuss an extension of the continual reassessment method (CRM) for use in phase I dose-finding studies. The extension enables the method to be applied to two groups of patients to determine the appropriate dose levels for each group. The method takes the specification of a simple relationship between the dose-toxicity curves for the two groups and runs the CRM on the bivariate model using maximum likelihood. We prove consistency of the method under fairly weak conditions and provide several simulations to give an idea how the method works in practice. We also undertake an evaluation of its performance by considering three possible situations: The first is the two-sample CRM, which directly uses a working model for the relationship between the two groups, carrying out a single trial using this method; the second situation carries out single trials for each of the two groups separately using the original (one-sample) CRM. The third situation is the case where such heterogeneity is ignored and the two groups are pooled into a single group, again using the original (one-sample) CRM. Simulations are carried out under a large class of model misspecifications, both of the dose-toxicity relationships and of the functional form linking the groups, and are backed up by asymptotic results. Our conclusions match intuition: The first scheme gives the most favorable results when the two groups are different but share some features. When the groups are very different, the second scheme performs similarly to the first for finite sample sizes while having some advantages in terms of asymptotic efficiency. The third, as expected, gives the best results in the absence of patient heterogeneity. The two-sample method appears particularly advantageous when there may not be enough subjects in one of the subgroups for it to be feasible to carry out two trials.

Clinical Trials, Phase I as Topic↗

PubMed1998

Phase I and pharmacokinetic study of intraperitoneal topotecan.

OBJECTIVE: To determine the maximum tolerated dose and pharmacokinetics of topotecan when administered by the intraperitoneal route. METHODS: A dose-escalating Phase I trial was conducted in which fifteen % of the total dose was given as an intraperitoneal bolus in two litres of D5W and the remainder was given as a continuous intraperitoneal infusion over 24 hours. Treatments were given every 21 days. Pharmacokinetic analyses were performed at the recommended phase II dose. RESULTS: Seventeen patients received a total of 43 cycles at 21-day intervals. The maximum tolerated dose was 4 mg/m2 and acute dose-limiting toxicity was neutropenia. Other toxicities included leukopenia, anemia, emesis, fever, and abdominal pain. Although no objective responses were achieved, five of ten patients with ascites had a decrease in fluid accumulation with administration of intraperitoneal topotecan. The recommended phase II dose is 3 mg/m2. Pharmacokinetic analysis performed at a dose of 3 mg/m2 demonstrated that elimination from the peritoneal cavity followed second-order kinetics with k1 = 1.6 hr(-1), k2 = 0.3 hr(-1) and first and second-phase half-lives of 0.49 and 2.7 hours, respectively. Plasma pharmacokinetic behavior was best described by first-order kinetics with k = 0.5 hr(-1) and a half-life of 3.9 hours. The pharmacologic advantage, expressed as the peritoneal to plasma AUC ratio was 31.2. CONCLUSIONS: Intraperitoneal administration of topotecan at 3 mg/m2 results in a substantial increase in drug exposure for the peritoneal cavity without compromising systemic exposure; this may be beneficial for the treatment of patients with ovarian cancer or intraperitoneal carcinomatosis.

Adult↗

PubMed1996

Continual reassessment method: a likelihood approach.

The continual reassessment method as described by O'Quigley, Pepe, and Fisher (1990, Biometrics 46, 33-48) leans to a large extent upon a Bayesian methodology. Initial experimentation and sequential updating are carried out in a natural way within the context of a Bayesian framework. In this paper we argue that such a framework is easily changed to a more classic one leaning upon likelihood theory. The essential features of the continual reassessment method remain unchanged. In particular, large sample properties are the same unless the prior is degenerate. For small samples and as far as the final recommended dose level is concerned, simulations indicate that there is not much to choose between a likelihood approach and a Bayesian one. However, for in-trial allocation of dose levels to patients, there are some differences and these are discussed. In contrast to the Bayesian approach, a likelihood one requires some extra effort to get off the ground. This is because the likelihood equation has no solution until we observe a toxicity. Initially then we suggest working with either a standard Up-and-Down scheme or standard continual reassessment method until toxicity is observed and then switching to the new scheme.

Bayes Theorem↗

PubMed1995

A two-stage procedure for survival studies with surrogate endpoints.

A two-stage procedure for survival studies with surrogate endpoints is proposed. The objective of the procedure is to reduce the duration of a survival study relative to classical procedure. A surrogate endpoint is an event which is related to survival time and may occur earlier during follow up. In the first stage, all patients are followed to the primary endpoint in order to evaluate the strength of the relationship between the surrogate endpoint and survival. In the second stage, follow up is terminated on patients who reach the surrogate endpoint. Indirect inferences on the survival endpoint is now possible by virtue of the first stage analysis. We present methods for data collected in the two-stage procedure, for estimating the survivorship function, S(t), and for comparing two treatment groups using a non-parametric permutation test. The methods are applied to the results of a study of resected lung cancer.

Biometry↗

PubMed1994

Predictive capability of proportional hazards regression.

A measure of the predictive capability of a proportional hazards regression is derived. The measure is based on the residuals appropriate to proportional hazards regression. A population version is presented and can be seen not to depend on the censoring mechanism under the provision that any such censoring be independent or conditionally independent of the failure mechanism given the covariate. For the special case of a Weibull regression model, for which the covariate distribution follows binary, uniform, normal, or exponential laws, we derive analytic results. These alone give credence to the measure which can be seen to reflect strength of regression effect, as quantified by the parameter estimate, although on a scale between 0 and 1, independently of the intercept or shape parameter of the particular Weibull law and only weakly dependent on the covariate distribution. Extensions to partial and multiple measures of predictive ability are straightforward. An example is provided.

Predictive Value of Tests↗

PubMed1994

Integral evaluation for continual reassessment method.

In this paper we show how the exact analytical solutions to the numerical integrals required in the implementation of the continual reassessment method can be evaluated. The formulas have been given but, with increasing sample size, rapidly become unwieldy. We develop an algorithm which uses the numerical binary representation of the subset size and, in the process, enables us to easily identify those members of the subset needed in the integral evaluation. The complex combinatorial problem of selecting the set of all subsets with a particular characteristic is then greatly simplified. We describe the nature and type of all the variables and parameters used in a PASCAL procedure.

Algorithms↗

PubMed1992

Estimating the probability of toxicity at the recommended dose following a phase I clinical trial in cancer.

The problem of point and interval estimation following a Phase I trial, carried out according to the scheme outlined by O'Quigley, Pepe, and Fisher (1990, Biometrics 46, 33-48), is investigated. A reparametrization of the model suggested in this earlier work can be seen to be advantageous in some circumstances. Maximum likelihood estimators, Bayesian estimators, and one-step estimators are considered. The continual reassessment method imposes restrictions on the sample space such that it is not possible for confidence intervals to achieve exact coverage properties, however large a sample is taken. Nonetheless, our simulations, based on a small finite sample of 20, not atypical in studies of this type, indicate that the calculated intervals are useful in most practical cases and achieve coverage very close to nominal levels in a very wide range of situations. The relative merits of the different estimators and their associated confidence intervals, viewed from a frequentist perspective, are discussed.

Antineoplastic Agents↗

PubMed1991

Methods for dose finding studies in cancer clinical trials: a review and results of a Monte Carlo study.

We discuss some of the statistical approaches to the design and analysis of phase I clinical trials in cancer. An attempt is made to identify the issues, particular to this type of trial, that should be addressed by an appropriate methodology. A brief review of schemes currently in use is provided together with our views of the extent to which any particular scheme addresses the main issues. Some simulations are provided together with graphical illustration of the operating characteristics of the particular methods. It appears that the continual reassessment method is preferable to other contending schemes.

Antineoplastic Agents↗

PubMed1991

The problem of a covariate-time qualitative interaction in a survival study.

We introduce a test for the equality of two survival distributions against the specific alternative of crossing hazards. Although this kind of alternative is somewhat rare, designing a test specifically aimed at detecting such departures from the null hypothesis in this direction leads to powerful procedures, upon which we can call in those few cases where such departures are suspected. Furthermore, the proposed test and an approximate version of the test are seen to suffer only moderate losses in power, when compared with their optimal counterparts, should the alternative be one of proportional hazards. Our interest in the problem is motivated by clinical studies on the role of acute graft versus host disease as a risk factor in leukemic children and we discuss the analysis of this study in detail. The model we use in this work is a special case of the one introduced by Anderson and Senthilselvan (1982. Applied Statistics 31, 44-51). We propose overcoming an inferential problem stemming from their model by using the methods of Davies (1977, Biometrika 64, 247-254; 1987, Biometrika 74, 33-43) backed up by resampling techniques. We also look at an approach relying directly on resampling techniques. The distributional aspects of this approach under the null hypothesis are interesting but, practically, its behaviour is such that its use cannot be generally recommended. Outlines of the necessary asymptotic theory are presented and for this we use the tools of martingale theory.

Biometry↗

PubMed1991

Nonparametric tests of association between survival time and continuously measured covariates: the logit-rank and associated procedures.

O'Brien's logit-rank procedure (1978, Biometrics 34, 243-250) is shown to arise as a score test based on the partial likelihood for a proportional hazards model provided the covariate structure is suitably defined. Within this framework the asymptotic properties claimed by O'Brien can be readily deduced and can be seen to be valid under a more general model of censoring than that considered in his paper. More important, perhaps, it is now possible to make a more natural and interpretable generalization to the multiple regression problem than that suggested by O'Brien as a means of accounting for the effects of nuisance covariates. This can be achieved either by modelling or stratification. The proportional hazards framework is also helpful in that it enables us to recognize the logit-rank procedure as being one member of a class of contending procedures. One consequence of this is that the relative efficiencies of any two procedures can be readily evaluated using the results of Lagakos (1988, Biometrika 75, 156-160). Our own evaluations suggest that, for non-time-dependent covariates, a simplification of the logit-rank procedure, leading to considerable reduction in computational complexity, is to be preferred to the procedure originally outlined by O'Brien.

Biometry↗

PubMed1990

Homogeneity of relative risk against two-step alternatives.

In this paper the computational aspects of testing the null hypothesis of homogeneity of relative risk against two-step alternatives are examined. This representation is the same as that introduced by Anderson and Senthilselvan (Appl. Stat. 31 (1982) 44-51), i.e. a two-step model. Such alternatives may be used to represent decay in effect or, perhaps, inversion of the regression effect or crossing hazards. For such models inferential aspects are slightly more involved than for instance with proportional hazards models having fixed effects, even when time dependent as in O'Quigley and Pessione (Biometrics 45 (1989) 135-144). The necessary techniques for carrying out tests based on the two-stage model have recently been developed (O'Quigley and Pessione (Biometrics (1990) (in press] and in this paper we outline the necessary steps to be taken in the construction of algorithms to implement the proposed procedures. Programs enabling analyses based on the assumption of homogeneity of risk are very widely available. These include software packages such as BMDP, SAS, SPSS and GLIM. In the output of these packages, as well as that from most other standard routines, is contained all the necessary information to carry out the tests proposed by O'Quigley and Pessione. Here we detail the explicit formulae needed for carrying out the calculations in practice. The special cases of crossing hazards are considered in detail.

Mathematical Computing↗

PubMed1990

Sequential design and analysis of dose finding studies in patients with life threatening disease.

We describe a recently developed approach to the design and analysis of dose finding studies in patients with life-threatening disease. Such patients may be terminally ill and, although interest is focussed on cancer, the methodology is more generally applicable. Of major interest is the identification of a dose with a given targeted toxicity level. Experimentation is carried out at a level for which all current available evidence, ie any information available before beginning the study, typically used in determining dose levels, together with information accumulated in the course of the study, indicates the best estimate of the target level. At the end of the study it is possible to obtain a statistical estimate of the probability of toxicity at the chosen level. Note that such estimation is not possible after a dose finding study carried out along traditional lines.

Drug Evaluation↗

PubMed1990

Continual reassessment method: a practical design for phase 1 clinical trials in cancer.

This paper looks at a new approach to the design and analysis of Phase 1 clinical trials in cancer. The basic idea and motivation behind the approach stem from an attempt to reconcile the needs of dose-finding experimentation with the ethical demands of established medical practice. It is argued that for these trials the particular shape of the dose toxicity curve is of little interest. Attention focuses rather on identifying a dose with a given targeted toxicity level and on concentrating experimentation at that which all current available evidence indicates to be the best estimate of this level. Such an approach not only makes an explicit attempt to meet ethical requirements but also enables the use of models whose only requirements are that locally (i.e., around the dose corresponding to the targeted toxicity level) they reasonably well approximate the true probability of toxic response. Although a large number of models could be contemplated, we look at a particularly simple one. Extensive simulations show the model to have real promise.

Antineoplastic Agents↗

PubMed1990

Gynecological abnormalities following allogeneic bone marrow transplantation.

Forty-four post-pubertal women were studied 261-4628 days after allogeneic transplantation to determine the nature and degree of gynecological abnormalities following bone marrow transplantation. Evaluations included pelvic examinations, exfoliative cytology, serum gonadotropin levels, direct preparations for micro-organisms, and microbial cultures. Pelvic abnormalities were detected in 35 of 44 (80%) women and resembled atrophic changes known to occur after ovarian failure. Findings included reduced vaginal elasticity and rugal folds, pale tissues, small vaginal, uterine and cervical size, atrophic vulvovaginitis, introital stenosis, and loss of pubic hair. Atrophic abnormalities were noted in 33 of 36 recipients of total body irradiation (TBI) compared to two of eight women not prepared with TBI (p = 0.02). Vasomotor symptoms were reported in 67% of TBI recipients compared to 38% of those not given TBI. Elevated serum gonadotropin levels suggested that TBI had caused the ovarian failure. Recognition of these gynecological abnormalities can lead to earlier hormone replacement, alleviating unnecessary discomfort and improving the well-being of the marrow transplant recipient.

Adolescent↗

PubMed1989

Estimating the size of the dividing stem cell pool after allogeneic bone marrow transplantation.

Currently the problem of estimation of the number of pluripotent stem cells reconstituting marrow grafts following bone marrow transplantation, or studies looking at questions of clonal dominance in hematopoietic cell populations, rely on indirect measurement and a simple application of the formula for the sampling variation of a binomial proportion. This approach, from a statistical viewpoint, can be seen to be flawed. It is very easily remedied though and only requires appropriate use of variance stabilizing transformations. These lead to a very simple estimator for the number of hematopoietic stem cells involved in repopulating the marrow and require little in the way of additional calculation. We give the distribution theory for this estimator as well as simple approximations for practical application. As an illustration we rework data recently gathered to address the question as to whether or not reconstitution of marrow grafts in the clinical setting is oligoclonal.

Bone Marrow Transplantation↗

PubMed1989

Analysis of the origin of marrow cells in bone marrow transplant recipients using a Y-chromosome-specific in situ hybridization assay.

A Y-chromosome-specific in situ hybridization assay was used to assess the frequency with which host bone marrow cells are retained after marrow grafting. The majority of patients (74%) showed the presence of both host and donor marrow cells when assayed 14 days after transplant. By 84 days posttransplant only 4% of the patients retained host marrow cells. Only 1 of 19 evaluable patients analyzed over 1 year posttransplant showed minimal retention of host cells. No statistical correlation was found between retention of host cells posttransplant and the development of relapse or acute or chronic graft-versus-host disease. Pretransplant conditioning regimen, HLA-matching, diagnosis, disease status at transplant, ABO-matching, and patient age also showed no correlation with the retention of host cells posttransplant.

Bone Marrow Cells↗

PubMed1989

Acute renal failure following bone marrow transplantation: a retrospective study of 272 patients.

To assess the incidence, risk factors, and course of acute renal failure (ARF) following bone marrow transplantation (BMT), a retrospective analysis of 272 patients receiving transplants at the Fred Hutchinson Cancer Research Center during 1986 was undertaken. The patients were divided into three groups: group 1, hemodialysis requiring ARF; group 2, mild renal insufficiency (doubling of serum creatinine, Scr, but no dialysis); group 3, relatively normal post-BMT renal function (no doubling of Scr). Fifty-three percent of patients at least doubled their Scr (Groups 1 and 2), and 24% required dialysis. The degree of renal functional impairment had a dramatic impact on patient mortality rates (84%, 37%, and 17% in groups 1, 2, and 3, respectively). Jaundice (bilirubin greater than or equal to 2.0 mg/dL), weight gain (greater than or equal to 2.0 kg), amphotericin B use, and a pretransplant Scr greater than or equal to 0.7 mg/dL were independently associated with the subsequent development of dialysis-requiring ARF (P less than 0.001; relative risks, 3.0 to 7.7). Neither aminoglycoside/vancomycin/cyclosporine A use nor acute graft v host disease correlated with the development of ARF. A mismatched graft was a significant risk factor for ARF by univariate but not by multivariate analysis. Within 48 hours before doubling the Scr, 63% of group 1 patients had positive blood cultures and 39% developed hypotension. Of the 26 group 1 patients who had urine Na concentrations measured, 85% had values less than or equal to 40 mEq/L. Autopsy kidney specimens provided no clear explanation for ARF in the vast majority of patients in group 1.(ABSTRACT TRUNCATED AT 250 WORDS)

Acute Kidney Injury↗

PubMed1989

Score tests for homogeneity of regression effect in the proportional hazards model.

A simple model, containing the proportional hazards regression model as a special case, is presented. The purpose of the model is to provide a framework in which specific alternatives to the proportional hazards assumption may be tested. Rank-invariant score tests for linear, quadratic, or exponential trends can, for instance, all be undertaken within this framework. In the case of the two-sample problem the required calculations are shown to take a particularly simple form. Special consideration is given to the two-sample case in which there is an inversion of the regression effect, i.e., where the hazard functions cross at some given point. Both of the motivating examples are concerned with this problem. Computational aspects are relatively straightforward and some discussion on this is provided.

Actuarial Analysis↗