Association of fasting glucose with subclinical cerebrovascular disease in older adults without Type 2 diabetes.

AIMS: To examine how fasting glucose and glucose tolerance are related to magnetic resonance imaging-assessed indicators of subclinical cerebrovascular disease and brain atrophy and their variation according to age, sex and education. METHODS: Participants in the present study were 172 healthy, community-dwelling older adults. An oral glucose tolerance test was administered and magnetic resonance imaging performed. Fasting, 2-h, and 2-h area-under-the-curve glucose levels, their associations with subclinical cerebrovascular disease and brain atrophy, and their respective interactions with age, sex and education were examined. RESULTS: A positive association between fasting glucose and subclinical cerebrovascular disease (but not brain atrophy) emerged; this association was more pronounced for participants with < 12 years of education; however, glucose tolerance was not related to subclinical cerebrovascular disease or brain atrophy. CONCLUSIONS: Findings revealed a potential link between fasting glucose levels and the presence of subclinical cerebrovascular disease indicators - white matter hyperintensities and silent brain infarction - in older adults without diabetes and with an education level below high school. Additional research is needed to confirm these associations and to determine the need for interventions aimed at closely monitoring and preventing elevated glucose levels in this population to reduce the prevalence of subclinical cerebrovascular disease.

A Bayesian Hybrid Adaptive Randomisation Design for Clinical Trials with Survival Outcomes.

BACKGROUND: Response-adaptive randomisation designs have been proposed to improve the efficiency of phase III randomised clinical trials and improve the outcomes of the clinical trial population. In the setting of failure time outcomes, Zhang and Rosenberger (2007) developed a response-adaptive randomisation approach that targets an optimal allocation, based on a fixed sample size. OBJECTIVES: The aim of this research is to propose a response-adaptive randomisation procedure for survival trials with an interim monitoring plan, based on the following optimal criterion: for fixed variance of the estimated log hazard ratio, what allocation minimizes the expected hazard of failure? We demonstrate the utility of the design by redesigning a clinical trial on multiple myeloma. METHODS: To handle continuous monitoring of data, we propose a Bayesian response-adaptive randomisation procedure, where the log hazard ratio is the effect measure of interest. Combining the prior with the normal likelihood, the mean posterior estimate of the log hazard ratio allows derivation of the optimal target allocation. We perform a simulation study to assess and compare the performance of this proposed Bayesian hybrid adaptive design to those of fixed, sequential or adaptive - either frequentist or fully Bayesian - designs. Non informative normal priors of the log hazard ratio were used, as well as mixture of enthusiastic and skeptical priors. Stopping rules based on the posterior distribution of the log hazard ratio were computed. The method is then illustrated by redesigning a phase III randomised clinical trial of chemotherapy in patients with multiple myeloma, with mixture of normal priors elicited from experts. RESULTS: As expected, there was a reduction in the proportion of observed deaths in the adaptive vs. non-adaptive designs; this reduction was maximized using a Bayes mixture prior, with no clear-cut improvement by using a fully Bayesian procedure. The use of stopping rules allows a slight decrease in the observed proportion of deaths under the alternate hypothesis compared with the adaptive designs with no stopping rules. CONCLUSIONS: Such Bayesian hybrid adaptive survival trials may be promising alternatives to traditional designs, reducing the duration of survival trials, as well as optimizing the ethical concerns for patients enrolled in the trial.

Perception of timing of kinesthetic stimuli.

A psychophysical method was used to estimate the timing of perception of kinesthetic stimuli with different velocities in normal volunteers. A 1 ms auditory click occurred randomly before or after an imposed flexion movement at either 20, 40 or 60 deg/s of the metacarpophalangeal joint. Subjects reported whether the click was perceived before or after the movement onset (experiment 1) or perception of movement velocity (experiment 2). The time at which there was a 50% chance that subjects reported movement or velocity perception after the click was taken as an estimate of the time subjects perceived the stimuli. The difference in time of perceived movement velocity discrimination and movement onset was only significant when the velocity was 20 deg/s (52 ms). This suggests that movement onset and identification of the velocity of the faster movements are perceived nearly simultaneously.

Dealing with multiplicities in pharmacoepidemiologic studies.

The problem of multiplicities arises in almost all aspects of scientific studies. When multiple hypotheses are tested in a study, one must adjust the significance level of individual tests to maintain an overall probability error rate. The reasons for this are explored in a non-technical way. Techniques for dealing with multiplicities are described and motivated and guidelines are given, with implications for both study design and publication.

Randomized play-the-winner clinical trials: review and recommendations.

The randomized play-the-winner rule is an adaptive randomized design, based on an urn model, that is used occasionally in clinical trials. This paper discusses practical and theoretical issues arising from its use, including stratification, delayed response, operating characteristics, selection of urn parameters, and inference. The paper also discusses recent experience with adaptive clinical trials within the pharmaceutical industry. The author concludes that the randomized play-the-winner rule is appropriate for some clinical trials, but intense and thoughtful planning must take place in the design phase. Such planning should incorporate considerations of variability, power, and appropriate techniques.

Analysis of time trends in adaptive designs with application to a neurophysiology experiment.

Time trends are present in many sequential experiments. Adaptive designs use accruing data to select future design points. It has been observed that the presence of time trends in adaptive designs can bias results of the study. We propose one method of dealing with time trends in analysing adaptive designs. The method, relevance weighted likelihood, weights individual components of the likelihood differently. Consequently, one can downweight earlier data if there is a clear time trend that converges at some point in the study. We apply this methodology to a data set from an adaptive design in neurophysiology. We find that the method is robust and useful in getting more precise estimates of an individual subject's median response.

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.

A comparison of urn designs for randomized clinical trials of K > 2 treatments.

Response-adaptive designs in clinical trials involve incorporating accruing information from patient responses to treatment into the randomization scheme in order to assign more patients to the treatment that has performed better in the trial up to that point. One probability model useful in generating an adaptive randomization scheme is an urn model. We will give a short overview of such adaptive models and compare four of them. We will be interested in how these four models minimize the number of treatment failures in a clinical trial with dichotomous response treatments. Comparison will be done via simulations for four treatments and exactly for three treatments for moderate sample sizes. We compare designs under the assumption that the results of treatments are known immediately, and we also allow some delay in response. Power is analyzed under various alternatives. Our results indicate that a birth and death urn with immigration is the best unless success probabilities are very small, in which case a randomized version of Polya's urn is preferred.

Adaptive survival trials.

We present a design for adaptive survival trials, where the probability of randomization to one of two treatments is skewed away from 0.5 according to the current value of the logrank statistic. A formula mapping the logrank statistic onto [0,1] is given, which is then used to bias a coin used for randomization. Simulation evidence shows that the allocation scheme works well and offers a more ethical alternative when lifetime data are available from other patients during the recruitment period. Power is not adversely affected by the resulting unequal allocation. The usual test statistic appears to be standard normal under the proposed allocation scheme.

A sequential design for psychophysical experiments: an application to estimating timing of sensory events.

An experimental subject sequentially receives different levels of a stimulus, and data are recorded on response or non-response to the stimulus. To ensure that the subject cannot predict the next stimulus level based on previous stimulus levels, a randomized design, based on a generalized Pólya urn model, is used to allocate the stimulus levels. The goal of the experiment is to elicit information efficiently about the relationship between stimulus level and response (either for an individual subject or a group of independent subjects), by estimating quantiles of the stimulus-response curve. Our design allocates stimulus levels unimodally and symmetrically around the unknown median of the stimulus-response curve. We discuss estimation under a broad family of distributions and also fully discuss design issues and options. This design was used for an experiment in neurophysiology in humans to estimate the timing of onset of kinesthetic stimuli. Such psychophysical studies can increase our understanding of normal and pathological function. We present data from that experiment.

A comparison of the randomized play-the-winner rule and the triangular test for clinical trials with binary responses.

We consider a clinical trial model comparing an experimental treatment with a control treatment when the responses are binary. For fixed significance level and power, we compare the expected number of treatment failures for two designs--the randomized play-the-winner rule and the triangular test. The former is an example of an adaptive design while the latter is an example of a fully sequential design. We show how to determine the sample size for the randomized play-the-winner rule and how to choose the stopping boundaries for the triangular test so that the two designs have similar power functions. With this choice of design parameters, simulation indicates that the triangular test is generally more effective at reducing the expected number of treatment failures, particularly when there is a large difference between the two probabilities of success. The expected number of treatment failures can be further reduced if the triangular test is applied using the randomized play-the-winner rule to assign each patient to one of the two treatments.

Ethics and practice: alternative designs for phase III randomized clinical trials.

For decades, biostatisticians have developed and refined the methodology for clinical trials with the intent of giving trial participants a better representation than traditional, equal-allocation, fixed sample-size designs. Despite these methodologic advances and ethical advantages, alternative or data-dependent designs for phase III clinical trials, including sequential designs, Bayesian methods, and adaptive designs, have not been widely adopted in practice. We attempt to characterize situations under which these designs are feasible and desirable from ethical and logistical standpoints. In particular, we describe the role of individual and collective ethics in designing clinical trials and argue that greater attention should be paid to the former. We give examples of those alternative designs that have been used in practice, including discussion of their strengths and shortcomings. We conclude that alternative designs are applicable in limited classes of trials and that investigators should consider them more often when planning clinical trials.

Bootstrap methods for adaptive designs.

Adaptive designs generate dependent sequences of random variables that are not exchangeable. Therefore, it is not obvious how to employ a resampling scheme for confidence interval estimation. We propose a simple procedure where observed response rates from an adaptive experiment are input to a simulation program. The program then generates sequences from the adaptive sampling scheme. We compare, via simulation, three bootstrap confidence intervals with the asymptotic confidence interval for two adaptive designs useful for clinical trials. A simple ranking of simulated response rates yields a confidence interval approximation with coverage close to 1-alpha in most cases. The method allows us to incorporate such complexities as staggered entry and delayed response. We give an example of its utility on a clinical trial of fluoxetine in depression.

Use of spending functions for occasional or continuous monitoring of data in clinical trials.

In many clinical trials, data are monitored periodically by an external data monitoring committee (DMC). Usually the frequency of these interim 'looks' at the data is prespecified. However, the progress of a clinical trial is unpredictable; often the schedule of looks must be modified. The Lan-DeMets procedure provides a spending function approach which does not require prespecification of the frequency or timing of interim looks. The procedure was developed based on the principle of a continuous Brownian motion process. In this paper we employ more elementary concepts to describe a procedure which is based upon the continuous monitoring of emerging data. The approach is flexible in that it applies to both continuous data monitoring and occasional interim monitoring. Examples are given from real clinical trials.

Sequential monitoring of survival data with the Wilcoxon statistic.

When a spending function is used in sequential data monitoring of a clinical trial, it is important to know the information fraction at the times of interim analysis. In a maximum duration designed study, the information fraction is unknown when data are monitored, and it has to be estimated. The modified Wilcoxon statistic developed by Peto and Peto and modified by Prentice is often used to compare two survival curves in a clinical trial. We give guidelines for estimating the information fraction in a maximum duration trial when this statistic is employed. When there is a relatively low event rate or the survival time is approximately exponential, the information fraction for the Peto-Peto-Prentice Wilcoxon statistic is very close to that of the popular logrank statistic. In other cases, it would be helpful to estimate the information fraction as a function of elapsed calendar time. We discuss both group sequential and continuous monitoring.

Calories, fat and cholesterol: intake patterns in the US population by race, sex and age.

Nutrient intakes were investigated for Blacks and Whites using data from the NHANES II survey (1976-80). Intake of energy, total fat, saturated fat, dietary cholesterol, P/S ratio, and per cent of calories derived from total and saturated fat are examined by sex and age, both in absolute terms and per unit of body weight. For most age and sex categories, Blacks are found to have a lower intake of energy and fats than Whites; however, Blacks have a consistently higher intake of dietary cholesterol. The ratio of polyunsaturated fats to saturated fats is higher in females than in males, but all age-sex groups are substantially below recommended levels. Per cent of calories from total and saturated fat are similar in most age-sex groups. Possible explanations of the observed patterns include activity level and metabolic differences.

Closed-form estimates for missing counts in two-way contingency tables.

One method for analyzing contingency tables with missing observations is to model the missing-data mechanism using log-linear models. Previous methods for obtaining estimates (of missing counts and parameters) have required an iterative algorithm. In many cases, however, one can obtain estimates by use of a simple algebraic formula. We illustrate the method with data on smoking and birth weight.

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 patient's 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.

Nonparametric test of stochastic ordering for multiple longitudinal measures.

We present a nonparametric approach that tests whether multiple longitudinal measures tend in the same direction over time. It is not required that each measure have the same number of serial observations, or that the observations be evenly spaced. The test and related estimators of group differences are based on the multivariate rank test of Wei and Lachin (1) and multivariate Mann-Whitney shift estimators of Thall and Lachin (2) and Lachin (3). An example is given using a subset of exercise data from a clinical trial of vesnarinone in congestive heart failure.

Covariate-adjusted response-adaptive designs for binary response.

An adaptive allocation design for phase III clinical trials that incorporates covariates is described. The allocation scheme maps the covariate-adjusted odds ratio from a logistic regression model onto [0, 1]. Simulations assume that both staggered entry and time to response are random and follow a known probability distribution that can depend on the treatment assigned, the patient's response, a covariate, or a time trend. Confidence intervals on the covariate-adjusted odds ratio is slightly anticonservative for the adaptive design under the null hypothesis, but power is similar to equal allocation under various alternatives for n = 200. For similar power, the net savings in terms of expected number of treatment failures is modest, but enough to make this design attractive for certain studies where known covariates are expected to be important and stratification is not desired, and treatment failures have a high ethical cost.