bluuebunny/crossref_metadata_2025 huggingface dataset:
Discrimination techniques applied to the NCI in vitro anti‐tumour drug screen: Predicting biochemical mechanism of action
Authors: Koutsoukos Antonis D. , Rubinstein Lawrence V. , Faraggi David , Simon Richard M. , Kalyandrug Sivaram , Weinstein John N. , Kohn Kurt W. , Paull Kenneth D.
Published: April 2007

The National Cancer Institute currently tests approximately 400 compounds per week against a panel of human tumour cell lines in order to identify potential anti‐cancer drugs. We describe several approaches, based on these in uitro data, to the problem of identifying the primary biochemical mechanism of action of a compound. Using linear and non‐parametric discriminant procedures and cross‐validation, we find that accurate identification of the mechanism of action is achieved for approximately 90 percent of a diverse collection of 141 known compounds, representing six different mechanistic categories. We demonstrate that two‐dimensional graphical displays of the compounds in terms of the initial three principal components (of the original data) result in suggestive visual clustering according to mechanism of action. Finally, we compare the classification accuracy of the statistical discrimination procedures with the accuracy obtained from a neural network approach and, for our example, we find that the results obtained from the various approaches are similar.
Measures of agreement: A single procedure
Authors: Bartko John J.
Published: April 2007

An assessment of measurement agreement made by devices, laboratories, or raters is important in medical practice and research. The setting in which each randomly selected subject is rated by the same two raters raises assorted questions regarding rater agreement. The intraclass correlation coefficient (ICC) is one measure of reliability. The paired t‐test can be used to evaluate the overall ratings or bias of the two raters, while their variances can be assessed with Pitman's test. The Bradley‐Black wood test can be used for a simultaneous test of their means and variances. A single method that provides results for these approaches is proposed and the bivariate confidence ellipse is suggested to provide boundaries for dispersion.
On non‐parametric and generalized tests for the two‐sample problem with location and scale change alternatives
Authors: Podgor Marvin J. , Gastwirth Joseph L.
Published: April 2007

Various tests have been proposed for the two‐sample problem when the alternative is more general than a simple shift in location: non‐parametric tests; O'Brien's generalized t and rank sum tests; and other tests related to the t. We show that the generalized tests are directly related to non‐parametric tests proposed by Lepage. As a result, we obtain a wider, more flexible class of O'Brien‐type procedures which inherit the level robustness property of non‐parametric tests. We have also computed the tests' empirical sizes and powers under several models. The non‐parametric procedures and the related O'Brien‐type tests are valid and yield good power in the settings investigated. They are preferable to the t‐test and related procedures whose type I errors differ noticeably from nominal size for skewed and long‐tailed distributions.
Tudying the relationship between change and initial value in longitudinal studies
Authors: Zerbe Gary O. , Wu Margaret C. , Zucker David M.
Published: April 2007

Blomqvist's problem of studying the relationship between change and initial value in a linear growth curve setting is reformulated from a random effects model perspective. First, a maximum likelihood estimate of the between‐individual covariance matrix for a simple linear regression model with stochastic parameters is obtained via an EM algorithm as discussed by Laird and Ware. Second, the regression coefficient of the individual‐specific slopes on the individual‐specific intercepts is estimated as a ratio of elements of the between‐individual covariance matrix as discussed by Zucker et al. Then a Fieller's type confidence interval for this ratio is proposed. Discussion is facilitated by recognizing the Laird‐Ware model as a special case of a more general model discussed by Hocking.
Some power considerations when deciding to use transformations
Authors: Kingman Albert , Zion Gary
Published: April 2007

Conventional wisdom suggests that for small data sets having substantial skew, one should attempt to determine the correct distributional form, if possible, and apply statistical methods appropriate for that distribution. Transformations such as the log or square root are often used. If an appropriate distributional form cannot be determined, a distribution‐free procedure such as a rank transformation or a randomization test procedure can be used. To better appreciate the effect of such alternatives on both the type I error and power of detecting differences between treatment groups, simulation studies were conducted for responses having specific gamma G(r, Ø) and log‐normal In(M, V) distributions. The gamma and log‐normal distributions were selected so that they had the same first two moments. A simple two group design was assumed. The reference group always had an average disease level μ = 3.0 (μ = rø for gamma, μ =Mfor log‐normal), and the treatment group always had means whose reductions ranged from 0 per cent to 50 per cent. The effect of distributional type and the degree of skewness was investigated by varying the population parameter values. Six statistical test procedures were compared for the gamma distributions. All test procedures were robust relative to the type I error. The UMP test based on a ratio of sample means produced the greatest power for all combinations ofn, randRT. The power losses associated with the randomization test, thet‐test on original scale, and thet‐test on the square root scale were very small, (3 per cent to 6 per cent in absolute value) forn= 10 and 15, and less than 2 per cent for group sizes of 25 or more. The power loss associated with thet‐test on the log scale was much larger, ranging from 5 per cent to 10 per cent smaller power than thet‐test on original scale. The Wilcoxon rank test produced similar results to that of the LOGt‐test for small samples. The power for the shifted LOG (X+c) test increased monotonically to the asymptotic value of the OREt‐test. The same five test procedures based on differences in sample means were then compared for the corresponding log‐normal distributions. The UMP test, that is, LOG(X), produced the highest power. There was very little power lost for the SQRTt‐test. The loss in power varied between 2 per cent and 5 per cent for the RANK test. The RANK test performed considerably better than thet‐test on the original scale. In contrast to the results for the gamma the power for the shifted LOG (X+c) test had its maximum forc= 0, and decreased monotonically to the asymptotic value of the ORIGt‐test.

The results suggest that statistical inferences can be highly dependent on the distributional form and the scale of measurement of the response used in the statistical analysis.
The first t‐test
Authors: Senn Stephen , Richardson William
Published: April 2007

The data with which Student illustrated the application of his famous distribution are examined from a number of aspects. Central to the discussion is the within‐patient clinical trial at Kalamazoo whose results were published by Cushny and Peebles and misquoted by Student and Fisher. This trial is discussed from historical, pharmacological and statistical perspectives. Student's and Fisher's analyses and a more modern analysis by Preece are considered as is Cushny's and Peebles's interpretation. Brief biographies of the five physicians involved in running the trial are presented.
Multi‐state Markov models for analysing incomplete disease history data with illustrations for hiv disease
Authors: Gentleman R. C. , Lawless J. F. , Lindsey J. C. , Yan P.
Published: April 2007

Multi‐state Markov models can be useful in analysing disease history data. We apply the general estimation methods of Kalbfleisch and Lawless to panel data in which individuals are viewed over only a portion of their life history and complete information about transition times between states is unavailable. Methods to assess goodness‐of‐fit are proposed. To illustrate the methods, we consider models of HIV disease relating important immunological marker measurements to the onset of AIDS.
Bayesian analysis of survival on multiple time scales
Authors: Berzuini Carlo , Clayton David
Published: April 2007

We propose a Bayesian approach to the analysis of survival data on multiple time scales. Non‐parametric modelling of variation of rates with more than one time scale is achieved using priors which specifysmoothvariation. Computations are conveniently carried out using Gibbs sampling. We discuss the extension of the method to Bayesian forecasting of rates. Numerical experience of two examples is described.
Assessing the significance of chromosome‐loss data: Where are suppressor genes for bladder cancer?
Authors: Newton Michael A. , Wu Shi‐Qi , Reznikoff Catherine A.
Published: April 2007

Cytogenetic analysis reveals alterations of chromosome structure (losses, gains, and rearrangements of genetic material) in bladder cancer cells generated using an in oitrolin oioo transformation system. To predict possible locations of bladder cancer suppressor genes, we performed a robust Bayesian analysis of the chromosome‐loss data. We postulated a simple stochastic model to describe chromosome loss during tumour progression. Posterior computations are enabled by a dynamic simulation algorithm. Ordered by decreasing posterior probability of putatively harbouring a suppressor gene, we observe significant losses on chromosomes 3, 18, 13, 10, 11, andy.
Sample size calculation for clinical trials in which entry criteria and outcomes are counts of events
Authors: McMahon Robert P. , Proschan Michael , Geller Nancy L. , Stone Peter H. , Sopko George
Published: April 2007

In many chronic diseases, therapy aims to prevent or reduce the frequency of episodes of a disease manifestation, for example cardiac ischaemic episodes or epileptic seizures. Entry criteria for clinical trials typically include a minimum number of episodes within a baseline period, and regression to the mean should be anticipated. The distribution of the number of episodes at follow‐up, the statistical power for treatment comparisons, and the difficulty of recruitment will depend on the entry criterion chosen. A gamma‐Poisson mixture model is employed to describe the regression to the mean when the entry criterion and outcome measure are counts of discrete events. Sample size formulae which take account of the entry criterion are derived for comparison of mean number of events at follow‐up and the proportion of patients with zero events at follow‐up. Application of these formulae to screening data from the Aymptomatic Cardiac Ischemia Pilot (ACIP) Study is presented as an example.
Empirical Bayes methods for estimating hospital‐specific mortality rates
Authors: Thomas Neal , Longford Nicholas T. , Rolph John E.
Published: April 2007

We present alternative methods for estimating hospital‐level mortality rates to those used by the Health Care Finance Administration for Medicare patients. We use an empirical Bayes model to represent the different sources of variation in observed hospital‐specific mortality rates and we use a logistic regression model to adjust for severity differences (in patient mix) across hospitals. In addition to providing a principled derivation of a standard error for the commonly used estimator, our fully model‐based formulation produces much more accurate estimates and resolves the severe problem of multiple comparisons that arises when extreme estimates are used to identify exceptional hospitals. We estimate models for each of four disease conditions using the national Medicare mortality data base which does not contain patient severity descriptors, and mortality data from national samples which do include patient severity descriptors. We find substantial between‐hospital variation in the unadjusted death rates from the national data base. Mortality rates differ substantially with patient severity in our models, but the sample sizes are too small to yield reliable estimates of the between‐hospital variation in adjusted mortality rates.
Extending a Bayesian analysis of the two‐period crossover to allow for baseline measurements
Authors: Grieve Andrew P.
Published: April 2007

In the context of a two‐period crossover study with baseline measurements a graphical method is developed for displaying the dependence of posterior inferences concerning the treatment effect on unavoidable prior beliefs about the ‘correct’ model. The method is a generalization of the approach developed by Grieve for the corresponding case without baselines.
Fitting multivariate polynomial growth curves in two‐period crossover designs
Authors: Grender Julie Myers , Johnson William D.
Published: April 2007

We discuss the statistical analysis of data from two clinical trials using crossover designs. In both studies, response was observed repeatedly over time in each treatment period. The first study involves repeated measurements of a single response variable whereas the second involves bivariate response. Methods are described for fitting polynomial growth curves to achieve data reduction in a two‐stage approach to the analysis of crossover designs. Thus, a multivariate parametric analysis frequently can be conducted even when the sample sizes are somewhat small as is the case in many crossover designs. Hypotheses that are usually of interest in crossover designs can be tested in the second stage of the analysis. Methods for testing the multivariate general linear hypothesis as a basis for statistical inference in such problems are discussed.
Use of surrogate information time for monitoring the effect of treatment on the change in a response variable in clinical trials
Authors: Wu Margaret C. , Lan K. K. Gordon , Connett John E.
Published: April 2007

We discuss the monitoring of clinical trials data and propose two surrogates for total information for use with the spending function approach where there are repeated measurements and we wish to compare the rates of change in a response variable under different treatments. These surrogates are applied to a setting similar to the Lung Health Study. Although the surrogates do not require estimation of the variance parameters, they do require some knowledge of the ratio R of the within‐ to the between‐individual variances. The effects of overestimating and underestimating R are illustrated. In situations of uncertainty we recommend overestimating R, to provide a conservative estimate of information time at each interim analysis.
Surrogate and auxiliary endpoints in clinical trials, with potential applications in cancer and aids research
Authors: Fleming Thomas R. , Prentice Ross L. , Pepe Margaret S. , Glidden David
Published: April 2007

Surrogate endpoints have been defined by Prentice as response variables that can substitute for a ‘true’ endpoint for the purpose of comparing specific interventions or treatments in a clinical trial. The applicability of this definition, and of related surrogate endpoint criteria, is discussed, with emphasis on cancer and AIDS research settings. Auxiliary endpoints are defined as response variables, or covariates, that can strengthen true endpoint analyses. Specifically, such response variables provide some additional information on true endpoint occurrence times for study subjects having censored values for such times. Auxiliary variables will very frequently be available, and they may be able to be used without making additional strong assumptions. Approaches to the use of auxiliary variables using ideas based on augmented score and augmented likelihood methods are described.
The interpretation of multiplicative‐model parameters as standardized parameters
Authors: Greenland Sander , Maldonado George
Published: April 2007

Under current conventions, relative‐risk estimates obtained from multiplicative models are interpreted as estimates of a homogeneous, effect. Such interpretations condition on the unverifiable assumption that the relative risk under study is homogeneous, an assumption that is not likely to be correct even if the model fits well. We propose that such estimates are better interpreted as estimates of standardized relative risks, with a bias component that depends on the degree of model misspecification and on the study design. To evaluate our proposal, we present a study of the maximum‐likelihood estimators from Poisson and logistic regression compared to the population‐standardized rate ratio. The results indicate that our proposed interpretation would in practice be more cautious and accurate than the homogeneous‐effect interpretation.
Evaluating risk adjustment by partitioning variation in hospital mortality rates
Authors: Smith David W.
Published: April 2007

The variation in mortality rates among hospitals has often been described informally as having three major components: patient severity, quality of care and random variation. These informal concepts are characterized formally by partitioning sums of squares and finding their expected values. The partition relates to commonly used tests for whether individual hospitals have unusual mortality rates. Application of the partition to the hospital mortality reports by the Health Care Financing Administration shows that their models for patient risk account for about one‐half the variation among hospital mortality rates. An example using clinical measures of severity accounts for about two‐thirds of mortality variation among hospitals.
Heart rate variability and susceptibility for sudden cardiac death: An example of multivariable optimal discriminant analysis
Authors: Yarnold Paul R. , Soltysik Robert C. , Martin Gary J.
Published: April 2007

The statistical classification problem motivates the search for an analytical procedure capable of classifying observations accurately into one of two or more groups on the basis of information with respect to one or more attributes, and constitutes a fundamental challenge for all scientific disciplines. Although there are many classification methodologies, only optimal discriminant analysis (ODA) explicitly guarantees that the discriminant classifier will maximize classification accuracy in the training sample. This paper presents the first example of multivariable ODA (MultiODA) in medicine, for an application in which we employ three attributes (age and two measures of heart rate variability) to predict susceptibility to sudden cardiac death for a sample of 45 patients. MultiODA outperformed logistic regression analysis on every classification performance index (overall accuracy, sensitivity, specificity, and positive and negative predictive values). In fact, the worst performance result achieved by MultiODA (in total sample or leave‐one‐out validity analysis) exceeded the best performance achieved by logistic regression analysis. We conclude that ODA offers promise as a methodology capable of improving the classification performance achieved by medical researchers, and that clearly merits investigation in future research.
A simple test for spatial pattern in regional health data
Authors: Walter S. D.
Published: April 2007

The rank adjacency statistic D has been used to summarize the spatial autocorrelation in regional health data. It is defined as the average absolute difference in ranks of the data, taken over all possible pairs of adjacent regions. In this paper, the mean and approximate variance are derived for D, including provision for general weighting of regional pairs. Special results are given for the case of binary weights representing regional adjacencies. Empirical analysis with cancer maps from three countries shows excellent properties of a normal approximation to test the significance of D, eliminating the previous need for simulation. The calculations are further illustrated with data on the spatial pattern of urban/rural residence.
Assessing time‐by‐covariate interactions in proportional hazards regression models using cubic spline functions
Authors: Hess Kenneth R.
Published: April 2007

Proportional hazards (or Cox) regression is a popular method for modelling the effects of prognostic factors on survival. Use of cubic spline functions to model time‐by‐covariate interactions in Cox regression allows investigation of the shape of a possible covariate‐time dependence without having to specify a specific functional form. Cubic spline functions allow one to graph such time‐by‐covariate interactions, to test formally for the proportional hazards assumption, and also to test for non‐linearity of the time‐by‐covariate interaction. The functions can be fitted with existing software using relatively few parameters; the regression coefficients are estimated using standard maximum likelihood methods.
Crossover designs for clinical trials
Authors: Carriere K. C.
Published: April 2007

I discuss three‐period crossover designs for an efficient comparison of two test treatments with special application to clinical trials which often have many practical limitations. In this paper I specify a subset of three‐period crossover designs so that the investigators are not left with the problematic two‐period two‐sequence design, should the trials be terminated after the second period. I show that there is a dramatic reduction in variability for estimating the direct and residual treatment effects in three‐period designs compared to two‐period designs. I also show that the universally optimal design with ABB and BAA sequences is unsuitable when a complex form of residual effects is suspected, such as the second‐order residual effects or treatment by period interactions. The design with ABB, BAA, AAB, and BBA sequences is relatively robust to these uncertain model assumptions. I also discuss missing data problems and conclude that, even with a large proportion of missing values, the three‐period design is far more efficient than the two‐period design.
Confidence intervals for weighted proportions
Authors: Waller Jennifer L. , Addy Cheryl L. , Jackson Kirby L. , Garrison Carol Z.
Published: April 2007

We investigate methods for the construction of confidence intervals for a proportion in a stratified two‐stage sampling design with few events occurring in a small number of large, unequal size strata. The critical aspect is the incorporation of the weighting scheme into the construction of a single overall confidence interval. With small numbers of events, the binomial based methods may be inadequate since the normal approximation is not valid. Computer simulations compare coverage probability and bias for five methods of obtaining confidence intervals for proportions by combining: (1) binomial variances; (2) confidence intervals based on the F‐distribution approximation to the cumulative binomial; (3) the binomial variance method with exact confidence limits when a zero prevalence occurs in any stratum; (4) confidence intervals based on the F‐distribution using a rescaling factor; and (5) the binomial variance method with exact confidence limits using a rescaling factor. The method that performs best in terms of coverage probability is the combination of stratum specific confidence intervals based on the F‐distribution using a rescaling factor. The methods involving the binomial variance tend to be negatively biased and the methods based on the F‐distribution tend to be positively biased. Application of these methods with data from a study of adolescent depression that employs a stratified two‐stage sampling design is consistent with these results.
Causal estimation of time‐varying treatment effects in observational studies: Application to depressive disorder
Authors: Lavori Philip W. , Dawson Ree , Mueller Timothy B.
Published: April 2007

Clinicians recognize three phases of the treatment of major depression: an acute phase to control disabling symptoms, a continuation phase to avoid relapses of a single episode, and a preventive phase to avoid recurrences of new episodes over time. With no directly measurable trace of the underlying pathological process, the distinction is based arbitrarily on the passage of time in remission. The clinician who has successfully treated a patient with antidepressant medications in the acute phase has a critical clinical decision to make for the continuation and preventive phases: whether to continue to prescribe the medication, for how long, and at what dose. This decision, like most clinical decisions in psychiatry, is not yet completely determined by the results of randomized clinical trials. Only a handful of such trials have been completed, covering just a fraction of the possible maintenance strategies (defined by treatment drop times). For many reasons, observational studies of the outcome of naturally occurring treatment choices play an important supporting role, helping to extend the reach of completed studies and to design new studies. Causal inference from observational studies has usually been considered in the context of a decision among a few fixed alternatives at a single time. The particular causal effect of interest in the maintenance of remission dictates that treatment be studied over remission time. This challenges the causal analysis of the observational study. We present issues arising from assessing temporal treatment effects due to non‐randomized treatment assignment over time. We use data from a large observational study of the course of affective illness, to illustrate an approach to this problem.
Estimating patterns of CD4 lymphocyte decline using data from a prevalent cohort of HIV infected individuals
Authors: Vittinghoff Eric , Malani Hina Mehta , Jewell Nicholas P.
Published: April 2007

In natural history studies of chronic disease, it is of interest to understand the evolution of key variables that measure aspects of disease progression. This is particularly true for immunological variables among persons infected with the human immunodeficiency virus (HIV). The natural time scale for such studies is time since infection. Most data available for analysis, however, arise from prevalent cohorts, where the date of infection is unknown for most or all individuals. As a result, standard curve fitting algorithms are not immediately applicable. Here we propose two methods to circumvent this difficulty. The first uses repeated measurement data to provide information not only on the level of the variable of interest, but also on its rate of change, and is based on the principal curves algorithm of Hastie and Stuetzle. The second uses an external estimate of the expected time since infection. Both methods use locally‐weighted linear smoothers, and are applied to data from a prevalent cohort of HIV‐infected homosexual men, giving estimates of the average pattern of CD4 lymphocyte decline. These methods apply to natural history studies that use data from prevalent cohorts where the time of disease origin is uncertain, provided availability of certain information from external sources.
A constrained empirical bayes estimator for incidence rates in areas with small populations
Authors: Devine Owen J. , Louis Thomas A.
Published: April 2007

Maps that show the geographic distribution of incidence rates can be useful tools for analysing spatial variation in mortality and morbidity. To attain the necessary geographic resolution, however, production of such maps often requires estimation of incidence in areas with small populations where the observed rates may be highly unstable. Manton et al. have presented an empirical Bayes stabilization procedure in which the observed rate is combined with an area‐specific estimate of the underlying incidence. The approach allows for the mapping of outcomes with varied and possibly unknown etiologies without necessitating covariate dependent modelling of the expected rate. The empirical distribution of a collection of these estimates, however, may not provide an adequate description of the dispersion among the true rates. As a result, decisions based on the histogram of the empirical Bayes estimates may be suspect. We propose a modified version of the approach in which the mean and sample variance of the ensemble of estimates are constrained to equal the appropriate moments of the posterior distribution. The resulting collection of constrained empirical Bayes estimators has nearly the stability of the unconstrained approach and provides an improved estimator of the true rate distribution. We illustrate use of the estimator by producing stabilized county‐level maps of U.S. fire‐ and burn‐related mortality rates and validate the analytic results using a simulation analysis.
Corrections for exposure measurement error in logistic regression models with an application to nutritional data
Authors: Kuha Jouni
Published: April 2007

Two correction methods are considered for multiple logistic regression models with some covariates measured with error. Both methods are based on approximating the complicated regression model between the response and the observed covariates with simpler models. The first model is the logistic approximation proposed by Rosner et al., and the second is a second‐order extension of this model. Only the mean and covariance matrix of the true values of the covariates given the observed values have to be specified, but no distributional assumptions about the measurement error are made. The parameters related to the conditional moments are estimated from a separate validation data set. The correction methods considered here are compared to other methods proposed in the literature. They are also applied to a multiple logistic model describing the effect of nutrient intakes on the ratio of serum HDL cholesterol to total cholesterol. The data constitute baseline data from an epidemiological cohort study, in which a separate pilot study has been carried out to obtain validation information. In the example the corrected parameter estimates from the two approximate models are very similar. Both differ considerably from the naive logistic estimates, indicating a large effect of the measurement error. The various assumptions required by the correction methods are also discussed.
Analysis of repeated categorical data using generalized estimating equations
Authors: Lipsitz Stuart R. , Kim Kyungmann , Zhao Lueping
Published: April 2007

Moment methods for analysing repeated binary responses have been proposed by Liang and Zeger, and extended by Prentice and Zhao and Prentice. In these estimating equations, models are proposed for the correlation between the repeated binary responses. We extend Liang and Zeger's method to models for the correlation between repeated nominal or ordinal categorical responses; in particular, when the repeated responses are binary, our methods reduce to Liang and Zeger's method. Our method is illustrated with two datasets. One dataset contains repeated observations of self‐assessment of arthritis, an ordered variable with three categories, collected during a randomized comparative study of alternative treatments of patients with rheumatoid arthritis. The second dataset is a longitudinal study of the health effects of air pollution, in which the repeated ordered multinomial response is the wheezing status (no wheeze, wheeze with cold, wheeze apart from cold) of a child at ages 9, 10, 11 and 12 years.
Assays for recombinant proteins: A problem in non‐linear calibration
Authors: Giltinan David M. , Davidian Marie
Published: April 2007

Quantification of protein levels in biological matrices such as serum or plasma frequently relies on the techniques of immunoassay or bioassay. The relevant statistical problem is that of non‐linear calibration, where one estimates analyte concentration in an unknown sample from a calibration curve fit to known standard concentrations. This paper discusses a general framework for calibration inference, that of the non‐linear mixed effects model. Within this framework, we consider two issues in depth accurate characterization of intra‐assay variation, and the use of empirical Bayes methods in calibration. We show that proper characterization of intra‐assay variability requires pooling of information across several assay runs. Simulation work indicates that use of empirical Bayes methods may afford considerable gain in efficiency; one must weigh this gain against practical considerations in the implementation of Bayesian techniques. We illustrate the methods discussed using a cell‐based bioassay for the recombinant hormone relaxin.
Survival analysis and neural nets
Authors: Liestbl Knut , Andersen Per Kragh , Andersen Ulrich
Published: April 2007

We consider feed‐forward neural nets and their relation to regression models for survival data. We show how the back‐propagation algorithm may be used to obtain maximum likelihood estimates in certain standard regression models for survival data, as well as in various generalizations of these. Examples concerning malignant melanoma and post‐partum amenorrhoea during lactation are used as illustration. We conclude that although problems with the substantial number of parameters and their interpretation remain, the feed‐forward neural network models are flexible extensions to the standard regression models and thereby candidates for use in prediction and exploratory analyses in larger data sets.
A fourier series approach for comparing groups of subjects on ambulatory blood pressure patterns
Authors: Somes Grant W. , Harshfield G. A. , Arheart K. L. , Miller S. T.
Published: April 2007

We develop an approach to the statistical analysis of 24‐hour ambulatory blood pressure monitoring where we represent each subject's profile by a different mathematical model. We first smooth the data and then use a Fourier series approach to determine the best model for each subject. We then estimate summary variables based on each subject's model to compare distinct groups of subjects. In comparing 15 adult black male hypertensives to eight adult black male normotensives we found that the two groups differ on the shift away from the mesor for both the systolic and the diastolic blood pressure profile.
Analysis of longitudinal data: Random coefficient regression modelling
Authors: Rutter Carolyn M. , Elashoff Robert M.
Published: April 2007

We review random coefficient regression (RCR) models and methods for fitting these models from an applications perspective. Methods for data with exponential family distributions are presented with the Gaussian distribution as a special case. Attention is given to interpretation of fixed effects and the correlation structures implied by RCR models. Estimation methods are presented with computational approaches. Problems associated with testing fixed effects include accurate variance estimation and robustness to misspecification of the covariance structure. Methods for model selection and assessment are presented. An example is used to demonstrate recommended approaches.
Sample size for repeated measures studies with binary responses
Authors: Lipsitz Stuart R. , Fitzmaurice Garrett M.
Published: April 2007

We consider the sample size required for repeated measures studies when the response variable is binary. We propose the use of weighted least squares (WLS) for calculating the minimum sample size required to detect some minimum clinically important treatment effect. We provide tabulated values of the estimated sample sizes for a simple example and we discuss some practical considerations in determination of sample size with repeated binary responses.
Estimation and sample size considerations for clustered binary responses
Authors: Lee Eric W. , Dubin Neil
Published: April 2007

Although there is much literature on sample size determination for clinical trials or experiments with independent responses, there is a lack of methodology to obtain sample sizes for dependent outcomes. This paper presents a simple way to calculate sample size for estimating treatment effects and diagnostic accuracy in the case of correlated binary outcomes. The proposed weighted procedure also has use in estimation, whose advantages we demonstrate through simulation. Recommendations are made for practical application.
Measurement error, instrumental variables and corrections for attenuation with applications to meta‐analyses
Authors: Carroll R. J. , Stefanski L. A.
Published: April 2007

MacMahon et al. present a meta‐analysis of the effect of blood pressure on coronary heart disease, as well as new methods for estimation in measurement error models for the case when a replicate or second measurement is made of the fallible predictor. The correction for attenuation used by these authors is compared to others already existing in the literature, as well as to a new instrumental variable method. The assumptions justifying the various methods are examined and their efficiencies are studied via simulation. Compared to the methods we discuss, the method of MacMahon et al. may have bias in some circumstances because it does not take into account: (i) possible correlations among the predictors within a study; (ii) possible bias in the second measurement; or (iii) possibly differing marginal distributions of the predictors or measurement errors across studies. A unifying asymptotic theory using estimating equations is also presented.
The chart trials: Bayesian design and monitoring in practice
Authors: Parmar Mahesh K. B. , Spiegelhalter David J. , Freedman Laurence S. ,   Chart Steering Committee
Published: April 2007

In this paper we describe the design and monitoring of two large randomized trials being conducted through the British Medical Research Council (MRC) Cancer Trials Office. In particular we discuss the issues involved in the design of the trials, specifying the null and alternative hypotheses and how the design has influenced the monitoring procedure used. From these two examples, and some other MRC experiences, we suggest some basic considerations which could be applied to many MRC and other trials.
Stopping a clinical trial early: Frequentist and bayesian approaches applied to a CALGB trial in non‐small‐cell lung cancer
Authors: George Stephen L. , Li Chengchang , Berry Donald A. , Green Mark R.
Published: April 2007

In May 1984, the Cancer and Leukemia Group B (CALGB) opened a phase III clinical trial for patients with stage III non‐small‐cell lung cancer (NSCLC). The experimental design entailed randomization of 240 patients equally to one of two treatments: radiotherapy alone or chemotherapy followed by radiotherapy. The original design was a fixed sample size design with the intent to analyse the results after 190 deaths. Shortly after the trial began, it was decided to apply group sequential concepts by using a truncated O'Brien‐Fleming stopping rule, implemented via a Lan‐DeMets α‐spending function. A study monitoring committee was established to review the analyses as they were produced. The study was stopped at the fifth interim analysis in May 1987 after 155 eligible patients had been entered. This paper reviews the statistical and other considerations leading to this decision and presents later follow‐up information on these patients. Some Bayesian alternatives to the standard frequentist approaches are also explored and it is demonstrated how these alternatives provide a natural way to address many of the issues raised in monitoring clinical trials.
A reversal of fortune: Practical problems in the monitoring and interpretation of an EORTC breast cancer trial
Authors: Sylvester Richard , Bartelink Harry , Rubens Robert ,   Eortc Breast Cancer Cooperative Group
Published: April 2007

An example of some of the practical problems associated with the interim monitoring of accumulating time to event data is illustrated through an EORTC clinical trial in locally advanced breast cancer which studied the effects of both hormonal therapy and chemotherapy using a 2 × 2 factorial design. The data monitoring process is described and summaries of the interim statistical analyses and the analysis for publication of the clinical results are provided. The results 2.5 years after closing the trial to patient entry differed quite markedly from those observed during the period of patient accrual, thus showing the potential dangers of drawing conclusions based on only short term follow‐up data.
Timing the collaborative analysis of three trials comparing 5‐FU plus folinic acid (FUFA) to surgery alone in the management of resected colorectal cancer: A national cancer institute of canada clinical trials group (NCIC‐CTG) perspective
Authors: Pater Joseph L.
Published: April 2007

Three cancer trials groups have decided to combine data from their separate trials examining the role of chemotherapy with 5‐FU and folinic acid in the adjuvant treatment of resected colorectal carcinoma. Decisions regarding the timing of the analyses of these data have had to consider the same issues that surround early termination of ongoing trials. This paper reviews this experience from the perspective of one of the groups. It emphasizes the significant impact on decision‐making of international differences of opinion concerning treatment standards.
Interim analysis: The alpha spending function approach
Authors: Demets David L. , Lan K. K. Gordon
Published: April 2007

Interim analysis of accumulating data in a clinical trial is now an established practice for ethical and scientific reasons. Repeatedly testing interim data can inflate false positive error rates if not handled appropriately. Group sequential methods are a commonly used frequentist approach to control this error rate. Motivated by experience of clinical trials, the alpha spending function is one way to implement group sequential boundaries that control the type I error rate while allowing flexibility in how many interim analyses are to be conducted and at what times. In this paper, we review the alpha spending function approach, and detail its applicability to a variety of commonly used statistical procedures, including survival and longitudinal methods.
Sequential methods based on the boundaries approach for the clinical comparison of survival times
Authors: Whitehead John
Published: April 2007

The earliest formal sequential procedure, the sequential probability ratio test, involved the plotting of certain test statistics and comparison with straight line parallel boundaries. The boundaries approach can now be used with a wide variety of test statistics, including those appropriate to the analysis of survival data. The boundaries can take various forms, although the use of straight lines still eases the underlying mathematical theory while at least approximating to the requirements of the majority of clinical trials. The implementation of sequential methods needs to be made flexibly and sensitively, with each clinical trial meriting an individualized approach.
The what, why and how of bayesian clinical trials monitoring
Authors: Freedman Laurence S. , Spiegelhalter David J. , Parmar Mahesh K. B.
Published: April 2007

We discuss the advantages and limitations of group sequential methods for monitoring clinical trials data. We describe a Bayesian approach, based upon the use of sceptical prior distributions, that avoids some of the limitations of group sequential methods. We illustrate its use with data from a trial of levamisole plus 5‐Fluorouracil for colorectal cancer.
Data monitoring committees and early stopping guidelines: The southwest oncology group experience
Authors: Crowley J. , Green S. , Liu P. Y. , Wolf M.
Published: April 2007

Data Monitoring Committees in the Southwest Oncology Group comprise the study leadership and the leadership of the Group, augmented by some outside representation. The early stopping guidelines used by these committees are explicit in each protocol, and can be characterized as specifying a few interim analyses at conservative statistical levels. We describe our ten years of experience with this system, and give examples of different trials which have stopped either for positive or negative results.
Some practical aspects of the interim monitoring of clinical trials
Authors: Simon Richard
Published: April 2007

The decision to stop accrual early to a clinical trial is often difficult and multifaceted. Interim monitoring boundaries have been found useful in U.S. oncology trials for such decisions for reasons described here. This paper also discusses rationale that lead to more conservative approaches to early stopping decisions than are currently employed. A recent initiative of the National Cancer Institute to achieve the objectives of independent data monitoring committees in the phase III clinical trials which it sponsors is also described.
Surrogate markers in aids and cancer trials
Authors: Fleming Thomas R.
Published: April 2007

There is significant need for rapid yet reliable evaluation of promising new interventions for the treatment of patients with cancer or HIV infection. Considerable attention has been given to identifying replacement or ‘surrogate’ endpoints for the true clinical efficacy endpoints, in order to reduce the cost, size and duration of clinical trials. We discuss issues which affect the validity of surrogate markers. The reliability of the CD4 lymphocyte count marker is carefully considered in clinical trials of anti‐retroviral agents in HIV infected individuals. The nature of surrogate markers and their reliability is discussed in cancer prevention, screening and treatment trials. Some suggested uses of marker information are also considered.
Monitoring multi‐armed trials
Authors: Proschan Michael A. , Follmann Dean A. , Geller Nancy L.
Published: April 2007

We propose and discuss several methods of monitoring multi‐armed trials comparing means or survival. These methods combine multiple comparison procedures such as Fisher's LSD, Newman‐Keuls and Tukey's with monitoring boundaries such as those of O'Brien and Fleming and Lan and DeMets. Tables of boundaries are provided for the equal variance or equal censoring distribution case.
Stopping when the experimental regimen does not appear to help
Authors: Wieand Sam , Schroeder Georgene , O'Fallon Judith Rich
Published: April 2007

We discuss a procedure that offers the possibility of reducing the number of patients assigned to experimental regimens which are no more effective than a standard regimen. The procedure is useful in advanced cancer trials in which the length of the accrual period might be more than twice the median survival time for the standard regimen. The procedure is intuitively appealing and offers a 50 per cent chance of stopping accrual to ineffective therapies, yet is associated with a loss of power of less than 0.02.
Early stopping rules ‐ clinical perspectives and ethical considerations
Authors: Baum M. , Houghton J. , Abrams K.
Published: April 2007

A clinical trial should only be launched in the presence of equipoise both amongst clinicians responsible for treating the disease and their patients. However, during the period of patient recruitment the chances are that the levels of equipoise will modify. In some cases, where toxicity or inferiority of efficacy are readily demonstrable, the trial needs to be stopped prematurely to prevent harm to patients. In other cases, the degree of equipoise may be increased by the very existence of the trial or from conflicting evidence reported from other studies. By reference to four specific trials it is argued that instead of definitive rules, the decision as to whether recruitment should be continued must be the role of a Data Monitoring Committee which is able to consider and respond to all the available evidence.
Early stopping of prevention trials when multiple outcomes are of interest: A discussion
Authors: Green Sylvan B. , Freedman Laurence S.
Published: April 2007

In large prevention and screening trials, we want to answer multiple questions simultaneously. When monitoring prevention trials with multiple disease outcomes, composite measures may be useful, such as a count of important events, or a comparison of expected mortality, There are advantages to investigating multiple interventions in the same prevention or screening trial, using all‐versus‐none, factorial, or reciprocal control designs. In these situations it may be important to answer some questions early while others are still being investigated.