bluuebunny/crossref_metadata_2025_split huggingface dataset:
PLANNING AND REVISING THE SAMPLE SIZE FOR A TRIAL
Authors: Gould A. Lawrence 
Published: April 2007

AbstractThe sample size for a trial depends on the type I and type II error rates and on the minimum relevant clinical difference, all of which are known, and on the anticipated, but unknown, value of a measure of variation for the key response. This measure is the overall response rate when the key response is binomially distributed, or the residual variance in each treatment group when the key response is continuous and normally distributed. Since the true value of the measure is unknown, it must be guessed or estimated from previous trials. We describe approaches to determining an appropriate value for it, both before the trial begins and after it has begun, for use in calculating the final sample size. These approaches differ from previously described ‘internal pilot’ methods in not requiring unblinding of the treatment assignments in the trial. They preserve the power and do not affect the type I error rate materially. The approaches can be applied to longitudinal studies where the rate of change over time is the response of interest, and to group sequential trials.
BAYESIAN PREDICTIVE APPROACH FOR INFERENCE ABOUT PROPORTIONS
Authors: Lecoutre Bruno , Derzko Gérard , Grouin Jean‐Marie 
Published: April 2007

AbstractThis paper investigates the Bayesian procedures for comparing proportions. These procedures are especially suitable for accepting (or rejecting) the equivalence of two population proportions. Furthermore the Bayesian predictive probabilities provide a natural and flexible tool in monitoring trials, especially for choosing a sample size and for conducting interim analyses. These methods are illustrated with two examples where antithrombotic treatments are administrated to prevent further occurrences of thromboses.
ASSESSING SENSITIVITY IN SUSPENSION TO CYTOSINE ARABINOSIDE: STATISTICAL ANALYSIS, ASSOCIATIONS WITH CLINICAL OUTCOME AND EXPERIMENTAL DESIGN
Authors: Minkin Salomon , Chow Andrew 
Published: April 2007

AbstractAs part of the treatment protocol in 40 leukaemia patients using cytosine arabinoside for remission induction therapy, the sensitivity of each patient's blast cell progenitors to the drug was determined by the liquid suspension assay. To summarize the dose–response curves obtained in this assay, we considered a model that assumes the existence of a resistant subpopulation of progenitor cells. We also considered the median effect model popular in pharmacokinetics. Both models were similar in their ability to describe the data. Parameter values that characterize a low baseline number of progenitor cells exhibiting high sensitivity both at low and high dosages were found to indicate good prognosis. We also identified a simple, economical, robust and efficient design for conducting future assays.
STATISTICAL CONCEPTS IN THE PLANNING AND EVALUATION OF DRUG SAFETY FROM CLINICAL TRIALS IN DRUG DEVELOPMENT: ISSUES OF INTERNATIONAL HARMONIZATION
Authors: O'Neill Robert T. 
Published: April 2007

AbstractThe assessment of the safety of new drugs during pre‐marketing clinical studies is an important and integral part of the drug development and regulatory evaluation process. The International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) is a project that brings together the regulatory decision‐makers of Europe, Japan and the United States of America and the experts from the pharmaceutical industry in the three regions to seek ways to eliminate redundant and duplicative technical requirements among the developed countries for registering new medicinal substances and products. The ICH is developing guidelines or position papers to achieve the goal of harmonizing technical standards in three broad areas, namely, drug efficacy, safety and quality. Within the area of drug safety, this paper will discuss three of the safety topics because of their relevant statistical framework, and because these topics have not, to date, received any attention by the statistical community. The three issues under consideration by the International Conference on Harmonization (ICH), are.1. Dose–response information to support drug registration (especially dose/toxicity relationship).2. Studies in support of special populations; Geriatrics, A Draft Guideline.3. ICH Draft Guideline 3 on The extent of population exposure required to assess clinical safety for drug intended for long‐term‐treatment of non‐life‐threatening conditions'.The ICH special population guideline concerning studies in geriatric patients is closely related to a recent Food and Drug Administration ‘Guideline for the study and evaluation of gender differences in the clinical evaluation of drugs’,4 which is another example of a ‘subgroup’ for whom specific interest exists to evaluate drug safety and efficacy.
Longitudinal data analysis for linear gaussian models with random disturbed‐highest‐derivative‐polynomial subject effects
Authors: Wilson P. David 
Published: April 2007

AbstractFor linear regression analysis of longitudinal data with Gaussian response, I propose a new model to generalize the traditional class of random effects models in which the random effects are deterministic polynomials with coefficients randomly distributed over subjects with mean zero. The generalization is accomplished by adding zero mean Gaussian ‘disturbances’ to the highest derivative of each random coefficient subject polynomial, independently at each observation time. The resulting random effects, which have mean zero at each observation time, are called disturbed highest derivative polynomials (DHDPs). The disturbances induce serial correlation and also allow the subject‐specific DHDP time trends to be non‐linear. I do not estimate the subject‐specific DHDP time trends. Analysis is based on the marginal model, that is, the fixed effects or population model obtained by integrating the random polynomial coefficients and all disturbances out of the joint distribution of themselves and the response vector. This allows a ‘population averaged’ interpretation. One can select the DHDP order by an information criterion. When the population time trend is not correctly modelled, the optimal DHDP order will be larger than when it is correctly modelled. One can make the covariance matrix of the regression coefficients robust to errors in modelling the within‐subject dependence. I describe the relationship of a DHDP to a smoothing polynomial spline, and show how to replace the DHDP model with a smoothing polynomial spline model for the within‐subject dependence in the marginal model.
Graphical models for panel studies, illustrated on data from the framingham heart study
Authors: Klein John P. , Keiding Niels , Kreiner Svend 
Published: April 2007

AbstractRecursive graphical models allow description of the simultaneous development of several (discrete) characteristics such as are recorded in a panel study. Irreversible events (death, irreversible disease occurrence) between exams are naturally included. This paper reviews these models and gives a detailed discussion of an example consisting of 49–50 year old males from the Framingham Heart study. The program DIGRAM is used throughout and the present paper may also serve as an introduction to this software.
Misclassification of a prognostic dichotomous variable: Sample size and parameter estimate adjustment
Authors: Tavaré C. J. , Sobel E. L. , Gilles F. H. 
Published: April 2007

AbstractUnder general conditions, Lagakos showed that for an explanatory variable observed with error, the asymptotic relative efficiency (ARE) when using the observed rather than the true values in linear models, logistic models and proportional hazards models for survival is the square of the correlation between the true and observed variables. The result is useful for samle size adjustment when this correlation is estimable. Often, one cannot observe correct values of the explanatory variable under any circumstances. We show, however, that under the models considered by Lagakos for a dichotomous explanatory variable, the ARE equals the kappa statistic in a read—reread protocol. Consequently, one need not know ‘truth’ in this situation to estimate the ARE and to adjust sample size to maintain desired power; divide the estimated sample size obtained with the assumption of no measurement error by the consistent estimate of the kappa statistic (which is unlikely to be zero or negative). We then develop heuristically an adjusted estimate of the β parameter in a proportional hazards survival model. The work was motivated by analyses of the Childhood Brain Tumour Consortium database. Examples from this database illustrate the method.
Logistic regression with incompletely observed categorical covariates — investigating the sensitivity against violation of the missing at random assumption
Authors: Vach Werner , Blettner Maria 
Published: April 2007

AbstractMissing values in the covariates are a widespread complication in the statistical inference of regression models. The maximum likelihood principle requires specification of the distribution of the covariates, at least in part. For categorical covariates, log‐linear models can be used. Additionally, themissing at randomassumption is necessary, which excludes a dependence of the occurrence of missing values on the unobserved covariate values. This assumption is often highly questionable. We present a framework to specify alternative missing value mechanisms such that maximum likelihood estimation of the regression parameters under a specified alternative is possible. This allows investigation of the sensitivity of a single estimate against violations of the missing at random assumption. The possible results of a sensitivity analysis are illustrated by artificial examples. The practical application is demonstrated by the analysis of two case‐control studies.
A bivariate cumulative probit regression model for ordered categorical data
Authors: Kim Kyungmann 
Published: April 2007

AbstractThis paper proposes a latent variable regression model for bivariate ordered categorical data and develops the necessary numerical procedure for parameter estimation. The proposed model is an extension of the standard bivariate probit model for dichotomous data to ordered categorical data with more than two categories for each margin. In addition, the proposed model allows for different covariates for the margins, which is characteristic of data from typical opthalmological studies. It utilizes the stochastic ordering implicit in the data and the correlation coefficient of the bivariate normal distribution in expressing intra‐subject dependency. Illustration of the proposed model uses data from the Wisconsin Epidemiologic Study of Diabetic Retinopathy for identifying risk factors for diabetic retinopathy among younger‐onset diabetics. The proposed regression model also applies to other clinical or epidemiological studies that involve paired organs.
Estimation of the proportion of genetic cases in late‐onset diseases: An application to alzheimer's disease
Authors: Tzourio C. , Maccario J. , Alpérovitch A. 
Published: April 2007

AbstractIn diseases with a complex etiology including a genetic component, an important issue is to determine the proportion of cases really having inherited the disease. This is not easy in late‐onset diseases where censoring might obscure the transmission pattern of the disease and give an apparently non‐genetic distribution of the cases. We present a model that allows the estimation of the proportion of genetic cases in late‐onset diseases. This model takes censoring into account by explicit modelling of the time dependency of the onset of the disease. The model is illustrated with an example based on an epidemiological survey in Alzheimer's disease and with simulated data.
Heterosexual transmission of HIV analysed by generalized estimating equations
Authors: Fielding Katherine L. , Brettle Raymond P. , Gore Sheila M. , O'Brien Fiona , Wyld Rona , Robertson J. Roy , Weightman Rosina 
Published: April 2007

AbstractA longitudinal analysis of a partner study is compared with a cross‐sectional analysis which identify behavioural and biological risk factors for heterosexual transmission of HIV. Using generalized estimating equations (GEEs) a random effects logistic model is used for the longitudinal analysis. These approaches are illustrated by the Edinburgh heterosexual partner study. The longitudinal analysis finds that ‘high‐risk’ sexual practices, unprotected intercourse for HIV and a low CD4 count in the index case significantly increase the risk of HIV transmission. The cross‐sectional analysis, however, only indicates ‘high‐risk’ sexual practices as favourable for HIV transmission.
Robust bayesian methods for monitoring clinical trials
Authors: Greenhouse Joel B. , Waserman Larry 
Published: April 2007

AbstractBayesian methods for the analysis of clinical trials data have received increasing attention recently as they offer an approach for dealing with difficult problems that arise in practice. A major criticism of the Bayesian approach, however, has focused on the need to specify a single, often subjective, prior distribution for the parameters of interest. In an attempt to address this critism, we describe methods for assessing the robustness of the posterior distribution to the specification of the prior. The robust Bayesian approach to data analysis replaces the prior distribution with aclassof prior distributions and investigations and investigates how the inferences might change as the prior varies over this class. The purpose of this paper is to illustrate the application of robust Bayesian methods to the analysis of clinical trials data. Using two examples of clinical trials taken from the literature, we illustrate how to use these methods to help a data monitoring committee decide whether or not to stop a trial early.
Model selection techniques for the covariance matrix for incomplete longitudinal data
Authors: Grady James J. , Helms Ronald W. 
Published: April 2007

AbstractIn longitudinal studies with incomplete data, where the number of time points can become numerous, it is often advantageous to model the covariance matrix. We describe several covariance models (for example, mixed models, compound symmetry, AR(1)‐type models, and combination models) that offer parsimonious alternatives to unstructured. We evaluate each covariance model with longitudinal data concerning cholesterol as the repeated outcome measure. We discuss strategies for deciding the ‘best’ model and show a graphical technique for judging goodness‐of‐fit of covariance models.
Estimating the reliability of an exposure variable in the presence of confounders
Authors: Kim Mimi Y. , Pasternack Bernard S. , Carroll Raymond J. , Koenig Karen L. , Toniolo Paolo G. 
Published: April 2007

AbstractIn this paper we discuss estimation of the reliability of an exposure variable in the presence of confounders measured without error. We give an explicit formula that shows how the exposure becomes less reliable as the degree of correlation between the exposure and confounders increases. We also discuss biases in the corresponding logistic regression estimates and methods for correction. Data from a matched case‐control study of hormone levels and risk of breast cancer are used to illustrate the methods.
A comparison of frailty models for multivariate survival data
Authors: Pickles Andrew , Crouchley Robert 
Published: April 2007

AbstractThis paper reviews some of the main approaches to the analysis of multivariate censored survival data. Such data typically have correlated failure times. The correlation can be a consequence of the observational design, for example with clustered sampling and matching, or it can be a focus of interest as in genetic studies, longitudinal studies of recurrent events and other studies involving multiple measurements. We assume that the correlation between the failure or survival times can be accounted for by fixed or random frailty effects. We then compare the performance of conditional and mixture likelihood approaches to estimating models with these frailty effects in censored bivariate survival data. We find that the mixture methods are surprisingly robust to misspecification of the frailty distribution. The paper also contains an illustrative example on the times to onset of chest pain brought on by three endurance exercise tests during a drug treatment trial of heart patients.
A hybrid paired and unpaired analysis for the comparison of proportions
Authors: Thomson P. C. 
Published: April 2007

AbstractA method is presented for making inferences about the difference between two binomial proportions when there is partial overlap of the two samples. The method will simplify to the usual cases of two independent samples or paired samples (McNemar'S method) depending on whether there is no or complete overlap. The method is used to compare sensitivities, specificities and Youden indices from a comparative study of trauma triage (screening) instruments.
Investigating a sequence of randomized phase II trials to discover promising treatments
Authors: Strauss Nurith , Simon Richard 
Published: April 2007

AbstractWe consider clinical trial strategies to study diseases in which there is rapidly developing technology. We assume the availability of a limited number of patients for screening treatments over a time horizon, and that availability of new tratements for test is staggered over time. We assume further that patient response is binary and rapidly observable. We consider the strategy of conducting a sequence of two‐armed randomized clinical trials. We carry over the treatment with the larger number of observed successes on the current trial to the next trial for comparison with a new treatment, with this process repeated at each step. For a fixed total number of patients (N), the number of trials one may conduct in sequence (k) is inversely related to the sample size per trial (2n), N = 2nk. We investigate how k and n influence (a) the expected success probability for the treatment selected at the end, and (b) the expected number of total success for the N patients. The ultimate objective is to select one treatment, the winner at stage k, to test against a standard regimen in a randomized comparative phase III trials.
Breaking the matches in a paired t‐test for community interventions when the number of pairs is small
Authors: Diehr Paula , Martin Donald C , Koepsell Thomas , Cheadle Allen 
Published: April 2007

AbstractThere is considerable interest in community interventions for health promotion, where the community is the experimental unit. Because such interventions are expensive, the number of experimental units (communities) is usually small. Because of the small number of communities involved, investigators often match treatment and control communities on demographic variables before randomization to minimize the possibility of a bad split. Unfortunately, matching has been shown to decrease the power of the design when the number of pairs is small, unless the matching variable is very highly correlated with the outcome variable (in this case, with change in the health behaviour). We used computer simulation to examine the performance of an approach in which we matched communities but performed an unmatched analysis. If the appropriate matching variables are unknown, and there are fewer than ten pairs, an unmatched design and analysis has the most power. If, however, one prefers a matched design, then for N > 10, power can be increased by performing an unmatched analysis of the matched data. We also discuss a variant of this procedure, in which an unmatched analysis is performed only if the matching ‘did not work’.
Computing back‐calculation estimates of aids epidemic
Authors: Ding Ye 
Published: April 2007

AbstractThe method of back‐calculation estimates the number of HIV infections from AIDS incidence data and projects future AIDS incidence. We explore a conditional likelihood approach for computing estimates of the number of HIV infections and the parameters in the epidemic density. This method is asymptotically equivalent to the usual likelihood method. The asymptotic normal distribution of the estimates facilitates the computation of confidence intervals. We compute standard deviations for the estimates of HIV incidence and project AIDS incidence from the underlying multinomial distributions. We illustrate the methods with applications to AIDS data in the United States.
A back‐calculation method to estimate the age and period HIV infection intensity, considering the susceptible population
Authors: Verdecchia Arduino , Mariotto Angela B. 
Published: April 2007

AbstractAn extension of the back‐calculation method to reconstruct the past history of HIV infections is presented. In this method the HIV infection intensity, as a function of age and calender time, is assumed to belong to a family of parametric functions and is back‐calculated from the observed AIDS incidence cases, knowledge of the incubation period and of the susceptible population, the final model being chosen as the one that gives the best fit. The model allows for the interaction between age and calendar time. The application to the main risk groups in Italy, male IVDUs and homosexuals, showed that age and the susceptible population were important in estimating the epidemic among IVDUs; interaction between age and calendar time was found to be significant, showing that among young IVDUs the risk of infection decreased faster than among older IVDUs. Sensitivity of the estimates to the assumption of population and choice of the infection function is investigated.
Estimation of mean sojourn time in breast cancer screening using a Markov chain model of both entry to and exit from the preclinical detectable phase
Authors: Duffy Stephen W. , Chen Hsiu‐Hsi , Tabar Laszlo , Day Nicholas E. 
Published: April 2007

AbstractThe sojourn time, time spent in the preclinical detectable phase (PCDP) for chronic diseases, for example, breast cancer, plays an important role in the design and assessment of screening programmes. Traditional methods to estimate it usually assume a uniform incidence rate of preclinical disease from a randomized control group or historical data. In this paper, a two‐parameter Markov chain model is proposed and developed to explicitly estimate the preclinical incidence rate (λ1) and the rate of transition from preclinical to clinical state (λ2, equivalent to the inverse of mean sojourn time) without using control data. A new estimate of sensitivity is proposed, based on the estimated parameters of the Markov process. When this method is applied to the data from the Swedish two‐county study of breast cancer screening in the age group 70–74, the estimate of MST is 2·3 with 95 per cent CI ranging from 2·1 to 2·5, which is close to the result based on the traditional method but the 95 per cent CI is narrower using the Markov model. The reason for the greater precision of the latter is the fuller use of all temporal data, since the continuous exact times to events are used in our method instead of grouping them as in the traditional method. Ongoing and future researches should extend this model to include, for example, the tumour size, nodal status and malignancy grade, along with methods of simultaneously estimating sensitivity and the transition rates in the Markov process.