bluuebunny/crossref_metadata_2025 huggingface dataset:
A comparison of tests for the k‐sample, non‐decreasing alternative
Authors: Mahrer Joanne M. , Magel Rhonda C.
Published: April 2007

This paper compares, by means of Monte Carlo simulations, three non‐parametric tests to detect a non‐decreasing trend in location parameters. The three tests are the Jonckheere—Terpstra test, the Cuzick test, and the Le test. We estimated power for each test across a wide variety of non‐decreasing location parameters under four different types of distributions. None of the tests produced an overwhelmingly higher power over their two competitors.
BAYESIAN DECISION PROCEDURES FOR DOSE DETERMINING EXPERIMENTS
Authors: Whitehead John , Brunier Hazel
Published: April 2007

This paper describes the Bayesian decision procedure and illustrates the methodology through an application to dose determination in early phase clinical trials. The situation considered is quite specific: a fixed number of patients are available, to be treated one at a time, with the choice of dose for any patient requiring knowledge of the responses of all previous patients. A continuous range of possible doses is available. The prior beliefs about the dose–response relationship are of a particular form and the gain from investigation is measured in terms of statistical information gathered. How all of these specifications may be varied is discussed. A comparison with the continual reassessment method is made.
DOSE FINDING STRATEGIES INVOLVING INTERIM ANALYSES AND UNBALANCED TREATMENT ALLOCATION
Authors: Wargenau Manfred
Published: April 2007

Dose finding studies usually require the application of multiple test procedures (MTP). A variety of procedures is available for data analyses. Focusing on testing only subset hypotheses of interest requires only limited α‐adjustment. Specific strategies based on MTP, in particular closed test procedures, which also consider dropping dose groups and unequal group sizes, lead to substantial reduction in total sample size. Of course, strategies discussed here can be extended to comparisons of more than five groups, and they may be generalized to cover equivalence tests, too. For example, one may want to demonstrate therapeutical equivalence of two dose regimens like b.i.d. and o.d. administrations of the drug.
CONSTRUCTING A BOOTSTRAP CONFIDENCE INTERVAL FOR THE UNKNOWN CONCENTRATION IN RADIOIMMUNOASSAY
Authors: Rosen Ori , Cohen Ayala
Published: April 2007

The statistical problem associated with radioimmunoassay is known as calibration or inverse regression. In the current study, we propose a bootstrap procedure aimed at constructing an inverse confidence interval for the univariate calibration problem. The calibration curve is estimated either parametrically or by non‐parametric regression. The methods are illustrated by an example.
INFERRING SYSTEMIC EXPOSURE FROM A PHARMACOKINETIC SCREEN: MODEL‐FREE AND MODEL‐BASED APPROACHES
Authors: Nedelman Jerry R. , Karara Adel H. , Chang Cheng‐Tao , Gibiansky Ekaterina , McDonald Scott , Robinson William T.
Published: April 2007

To infer patterns of average systemic exposure and to estimate individual exposures in phase III clinical trials of a new anxiolytic, two statistical methodologies were applied and compared: non‐linear mixed‐effect modelling, and a model‐free approach based on quartiles of dose‐normalized plasma concentrations of the drug. Although the model‐based approach provides more quantitative insight about relationships between average exposure and demographic covariates, the model‐free approach provides qualitatively similar results about average clearance and quantitatively similar results about individual exposures, and the model‐free approach is easy and inexpensive to implement.
AN APPLICATION OF BAYESIAN POPULATION PHARMACOKINETIC/PHARMACODYNAMIC MODELS TO DOSE RECOMMENDATION
Authors: Wakefield Jon , Racine‐Poon Amy
Published: April 2007

Population pharmacokinetic data consists of dose histories, individual covariates and measured drug concentrations with associated sampling times. Population pharmacodynamic data consist of dose histories, covariates and some response measure. Population analyses, whether they be pharmacokinetic or pharmacodynamic attempt to explain the variability observed in the recorded measurements and are increasingly being seen as an important aid in drug development. In this paper a general Bayesian population pharmacokinetic/pharmacodynamic model is described and an analysis of data for the drug recombinant hirudin is presented. The model we use allows for both outliers and censoring in the concentration data and outlying individual pharmacokinetic parameters. We attempt to address directly important questions such as recommended dose size using predictive distributions for response.
INFLUENCE OF CONFOUNDING FACTORS ON DESIGNS FOR DOSE–EFFECT RELATIONSHIP ESTIMATES
Authors: Girard Pascal , Laporte‐Simitsidis Silvy , Mismetti Patrick , Decousus Hervé , Boissel Jean‐Pierre
Published: April 2007

Three types of designs can be used to estimate the drug dose–effect relationship during phase II clinical trials: parallel‐dose designs (//); cross‐over designs (X), and dose‐escalation designs (↗). Despite the use of non‐linear mixed effect models, the potential influence of confounding factors on ↗ designs has not been previously fully elucidated; we undertook simulations to investigate this for all three experimental designs. We found that: (i) monotonic spontaneous evolution of the effect(EV)did not affect the maximum effect estimation (Emax) and the dose giving 50 per cent of this (ED50); (ii)EVsimilar to a regression to the mean gave rise to biases for ↗ designs; (ii) the introduction of a pharmacodynamic carry‐over generates important biases and imprecision for ↗ designs, even when the carry‐over is adjusted for; (iv) the introduction of non‐responders resulted in bias and imprecision for bothEmaxandED50in all three designs.
ANALYSIS OF PSEUDO‐PROFILES IN ORGAN PHARMACOKINETICS AND TOXICOKINETICS
Authors: Mager Harry , Göller Gernot
Published: April 2007

In general, the pharmacokinetic model parameters, like rate constants, area under the curve (AUC) etc. are estimated via a two‐stage procedure, where the values obtained from concentration–time relationships within one subject (experimental unit) are considered to be functionally related to the drug concentrations measured. In many cases ‘mean’ estimators and their respective standard errors are calculated afterwards. The determination of drug concentrations in organs as well as in the serum of small animals (mice, rats) in dependence of the time after administration often does not permit the establishment of reasonable profiles within one subject suited for conventional pharmacokinetic analyses and tolerability studies. Frequently, only one experimental value per organ or animal is recorded. The consequence is that most pharmacokinetic parameters are to be estimated on the basis of the mean concentrations rather than via the mean of individual parameter estimates. In all cases of a non‐linear relationship between a target item and the concentration, the mean‐concentration based estimators and the two‐stage profile based estimators need not coincide. In addition, in the former case variance estimators may be either difficult to obtain or not deducible. In order to get variance estimators as well as to enable comparisons between different treatment regimens, in addition to bioequivalence testing as a step towards human dose finding studies, various resampling techniques (parametric and non‐parametric bootstrap) were applied to generate pseudo‐profiles from independent measurements and compared to their more conventional counterparts where applicable. Simulation studies based on different predefined pharmacokinetic models (first‐order elimination after IV bolus, first‐order elimination after first‐order absorption, simple capacity‐limited kinetics) revealed that even the non‐parametric pseudo‐profile stratified ‘bootstrap’ (resampling with replacement per time point) performs quite satisfactorily.
THE CASE FOR CROSS‐OVER TRIALS IN PHASE III
Authors: Jones B. , Lewis J. A.
Published: April 2007

There is a common belief that cross‐over trials should not be used in phase III of drug development. This was reinforced by a statement in the draft CPMP Note for Guidance on biostatistical methodology in clinical trials which was circulated for review in March 1993: ‘Hence crossover trials in patients should be avoided as far as possible’. We do not share this belief. Historically, many successful drug developments in indications such as hypertension and asthma have depended heavily on cross‐over trials in their phase III programmes, leading to regulatory approval for a number of well established medicines. The evidence on which these developments were based appeared sound at the time, and has not been questioned by later experiences with these medicines. Furthermore, the general level of understanding of these medical indications is now even more well developed, and hence the circumstances under which cross‐over trials may be used to advantage for new drugs in phase III are even more likely to be correctly identified. There are some well‐known disadvantages of cross‐over trials relative to parallel group trials. These are reviewed and the ways in which early indications of such problems might be detected in phases I and II or elsewhere will be discussed. However, there are also two key advantages, the well‐known one of study size and a less well‐known one arising in the context of treatment‐by‐patient interaction. In phases I and II these advantages lead routinely to the use of the cross‐over design. Some methods of analysing cross‐over trials have been criticized in a number of recent articles. We compare the properties of a number of alternative analysis strategies by means of simulation and conclude that these concerns about methods of analysis do not imply that cross‐over trials should be avoided, especially if baseline measurements can be included in the design. Any small risks attached to their use should not normally concern the regulator as they will tend to diminish estimates of treatment effects rather than enhance them. In summary, cross‐over trials remain a potentially valuable research tool in the development of new medicines at all stages including phase III. It is unnecessary and counterproductive to exclude them from use.
PLANNING AND REVISING THE SAMPLE SIZE FOR A TRIAL
Authors: Gould A. Lawrence
Published: April 2007

The 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

This 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

As 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

The 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

For 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

Recursive 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

Under 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

Missing 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

This 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

In 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

A 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

Bayesian 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

In 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

In 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

This 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

A 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

We 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

There 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

The 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

An 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

The 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.
Analysis of ordinal data in a study of endometrial cancer under a matched pairs case‐control design
Authors: Ganguly S. S. , Naik‐Nimbalkar U. V.
Published: April 2007

While estimating odds ratios (ORs) in the context of dose levels of conjugated oestrogen exposure and development of endometrial cancer, the categories formed by the levels of the exposure are ordinal in nature. In the literature, the binary logistic model is used for estimating OR for each category relative to the baseline category. We describe the use of two ordinal logistic models, the cumulative logit and continuation‐ratio logit models, to estimate the ORs for the matched pairs case‐control data set of the Los Angeles endometrial cancer study. A test for equality of the cumulative ORs across the exposure levels is proposed. The test statistic follows asymptotically the chi‐square distribution.
Transient effects in the cox proportional hazards regression model
Authors: Mauger Elizabeth A. , Wolfe Robert A. , Port Friedrich K.
Published: April 2007

We consider a model for mortality rates that includes both the long and short term effects of switching from an initial to a second state, for example, when patients receive an initial treatment and then switch to a second treatment. We include transient effects associated with the switch in the model through the use of time‐dependent covariates. One can choose the form of the time‐dependent covariate to correspond with a variety of possible transition patterns. We use an exponential decay model to compare the survival experience of transplant versus dialysis treatment of end stage renal disease (ESRD) patients from the Michigan Kidney Registry (MKR). This model involves a hazard function that has an initial effect in mortality at the time of transplant, expected to be higher, followed by a smooth exponential decay to a long term effect, expected to be lower than the risk for those remaining on dialysis. Cox and Oakes used this model to analyse the Stanford Heart Transplant data. The model implicitly suggests there is a time at which the hazard curves (and survival curves) for the treatment groups cross. Those crossing times are useful in advising patients who have the option of receiving a transplant. We describe methods for obtaining estimates of the crossing times and their associated variances, and then apply them in analysing the MKR data.
A comparison of size and power calculations for the wilcoxon statistic for ordered categorical data
Authors: Kolassa John E.
Published: April 2007

This paper compares the accuracy of approximations to test size and power based on the score and the Fisher information, with calculations using Edgeworth and Cornish—Fisher approximations, and demonstrates that in some cases one must exercise much care in using the simpler approximations.
Comparison of tests and sample size formulae for proving therapeutic equivalence based on the difference of binomial probabilities
Authors: Roebruck Peter , Kühn Andreas
Published: April 2007

To prove the hypothesis that a new treatment is as effective as a standard one, a possibility is to test the one‐sided null hypothesis of a clear inferiority of the new treatment against the alternative hypothesis that, if at all, it is only negligibly inferior. Such a problem is of clinical relevance if, for instance, a new treatment with an effectiveness which is comparable to that of the standard one would be preferred if it is less toxic. If the difference between the two treatments is measured by the difference of failure rates, approximate statistical tests and sample size formulae have to be used. This paper reports the results of an extensive empirical investigation comparing the well known calculations proposed by Blackwelder, Rodary, Com‐Nougue and Tournade, which propose a sample size formula for the test of Dunnett and Gent, and Farrington and Manning. The investigation was conducted in order to allow more comprehensive conclusions than those which may be drawn from the limited examples given by the last authors. For the usual settings in clinical trials, the formulae of Farrington and Manning are recommended. However, there are combinations of statistical parameters for which they are not preferred.
An adaptive method for establishing a dose‐response relationship
Authors: Bauer P. , Röhmel J.
Published: April 2007

A multistage sampling method is proposed in dose—response trials, where dose adaptions can be performed in the preplanned adaptive interim analyses. The overall test for proving a dose—response relationship is performed by Fisher'S product criterion for the p‐values from the separate tests of a dose effect in the disjoint samples at the different stages. Based on these p‐values decision boundaries for early stopping with the rejection of the global null hypothesis of no existing dose—response relationship are introduced. The power of the adaptive two‐stage procedure using a particular adaptation rule is compared with the power of the test for a linear trend under analysis of variance assumptions in extensive simulations. The bias in estimation is also quantified. This procedure could be used for establishing a dose—response relationship without including a placebo treatment.
Testing independence when the form of the bivariate distribution is unspecified
Authors: Geisser Seymour , Johnson Wesley
Published: April 2007

We address the problem of testing for independence betweenXandYin two situations. In the first we assume that the joint distribution ofXandYis unknown but the observations onXandYare identifiable. In the second case we assume that the distribution of(X, Y)is exchangeable. Here we consider both when(X, Y)are identifiable and when they are not. We illustrate applications to the testing of independence in DNA databases and in twin studies.
The biostatistician in medical research: Allocating time and effort
Authors: Lesser Martin L. , Parker Robert A.
Published: April 2007

Biostatisticians in research juggle many responsibilities: short‐term consulting; long‐term collaboration; teaching/training, and statistical research. In an institutionally‐supported service group, the biostatistician frequently faces allocation of limited resources (time and effort) over multiple projects, none of which individually supports the biostatistician. In addition to the level of support provided by a specific project, there are several major issues with resource allocation: the quality of the science and data in the project; the possibility that long‐term support develops from the work; personal and institutional considerations that involve the specific investigator or project.

In this paper, we discuss these considerations along with some examples. We present guidelines for making decisions about allocation of time and effort and the possible implications of setting such priorities.
Effects of model misspecification in the estimation of variance components and intraclass correlation for paired data
Authors: Lyles Robert H. , Chambless Lloyd E.
Published: April 2007

Paired data occur in many experimental situations. When one views the subjects as a random sample from some large population, it may seem reasonable to model the data according to the typical one‐way random effects analysis of variance (ANOVA). It is then usually of interest to estimate variance components and intraclass correlation. These estimators can be biased if key assumptions are violated, leading to erroneous interpretations and conclusions. We focus upon assumptions about the equality or inequality of means and/or variances of the two measures on each subject. In the framework of the one‐way random effects ANOVA model, and three generalizations of it, we document estimators obtained as solutions to the likelihood equations. We consider the potentially serious effects of mistaken assumptions. Our findings suggest that the most general model considered is most desirable if consistent and efficient estimation of the between‐subject variance component and intraclass correlation is the main goal. We also briefly connect our exposition to the study of reliability or agreement.
Graphical methods for assessing violations of the proportional hazards assumption in cox regression
Authors: Hess Kenneth R.
Published: April 2007

A major assumption of the Cox proportional hazards model is that the effect of a given covariate does not change over time. If this assumption is violated, the simple Cox model is invalid, and more sophisticated analyses are required. This paper describes eight graphical methods for detecting violations of the proportional hazards assumption and demonstrates each on three published datasets with a single binary covariate. I discuss the relative merits of these methods. Smoothed plots of the scaled Schoenfeld residuals are recommended for assessing PH violations because they provide precise usable information about the time dependence of the covariate effects.
Age‐adjusted survival curves with application in the Framingham study
Authors: Cupples L. Adrienne , Gagnon David R. , Ramaswamy Ratna , D'Agostino Ralph B.
Published: April 2007

Kaplan—Meier curves provide descriptors of survival information for different subgroups within a data set. Investigators frequently use these figures for descriptive comparison of the effect of a particular measure upon survival. When subjects enter a study at different ages and age is associated with survival, it is often desirable to adjust survival information for age differences in the subgroups. This report discusses several methods for age‐adjustment of survival curves, including direct age‐adjustment and proportional hazard modelling with age as a covariate.
Assessment of cardiovascular risk factors in the elderly: The Framingham heart study
Authors: Larson Martin G.
Published: April 2007

In elderly Framingham men and women, systolic blood pressure and cigarette smoking status, as well as a subject's age and sex, strongly influenced the risk of developing cardiovascular disease during ten years of follow‐up. Multivariable proportional hazards models were used to assess the roles of several primary risk factors and to examine their secondary effects. The first three factors were noted in both sexes, separately and combined, but the risk function for blood pressure was steeper in men than in women (hazard ratio, HR, 1·53 per 20 mmHg, 95 per cent confidence interval, CI, 1·33 to 1·75 in men; HR = 1·19, 95 per cent CI 1·07 to 1·33 in women). Systolic pressure measured ten years earlier also contributed to CVD risk (HR = 1·16 per 20 mmHg, 95 per cent CI 1·04 to 1·30), even after accounting for current level. Smoking was associated with a 64 per cent elevation in risk, male sex with a 51 per cent increase, and each 5 years increment in age with a 22 per cent increase. Body mass index measured 10 years ago had a modest association, but current body mass index did not. Among diabetic subjects, total serum cholesterol had an asymmetrical U‐shaped risk function, the risk increasing to either side of sex‐specific median values; diabetesper se, however, was not significant in the final model. In non‐diabetic subjects, there was little change in CVD risk up to the median cholesterol values and a modest increase thereafter. None of the risk functions was age dependent.
Development of health risk appraisal functions in the presence of multiple indicators: The Framingham Study nursing home institutionalization model
Authors: D'Agostino Ralph B. , Belanger Albert J. , Markson Elizabeth W. , Kelly‐Hayes Maggie , Wolf Philip A.
Published: April 2007

A health risk appraisal function is a mathematical model designed to estimate the risk or probability of a person's mortality or morbidity for various diseases based upon risk factors such as age, medical history and smoking behaviour. The Framingham Study has contributed substantially to the development and use of these for endpoints such as mortality and incidence of coronary heart disease and other cardiovascular diseases. This paper discusses a methodology for the development of health risk appraisal functions when the number of potential risk factors is large and illustrates it with sex specific functions for nursing home institutionalization. The methodology involves grouping variables substantively into sets, applying principal component factor analysis and variable clustering to obtain substantively meaningful composite scores, ranking these in order of substantive importance, and then entering these with a hierarchical ordering into a Cox proportional hazard regression.
A quality‐adjusted survival meta‐analysis of adjuvant chemotherapy for premenopausal breast cancer
Authors: Cole Bernard F. , Gelber Richard D.
Published: April 2007

The purpose of this paper is to develop and apply a meta‐analysis methodology, that does not require patient‐level data, for comparing treatments in terms of quality‐of‐life‐adjusted survival. As a motivating example, we considered adjuvant chemotherapy for breast cancer. This therapy has been shown to offer an improvement in recurrence‐free and overall survival, especially for youger women, but its acute toxic effects discourage some physicians from prescribing it. To determine whether the benefit of adjuvant chemotherapy treatment outweighs its costs in terms of toxic effects, we performed a meta‐analysis of quality‐adjusted survival based on data from 1229 patients, 49 years of age or younger, randomized in eight clinical trials that compared chemotherapy versus no adjuvant systemic therapy. We conducted the meta‐analysis by performing a quality‐adjusted survival analysis known as a Q‐TWiST analysis on each trial. A Q‐TWiST analysis allows one to make treatment comparisons that incorporate differences in quality of life associated with various health states. In this analysis, we define as health states the periods of time patients spend: (i) with subjective toxic effects of chemotherapy; (ii) without symptoms of recurrence and toxicity, and (iii) following disease recurrence. We assigned weights to each health state which reflect their relative value in terms of quality of life and allowed them to vary in a sensitivity analysis. We then combined the individual trial results in a meta‐analysis, using a multivariate regression model, in such a way that we could easily perform an overall sensitivity analysis. Individual patient‐level data are not required to perform this meta‐analysis methodology if the individual Q‐TWiST analysis results for each trial are available.