bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing goodness‐of‐fit of parametric regression models for lifetime data – graphical methods
Authors: Cohen Ayala , Barnett Ofra
Published: April 2007

Graphical methods are often used to check goodness‐of‐fit of models to data. It is common to plot residuals against a reference distribution so that when the model fits the data, the configuration should be close to a straight line. Since the resemblance to a straight line is often unclear, it has been suggested to add simulated envelopes within which the configuration is expected to lie. The implementation of this method for survival data analysis is not straightforward. In this paper we point out the difficulties which arise in constructing envelopes on residual plots for randomly censored data. Methods are suggested to deal with the problems and evaluated; they are illustrated by simulated data and on a follow‐up study of myeloma.
Methods for maternal age‐standardization of the incidence of congenital abnormalities
Authors: Carothers Andrew D.
Published: April 2007

In order to compare the birth incidences of particular congenital abnormalities in different populations, it is often necessary to allow for the effects of maternal age. Three age‐adjusted indices are defined, based on analogous indices from the literature on mortality studies, namely, the Standardized Mortality Ratio, the Comparative Mortality Figure and Kerridge's Inverse Method. In most practical situations the differences between them are likely to be trival. However, the first index is maximally efficient under multiplicative risk models and is easily adjusted for incomplete data. The second is the only one to provide valid comparisons under additive, as well as multiplicative, models. The third has the advantage that it does not require a knowledge of the maternal age distribution of all births in the population. The use of the three indices is illustrated with published data on Down's syndrome.
Predicting the success of in vitro fertilization: Conventional semen analysis compared to the hamster ova penetration test
Authors: Abt Markus , Strehler Erwin , Rosenbusch Bernd , Sterzik Karl
Published: April 2007

In vitro fertilization has been advocated as a possible treatment for male factor infertility. Using data collected on 154 men, conventional semen analysis and the hamster ova penetration test are compared with respect to their power in predicting the success of assisted conception. As performing the hamster test is more expensive than the semen analysis, we also investigate a combined strategy. It performs better than the discrimination based solely on the semen parameters, but does not lead to an improvement over the overall discriminatory power obtained from the hamster test alone.
Analysis of dependent survival data applied to lifetimes of amalgam fillings
Authors: Aalen Odd O. , Bjertness Espen , Sønju Torleif
Published: April 2007

When studying lifetimes of amalgam fillings, one is faced with the fact that each patient may contribute multiple survival data. This creates a dependence problem, and some methods for solving it are presented here. Analysis for individual patients with simple approaches for combining data is discussed first. Then a parametric frailty model is applied to analyse variation within and between patients. The results are presented in the form of an empirical Bayes estimate for each patient.
On the sample size for one‐sided equivalence of sensitivities based upon McNemar's test
Authors: Lu Ying , Bean Judy A.
Published: April 2007

In this paper we formalize the problem of testing the one‐sided equivalence in the sensitivities of two medical diagnostic tests under a matched‐pair study design. We derive conditional and unconditional sample size formulae which are decreasing functions of the probability of being diagnosed by both tests. We calculate upper boundary and midpoint sample sizes. Results of a Monte Carlo simulation study that compares the proposed sample size formulae with that of Lachenbruch suggest that our midpoint conditional sample size is the best choice to obtain the desired power for the type of equivalence studies discussed in the paper.
An analysis for menstrual data with time‐varying covariates
Authors: Murphy Susan A. , Bentley Gillian R. , O'Hanesian Mary Ann
Published: April 2007

This paper concerns the analysis of menstrual data; in particular, methodology to identify variables that contribute to the variability of menstrual cycles both within and between women. The basis for the proposed methodology is a parameterization of the mean length of a menstrual cycle conditional upon the past cycles and covariates. This approach accommodates the length‐bias and censoring commonly found in menstrual data. Data from a longitudinal study of menstrual patterns and other variables among Lese women of the Ituri Forest, Zaire, illustrate the methodology. A small simulation illustrates the bias caused by incorrectly deleting the censored cycles.
Simple confidence intervals for standardized rates based on the approximate bootstrap method
Authors: Swift Michael B.
Published: April 2007

This paper presents simple expressions for the confidence intervals of indirect and direct standardized rates, based on the approximate bootstrap confidence (ABC) method of DiCiccio and Efron. For indirect rates, the ABC method compares favourably with the exact methods. An exact method is not available for direct standardized rates. I, therefore, compared the ABC to other simple procedures for confidence intervals, namely: the standard normal, log‐normal, and a recent method proposed by Dobsonet al.Simulation studies of the coverage properties of these methods for direct standardized rates show that the ABC method performs best, with balanced tail probabilities close to their expected values.
Difference in clinical implications of CD4 counts among HIV‐infected homosexual men and injection drug using men and women
Authors: Paik Myunghee Cho , Begg Melissa Dowd , El‐Sadr Wafaa , Gorman Jack , Stein Zena
Published: April 2007

While the relationship between CD4 counts and clinical symptoms is well established among homosexual men, the same is not true for injection drug using men and women (IDUM and IDUW). In this paper we investigate whether CD4 counts have the same clinical implications for IDUM and IDUW as for homosexual men. We estimated the CD4 counts at which 50 per cent of the HIV‐infected but AIDS‐free population has AIDS related complex (ARC) based on three biannually measured CD4 counts. The analyses involve interval, right and left censored threshold data. We took the parametric approach, assuming that the threshold values for ARC arise from a family of distributions that includes symmetric, left or right skewed distributions, in which the logistic and extreme value distributions are embedded as special cases. The resulting estimates of median thresholds of CD4 counts for ARC were 249, 424 and 755 for homosexual men, IDUM, and IDUW, respectively. The results were robust with respect to the assumptions on the underlying distribution.
Combining 2 × 2 tables that contain structural zeros
Authors: Johnson William D. , May Warren L.
Published: April 2007

When one cell or more of a contingency table is necessarily zero, the table is said to contain structural zeros. These structural voids may be inherent to the problem at hand, or, in some applications, they may be introduced intentionally through the experimental design. With data classified by a third stratification variable, one needs a method for analysing a set of independent 2 × 2 tables. We propose Cochran‐Mantel—Haenszel type analyses that combine observations across strata. We also propose tests of hypotheses pertaining to the marginal and conditional probabilities.
A multiple imputation strategy for clinical trials with truncation of patient data
Authors: Lavori Philip W. , Dawson Ree , Shera David
Published: April 2007

Clinical trials of drug treatments for psychiatric disorders commonly employ the parallel groups, placebo‐controlled, repeated measure randomized comparison. When patients stop adhering to their originally assigned treatment, investigators often abandon data collection. Thus, non‐adherence produces a monotone pattern of unit‐level missing data, disabling the analysis by intent‐to‐treat. We propose an approach based on multiple imputation of the missing responses, using the approximate Bayesian bootstrap to draw ignorable repeated imputations from the postrior predictive distribution of the missing data, stratifying by a balancing score for the observed responses prior to withdrawal. We apply the method and some variations to data from a large randomized trial of treatments for panic disorder, and compare the results to those obtained by the original analysis that used the standard (endpoint) method.
Toward vital status sweeps: A case history in sequential monitoring
Authors: Hallstrom Al , McBride Ruth , Moore Rich
Published: April 2007

We use data from the Cardiac Arrhythmia Suppression Trial (CAST) to demonstrate the sensitivity of sequential monitoring to the timeliness of survival data. In CAST vital status sweeps were not routinely performed prior to the times of sequential analysis. Examination of the delay between death and reporting of death shows that the change in the time of sequential analysis by even as few as several months can have dramatic impact on the results of the sequential analysis.
Additivity test for composition of binomial effects in chromosome aberrations after radiation injury
Authors: Komornik J. , Mikulecky M. , Beno M.
Published: April 2007

Additivity of clastogenic effects of two doses of radiation to human peripheral blood lymphocytes is considered. We compare the effects of combined tritium and gamma irradiation with the sum of the effects of either irradiation administered separately. Our solution is based on combination of de Moivre and Cramèr—Slucky theorems. The results for 14 subjects where the deletion types of chromosome aberrations were counted indicate a Normal distribution of the sample additivity index, oscillating non‐significantly around zero, with one outlier towards adaptivity and another towards synergism of the effect of the two doses absorbed by the cells.
A comparison of methods for correlated ordinal measures with ophthalmic applications
Authors: Gange Stephen J. , Linton Kathryn L. P. , Scott Alastair J. , Demets David L. , Klein Ronald
Published: April 2007

For many clinical trials and epidemiologic investigations in the field of ophthalmology, paired ordinal data are often collected through the detailed grading of retinal photographs. One method for analysis of these data is the extension of the generalized estimating equation (GEE) methodology to multinomial data with cumulative link functions. Prior to the development of this advanced technique, however, ophthalmologists developed a method of combining the ordinal responses of both eyes of a patient into a single person‐level response on a new ordinal scale. A relationship between the regression coefficients of these two methods is derived as a function of the correlation between eyes. We investigate the applicability of this result and the relationship of the standard errors in simulation experiments and in an example from the Wisconsin Epidemiologic Study of Diabetic Retinopathy.
Multi‐state models and diabetic retinopathy
Authors: Marshall Guillermo , Jones Richard H.
Published: April 2007

This paper discusses the application of a multi‐state model to diabetic retinopathy under the assumption that a continuous time Markov process determines the transition times between disease stages. The multi‐state model consists of three transient states that represent the early stages of retinopathy, and one final absorbing state that represent the irreversible stage of retinopathy. By using a model with covariables, we explore the effects of factors that influence the onset, progression, and regression of diabetic retinopathy among subjects with insulin‐dependent diabetes mellitus. We can also introduce time‐dependent covariables in the model by assuming that the covariables remain constant between two observations. We can also obtain survival‐type curves from each stage of the disease and for any combination of patient risk factors.
Methods for bounding the marginal survival distribution
Authors: Dignam James J. , Weissfeld Lisa A. , Anderson Stewart J.
Published: April 2007

For time to event data with many potential failure types, one cannot uniquely determine the distribution of time to a specific event type, or marginal survival distribution, in the case where event types are mutually exclusive. In this paper we discuss several methods for estimating functions that bound the non‐identifiable marginal survival distribution in the competing risks problem. We compute and compare bounds for data simulated from two bivariate survival distributions. Results show that the methods provide a suitable estimate of the marginal survival probability when one has specified dependence correctly. Data from a large clinical trial for breast cancer illustrate the methods.
Construction, validation and updating of a prognostic model for kidney graft survival
Authors: van Houwelingen Hans C. , Thorogood Jane
Published: April 2007

The construction, validation and updating of a prognostic model for kidney graft survival is reported using data from the Eurotransplant database. First, a model is constructed for data from transplantations in the period 1984 to 1987. The model is later updated for the 1988–1990 data. The first data set was randomly split into a training set (two‐thirds of the data) and a validation set (one‐third). To prevent overfitting empirical Bayes estimation of the transplantation centre effect was employed. After that, the validation set was used for fine‐tuning by shrinkage. For updating with the 1988–1990 data parametric models were used after suitable transformation of the time axis; it appeared that survival had slightly improved. This necessitated a correction of the parameters in the exponential model. Correctness of the model was checked by extension to a Weibull model. The lack of fit was statistically significant, but practically ignorable. Recommendations are made to place less emphasis on the selection of variables and cut‐off points, and more emphasis on the fine‐tuning of the prognostic model by means of low‐dimensional parametric models in independent data sets.
Self‐modelling with random shift and scale parameters and a free‐knot spline shape function
Authors: Lindstrom Mary J.
Published: April 2007

The shape invariant model is a semi‐parametric approach to estimating a function relationship from clustered data (multiple observations on each of a number of individuals). The common response curve shape over individuals is estimated by adjusting for individual scaling differences while pooling shape information. In practice, the common response curve is restricted to some flexible family of functions. This paper introduces the use of a free‐knot spline shape function and reduces the number of parameters in the shape invariant model by assuming a random distribution on the parameters that control the individual scaling of the shape function. New graphical diagnostics are presented, parameter identifiability and estimation are discussed, and an example is presented.
Residual plots for the censored data linear regression model
Authors: Hillis Stephen L.
Published: April 2007

To be consistent, censored data linear regression estimators typically require a correctly specified linear regression function and independent and identically distributed errors. For uncensored data one can assess these model assumptions informally by examining plots of the residuals against the independent variables or fitted values. In this paper I propose plots for censored data analogous to these uncensored data residual plots. One can use such plots in the same way as their uncensored data counterparts for checking model assumptions; if the model assumptions are correct, then the plots should exhibit a random scatter. I show that the proposed plots are useful in selecting a linear regression model for the Stanford heart transplant data.
Notes on conditional confidence limits under inverse sampling
Authors: Lui Kung‐Jong
Published: April 2007

When the number of subjects in a two‐by‐two table is small or moderate, we may commonly use the exact conditional distribution with all marginals fixed to derive the conditional confidence limits on the underlying parameter. Under inverse sampling, in which we continue to sample subjects until we obtain exactly a pre‐determined number of subjects falling into a specific category, this paper notes that derivation of a confidence interval, which has the coverage probability equal to or larger than a nominal 1 – α confidence level, for relative risk and relative difference in cohort studies is straightforward. This paper further finds that, when the underlying disease is rare, we can similarly apply an inverse sampling to produce an approximate 1 – α conditional confidence limits on attributable risk in case‐control studies as well. When the number of subjects is small and the test statistic derived on the basis of large sample theory is not strictly adequate for use, this paper also presents an exact hypothesis testing procedure for the above parameters in the corresponding study designs.
Practical methodology of meta‐analyses (overviews) using updated individual patient data
Authors: Stewart Lesley A. ,   Cochrane Working Group On Meta‐Analysis Using Individual Patient Data
Published: April 2007

Meta‐analyses using updated individual patient data may provide the most reliable means of combining data from similar randomized controlled trials. The benefits of this approach to systematic reviews are described. Guidance, based on the experience of several groups who have undertaken such projects, is given. This includes practical advice on initiating and maintaining collaboration, the time and resources required to undertake these usually international projects and methods of data checking and validation. Example proforma are included.
The design and analysis of randomized trials with recurrent events
Authors: Cook Richard J.
Published: April 2007

This paper describes a method for planning the duration of a randomized parallel group study in which the response of interest is a potentially recurrent event. At the design stage we assume patients accrue at a constant rate, we model events via a homogeneous Poisson process, and we utilize an independent exponential censoring mechanism to reflect loss to follow‐up. We derive the appropriate study duration to ensure satisfaction of power requirements for the effect size of interest under a Poisson regression model. An application to a kidney transplant study illustrates the potential savings of the Possion‐based design relative to a design based on the time to the first event. Revised design criteria are also derived to accomodate overdispersed Poisson count data. We examine the frequency properties of two non‐parametric tests recently proposed by Lawless and Nadeau for trials based on the above design criteria. In simulation studies involving homogeneous and non‐homogeneous Poisson processes they performed well with respect to their type I error rate and power. Results from supplementary simulation studies indicate that these tests are also robust to extra‐Posson variation and to clustering in the event times, making these tests attractive in their generality. We illustrate both tests by application to data from a completed kidney transplant study.
An application of decision theory to patient screening for an autologous tumour vaccine trial
Authors: Shannon William D. , Bryant John , Logan Theodore F. , Day Roger
Published: April 2007

Patients are eligible for accrual onto a phase I autologous tumour vaccine clinical trial if their resected and dissociated tumour achieves a minimum viable cell count. Because tumour pre‐processing and cell count determination are expensive, there has been developed a screening procedure based on tumour mass to screen out those tumours unlikely to yield sufficient viable cells. If θ is the ratio of the expected benefit of an accrual onto the study to the cost of tumour pre‐processing and cell counting, then we maximize long‐run benefit by pre‐processing and counting only those tumours whose masses exceed a cutoff mc, such that Pr {sufficient tumour cells mass = mc} = 1/θ. We derive algorithms for estimating mc and evaluate them under a variety of assumptions concerning the cell count/mass relationship. These include explicit equations for mc under parametric assumptions as well as more general algorithms based on non‐parametric smoothing techniques. We show that when θ deviates substantially from 2, these methods outperform simple inverse interpolation.
Two‐sample rank tests for acceleration in cure models
Authors: Lee Jae Won
Published: April 2007

I derive the locally most powerful rank tests for acceleration against semi‐parametric alternatives when some patients are cured of the disease. I consider some particular classes of alternatives and present simulation results to verify the validity of the proposed tests. Real data from clinical trials for childhood leukaemia illustrate the proposed tests.
The restricted cubic spline as baseline hazard in the proportional hazards model with step function time‐dependent covariables
Authors: Herndon James E. , Harrell Frank E.
Published: April 2007

We incorporate a cubic spline function where the tails are linearly constrained, as the baseline hazard, into the proportional hazards model. We show estimation of covariable coefficients and survival probabilities with this model to be as efficient statistically as with the Cox proportional hazards model when covariables are fixed. Examples show that the inclusion of time‐dependent covariables defined as step functions into the restricted cubic spline proportional hazards model reduces computation time by a factor of 213 over the Cox model. Advantages of the spline model also include flexibility of the hazard, smooth survival curves, and confidence limits for the survival and hazard estimates when there are time‐dependent covariables present.
Standardized estimates from categorical regression models
Authors: Joffe Marshall M. , Greenland Sander
Published: April 2007

We consider the problem of interpreting categorical regression models, such as the polytomous logistic model, the continuation‐ratio model, the stereotype model, and the cumulative‐odds model. We present a method to convert categorical regression coefficients into estimates of standardized fitted probabilities, probability differences and probability ratios. We use a delta‐method approach to estimate standard errors. We then present a small simulation study to compare different transforms for setting confidence limits, and provide an illustration of our approach in an observational study of drug therapy of polymyositis.
Exact logistic regression: Theory and examples
Authors: Mehta Cyrus R. , Patel Nitin R.
Published: April 2007

We provide an alternative to the maximum likelihood method for making inferences about the parameters of the logistic regression model. The method is based appropriate permutational distributions of sufficient statistics. It is useful for analysing small or unbalanced binary data with covariates. It also applies to small‐sample clustered binary data. We illustrate the method by analysing several biomedical data sets.
Modelling mortality and morbidity of newborns
Authors: Ritter C. , Bouckaert A. , van Lierde M. , Theunissen I.
Published: April 2007

Data of 10,514 singleton births collected over a ten year period at a single hospital are analysed and models linking birthweight and gestational age with mortality and morbidity defined by low Apgar scores are constructed and compared. Based on these models, charts of mortality and morbidity are drawn and compared with common charts of birthweight centiles. Classification rules for newborns at risk are defined by iso‐mortality contours, marginal birthweight centiles, and birthweight centiles adjusted by gestational age, respectively, and compared using receiver operating characteristic (ROC) curves. The results suggest that, as far as neonatal mortality is concerned, classification rules based on simple marginal birthweight centiles perform almost as well as iso‐mortality contours and considerably better than birthweight centiles adjusted for gestational age.
Longitudinal models for analysis of respiratory function
Authors: D'Agostino Ralph B. , Sparrow David , Weiss Scott , Rosner Bernard
Published: April 2007

We compare the results of fitting three longitudinal models, two autoregressive models (the serial correlation model and a damped autoregressive model) and a compound symmetry model, to data on a cohort of 1154 adult men in Boston. The serial correlation model assumes that the error terms are autocorrelated with correlation of the form λt for visits t years apart while the damped autoregressive assumes that the correlation between error terms of observations t years apart is of the form λtθ. The compound symmetry model assumes that the errors are correlated, with the same correlation regardless of how far apart observations are in time. These three models are all related in that the serial correlation and compound symmetry models are particular cases of the damped autoregressive models (that is, θ = 1 corresponds to the serial correlation model and θ = 0 corresponds to the compound symmetry model). For current smokers, the damped autoregressive model provided a significantly better fit than either of the other two models (p < 0·001); for never smokers the damped autoregressive and compound symmetry models were almost identical with both providing a significantly better fit than the serial correlation model (p < 0·001).
A statistic for comparing two correlated markers which are prognostic for time to an event
Authors: Jung Sin‐Ho , Wieand Sam , Cha Stephen S.
Published: April 2007

A proliferation of biologic markers has led to increased interest in methods for comparing the prognostic value of markers in predicting time to recurrence of or death from disease. This paper proposes a non‐parametric test statistic for comparing two continuous markers when the outcome of interest is time to an event and the outcome is subject to right censoring. Results from Monte Carlo studies show that the new testing procedure is reliable for practical use. We present an example in which we use the statistic successfully in a colon cancer study to select between two potentially useful markers.
A simulation study used to design the sequential monitoring plan for a clinical trial
Authors: Brooks Maria Mori , Hallstrom Al , Peckova Monika
Published: April 2007

In designing a sequential monitoring plan for the AVID (Antiarrhythmics Versus Implantable Defibrillators) study, a multi‐centred clinical trial, we completed a simulation study to help ourselves and the Data Safety Monitoring Board understand the implications of various monitoring plans. In particular, we compared three test statistics and three use functions. Simulation was based on approximate trial parameters and two plausible but distinct alternative hypotheses. The power to detect a difference between the two treatment arms was computed as a function of time. The simulated results illuminate several of the important statistical and ethical issues involved in selecting a sequential monitoring plan and illustrate how one can use simulations to design a clinical trial.
Exact power computation for dose—response studies
Authors: Tang Man‐Lai , Hirji Karim F. , Vollset Stein E.
Published: April 2007

The toxicity of an agent or the therapeutic effect of a drug may be assessed by a dose‐response study. We present a method for computing the exact power of exact and large sample statistical tests employed for binary response data from such a study. This method, based on recursive polynomial multiplications, enables fast computation of exact power for studies with up to a moderately large sample size. We demonstrate the efficiency of our method using three examples. The method is suitable for the design and power analysis of dose—response studies in which the usual asymptotic approximations are suspect.
The power of focused tests to detect disease clustering
Authors: Waller Lance A. , Lawson Andrew B.
Published: April 2007

Statistical tests have been proposed for determining whether incident cases of adverse health effects are ‘clustered’ together. Several procedures, termed ‘focused’, specifically analyse disease surveillance data around pre‐specified putative sources of environmental hazard. Little has been done to compare the performance of various proposed methods on actual models of clustering. Analytic power functions are derived for three tests of focused clustering. These functions are based on the probabilistic structure of the clustering tests and do not require simulation. The three tests are compared with respect to statistical power on hypothetical data where monotone multiplicative increases in disease risk near a putative hazard define disease clusters of varying intensity.
The choice of test for detecting raised disease risk near a point source
Authors: Bithell J. F.
Published: April 2007

This paper considers the problem of testing for excess risk near a point source of risk S, as might be postulated to exist near a nuclear installation, for example. The data are assumed to be in the form of counts in small areas for which expected numbers of cases have also been calculated by reference to national rates. It is shown how to derive the most powerful test against any given alternative hypothesis; such ‘linear risk score’ tests are further considered, particularly that which scores each case according to the reciprocal of the rank of the distance from S. These tests are compared with two tests due to Stone for general ordered alternatives and the important distinction is drawn between conditional and unconditional versions of the tests. Their behaviour is illustrated by application to data on childhood leukaemia in relation to nuclear installations in Britain.
Non‐parametric estimation of spatial variation in relative risk
Authors: Kelsall Julia E. , Diggle Peter J.
Published: April 2007

We consider the problem of estimating the spatial variation in relative risks of two diseases, say, over a geographical region. Using an underlying Poisson point process model, we approach the problem as one of density ratio estimation implemented with a non‐parametric kernel smoothing method. In order to assess the significance of any local peaks or troughs in the estimated risk surface, we introduce pointwise tolerance contours which can enhance a greyscale image plot of the estimate. We also propose a Monte Carlo test of the null hypothesis of constant risk over the whole region, to avoid possible over‐interpretation of the estimated risk surface. We illustrate the capabilities of the methodology with two epidemiological examples.
The map comparison problem: Tests for the overlap of geographic boundaries
Authors: Jacquez Geoffrey M.
Published: April 2007

The quality of environmental studies is often compromised by the use of statistics, such as correlation and regression for example, which presuppose a statistical model, linear or otherwise, between two variables. When investigating hypotheses about relationships among geographically distributed variables, an alternative approach is to measure the amount of boundary overlap. Boundaries are geographic zones of rapid change in the intensity of a variable, and are often of scientific interest in their own right. Examples of boundaries include ecotones, genetic hybrid zones, pollution plumes, and the front of the wave of advance of an epidemic. Boundary overlap describes zones where boundaries from two or more variables coincide, and are useful for evaluating epidemiologic hypotheses relating health to environmental exposures. This paper proposes four statistics of boundary overlap, and explores their performance using simulation models and real data describing ozone concentrations and hospital admissions for respiratory conditions. The statistics are found sensitive to different aspects of boundary overlap, and provide an additional diagnostic tool in the analysis of geographically distributed variables. Overlap statistics are expected to come into increasing use as the installed base of geographic information systems increases.
The spatial analysis of covariates in a study of environmental epidemiology
Authors: Donnelly Christl A.
Published: April 2007

Kanawha County, West Virginia, includes a deep river valley with a large population living in close proximity to many chemical manufacturing centres. One study of this area combined an epidemiologic survey of children attending Kanawha County schools with the measurement of the concentrations of 15 volatile organic compounds at each of the 75 elementary schools. Results have been reported by Ware et al. This paper further examines the associations of the concentrations of the ten individual volatile organic compounds related to chemical manufacturing processes with observed lower respiratory symptoms, generalizing the model of spatial variability and measurement error proposed by Donnelly et al. and using semi‐variograms to assess the validity of model assumptions.
Bayesian estimates of disease maps: How important are priors?
Authors: Bernardinelli L , Clayton D. , Montomoli C.
Published: April 2007

In the fully Bayesian (FB) approach to disease mapping the choice of the hyperprior distribution of the dispersion parameter is a key issue. In this context we investigated the sensitivity of the rate ratio estimates to the choice of the hyperprior via a simulation study. We also compared the performance of the FB approach to mapping disease risk to the conventional approach of mapping maximum likelihood (ML) estimates andp‐values. The study was modelled on the incidence data of insulin dependent diabetes mellitus (IDDM) as observed in the communes of Sardinia.
Bayesian analysis of space—time variation in disease risk
Authors: Bernardinelli L. , Clayton D. , Pascutto C. , Montomoli C. , Ghislandi M. , Songini M.
Published: April 2007

The analysis of variation of risk for a given disease in space and time is a key issue in descriptive epidemiology. When the data are scarce, maximum likelihood estimates of the area‐specific risk and of its linear time‐trend can be seriously affected by random variation. In this paper, we propose a Bayesian model in which both area‐specific intercept and trend are modelled as random effects and correlation between them is allowed for. This model is an extension of that originally proposed for disease mapping. It is illustrated by the analysis of the cumulative prevalence of insulin dependent diabetes mellitus as observed at the military examination of 18‐year‐old conscripts born in Sardinia during the period 1936–1971. Data concerning the genetic differentiation of the Sardinian population are used to interpret the results.
Spatial variation of natural radiation and childhood leukaemia incidence in Great Britain
Authors: Richardson Sylvia , Monfort Christine , Green Martyn , Draper Gerald , Muirhead Colin
Published: April 2007

This paper describes an analysis of the geographical variation of childhood leukaemia incidence in Great Britain over a 15 year period in relation to natural radiation (gamma and radon). Data at the level of the 459 district level local authorities in England, Wales and regional districts in Scotland are analysed in two complementary ways: first, by Poisson regressions with the inclusion of environmental covariates and a smooth spatial structure; secondly, by a hierarchical Bayesian model in which extra‐Poisson variability is modelled explicitly in terms of spatial and non‐spatial components. From this analysis, we deduce a strong indication that a main part of the variability is accounted for by a local neighbourhood ‘clustering’ structure. This structure is furthermore relatively stable over the 15 year period for the lymphocytic leukaemias which make up the majority of observed cases. We found no evidence of a positive association of childhood leukaemia incidence with outdoor or indoor gamma radiation levels. There is no consistent evidence of any association with radon levels. Indeed, in the Poisson regressions, a significant positive association was only observed for one 5‐year period, a result which is not compatible with a stable environmental effect. Moreover, this positive association became clearly non‐significant when over‐dispersion relative to the Poisson distribution was taken into account.
A Bayesian analysis of bivariate survival data from a multicentre cancer clinical trial
Authors: Gustafson Paul
Published: April 2007

I present an analysis of data from a clinical trial of new chemotherapies for colorectal cancer. I model participating clinical sites via random effects, to allow for heterogeneous patient responses across centres. Patient response is measured by both progression and survival times. The data suggest that patient responses are homogeneous before, but heterogeneous after, disease progression. For one new therapy, an increase in efficacy relative to standard therapy is detected before, but not after, progression.
A new approach to selection and weighting of items in evaluative composite measurement scales
Authors: Coste Joël , Walter Eric , Venot Alain
Published: April 2007

Evaluative composite measurement scales (CMS) are increasingly used in medicine to measure complex constructs in the absence of a reference criterion or ‘gold standard’. However, the level of measurement provided by these instruments has usually been given little attention. This paper explores the influence of the characteristics of the item set and weighting on the level of measurment of an evaluative CMS. The approach is illustrated with an application to the scoring system of the Nottingham Health Profile. Simulations are provided to indicate when the composite score of item responses, on an ordinal or pass—fail scale, could be considered to achieve the properties of an interval scale.
Determining the value of additional surrogate exposure data for improving the estimate of an odds ratio
Authors: Dahm P. Fred , Gail Mitchel H. , Rosenberg Philip S. , Pee David
Published: April 2007

We consider the design of both cohort and case‐control studies in which an initial (‘stage 1’) sample of complete data on an error‐free disease indicator (D), a correct (‘gold‐standard’) dichotomous exposure measurement (X) and an error‐prone exposure measurement (Z) are available. We calculate the amount of additional information on the odds ratio relating D to X that one can obtain from a second (‘stage 2’) sample of measurements only on D and Z. If one allows for differential measurement error in Z, there is often little advantage in having more than four times as much data in stage 2 data as in stage 1. With the assumption that a non‐differential measurement error model is reasonable, larger amounts of stage 2 data can be useful. Simulations indicate that stage 1 samples of modest size (50 cases in case‐control studies and 50 failures in cohort studies) yield sufficiently reliable estimates of needed parameters to assist in determining an appropriate size for the stage 2 sample. These ideas apply in settings either where the amount of stage 1 data is limited and fixed by external constraints or where one has gathered stage 1 data in advance to avoid collecting superfluous stage 2 data.
Using a mixture model to predict the occurrence of diabetic retinopathy
Authors: Young Philip , Morgan Byron , Sonksen Peter , Till Sebastian , Williams Charles
Published: April 2007

Diabetes mellitus is a common condition which has several serious complications associated with it. In this paper a mixture model, based on one previously used to predict the onset of AIDS, is used to predict the onset of one of these complications, diabetic retinopathy, the major cause of adult blindness in the U.K. This model differs from the previous AIDS model by introducing covariates into the model and using a wider choice of mixture distributions. The fit and distributional assumptions of the model are then discussed for this example. The model is fitted to the data by maximum likelihood. It is important that the training set contains balanced numbers of individuals with and without retinopathy.
Optimal and practicable designs for measuring plaque pH‐profiles
Authors: Fedorov V. V. , Hackl P. , Müller W. G.
Published: April 2007

The shape of the plaque pH‐profile after consumption of a food item determines the food's potential caries risk. This and related facts have caused an increased interest in measurements of plaque pH‐profiles during the last decade. A standard design for these measurements is to take equally spaced observations over a certain period of time. The theory of optimal experimental design gives methods for positioning the measurements of a pH‐profile in an optimized way. The objective is to minimize the effort given a desired precision of the estimated profile characteristics or to maximize the precision given the number of measurements. We show that a reduction in the number of measurements up to 50 per cent or a respective improvement in precision of the estimates as compared to standardly applied designs is possible. This implies less inconvenience for the subjects and better compliance with the needs of investigation. Our study is intended to demonstrate the applicability of the theory of optimal experimental design which has the potential to improve the efficiency of medical research.
Validity of the recording of ischaemic heart disease and chronic obstructive pulmonary disease in the Saskatchewan health care datafiles
Authors: Rawson Nigel S. B. , Malcolm Edith
Published: April 2007

The internal validity of the recording of information about ischaemic heart disease (IHD) and chronic obstructive pulmonary disease (COPD) in the administrative health care datafiles of the Canadian province of Saskatchewan is investigated. Comparisons between hospital data and medical charts for acute myocardial infarction and chronic airways obstruction patients showed excellent diagnostic agreement: 97 per cent and 94 per cent, respectively. Appropriate physician service claims were identified for 89 per cent of hospitalizations for IHD and COPD and exact concordance between diagnoses in the two datafiles varied between 15 per cent for acute/sub‐acute IHD and 80 per cent for asthma; including any physician diagnosis within the same broad category (IHD or COPD) increased concordance to 79–94 per cent for IHD and 64–88 per cent for COPD. Contextual information related to the hospitalizations was clinically and epidemiologically realistic.
A personal view of some controversies in allocating treatment to patients in clinical trials
Authors: Senn Stephen
Published: April 2007

A non‐exhaustive but nevertheless wide‐ranging if biased and personal view of various matters affecting the allocation of treatments to patients in controlled clinical trials will be undertaken from the standpoint of a statistician working in drug development. The following topics are considered: the use of ‘placebos’ in the run‐in phase; the use of crossover trials; selection of patients; various matters concerning centres; two extreme alternatives to randomization, and various matters linking blinding and allocation.
Effect of non‐random missing data mechanisms in clinical trials
Authors: Choi S. C. , Lu I. L.
Published: April 2007

A simple form of non‐ignorable missing data mechanisms based on two parameters is used to characterize the amount of missing data and the severity of non‐randomness in clinical trials. Based on the formulation, the effect of non‐randomly missing data on simple analyses which ignore the missing data is studied for binary and normally distributed response variables. In general, the effect of the non‐randomly missing data on the bias and the power increases with the severity of non‐randomness. The bias can be positive or negative and the power can be less than or greater than when the data are missing at random. The results of the analysis, ignoring the missing data, can be seriously flawed if the non‐randomness is severe, even when only a small proportion of the sample is missing. The problem is more pronounced in the case of normally distributed response variables with unequal variances.
Bayesian approaches to random‐effects meta‐analysis: A comparative study
Authors: Smith Teresa C. , Spiegelhalter David J. , Thomas Andrew
Published: April 2007

Current methods for meta‐analysis still leave a number of unresolved issues, such as the choice between fixed‐ and random‐effects models, the choice of population distribution in a random‐effects analysis, the treatment of small studies and extreme results, and incorporation of study‐specific covariates. We describe how a full Bayesian analysis can deal with these and other issues in a natural way, illustrated by a recent published example that displays a number of problems. Such analyses are now generally available using the BUGS implementation of Markov chain Monte Carlo numerical integration techniques. Appropriate proper prior distributions are derived, and sensitivity analysis to a variety of prior assumptions carried out. Current methods are briefly summarized and compared to the full Bayes analysis.
Non‐parametric estimation of the post‐lead‐time survival distribution of screen‐detected cancer cases
Authors: Xu Jian‐Lun , Prorok Philip C.
Published: April 2007

The goal of screening programmes for cancer is early detection and treatment with a consequent reduction in mortality from the disease. Screening programmes need to assess the true benefit of screening, that is, the length of time of extension of survival beyond the time of advancement of diagnosis (lead‐time). This paper presents a non‐parametric method to estimate the survival function of the post‐lead‐time survival (or extra survival time) of screen‐detected cancer cases based on the observed total life time, namely, the sum of the lead‐time and the extra survival time. We apply the method to the well‐known data set of the HIP (Health Insurance Plan of Greater New York) breast cancer screening study. We make comparisons with the survival of other groups of cancer cases not detected by screening such as interval cases, cases among individuals who refused screening, and randomized control cases. As compared with Walter and Stitt's model, in which they made parametric assumptions for the extra survival time, our non‐parametric method provides a better fit to HIP data in the sense that our estimator for the total survival time has a smaller sum of squares of residuals.
Disentangling compensation and employment risks using the behavioral agency model
Authors: Larraza‐Kintana Martin , Wiseman Robert M. , Gomez‐Mejia Luis R. , Welbourne Theresa M.
Published: May 2007

Employing survey and archival data from a sample of IPO firms, and extending the ideas of the Behavioral Agency Model, this study examines the influence of various forms of risk bearing created within the compensation contract on perceived risk taking. The results show that employment risk and variability in compensation each corresponds to greater risk taking, while downside risk and the intrinsic value of stock options correspond to lower risk taking. Among the implications from these results are the importance CEOs attach to relatively stable forms of pay, and to drawing distinctions between the potential for loss of pay and uncertainty about the amount of future pay. Copyright © 2007 John Wiley & Sons, Ltd.