bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mean square error of estimates of HIV prevalence and short‐term aids projections derived by backcalculation
Authors: Rosenberg P. S. , Gail M. H. , Pee D. 
Published: April 2007

AbstractWe simulated multinomial AIDS incidence counts from 27 ‘representative’ AIDS epidemics that spanned a period corresponding to previous applications of backcalculation (1 January 1977 to 1 July 1987) and assessed mean square error for several back‐calculated estimators of HIV prevalence and short‐term AIDS projections. Estimators were based on flexible model selection procedures that chose the best‐fitting non‐negatively constrained model of the infection curve from a family of possible step‐function models. Selection of the best‐fitting model from a family of four‐step models each with a long last step of width of 4 or 4.5 years offered a favourable tradeoff between bias and variance when compared with selection from families of models with three steps or from families with a short last step. Five‐step models performed as well as four‐step models. Three‐step models had substantially larger mean square error in some epidemic situations. Percentage root mean square error (PRMSE) for estimates of cumulative HIV prevalence as of 1 January 1985 was less than 14 per cent over a range of hypothetical epidemics of N = 50,000 infected individuals. PRMSE for short‐term projections was less than 18 per cent. Estimates of cumulative HIV prevalence as of 1 July 1987 were substantially more uncertain and had a PRMSE of 33 per cent in the unfavourable case of a rapidly rising HIV epidemic. Estimates of cumulative HIV prevalence as of 1 July 1987 were positively biased in HIV epidemics with a rapidly decreasing recent HIV incidence rate and negatively biased in rapidly increasing HIV epidemics. Despite these uncertainties, we obtained useful estimates even for HIV epidemics with as few as 5000 infected individuals.
Misclassification of covariates
Authors: Walker Alexander M. , Lanes Stephan F. 
Published: April 2007

AbstractIn the context of reported data on asthma mortality, we examine two types of covariate misclassification. The first type is non‐differential misclassification, in which the proportion of subjects misclassified is invariant over exposure and disease status. Building on work by Cox and Elwood and by Blettner and Wahrendorf, we find that the range of admissible values for misclassification proportions is bounded by the observed data, and may not include any values that account for observed heterogeneity of effect estimates. The second type is differential misclassification, in which the classification error differs according to the disease or exposure classes to which study subjects belong. If the relation between exposure and the confounding variable or between the confounding variable and disease is strong, differential misclassification can produce large variations in the stratum‐specific odds ratio estimates.
Validation techniques for logistic regression models
Authors: Miller Michael E. , Hui Siu L. , Tierney William M. 
Published: April 2007

AbstractThis paper presents a comprehensive approach to the validation of logistic prediction models. It reviews measures of overall goodness‐of‐fit, and indices of calibration and refinement. Using a model‐based approach developed by Cox, we adapt logistic regression diagnostic techniques for use in model validation. This allows identification of problematic predictor variables in the prediction model as well as influential observations in the validation data that adversely affect the fit of the model. In appropriate situations, recommendations are made for correction of models that provide poor fit.
Statistical analysis of repeated events forming renewal processes
Authors: Aalen Odd O. , Husebye Einar 
Published: April 2007

AbstractFor each of several individuals a sequence of repeated events, forming a renewal process, is observed up to some censoring time. The object is to estimate the average interevent time over the population of individuals as well as the variation of interevent times within and between individuals. Medical motivation comes from gastroenterology, and concerns the occurrence of certain cyclic movements in the small bowel during the fasting state. Two statistical models are considered: one is the standard variance component model adapted to censored data, and the other is a recent intensity based model with a random proportionality factor representing interindividual variation. These models are applied to the motility data, and their advantages are discussed. The intensity based model allows simple empirical Bayes estimation of the expected interevent times for an individual in the presence of censoring.
Homogeneity of the relative risk in cohort studies
Authors: Le Chap T. , Lindgren Bruce R. , Zelterman Daniel , Umen Andrew J. 
Published: April 2007

AbstractWe propose a statistical method to test the homogeneity of relative risk in cohort studies. We illustrate its application to an original data set from a low‐risk group of end‐stage renal disease patients on haemodialysis. The use of the method is as a global test against the omnibus alternative.
Exploring the order of odds ratios using the bootstrap
Authors: Schwartzbaum Judith A. , Hirschberg Joseph G. 
Published: April 2007

AbstractWe show the use of the bootstrap resampling method to examine further the order of a series of odds ratios. Specifically, the bootstrap provides a method for estimating the probabilities that one would find in subsequent independent samples from the same population the observed odds ratio rankings. To illustrate this use of the bootstrap, we modelled the responses of 77 white male physicians to an ethical dilemma involving hypothetical patients. Would the physician report positive HIV status to the health department or would he maintain patient confidentiality? To see if a patient's sex, race, or sexual preference would influence the physicians' decisions, each physician received one of eight randomly selected descriptions of a hypothetical patient. To evaluate the initial order of the patient categories, we constructed 1000 bootstrap samples. Black heterosexual males ranked first or second in 92.2 per cent of the bootstrap samples; black homosexual males ranked first, second or third in 88.6 per cent; and white homosexual females ranked sixth or seventh in 82.9 per cent. Thus we would expect to observe these rankings of the categories in a high percentage of subsequent independent samples.
Effect of regression to the mean in the presence of within‐subject variability
Authors: Johnson William D. , George Varghese T. 
Published: April 2007

AbstractRegression to the mean arises often in statistical applications where the units chosen for study relate to some observed characteristic in the extreme of its distribution. Gardner and Heady attribute the effect of regression to the mean to measurement errors. They assume the modelYi = U + ei, where U is a fixed within‐subject component and ei is the random measurement error. They suggest several replicate measurements to reduce the regression effect under the assumption that the measurement errors ei are independent within subjects. While measurement errors play an important role in regression to the mean, one should not overlook within‐subject variation. In this paper, we consider a model to estimate the regression effect in the presence of correlated within‐subject effects as well as independent measurement errors.
Detection of outlying data in bioavailability/bioequivalence studies
Authors: Liu Jen‐Pei , Weng Chung‐Sing 
Published: April 2007

AbstractThis paper considers the problem of detecting outlying data in bioavailability/bioequivalence studies. We define outlying subjects as those whose responses in bioavailability to all formulations differ from the rest of the subjects. We also define an outlying observation as the response in bioavailability of a subject to a particular formulation which is grossly different from the average bioavailability of that formulation calculated from all subjects. We propose two test procedures. The first, based on two‐sample Hotelling T2, is to detect possible outlying subjects. The second, based on residuals from formulation means, is to identify possible outlying observations within subjects. Both procedures take into account the covariance structure of the responses to formulations, dependence of test statistics, and multiplicity of test procedures. We apply the Monte Carlo or bootstrap simulation to evaluate the sampling distributions of test statistics. An example from a 3‐way crossover bioequivalence study illustrates the two procedures.
Stochastic methods for short term projections of symptomatic HIV disease
Authors: Mode Charles J. , Fife Daniel , Troy Steven M. 
Published: April 2007

AbstractWe designed and implemented stochastic methods for short term projections of HIV disease at the local level, that accommodate various states or stages of the disease. We gave particular attention to projection of the number of patients with HIV disease who need care, when durations of stay in these various states depend on current methods for treating opportunistic infections. We consider two types of data as input to these projections. One concerns seroprevalence surveys conducted over time and from which we can obtain time series estimates of the numbers of HIV‐infected individuals. The other is a reported time series of AIDS cases adjusted for delays in reporting. Several projections, with data from the City of Philadelphia, illustrate this method. In addition, we consider a Monte Carlo method for computing confidence bounds on a projection.
Estimation of variance components for censored data with applications to blood pressure variability
Authors: Smith L. , Rosner B. 
Published: April 2007

AbstractIdeally, the basis for estimation of variance components is large random samples selected from a well‐defined reference population. Some large biomedical studies, however, consist of a random sample (S) of individuals ascertained at an initial visit, with a selected subsample from S seen on one or more follow‐up visits. In this setting, the usual formulae for estimation of variance components are problematic since they do not take into account the censored nature of the data. For this purpose, we consider both maximum likelihood and moments estimation methods that take the censoring into account, and we compare their performance, in terms of bias and mean squared error, with that of the usual variance components estimators that ignore censoring. We find the maximum likelihood estimators somewhat more efficient than method of moments estimators, provided that the assumption of multivariate normality is met; furthermore, these estimators are substantially more efficient than those that ignore the censoring. It is important to record data on all individuals, even those who do not meet screening criteria; one can estimate between‐ and within‐person variance more accurately with use of all available data. The resulting estimates are crucial in calculation of power for the design of future studies.
A method of non‐parametric back‐projection and its application to aids data
Authors: Becker Niels G. , Watson Lyndsey F. , Carlin John B. 
Published: April 2007

AbstractThe method of back‐projection has been used to estimate the unobserved past incidence of infection with the human immunodeficiency virus (HIV) and to obtain projections of future AIDS incidence. Here a new approach to back‐projection, which avoids parametric assumptions about the form of the HIV infection intensity, is described. This approach gives the data greater opportunity to determine the shape of the estimated intensity function. The method is based on a modification of an EM algorithm for maximum likelihood estimation that incorporates smoothing of the estimated parameters. It is easy to implement on a computer because the computations are based on explicit formulae. The method is illustrated with applications to AIDS data from Australia, U.S.A. and Japanese haemophiliacs.
Falsificationism and clinical trials
Authors: Senn S. J. 
Published: April 2007

AbstractThe relevance of the philosophy of Sir Karl Popper to the planning, conduct and analysis of clinical trials is examined. It is shown that blinding and randomization can only be regarded as valuable for the purpose of refuting universal hypotheses. The purpose of inclusion criteria is also examined. It is concluded that a misplaced belief in induction is responsible for many false notions regarding clinical trials.
Calculating centile curves using kernel density estimation methods with application to infant kidney lengths
Authors: Rossiter J. E. 
Published: April 2007

AbstractObserving a clinical measurement for an individual is of little value, unless it can be compared with measurements obtained from a healthy population, thought of as standard. The range of measurements observed will, in general, vary with age or some function of time. The usual approach is to assume a distributional form for the population density, but this is inappropriate for variables which do not follow a simple distribution. A method of estimating the centiles of a conditional distribution using multidimensional kernel density estimation, to allow conditioning on the value of one or more covariates, is proposed. By careful choice of the kernel used, the percentiles may easily be calculated using a Newton‐Raphson procedure. The method is illustrated using kidney lengths and birthweights of a sample of newborn infants.
Comparative efficiency of a survival‐based case‐control design and a random selection cohort design
Authors: Commenges Daniel , Moreau Thierry 
Published: April 2007

AbstractAmbidirectional studies are useful when information about disease status is available on a cohort but a risk factor has still to be recorded. An example is the study of the influence of HLA phenotypes on the progression of HIV carriers towards AIDS. An ambidirectional design is proposed in which the cases and controls are defined by the survival duration of the subjects; it includes as special cases some other ambidirectional designs. Its efficiency is compared with that of a random selection cohort design both analytically and by computer simulation. It is shown that when the size of the cohort is large, appreciable gains in power can be achieved by this type of design even when there is no censoring.
The unseen sample in cohort studies: Estimation of its size and effect
Authors: Hoover D. R. , Muñoz A. , Carey V. , Odaka N. , Taylor J. M. G. , Chmiel J. S. , Armstrong J. , Vermund S. H. 
Published: April 2007

AbstractRecruitment of disease‐free subjects into cohort studies and measurement of their time from exposure/infection to disease selectively excludes individuals (the unseen sample) who had earlier exposure and who have shorter times to disease. The unseen and observed samples may differ in other characteristics in addition to incubation period and exposure/infection time. For data with known truncation times, we develop non‐parametric maximum likelihood estimates of the size, exposure/infection dates and distribution of incubation time in the unseen sample. We provide procedures to estimate and compensate for the biasing effects due to exclusion of the unseen sample in descriptive and survival analysis. We give consistency properties of these estimates and assess variability using bootstrap methods. One can use imputation to derive the above estimates from data with unknown truncation times that have been estimated parametrically. Application is made to an AIDS cohort study of over 5000 homosexual men. Important estimates obtained from this application are the annual seroconversion rates from 1978 to 1983, not otherwise obtainable in this study population.
Collinearity diagnosis for a relative risk regression analysis: An application to assessment of diet‐cancer relationship in epidemiological studies
Authors: Wax Yohanan 
Published: April 2007

AbstractIn epidemiologic studies, two forms of collinear relationships between the intake of major nutrients, high correlations, and the relative homogeneity of the diet, can yield unstable and not easily interpreted regression estimates for the effect of diet on disease risk. This paper presents tools for assessing the magnitude and source of the corresponding collinear relationships among the estimated coefficients for relative risk regression models. I show how to extend three tools (condition indices, variance decomposition proportions, and standard inflation factors) for diagnosing collinearity in standard regression models to likelihood and partial likelihood estimation for logistic and proportional hazards models. This extension is based on the analogue role of the information matrix in such analyses and the cross‐product matrix in the standard linear model. I apply the methodology to relative risk models that relate crude intakes (on the log scale) and nutrient densities to breast cancer cases in the NHANES‐I follow‐up study. The three diagnostic tools provide complementary evidence of the existence of a strong collinearity in all models that is due largely to homogeneity of the population with respect to our risk scale for the crude intakes. The analysis suggests that the non‐significant relative risks for the crude intakes in these models may be due to their involvement in collinear relationships, while the nonsignificant relative risks for the nutrient densities are far less affected by multicollinearity.
The area between curves (ABC)—measure in nutritional anthropometry
Authors: Böhning Dankmar , Hempfling Axel , Schelp Frank‐Peter , Schlattmann Peter 
Published: April 2007

AbstractThis paper considers a statistic — recently suggested by Mora — for the deviation of a sample distribution from a reference distribution which typically arises in anthropometry when using the nutritional indicators height/age, weight/age or weight/height. The statistic measures the area between curves (ABC) and stands for the mass of the sample distribution which is not covered by the reference distribution. The paper provides a statistical framework for the ABC and includes some minor corrections of Mora's original paper. For the normal distribution situation with common or different variances, formulae are derived which include a partition of ABC into parts corresponding to malnourished and well‐nourished groups. However, the main result is a non‐parametric generalization of the ABC, motivated by the fact that the nutritional indicators often have skewed distributions with heavier left tails. Non‐parametric statistical inference is provided by linking the ABC to the Kolmogorov‐Smirnov statistic.
A retrospective assessment of the 75/75 rule in bioequivalence
Authors: Dobbins Thomas W. , Thiyagarajan Balasamy 
Published: April 2007

AbstractThe 75/75 rule was originally proposed by the U.S. Food and Drug Administration (FDA) as an alternative means of testing the bioequivalence of two formulations of a pharmaceutical agent. The rule specified that the ratio of test‐to‐reference formulation of a bioavailability measure arising in a bioequivalence study must be between 75 and 125 per cent of unity in at least 75 per cent of subjects to declare two formulations bioequivalent. The rule has garnered criticism in the literature and the FDA no longer uses the rule formally in assessing bioequivalence. The basis, however, for all criticism of the rule has been simulation arguments. In this paper, we derive the sampling model implied by the rule and place the rule in the framework of a statistical hypothesis test. We show how the significance level of the test depends upon variability of the formulations, and thus why the rule performs in the way that has received criticism.
Creation of a semiannual report for a multicentre co‐operative clinical trials group
Authors: Goodman Phyllis J. , Crowley John J. , Benson Carl 
Published: April 2007

AbstractThe Southwest Oncology Group (SWOG) is a multicentre co‐operative group that performs prospective clinical trials in cancer. The SWOG Statistical Center is responsible for preparing a report of studies for the semiannual meetings. The report includes a summary of study design and eligibility criteria, current accrual, baseline patient characteristics, toxicity, and, for studies closed to accrual and with sufficiently mature outcome data, tumour response or patient survival. A software program, Statisticians' Report Worksheet (SRW), has been developed by the programming staff at the SWOG Statistical Center. This program enables each disease specific chapter to be created independently, but ensures that tables and graphs for all chapters are of uniform style. We describe here the coordination of the report, and the use of the SRW program. This reporting system can serve as a model for other groups.
Bayesian methods for phase I clinical trials
Authors: Gatsonis Constantine , Greenhouse Joel B. 
Published: April 2007

AbstractPhase I clinical trials are conducted to determine the dose‐response curve of a new drug with respect to toxic side effects and, in particular, to estimate the maximum tolerated dose (MTD). In this paper we take a Bayesian approach to the problem of making inferences about the MTD. Working with broad classes of priors, we obtain the posterior distribution of the MTD and study its properties. We also address the question of providing updated assessments of the risk of toxicity for new patients entering the study at a specific dose level. These assessments would be useful in deciding issues of study management and ethics. Our analysis pays particular attention to the sensitivity of the inferences and risk assessments to the choice of prior and the choice of model for the dose‐response relationship.
The changing geographical coherence of measles morbidity in the United States, 1962–88
Authors: Cliff Andrew D. , Haggett Peter , Stroup Donna F. , Cheney Elizabeth 
Published: April 2007

AbstractGeographical coherence may be defined as the degree to which the behaviour of a time series in one geographical area corresponds with the time series behaviour in another. This paper illustrates the concept using the epidemiological time series of reported monthly measles morbidity for the states of the United States for the 27 years from January 1962 to December 1988. Over this period, as measles morbidity has declined in response to vaccination campaigns, and as the seasonal peaking of the disease in late spring has become less pronounced, the geographical coherence has altered at the national, divisional, regional and state levels. There was a steady decline in coherence from 1962 to 1980. In 1981, a dramatic reduction occurred, but there has been some recovery since. The implications for spatial forecasting models of these reducing levels of coherence are discussed.
Calculating the prevalence of cancer
Authors: Coldman Andrew J. , McBride Mary L. , Braun Terry 
Published: April 2007

AbstractIncidence, prevalence and mortality are commonly used measures to assess the impact of disease on human populations. Prevalence, although regularly assessed for a number of different diseases, has only had recent use to measure the impact of cancer. The calculation of the prevalence of cancer presents several difficulties since there is no reporting mechanism established to measure the proportion of the community that has the disease. In the absence of such a mechanism, mortality data linked to incidence data from cancer registries have been used. The assumption is made that once diagnosed with cancer an individual remains a prevalent case until death. In this paper we present alternatives to this assumption and use them to produce estimates of cancer prevalence. We illustrate the effect of these assumptions on the calculated prevalence of cancer using data from the British Columbia Cancer Registry.