bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of the choice of age‐adjustment method on maps of death rates
Authors: Pickle Linda W. , White Andrew A. 
Published: April 2007

AbstractMaps of morbidity or mortality rates, whether considered individually or as a layer in a geographic information system application, invite multiple comparisons of area rates. However, comparisons of rates across different populations require standardization of the age‐specific rates to account for differences in population age structures. The indirect standardization method, or equivalently the standardized mortality ratio (SMR), has been recommended for small areas where age‐specific rates can be quite variable. Although theoretically equivalent to directly adjusted rates under the assumption of independent age and area effects, indirect summary measures are not comparable across areas when this assumption is violated. We tested the validity of this assumption for the 10 most common causes of death in the United States during 1980–84 and examined the geographic clustering apparent when categorized death rates, adjusted by different methods, are presented as thematic maps. Although overall agreement between the methods was good (rank correlation coefficient >82 per cent for each cause), when the adjusted rates were classified into quintiles 18 per cent of the states fell into different categories depending on the method of adjustment. Using an internal standard for the indirect method reduced this discrepancy to 4·9 per cent. However, both traditional chi‐square tests and a generalized logistic spline model identified significant interactions between age and area for each cause of death, a violation of the assumption required for equivalence of the methods. Potential variation in geographic inferences is illustrated by maps of direct and indirect rates and an empirical Bayes posterior mean, which is a function of these traditionally adjusted rates. Based on these results, we recommend the direct age‐adjustment method for rate maps.
Public health surveillance for chronic conditions: A scientific basis for decisions
Authors: Thacker Stephen B. , Stroup Donna F. , Rothenberg Richard B. , Brownson Ross C. 
Published: April 2007

AbstractIn this paper we investigate the important contribution of multiple public health surveillance systems to policy in chronic disease control and prevention. We show that, typically, surveillance for chronic diseases relies on multiple data sources, often created for another purpose. We also define the concept of burden for chronic conditions based on data from multiple sources. An example from a state illustrates a model for combining data for use in policy development. These applications illustrate the central role of statistical methods in ensuring the appropriate use of data from multiple surveillance systems.
Differences in morbidity measures and risk factor identification using multiple data sources: The case of coronary heart disease
Authors: Madans Jennifer H. , Reuben Cynthia A. , Rothwell Sandra T. , Eberhardt Mark S. 
Published: April 2007

AbstractThe NHANES I Epidemiologic Followup Study contains several sources of information that can be used to define case status. Incidence rates and relative risks associated with selected, documented risk factors for heart disease were estimated using nine different case definitions. Despite wide variation in the estimates of incidence, the characteristics of the cases were remarkably similar as were the risks associated with heart disease incidence. The main difference occurred when cases were defined on the basis of death certificate information. Cases defined this way are more severe and models based on this definition result in relative risks of greater magnitude.
Model inconsistency, illustrated by the cox proportional hazards model
Authors: Ford Ian , Norrie John , Ahmadi Susan 
Published: April 2007

AbstractWe consider problems involving the comparison of two or more treatments where we have the opportunity to adjust for relevant covariates either conditionally in a regression model or implicitly in repeated measures data, for example, in crossover trials. It is seen that for data arising from non‐Normal distributions there is the possibility that models adjusting for covariates and those not adjusting for covariates will be inconsistent, that is, at most one of the models can be valid. Alternatively, even if conditional and unconditional models are valid, parameters in each model may have different interpretations. We note that this presents difficulties for the specification and interpretation of the analysis. It is also clear that model validation is critical. Specific attention is paid to survival data analysed by the Cox proportional hazards model.
The analysis of repeated‐measures data on schizophrenic reaction times using mixture models
Authors: Belin Thomas R. , Rubin Donald B. 
Published: April 2007

AbstractReaction times for schizophrenic individuals in a simple visual tracking experiment can be substantially more variable than for non‐schizophrenic individuals. Current psychological theory suggests that at least some of this extra variability arises from an attentional lapse that delays some, but not all, of each schizophrenic's reaction times. Based on this theory, we pursue models in which measurements from non‐schizophrenics arise from a normal linear model with a separate mean for each individual, whereas measurements from schizophrenics arise from a mixture of (i) a component analogous to the distribution of response times for non‐schizophrenics and (ii) a mean‐shifted component. We fit four mixture models within this framework, where the distinctions between models arise from assumptions about the variance of the shifted observations and the exchangeability of schizophrenic individuals. Some of these models can be fit by maximum likelihood using the EM algorithm, and all can be fit using the ECM algorithm, where the covariance matrices associated with the parameters are calculated by the SEM and SECM algorithms, respectively. Bayesian model monitoring using posterior predictive checks is invoked to discard models that fail to reproduce certain observed features of the data and to stimulate the development of better models.
Effects of correlated and uncorrelated measurement error on linear regression and correlation in medical method comparison studies
Authors: Rifkin Robert D. 
Published: April 2007

AbstractIt is well known that when uncorrelated measurement error affects both variables in linear regression, there is attenuation of the correlation coefficient and regression slope. The effect of correlated measurement error, however, has received little attention. In medical method comparison studies, such error correlation results from the presence of other, unknown explanatory variables that affect the results of the new test method and the reference test method to which it is being compared. The contribution of correlated measurement error to the observed correlation coefficient can be accounted for by the expression ρt1t2 = ρ1ρ2 + ρE1E2 (1–ρ)1/2(1–ρ)1/2 where ρt1t2 is the observed correlation between tests 1 and 2, ρ1 and ρ2 are the correlation with true values for tests 1 and 2, respectively, and ρE1E2 is the correlation between the test errors. The first term describes the attenuation due to uncorrelated error, the second term describes the effect of correlated error. A positive correlation between the measurement errors reduces the attenuation of observed correlation and slope, but, when the reference method is excellent, the effect is very small. For poorer reference tests whose correlations with true values are less than 0·9, however, error correlation may result in a slope and correlation coefficient that differ importantly from the values obtained with either uncorrelated error or with no reference test error. Negatively correlated measurement errors magnify the attenuation of slope and correlation. One might suspect the presence of correlated error when the observed regression slope is close to or exceeds 1 and the reference test is known to have suboptimal reliability. This paper provides several clinical examples of potentially correlated diagnostic methods.
Spatial disease clusters: Detection and inference
Authors: Kulldorff Martin , Nagarwalla Neville 
Published: April 2007

AbstractWe present a new method of detection and inference for spatial clusters of a disease. To avoidad hocprocedures to test for clustering, we have a clearly defined alternative hypothesis and our test statistic is based on the likelihood ratio. The proposed test can detect clusters of any size, located anywhere in the study region. It is not restricted to clusters that conform to predefined administrative or political borders. The test can be used for spatially aggregated data as well as when exact geographic co‐ordinates are known for each individual. We illustrate the method on a data set describing the occurrence of leukaemia in Upstate New York.
Trends in mortality from melanoma in Canada and prediction of future rates
Authors: Macneill I. B. , Elwood J. M. , Miller D. , Mao Y. 
Published: April 2007

AbstractA long term increase in incidence of and mortality due to malignant melanoma has been observed in all well documented white populations. The major identified cause of melanoma is sun exposure. One would expect predictions of future atmospheric ozone depletion to lead to an increase in ultraviolet radiation (UVR) and in the effects of sun exposure. We consider age‐period data for Canadian malignant malanoma mortality. We fit a multiplicative exponential/logistic (MEL) model to the data and extrapolate to AD 2010 hence yielding point estimates of future rates. We obtain total mortality forecasts by multiplying rates by population estimates. We present standard errors for forecasts. We forecast that melanoma will be a much larger burden on the health care system in the early years of the next century than it is at present. We obtain an age‐cohort model by a simple transformation of the age‐period model. Also, we obtain unconditional probabilities of death due to melanoma both for age‐period and age‐cohort models. We discuss the assumptions underlying the MEL model that suggest possible relationships between UVR and melanoma.
An alternative approach to the optimal design of an LD50 bioassay
Authors: Markus Richard A. , Frank Jess , Groshen Susan , Azen Stanley P. 
Published: April 2007

AbstractIn this paper we propose an alternative approach to the optimal design of an LD50 bioassay. We adopt a Bayesian approach to make use of prior information about the location and scale parameters of the tolerance distribution function to select the design parameters (number of doses, total number of animals, centre of doses, space between doses), and we adopt a frequentist approach using the Spearman—Karber statistic to estimate the LD50. We define the optimal design as the one that produces the minimum expected mean squared error E(MSE) with respect to the joint prior distribution of the parameters of the tolerance distribution. For the design parameters investigated, we found: (i) the shape of the E(MSE) is relatively smooth and continuous, the magnitude of which is influenced by the underlying tolerance distribution; (ii) the amount of prior information about the location and scale parameters independently and jointly affect the optimal design; and (iii) as the amount of prior information decreases, one requires more doses and/or animals. Finally, we show the proposed method is robust for an incorrectly assumed tolerance distribution function.
Bias of two one‐sided tests procedures in assessment of bioequivalence
Authors: Liu Jen‐Pei , Weng Chung‐Sing 
Published: April 2007

AbstractThe current applications of Schuirmann's two one‐sided tests procedure for the original scale ignore the variability of the least squares mean of the reference formulation when it substitutes for the unknown reference average of pharmacokinetic responses. We propose a modified two one‐sided tests procedure that takes into account the variability of the least squares mean of the reference formulation. The non‐parametric version of the modified procedure is also available. We conducted a simulation study to examine the true level of significance and empirical power of four current parametric and non‐parametric two one‐sided tests procedures under a 2 × 2 crossover design with different combinations of sample size, intrasubject variability, and correlation between the two responses from a subject. Both theoretical results and empirical evidence show that the true level of significance of the current two one‐sided tests procedures converges to 0·5 when the correlation of the two responses approaches 1. However, not only is the modified two one‐sided tests procedure a test of size α, but empirical evidence indicates it is also an unbiased test. The modified non‐parametric procedure also controls the size of the tests and is competitive even under normality assumption.
A comparison of tests for the k‐sample, non‐decreasing alternative
Authors: Mahrer Joanne M. , Magel Rhonda C. 
Published: April 2007

AbstractThis 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

AbstractThis 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

AbstractDose 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

AbstractThe 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

AbstractTo 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

AbstractPopulation 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

AbstractThree 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

AbstractIn 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

AbstractThere 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.