bluuebunny/crossref_metadata_2025_split huggingface dataset:
Statistical validation of intermediate endpoints for chronic diseases
Authors: Freedman Laurence S. , Graubard Barry I. , Schatzkin Arthur 
Published: April 2007

AbstractWe discuss the implementation of a criterion due to Prentice for the statistical validation of intermediate endpoints for chronic disease. The criterion involves examining in a cohort or intervention study whether an exposure or intervention effect, adjusted for the intermediate endpoint, is reduced to zero. For example, to examine whether serum cholesterol level is an intermediate endpoint for coronary heart disease (CHD), we may investigate the effect of the cholesterol lowering drug cholestyramine on CHD incidence adjusted for serum cholesterol levels. We show that use of this criterion will usually demand some form of model selection. When the unadjusted exposure or treatment effect is less than four times its standard error, the analysis can usually lead only to a weak form of validation, a conclusion that the data are not inconsistent with the validation criterion. More significant unadjusted exposure effects offer the potential for stronger types of validation statement such as ‘the intermediate endpoint explains at least 50 per cent (or 75 per cent) of the exposure effect’.
A note on the effect of the intraclass correlation in the multiple reading procedure with a unanimity rule
Authors: Lui Kung‐Jong 
Published: April 2007

AbstractThe use of multiple reading procedures to improve the performance of a diagnostic test occurs often in practice. Evaluation of the utility of multiple reading procedures, however, usually ignores the effect of the intraclass correlation. This paper provides a quantitative assessment of this effect in the multiple reading procedure with a unanimity rule with respect to sensitivity, specificity, positive and negative predictive values. We have found that when the disease prevalence is rare or moderate (≤ 0·20), use of the multiple reading procedure with a unanimity rule is effective in increasing the positive predictive value of a single reading procedure for the situation in which the variation of responses among different subjects and the intraclass correaltion among repeated tests are small. This is, however, not true for the situation in which the disease is rare and the variation of responses among different subjects is large, even when the intraclass correlation is small or 0. Furthermore, when the disease is rare and the variation of responses among subjects is small, a small or moderate intraclass correlation can substantially decrease the positive predictive value that one calculates under the assumption that the intraclass correlation is equal to 0. In general, when the disease is rare or moderate (≤ 0.20), the intraclass correlation between repeated tests and the variation of responses among subjects have little effect on the negative predictive value.
Stochastic progression of new states in psychotherapy
Authors: Badalamenti Anthony F. , Langs Robert J. , Kessler Marc 
Published: April 2007

AbstractThis paper presents a study of the stochastic behaviour of the entry of a 5‐dimensional, 2400 state, system into new states. Each state consists of five measurements on variables that are sensitive measures of the degree of activated unconscious communication. The data come from six psychotherapy consultation sessions. The study sought to investigate the manner in which patient and therapist progress into new configurations (new states) of the five variables, and the way tension accumulates to the point of overcoming the inertia in the patient/therapist system. We show that the time series that consists of the time intervals between successive new states is a generalized Poisson process. That is, the process is inherently governed by a Poisson parameter that is a continuous piecewise linear function of time. It increases rapidly in the opening moments of an interview and decreases slowly thereafter. This non‐linear, non‐stationary Markov model fits the data well according to the Kolmogorov–Smirnoff criterion. We discuss the model's implications for the nature of psychotherapy and its participants.
A random coefficient growth curve analysis of mental development in low‐birth‐weight infants
Authors: Carter Randy L. , Resnick Michael B. , Ariet Mario , Shieh Gwowen , Vonesh Edward F. 
Published: April 2007

AbstractIn many medical studies, longitudinal data are collected on each of a sample of patients. The objectives of such studies often are: to estimate and test bivariate or multivariate relationships within each of several groups of patients from these repeated measures data; to compare these relationships among groups; and to test for the effects of baseline covariates on the relationships. This paper illustrates the use of statistical methods for growth curve analysis recently proposed by Vonesh and Carter for achieving these goals by relating a measure of preschool cognitive development to age in four race by sex groups of low‐birth‐weight infants. Significant declines in Bayley's Mental Development Index (MDI) with increasing age were found in all groups. Birth‐weight did not significantly influence the rate of decline but did influence the overall level of performance. Even so, in the group most comparable to Bayley's normative population, predicted MDI was near the norm even for extremely low‐birth‐weight infants (that is, 1000 grams). Although there is some risk of mental deficit associated with prematurity, eventual developmental delays in low‐birth‐weight infants frequently are acquired with age. The rate of decline in MDI was significantly associated with race and mother's education. Assumptions required for the valid application of these methods are discussed and tested in the setting of this applied problem. The assumptions appeared valid in this application. We conclude with a brief discussion of available alternatives when the assumptions are violated and point to areas for future research.
Model‐based screening by risk with application to down's syndrome
Authors: Royston Patrick , Thompson Simon G. 
Published: April 2007

AbstractScreening for a disorder may be carried out by assessing the risk that an individual is affected given the values of variables whose distributions alter when the disorder is present. An optimal screening policy is obtained by identifying those individuals whose risk is greater than some cut‐off value. This paper summarizes the way in which risk is derived from the likelihood ratio of being affected by the disorder, and compares three different methods of estimating the likelihood ratio, namely direct estimation, logistic regression and distribution modelling. For continuous variables that have a multivariate normal distribution, screening by risk is equivalent to the use of quadratic discrimination. The paper shows how estimates of the risk and associated detection and false positive rates can be derived for a screening policy which uses specified risk cut‐offs. Screening by risk has the counter‐intuitive property that as the separation in the distribution of screening variables between affected and unaffected individuals increases, the detection and false positive rates may both increase. The approach is explored using data on antenatal screening for Down's syndrome. The method of choice is model‐based; the model is described and tested for goodness of fit. Complications arising from outliers and non‐normality must be overcome before an appropriate assessment of risk can be made. The concept of shrinkage is used to estimate the detection and false positive rates that may be expected in a new data set.
Predicting the prevalence of a disease in a cohort at risk
Authors: Mackisack M. , Dobson A. J. , Heathcote C. R. 
Published: April 2007

AbstractA non‐homogeneous Markov chain model is used to describe the evolution of a progressive chronic disease. Transition probabilities are estimated from data on coronary heart disease in Australia. By varying the transition probabilities, the effects of changes in medical management or in the natural history of the disease are examined. For reducing rates of fatal and non‐fatal acute myocardial infarction a multifaceted strategy of primary and secondary prevention and improved acute care is found most effective. Primary prevention reducing the risk of early stages of the disease has the potential to yield the highest proportion of disease‐free people. By contrast, intensive medical treatment of acute disease would reduce mortality but increase the total burden of disease in the population.
Using an autoregressive model to detect departures from steady states in unequally spaced tumour biomarker data
Authors: Schlain Brian R. , Lavin Philip T. , Hayden Cheryl L. 
Published: April 2007

AbstractA new method, based on a continuous time autoregressive [CAR(1)] model of time series data, is provided for detecting departures of tumour markers from steady states in breast cancer patients following surgery. A Kalman filter recursive algorithm is used to calculate the likelihood function arising from the CAR(1) model and to calculate recursive residuals, which are monitored by a Shewhart–Cusum scheme. This approach can be used to monitor the serial marker data of large numbers of patients even when the series are short and the data are serially correlated and unequally spaced. Further, the methodology can be used to recommend appropriate testing intervals.
Analysis of the incubation period for measles in the epidemic in greenland in 1951 using a variance components model
Authors: Kronborg Dorte , Hansen Bettina , Aaby Peter 
Published: April 2007

AbstractThis paper presents a model for analysing the incubation period of highly infectious diseases in populations where almost all susceptibles become infected during an epidemic. The model leads to a simple method for estimating the variance of the duration of the incubation period without any distributional assumptions. Further, the influence of covariates on the duration of the incubation period can be analysed. Data from the epidemic of measles in Greenland in 1951 are analysed and it is found that intersymptom times are correlated within households, suggesting that secondary cases are infected almost simultaneously. This result is inconsistent with a variation in the times of infection of secondary cases within a household which is often assumed when analysing data on measles. Prophylactic treatment did not prevent infection in the epidemic in Greenland, but it is found that the incubation period tended to be shorter for persons not receiving prophylactic treatment.
Effects of different parametric estimates of seroconversion time on analysis of progression to aids among Italian HIV‐positive haemophiliacs
Authors: Chiarotti Flavia , Palombi Massimo , Schinaia Nicola , Ghirardini Alessandro , Prospero Luca 
Published: April 2007

AbstractThe purpose of this study was to estimate seroconversion time using different parametric methods and to assess their influence on the estimation of the incubation time between HIV infection and onset of AIDS. Study subjects were 712 HIV‐positive haemophiliacs enrolled in the Italian National Registry of patients with congenital coagulation disorders. Seroconversion time was estimated using the mid‐point of each seroconversion interval (MID), the median of each interval under an estimated uniform distribution with cutpoints at December 1981 and December 1985 (MUU), the median of each interval under an estimated Weibull distribution (MUW), and the median of three random values drawn from each interval under the Weibull distribution (RUW). Kaplan‐Meier survival analysis showed that the cumulative incidence of AIDS over a 7‐year period was 11·6 per cent (SE 1·3 per cent) when using the MID estimate of seroconversion time, 10·8 per cent (1·2 per cent) with the MUU estimate, and 13·4 per cent (1·3 per cent) and 12·3 per cent (1·3 per cent) when using MUW and RUW estimates, respectively. This study demonstrates that the estimate of seroconversion time does not seem to be a major factor affecting estimates of AIDS incidence since the different techniques for estimating HIV seroconversion time yielded very similar results.
Closed‐form estimates for missing counts in two‐way contingency tables
Authors: Baker Stuart G. , Rosenberger William F. , Dersimonian Rebecca 
Published: April 2007

AbstractOne method for analyzing contingency tables with missing observations is to model the missing‐data mechanism using log‐linear models. Previous methods for obtaining estimates (of missing counts and parameters) have required an iterative algorithm. In many cases, however, one can obtain estimates by use of a simple algebraic formula. We illustrate the method with data on smoking and birth weight.
Mathematical models of complex dose‐response relationships: Implications for experimental design in psychopharmacologic research
Authors: Faraone Stephen V. , Simpson John C. , Brown Walter A. 
Published: April 2007

AbstractWe develop a mathematical model to account for the complex relationship between drug dose and clinical response in psychopharmacologic research. The model specifies relationships among drug dose, drug bioavailability, pharmacokinetic factors, course moderators, clinical response and the heterogeneity of the disorder, and allows for the derivation of results that have implications for experimental design in psychopharmacologic research. These results form the basis for computer simulations which indicate that random assignment to two fixed doses is more powerful and less sensitive to heterogeneity than assignment to clinically determined doses. Fixed dose designs, however, tend to overestimate the magnitude of drug bioavailability–clinical response relationships. Clinically determined dose designs are useful in some experimental situations; their effectiveness is enhanced by systematically reducing the clinically determined dose. Larger dose reductions improve the ability to detect bioavailability–clinical response relationships.
Estimating the number of clusters for the analysis of correlated binary response variables from unbalanced data
Authors: Shoukri M. M. , Martin S. W. 
Published: April 2007

AbstractDominating the literature on sample size determination are papers concerned with observations collected from independent responses. We discuss in this paper, under the common correlation model for binary responses, the issue of estimating the number of groups when the sampling units are clusters of variable sizes.
k‐ratio t tests for multiple comparisons involving several treatments and a control
Authors: Brant Larry J. , Duncan David B. , Dixon Dennis O. 
Published: April 2007

AbstractWe consider the problem of simultaneously comparing several treatment means with a control mean and also with one another. Following an elementary decision‐theoretic Bayesian approach requiring the choice of a type‐I to type‐II error‐seriousness ratio k, a posteriori t tests are derived for testing both treatment versus control (TvC) and treatment versus treatment (TvT) differences. These k‐ratio t tests are strictly comparisonwise in nature. That is, the test applied to any TvC or TvT difference d, depends in no way at all on whether the other differences are being tested. The test for d, however, does depend on the sizes of the other differences through tG, the standardized average of the observed TvC differences, and through FT, the observed between‐treatments F ratio. From these adaptive dependences on tG and FT, the critical t values can be large or small, thus avoiding the intuitive objections of under‐ or over‐conservatism in classical comparisonwise or experimentwise level testing rules.
Estimation of time since exposure for a prevalent cohort
Authors: Muñoz Alvaro , Carey Vincent , Taylor Jeremy M. G. , Chmiel Joan S. , Kingsley Lawrence , van Raden Mark , Hoover Donald R. 
Published: April 2007

AbstractIn natural history studies of human immunodeficiency virus type 1 (HIV‐1) infection a substantial proportion of participants are seropositive at time of enrolment in the study. These participants form aprevalent subcohort.Estimation of the unknown times since exposure to HIV‐1 in the prevalent subcohort is of primary importance for estimation of the incubation time of AIDS. The subset of the cohort that tested negative for antibody to HIV‐1 at study entry and was observed to seroconvert forms theincident subcohortthat provides longitudinal data on markers of maturity (that is, duration) of infection. We use parametric life table regression models incorporating truncation to describe the conditional distribution (imputing model) of the times since seroconversion given a vector of the markers of maturity. Using the fitted model and the values of the markers of maturity of infection provided by the seroprevalent subcohort at entry into the study, we can impute the unknown times since seroconversion for the prevalent subcohort. We implement multiple imputation based on a model‐robust estimate of the covariance matrix of parameters of the imputing model to provide confidence intervals for the geometric mean of the time since seroconversion in the prevalent subcohort, and to compare maturity of infection of cohorts recruited in different cities. The accuracy of imputation is further validated by comparisons of imputation‐based estimates of AIDS incubation distribution in the seroprevalent subcohort with more direct estimates obtained from the seroincident subcohort.
Assessing response bias in random‐digit dialling surveys: The telephone‐prefix method
Authors: Diehr Paula , Koepsell Thomas D. , Cheadle Allen , Psaty Bruce M. 
Published: April 2007

AbstractKnowledge of the characteristics of survey non‐respondents is important to determine generalizability to the population of interest. In a recent random‐digit dialling survey of health behaviours only 73 per cent of the households contacted provided any information about household composition, and only 74 per cent of those actually completed the extended interview, for an overall response rate of 54 per cent. To identify possible biases we grouped all attempted phone numbers by their prefix, and looked for the association between the response rate for that prefix and other summary variables known about the prefix. A simulation study showed that the method can identify non‐response biases if certain assumptions are correct. The analysis suggested that our survey data under‐represent older people and those with a college education. We found no significant biases in health behaviours, possibly because the basic assumptions did not hold. This method may assist in identification of non‐response bias in other studies.
Discriminant analysis when all variables are ordered
Authors: Johnston B. , Seshia S. S. 
Published: April 2007

AbstractDetermination of the equation that relates an ordered dependent variable to ordered independent variables is sought. One solution, non‐parametric discriminant analysis (NPD), involves obtaining the best monotonic step function by means of a computer search procedure. Although one can use alternative selection criteria in obtaining the equation, the illustrative examples use absolute distance. This paper compares the prediction procedures obtained from NPD with those from linear discriminant analysis, linear regression (with and without transformed variables), and logistic regression. We show that NPD is analogous to regression tree analysis with incorporation of ordered variables and monotonicity. We use various prediction functions to predict the example data, the data using the leave‐one‐out technique, and a verification set. Consistently, non‐parametric discriminant analysis performs as good as or better than the tested alternatives.
Analysis of variance for repeated measures data: A generalized estimating equations approach
Authors: Carr Gregory J. , Chi Eric M. 
Published: April 2007

AbstractVarious techniques are available for the analysis of repeated measures data, and the appropriate choice depends on distributional assumptions and study design features. A correct analysis must account for potential dependence between repeated observations on the same subject. Liang and Zeger proposed a more unified approach to the analysis of repeated measures data based on the application of generalized estimating equations. We examine the application of these methods to several types of data in which one estimates the mean response directly for each combination of discrete covariates, and uses an identity link. Computations for fitting this type of model are exceptionally simple. Numerical examples suggest that the proposed approach yields estimation and hypothesis testing results consistent with more specialized methods.
Comparisons between a new drug and active and placebo controls in an efficacy clinical trial
Authors: Dunnett Charles W. , Tamhane Ajit C. 
Published: April 2007

AbstractD'Agostino and Heeren (DH) discussed the multiple comparison issues that arise in evaluating both sensitivity and efficacy in over‐the‐counter drug trials. We propose a general definition of sensitivity that includes DH's definition as a special case. We also propose a test for sensitivity that coincides with the MIN test of Laska and Meisner at one extreme but has the advantage of identifying specific drugs satisfying the sensitivity criterion when some fail to do so. We suggest that the test of Dunnett as well as an extension of it may be useful for the efficacy comparisons.
A method of designing clinical trials for combination drugs
Authors: Pigeon Joseph G. , Copenhaver Margaret Diponzio , Whipple James P. 
Published: April 2007

AbstractMany pharmaceutical companies are now exploring combination drug therapies as an alternative to monotherapy. Consequently, it is of interest to investigate the simultaneous dose response relationship of two active drugs to select the lowest effective combination. In this paper, we propose a method for designing clinical trials for drug combinations that seems to offer several advantages over the 4 × 3 or even larger factorial studies that have been used to date. In addition, our proposed method provides a convenient formula for calculating the required sample size.
Comparing sample size formulae for trials with unbalanced allocation using the logrank test
Authors: Hsieh F. Y. 
Published: April 2007

AbstractThis paper compares the sample size formulae given by Schoenfeld, Freedman, Hsieh and Shuster for unbalanced designs. Freedman's formula predicts the highest power for the logrank test when the sample size ratio of the two groups equals the reciprocal of the hazard ratio. The other three formulae predict highest powers when sample sizes in the two groups are equal. Results of Monte Carlo simulations performed for the power of the logrank test with various sample size ratios show that the power curve of the logrank test is almost flat between a sample size ratio of one and a sample size ratio close to the reciprocal of the hazard ratio. An equal sample‐size allocation may not maximize the power of the logrank test. Monte Carlo simulations also show that, under an exponential model, when the sample size ratio is toward the reciprocal of the hazard ratio, Freedman's formula predicts more accurate powers. Schoenfeld's formula, however, seems best for predicting powers with equal sample size.