bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of ordinal data in a study of endometrial cancer under a matched pairs case‐control design
Authors: Ganguly S. S. , Naik‐Nimbalkar U. V. 
Published: April 2007

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

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

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

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

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

AbstractBiostatisticians in research juggle many responsibilities: short‐term consulting; long‐term collaboration; teaching/training, and statistical research. In an institutionally‐supported service group, the biostatistician frequently faces allocation of limited resources (time and effort) over multiple projects, none of which individually supports the biostatistician. In addition to the level of support provided by a specific project, there are several major issues with resource allocation: the quality of the science and data in the project; the possibility that long‐term support develops from the work; personal and institutional considerations that involve the specific investigator or project.In this paper, we discuss these considerations along with some examples. We present guidelines for making decisions about allocation of time and effort and the possible implications of setting such priorities.
Effects of model misspecification in the estimation of variance components and intraclass correlation for paired data
Authors: Lyles Robert H. , Chambless Lloyd E. 
Published: April 2007

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

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

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

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

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

AbstractThe purpose of this paper is to develop and apply a meta‐analysis methodology, that does not require patient‐level data, for comparing treatments in terms of quality‐of‐life‐adjusted survival. As a motivating example, we considered adjuvant chemotherapy for breast cancer. This therapy has been shown to offer an improvement in recurrence‐free and overall survival, especially for youger women, but its acute toxic effects discourage some physicians from prescribing it. To determine whether the benefit of adjuvant chemotherapy treatment outweighs its costs in terms of toxic effects, we performed a meta‐analysis of quality‐adjusted survival based on data from 1229 patients, 49 years of age or younger, randomized in eight clinical trials that compared chemotherapy versus no adjuvant systemic therapy. We conducted the meta‐analysis by performing a quality‐adjusted survival analysis known as a Q‐TWiST analysis on each trial. A Q‐TWiST analysis allows one to make treatment comparisons that incorporate differences in quality of life associated with various health states. In this analysis, we define as health states the periods of time patients spend: (i) with subjective toxic effects of chemotherapy; (ii) without symptoms of recurrence and toxicity, and (iii) following disease recurrence. We assigned weights to each health state which reflect their relative value in terms of quality of life and allowed them to vary in a sensitivity analysis. We then combined the individual trial results in a meta‐analysis, using a multivariate regression model, in such a way that we could easily perform an overall sensitivity analysis. Individual patient‐level data are not required to perform this meta‐analysis methodology if the individual Q‐TWiST analysis results for each trial are available.
Assessing goodness‐of‐fit of parametric regression models for lifetime data – graphical methods
Authors: Cohen Ayala , Barnett Ofra 
Published: April 2007

AbstractGraphical 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

AbstractIn 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

AbstractIn 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

AbstractWhen 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

AbstractIn 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

AbstractThis 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

AbstractThis 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

AbstractWhile 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

AbstractWhen 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

AbstractClinical 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

AbstractWe 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.