bluuebunny/crossref_metadata_2025_split huggingface dataset:
Regression analysis with clustered data
Authors: Graubard Barry I. , Korn Edward L. 
Published: April 2007

AbstractClustered data are found in many different types of studies, for example, studies involving repeated measures, inter‐rater agreement studies, household surveys, crossover designs and community randomized trials. Analyses based on population average and cluster specific models are commonly used for estimating treatment (exposure) effects with clustered data. This paper discusses conditions when one or both types of models are appropriate for estimating causal effects and when there is agreement between population average and cluster specific analyses. Applications of survey sampling methods to the robust estimation of standard errors of estimated treatment parameters are discussed.
Spatial distribution of neurons in tissue culture wells: Implications for sampling methods to estimate population size
Authors: McShane Lisa M. , Palmatier Meg 
Published: April 2007

AbstractMany laboratory procedures require the counting of cells in culture. While many cultured cells may be counted by automated methods, neuronal cultures often require manual cell counting methods that are prohibitively time‐consuming. This paper examines methods of sampling from tissue culture wells for estimating total cell counts. Performance of sampling and estimation schemes will depend in part on how the cells distribute themselves within a well. Spatial statistical analysis techniques are applied to the known total number and distribution of neurons in two wells counted in a grid scheme to demonstrate some important features of the neuron distributional patterns. Based on these two wells and simulated realizations from other point processes, a new sampling and estimation technique using open wedge‐shaped sampling regions radiating from the centre of the well is proposed. This method is shown to result in more accurate estimates of the total number of neurons in the well than standard methods.
Selection bias in observational and experimental studies
Authors: Ellenberg Jonas H. 
Published: April 2007

AbstractThere has been a heightened awareness of the dangers of selection bias over the past two decades. Certainly coverage in statistical and ‘statistics for medicine’, and epidemiology textbooks have allocated pages to warn investigators and readers of investigations to be aware of its presence. The scientific community has not, however, yet accepted the necessity for critical assessment of the method of sample selection in the planning and execution of studies as a fundamental underpinning of observational and experimental studies. To wit, we are faced with a plethora of research studies receiving funding, being published in peer‐reviewed journals and influencing future studies, that may be reporting entirely spurious associations. It is the intent of this paper to present examples of selection bias in a variety of areas which have resulted in misleading or entirely incorrect results. We hope to help make such research scientifically ‘politically incorrect’ to the degree that the scientific community ‘just says no’ to such studies, either proposed or reported.
A data‐analytic approach for estimating lead time and screening benefit based on survival curves in randomized cancer screening trials
Authors: Kafadar Karen , Prorok Philip C. 
Published: April 2007

AbstractScreening tests are used frequently for control of diseases such as cancer. The increased survival time of screen‐detected cases over those that are detected clinically may be due in part to ‘lead time’, or the length of time by which the disease is diagnosed earlier by screening in the presence or absence of any real extension in survival time. A realistic evaluation of screening needs to assess the true benefit of screening; that is, the length of time by which survival has been extended, beyond merely the time of the advanced diagnosis. The comparison of survival measured from time of entry between cases in a screening arm and in a control arm in randomized studies avoids the lead time bias. If the effects of average lead time and average benefit on survival are additive, these effects can be estimated by recognizing that (a) the difference in survival curves since time of diagnosis confounds benefit and lead time, but (b) the difference in survival curves since time of start of study involves benefit only. The method is evaluated on simulated data for its accuracy and may be used on data from randomized studies.
Mixed‐effects regression models for studying the natural history of prostate disease
Authors: Pearson Jay D. , Morrell Christopher H. , Landis Patricia K. , Carter H. Ballentine , Brant Larry J. 
Published: April 2007

AbstractAlthough prostate cancer and benign prostatic hyperplasia are major health problems in U.S. men, little is known about the early stages of the natural history of prostate disease. A molecular biomarker called prostate specific antigen (PSA), together with a unique longitudinal bank of frozen serum, now allows a historic prospective study of changes in PSA levels for decades prior to the diagnosis of prostate disease. Linear mixed‐effects regression models were used to test whether rates of change in PSA were different in men with and without prostate disease. In addition, since the prostate cancer cases developed their tumours at different (and unknown) times during their periods of follow‐up, a piece‐wise non‐linear mixed‐effects regression model was used to estimate the time when rapid increases in PSA were first observable beyond the background level of PSA change. These methods have a wide range of applications in biomedical research utilizing repeated measures data such as pharmacokinetic studies, crossover trials, growth and development studies, aging studies, and disease detection.
Composite linear models for incomplete multinomial data
Authors: Baker Stuart G. 
Published: April 2007

AbstractA composite linear model (CLM) is a matrix model for incomplete multinomial data. A CLM provides a unified approach for maximum likelihood inference which is applicable to a wide variety of problems involving incomplete multinomial data. By formulating a model as a CLM, one can simplify computation of maximum likelihood estimates and asymptotic standard errors. As an example, we use CLM to test marginal homogeneity for ordered categories, subject to both ignorable and non‐ignorable missing‐data mechanisms.
The partial questionnaire design for case‐control studies
Authors: Wacholder Sholom , Carroll Raymond J. , Pee David , Gail Mitchell H. 
Published: April 2007

AbstractWe propose an alternative to a long questionnaire that may increase quality while reducing the cost and effort of participants and researchers. In the ‘partial questionnaire design’, information about the exposure of interest is obtained from all subjects, while zero, one, or more disjoint subsets of questions about possible confounders are asked to randomly selected subgroups. The proposed analyses exploit the fact that the uncollected data can be considered to be missing at random. We show that it is possible to obtain high efficiency for estimating the effect of exposure of interest, adjusted for confounding, while substantially shortening average questionnaire length.
Testing for differences in changes in the presence of censoring: Parametric and non‐parametric methods
Authors: Wu Margaret C. , Hunsberger Sally , Zucker David 
Published: April 2007

AbstractSome commonly used parametric and non‐parametric methods for analysing repeated measures with incomplete observations are briefly reviewed. The performances of these methods in the presence of completely random, as well as informative censoring are compared in simulated experiments generated under the linear random effects model with parameter values derived from realistic examples. The effects of some moderate model deviations are also compared. The results indicate that in the presence of informative censoring, the usual parametric and nonparametric methods derived under the assumption of random censoring could either suffer severe loss of power or provide false positive results. The conditional linear model for informative censoring when used in conjunction with the bootstrap variance estimation procedure performed well under both random and informative censoring mechanisms. The non‐parametric procedure obtained by ranking the individual summary statistics, although not as efficient as the conditional linear model with robust variance, also performed relatively well in most situations. Therefore, in situations in which informative censoring is likely to occur it is important to select the proper method of analysis to test for the informativeness of censoring and to account for its effects.
Stochastic curtailing and conditional power in matched case‐control studies
Authors: Hunsberger Sally , Sorlie Paul , Geller Nancy L. 
Published: April 2007

AbstractNew methodology for early stopping of observational epidemiologic studies is developed. To indicate the likelihood of a positive result at the end of a study, the conditional power (CP) is calculated using an approach similar to that of Halperin et al. Given the current result, the conditional probability of a positive result for the entire study is determined. An expression relating the new approach to an approach given by Lan and Wittes is derived. As an example, the CP is calculated based on information from the first third of a matched case‐control study embedded in an epidemiological study.
Sources of bias in studies of time to pregnancy
Authors: Weinberg Clarice R. , Baird Donna D. , Wilcox Allen J. 
Published: April 2007

AbstractAmong sexually active couples who are not using contraception, there is considerable heterogeneity in fertility, even among those who eventually achieve pregnancy. The number of menstrual cycles required, measured in integer time, is greatly overdispersed compared to the geometric, suggesting that the per cycle probability of conception varies considerably among couples. Some of this variability may reflect the effect of reproductive toxicants on fertility, and studies of time to pregnancy can be useful in identifying such toxic effects. We describe models for analysing time‐to‐pregnancy data, and discuss seven sources of bias that can lead the reproductive epidemiologist to spurious conclusions. Certain analytic and design strategies can help protect against some of the pitfalls.
A comparison of tumour incidence analyses applicable in single‐sacrifice animal experiments
Authors: Dinse Gregg E. 
Published: April 2007

AbstractMonte Carlo methods are used to evaluate the operating characteristics of several trend tests for comparing incidence rates of occult tumours. The candidate tests are applicable in long‐term animal experiments that have just one sacrifice time and no information on cause of death. When survival decreases with dose and the tumour is lethal, the tests that do not adjust for survival or that assume tumours are non‐lethal behave conservatively, whereas the test that assumes tumours are rapidly lethal rejects the hypothesis of equal incidence rates too frequently. The only test which consistently operates at the correct level is one that specifies a constant difference between the death rates for animals with and without the tumour. Surprisingly, the test that specifies a constant ratio of these death rates often is conservative. Finally, a test based on a simple modification of the unadjusted test, which accounts for differential mortality by scaling down the size of the risk set, performs reasonably well in many cases. Among the tests that operate at the proper level, the constant risk difference test consistently exhibits high power across a wide range of situations.
Meta‐analysis of animal experiments on dietary fat intake and mammary tumours
Authors: Freedman Laurence S. 
Published: April 2007

AbstractI describe a meta‐analysis of animal experiments designed to investigate the effects of dietary fat intake upon mammary tumour development. These experiments involve one or more of three types of dietary manipulation: (i) varying the proportion of fat in the diet; (ii) varying the source of fat; and (iii) varying the amount of energy consumed. I use linear logistic regression models to investigate questions about the relative effects of fat and total energy intake upon mammary tumour development and about the effects of different sources of fat. The results of these analyses are presented and discussed. The regression models employed are characterized by the inclusion of an experiment ‘effect’ to ensure that estimated nutrient effects are based only upon within‐experiment comparisons. Homogeneity of nutrient effects across experiments is assumed. The method differs from the usual meta‐analysis of clinical trials in that the nutrient effects may not be estimable from each individual experiment, but only from combinations of experiments. This poses problems for the development of a model that allows heterogeneity of nutrient effects.
Discrimination techniques applied to the NCI in vitro anti‐tumour drug screen: Predicting biochemical mechanism of action
Authors: Koutsoukos Antonis D. , Rubinstein Lawrence V. , Faraggi David , Simon Richard M. , Kalyandrug Sivaram , Weinstein John N. , Kohn Kurt W. , Paull Kenneth D. 
Published: April 2007

AbstractThe National Cancer Institute currently tests approximately 400 compounds per week against a panel of human tumour cell lines in order to identify potential anti‐cancer drugs. We describe several approaches, based on these in uitro data, to the problem of identifying the primary biochemical mechanism of action of a compound. Using linear and non‐parametric discriminant procedures and cross‐validation, we find that accurate identification of the mechanism of action is achieved for approximately 90 percent of a diverse collection of 141 known compounds, representing six different mechanistic categories. We demonstrate that two‐dimensional graphical displays of the compounds in terms of the initial three principal components (of the original data) result in suggestive visual clustering according to mechanism of action. Finally, we compare the classification accuracy of the statistical discrimination procedures with the accuracy obtained from a neural network approach and, for our example, we find that the results obtained from the various approaches are similar.
Measures of agreement: A single procedure
Authors: Bartko John J. 
Published: April 2007

AbstractAn assessment of measurement agreement made by devices, laboratories, or raters is important in medical practice and research. The setting in which each randomly selected subject is rated by the same two raters raises assorted questions regarding rater agreement. The intraclass correlation coefficient (ICC) is one measure of reliability. The paired t‐test can be used to evaluate the overall ratings or bias of the two raters, while their variances can be assessed with Pitman's test. The Bradley‐Black wood test can be used for a simultaneous test of their means and variances. A single method that provides results for these approaches is proposed and the bivariate confidence ellipse is suggested to provide boundaries for dispersion.
On non‐parametric and generalized tests for the two‐sample problem with location and scale change alternatives
Authors: Podgor Marvin J. , Gastwirth Joseph L. 
Published: April 2007

AbstractVarious tests have been proposed for the two‐sample problem when the alternative is more general than a simple shift in location: non‐parametric tests; O'Brien's generalized t and rank sum tests; and other tests related to the t. We show that the generalized tests are directly related to non‐parametric tests proposed by Lepage. As a result, we obtain a wider, more flexible class of O'Brien‐type procedures which inherit the level robustness property of non‐parametric tests. We have also computed the tests' empirical sizes and powers under several models. The non‐parametric procedures and the related O'Brien‐type tests are valid and yield good power in the settings investigated. They are preferable to the t‐test and related procedures whose type I errors differ noticeably from nominal size for skewed and long‐tailed distributions.
Tudying the relationship between change and initial value in longitudinal studies
Authors: Zerbe Gary O. , Wu Margaret C. , Zucker David M. 
Published: April 2007

AbstractBlomqvist's problem of studying the relationship between change and initial value in a linear growth curve setting is reformulated from a random effects model perspective. First, a maximum likelihood estimate of the between‐individual covariance matrix for a simple linear regression model with stochastic parameters is obtained via an EM algorithm as discussed by Laird and Ware. Second, the regression coefficient of the individual‐specific slopes on the individual‐specific intercepts is estimated as a ratio of elements of the between‐individual covariance matrix as discussed by Zucker et al. Then a Fieller's type confidence interval for this ratio is proposed. Discussion is facilitated by recognizing the Laird‐Ware model as a special case of a more general model discussed by Hocking.
Some power considerations when deciding to use transformations
Authors: Kingman Albert , Zion Gary 
Published: April 2007

AbstractConventional wisdom suggests that for small data sets having substantial skew, one should attempt to determine the correct distributional form, if possible, and apply statistical methods appropriate for that distribution. Transformations such as the log or square root are often used. If an appropriate distributional form cannot be determined, a distribution‐free procedure such as a rank transformation or a randomization test procedure can be used. To better appreciate the effect of such alternatives on both the type I error and power of detecting differences between treatment groups, simulation studies were conducted for responses having specific gamma G(r, Ø) and log‐normal In(M, V) distributions. The gamma and log‐normal distributions were selected so that they had the same first two moments. A simple two group design was assumed. The reference group always had an average disease level μ = 3.0 (μ = rø for gamma, μ =Mfor log‐normal), and the treatment group always had means whose reductions ranged from 0 per cent to 50 per cent. The effect of distributional type and the degree of skewness was investigated by varying the population parameter values. Six statistical test procedures were compared for the gamma distributions. All test procedures were robust relative to the type I error. The UMP test based on a ratio of sample means produced the greatest power for all combinations ofn, randRT. The power losses associated with the randomization test, thet‐test on original scale, and thet‐test on the square root scale were very small, (3 per cent to 6 per cent in absolute value) forn= 10 and 15, and less than 2 per cent for group sizes of 25 or more. The power loss associated with thet‐test on the log scale was much larger, ranging from 5 per cent to 10 per cent smaller power than thet‐test on original scale. The Wilcoxon rank test produced similar results to that of the LOGt‐test for small samples. The power for the shifted LOG (X+c) test increased monotonically to the asymptotic value of the OREt‐test. The same five test procedures based on differences in sample means were then compared for the corresponding log‐normal distributions. The UMP test, that is, LOG(X), produced the highest power. There was very little power lost for the SQRTt‐test. The loss in power varied between 2 per cent and 5 per cent for the RANK test. The RANK test performed considerably better than thet‐test on the original scale. In contrast to the results for the gamma the power for the shifted LOG (X+c) test had its maximum forc= 0, and decreased monotonically to the asymptotic value of the ORIGt‐test.The results suggest that statistical inferences can be highly dependent on the distributional form and the scale of measurement of the response used in the statistical analysis.
The first t‐test
Authors: Senn Stephen , Richardson William 
Published: April 2007

AbstractThe data with which Student illustrated the application of his famous distribution are examined from a number of aspects. Central to the discussion is the within‐patient clinical trial at Kalamazoo whose results were published by Cushny and Peebles and misquoted by Student and Fisher. This trial is discussed from historical, pharmacological and statistical perspectives. Student's and Fisher's analyses and a more modern analysis by Preece are considered as is Cushny's and Peebles's interpretation. Brief biographies of the five physicians involved in running the trial are presented.
Multi‐state Markov models for analysing incomplete disease history data with illustrations for hiv disease
Authors: Gentleman R. C. , Lawless J. F. , Lindsey J. C. , Yan P. 
Published: April 2007

AbstractMulti‐state Markov models can be useful in analysing disease history data. We apply the general estimation methods of Kalbfleisch and Lawless to panel data in which individuals are viewed over only a portion of their life history and complete information about transition times between states is unavailable. Methods to assess goodness‐of‐fit are proposed. To illustrate the methods, we consider models of HIV disease relating important immunological marker measurements to the onset of AIDS.
Bayesian analysis of survival on multiple time scales
Authors: Berzuini Carlo , Clayton David 
Published: April 2007

AbstractWe propose a Bayesian approach to the analysis of survival data on multiple time scales. Non‐parametric modelling of variation of rates with more than one time scale is achieved using priors which specifysmoothvariation. Computations are conveniently carried out using Gibbs sampling. We discuss the extension of the method to Bayesian forecasting of rates. Numerical experience of two examples is described.
Assessing the significance of chromosome‐loss data: Where are suppressor genes for bladder cancer?
Authors: Newton Michael A. , Wu Shi‐Qi , Reznikoff Catherine A. 
Published: April 2007

AbstractCytogenetic analysis reveals alterations of chromosome structure (losses, gains, and rearrangements of genetic material) in bladder cancer cells generated using an in oitrolin oioo transformation system. To predict possible locations of bladder cancer suppressor genes, we performed a robust Bayesian analysis of the chromosome‐loss data. We postulated a simple stochastic model to describe chromosome loss during tumour progression. Posterior computations are enabled by a dynamic simulation algorithm. Ordered by decreasing posterior probability of putatively harbouring a suppressor gene, we observe significant losses on chromosomes 3, 18, 13, 10, 11, andy.
Sample size calculation for clinical trials in which entry criteria and outcomes are counts of events
Authors: McMahon Robert P. , Proschan Michael , Geller Nancy L. , Stone Peter H. , Sopko George 
Published: April 2007

AbstractIn many chronic diseases, therapy aims to prevent or reduce the frequency of episodes of a disease manifestation, for example cardiac ischaemic episodes or epileptic seizures. Entry criteria for clinical trials typically include a minimum number of episodes within a baseline period, and regression to the mean should be anticipated. The distribution of the number of episodes at follow‐up, the statistical power for treatment comparisons, and the difficulty of recruitment will depend on the entry criterion chosen. A gamma‐Poisson mixture model is employed to describe the regression to the mean when the entry criterion and outcome measure are counts of discrete events. Sample size formulae which take account of the entry criterion are derived for comparison of mean number of events at follow‐up and the proportion of patients with zero events at follow‐up. Application of these formulae to screening data from the Aymptomatic Cardiac Ischemia Pilot (ACIP) Study is presented as an example.