bluuebunny/crossref_metadata_2025_split huggingface dataset:
Instream flows for recreation are closely correlated with mean discharge for rivers of western North America
Authors: Rood Stewart B. , George Chris , George Bobbi , Tymensen Wilco 
Published: November 2005

AbstractEffective river regulation requires consideration for environmental and economic aspects and also for social aspects including recreation. Our study investigated relationships between river hydrology and recreational flows (RF) for canoes, kayaks, rafts and other non‐motorized boats, for 27 river reaches in the Red Deer and Bow river basins of southern Alberta, Canada. A subjective RF method involved regression analyses of data from River Trip Report Cards, volunteer postcard‐style surveys rating flow sufficiency. A total of 958 trip reports were submitted for the rivers between 1983 and 1997 and about 30 reports permitted confident regression analysis for a river reach. Values from these analyses were very consistent with values from the ‘depth discharge method’, a hydraulic modelling approach that used stage–discharge ratings to determine flows that would produce typical depths of 60 and 75 cm for minimal and preferred flows, respectively. Values were also consistent with expert opinions from river guidebooks and maps and aggregate values were calculated from the combined RF methods. These were very closely correlated with mean discharge (Qm) across the rivers (r2 = 0.94 for minimal and 0.96 for preferred flows). The relationship best fitted a power function (straight plot on log versus log scales) with a consistent slope but vertical offset for minimal versus preferred flows. Close relationships between guidebook estimates of RF and Qm were also observed for rivers in the American Rocky Mountain states of Idaho (r2 = 0.55 and 0.74), Montana (r2 = 0.34 and 0.80) and Colorado (r2 = 0.43 and 0.51), but the association was weaker for the Pacific Northwest state of Oregon (r2 = 0.35 and 0.26). These analyses indicate that RF can be confidently determined through a combination of subjective and hydraulic methods and reveal that RF values represent a systematic function of discharge for a broad range of alluvial and constrained river reaches. From these analyses we provide the ‘Alberta equation’: minimal recreational flow = 3 × Qm0.59 (Qm in m3/s), and preferred flows would typically be 1.5 times higher. For other river regions the exponent ‘0.59’ may be relatively constant but adjustments to the coefficient ‘3’ could be applicable. Copyright © 2005 John Wiley & Sons, Ltd.
Nitrogen processing by biofilms along a lowland river continuum
Authors: Baldwin D. S. , Mitchell A. M. , Rees G. N. , Watson G. O. , Williams J. L. 
Published: November 2005

AbstractWhile numerous studies have examined N dynamics along a river continuum, few have specifically examined the role of biofilms. Nitrogen dynamics and microbial community structure were determined on biofilms at six sites along a 120 km stretch of the lowland Ovens River, South Eastern Australia using artificial substrates. Terminal restriction fragment length polymorphism (T‐RFLP), chlorophyllaand protein analyses were used to assess biofilm microbial community composition. N dynamics was determined on the biofilms using the acetylene (C2H2) block technique and assessing changes in NH, NOxand N2O. Unlike microbial community structure, N dynamics were spatially heterogeneous. Nitrification, determined from the difference in accumulation of NHbefore and after addition of C2H2, occurred mostly in the upper sites with rates up to 1.4 × 10−5 mol m−2 h−1. The highest rates of denitrification occurred in the mid‐reaches of the river (with rates up to 1 × 10−5 mol m−2 h−1) but denitrification was not detected in the lower reaches. At the very most, only 50% of the observed uptake of NOxby the biofilms following addition of C2H2could be accounted for by denitrification. Copyright © 2005 John Wiley & Sons, Ltd.
Analysis of interval‐censored longitudinal data with application to onco‐haematology
Authors: De Lorenzo Paola , Henderson Robin , Valsecchi Maria Grazia 
Published: November 2005

AbstractThe analysis of repeated measurements on a biomarker, either alone or jointly with the analysis of time to the event of interest, is an area of active research. Nevertheless, we are not yet able to deal in complete generality with these complex data, which frequently consist of error‐prone, sparse and intermittent values. In many cancer studies, they arise in the framework of clinical trials and thus their relationship with prognosis is a primary focus. In such a setting, the Cox model is regarded as the standard technique for analysis. The aim of this work is to illustrate an alternative approach to the analysis of studies in which the biomarker values are complicated by interval censoring and an event occurs when the biomarker itself passes a certain threshold. We propose a linear mixed model with a Gaussian stochastic process that allows for interval‐censored data and can be used both to track the biomarker trajectory and to estimate the probability of event occurrence. It is developed within the classic approach to longitudinal data analysis that was previously adapted for left‐censored data, only. We apply this method to a study on the minimal residual disease (MRD) in childhood leukaemia. MRD is an interval‐censored measurement of residual leukaemic cells that was scheduled at 9 time‐points during treatment. The aim is to investigate the relationship between MRD and the disease process. Relapse, the event of interest, may conveniently be represented as MRD over a pre‐defined threshold. Our focus is on modelling the probability of relapse conditional on MRD observed prior to it. Results show that the approach is promising as it allows proper description of the data, while maintaining flexibility of modelling, feasibility of computations and interpretability of results. Copyright © 2005 John Wiley & Sons, Ltd.
Testing and estimation in flexible group sequential designs with adaptive treatment selection
Authors: Posch Martin , Koenig Franz , Branson Michael , Brannath Werner , Dunger‐Baldauf Cornelia , Bauer Peter 
Published: November 2005

AbstractIntegrating selection and confirmation phases into a single trial can expedite the development of new treatments and allows to use all accumulated data in the decision process. In this paper we review adaptive treatment selection based on combination tests and propose overall adjusted p‐values and simultaneous confidence intervals. Also point estimation in adaptive trials is considered. The methodology is illustrated in a detailed example based on an actual planned study. Copyright © 2005 John Wiley & Sons, Ltd.
Minimization—reducing predictability for multi‐centre trials whilst retaining balance within centre
Authors: Brown Sarah , Thorpe Helen , Hawkins Kim , Brown Julia 
Published: November 2005

AbstractMinimization is often used to assign patients to treatment groups to ensure good balance in patient numbers within centre and other prognostic factors. Balance within centre is preferable since large imbalances between treatment arms may have logistical implications for centres, such as cost and resource implications. However, recent concern over high predictability of treatment allocation by centres when using minimization has caused this method to be questioned. We used data from current clinical trials to assess predictability and summarize subsequent within‐centre imbalances with the aim of finding the most effective minimization method for reducing predictability whilst still retaining sufficient balance within centre, when randomization is to one of two treatments. We compared prediction rates and imbalances for deterministic minimization, and minimization incorporating various random elements, p (p=0.95,0.90,0.80,0.75,0.70). We also compared prediction rates and imbalance when centre was and was not included as a stratification factor. Incorporating a random element proved successful in reducing prediction rates whilst minimizing the inevitable increase in within‐centre imbalance, whereas excluding centre as a stratification factor incurred major within‐centre imbalance. We therefore suggest that minimization can still be used, and that centre can be included as a stratification factor, but a random element has to be incorporated into the minimization algorithm. Minimization incorporating a random element of 0.80 is the most efficient method to use based upon the simulations undertaken in this study of real clinical trial data using different probabilities of allocation. Copyright © 2005 John Wiley & Sons, Ltd.
Bayesian implementation of a genetic model‐free approach to the meta‐analysis of genetic association studies
Authors: Minelli Cosetta , Thompson John R. , Abrams Keith R. , Lambert Paul C. 
Published: November 2005

AbstractA genetic model‐free method for the meta‐analysis of genetic association studies is described that estimates the mode of inheritance from the data rather than assuming that it is known. For a bi‐allelic polymorphism, with G as risk allele and g as wild‐type, the genetic model depends on the ratio of the two log odds ratios, λ = log ORGg/log ORGG, where ORGGcompares GG with gg and ORGgcompares Gg with gg. Modelling log ORGGas a random effect creates a hierarchical model that can be implemented within a Bayesian framework.In Bayesian modelling, vague prior distributions have to be specified for all unknown parameters when no external information is available. When the data are sparse even supposedly vague prior distributions may have an influence on the posterior estimates. We investigate the impact of different vague prior distributions for the between‐study standard deviation of log ORGGand for λ, by considering three published meta‐analyses and associated simulations. Our results show that depending on the characteristics of the meta‐analysis the results may indeed be sensitive to the choice of vague prior distribution for either parameter.Genetic association studies usually use a case‐control design that should be analysed by the corresponding retrospective likelihood. However, under some circumstances the prospective likelihood has been shown to produce identical results and it is usually preferred for its simplicity. In our meta‐analyses the two likelihoods give very similar results. Copyright © 2005 John Wiley & Sons, Ltd.
Permutation based methods for comparing quality of life between observed treatments
Authors: Moerkerke Beatrijs , Goetghebeur Els , Van Steen Kristel , Van Belle Simon , Cocquyt Veronique 
Published: November 2005

AbstractQuality of life is becoming an important outcome for the comparison of aggressive therapies. To measure quality of life (QOL), questionnaires have been designed that ask patients about symptoms and functionality in several aspects of daily life. Primary analyses of such questionnaires typically focus on a summary statistic, such as a sum score or a single global question. This avoids inflated type I errors or loss of power due to multiple testing of individual items. In return, specific questions and answers that initially mattered to the patient may unfortunately get buried. To avoid reduced specificity and interpretability for both patients and physicians, we propose to also analyse all original questions. In this paper, we seek to detect items of the QOL questionnaire that differ significantly over observed treatments even in the face of multiple testing. We sequentially build a model that combines features which additionally discriminate between treatments. To achieve this, we draw on insights gained in the field of statistical genetics where one is often confronted with a vast amount of predictors, e.g. of a genotypic nature. Specifically, we adopt a permutation based approach to evaluate the null distribution of the maximum of many correlated test statistics and use it to build a regression model that explains QOL differences between treatment arms. We apply the new methodology to analyse QOL data in an observational study of four different treatments of breast cancer. We discover that a single question captures most of the observed treatment differences in this population. Copyright © 2005 John Wiley & Sons, Ltd.
Bayesian proportional hazards model with time‐varying regression coefficients: a penalized Poisson regression approach
Authors: Lambert Philippe , Eilers Paul H. C. 
Published: November 2005

AbstractOne can fruitfully approach survival problems without covariates in an actuarial way. In narrow time bins, the number of people at risk is counted together with the number of events. The relationship between time and probability of an event can then be estimated with a parametric or semi‐parametric model. The number of events observed in each bin is described using a Poisson distribution with the log mean specified using a flexible penalized B‐splines model with a large number of equidistant knots. Regression on pertinent covariates can easily be performed using the same log‐linear model, leading to the classical proportional hazard model. We propose to extend that model by allowing the regression coefficients to vary in a smooth way with time. Penalized B‐splines models will be proposed for each of these coefficients. We show how the regression parameters and the penalty weights can be estimated efficiently using Bayesian inference tools based on the Metropolis‐adjusted Langevin algorithm. Copyright © 2005 John Wiley & Sons, Ltd.
Concordance correlation coefficient applied to discrete data
Authors: Carrasco Josep L. , Jover Lluis 
Published: November 2005

AbstractIn any field in which decisions are subject to measurements, interchangeability between the methods used to obtain these measurements is essential. To consider methods as interchangeable, a certain degree of agreement is needed between the measurements they provide. The concordance correlation coefficient is an index that assesses the strength of agreement and it has been widely applied in situations in which measurements are made on a continuous scale. Recently the concordance correlation coefficient has been defined as a specific intraclass correlation coefficient estimated by the variance components of a Normal–Normal mixed linear model. Although this coefficient was defined for the continuous scale case, it may also be used with a discrete scale. In this case the data are often transformed and normalized, and the concordance correlation is applied. This study discusses the expression of the concordance correlation coefficient for discrete Poisson data by means of the Poisson–Normal generalized linear mixed model. The behaviour of the concordance correlation coefficient estimate is assessed by means of a simulation study, in which the estimates were compared using four models: three Normal–Normal mixed models with raw data, log‐transformed data and square‐root transformed data, and the Poisson–Normal generalized linear mixed model. An example is provided in which two different methods are used to measure CD34+cells. Copyright © 2005 John Wiley & Sons, Ltd.
Additive and multiplicative covariate regression models for relative survival incorporating fractional polynomials for time‐dependent effects
Authors: Lambert Paul C. , Smith Lucy K. , Jones David R. , Botha Johannes L. 
Published: November 2005

AbstractRelative survival is used to estimate patient survival excluding causes of death not related to the disease of interest. Rather than using cause of death information from death certificates, which is often poorly recorded, relative survival compares the observed survival to that expected in a matched group from the general population. Models for relative survival can be expressed on the hazard (mortality) rate scale as the sum of two components where the total mortality rate is the sum of the underlying baseline mortality rate and the excess mortality rate due to the disease of interest. Previous models for relative survival have assumed that covariate effects act multiplicatively and have thus provided relative effects of differences between groups using excess mortality rate ratios. In this paper we consider (i) the use of an additive covariate model, which provides estimates of the absolute difference in the excess mortality rate; and (ii) the use of fractional polynomials in relative survival models for the baseline excess mortality rate and time‐dependent effects. The approaches are illustrated using data on 115 331 female breast cancer patients diagnosed between 1 January 1986 and 31 December 1990. The use of additive covariate relative survival models can be useful in situations when the excess mortality rate is zero or slightly less than zero and can provide useful information from a public health perspective. The use of fractional polynomials has advantages over the usual piecewise estimation by providing smooth estimates of the baseline excess mortality rate and time‐dependent effects for both the multiplicative and additive covariate models. All models presented in this paper can be estimated within a generalized linear models framework and thus can be implemented using standard software. Copyright © 2005 John Wiley & Sons, Ltd.
Analysis of time‐dependent covariates in a regressive relative survival model
Authors: Giorgi Roch , Gouvernet Joanny 
Published: November 2005

AbstractRelative survival is a method for assessing prognostic factors for disease‐specific mortality. However, most relative survival models assume that the effect of covariate on disease‐specific mortality is fixed‐in‐time, which may not hold in some studies and requires adapted modelling. We propose an extension of the Esteveet al.regressive relative survival model that uses the counting process approach to accommodate time‐dependent effect of a predictor's on disease‐specific mortality. This approach had shown its robustness, and the properties of the counting process give a simple and attractive computational solution to model time‐dependent covariates. Our approach is illustrated with the data from the Stanford Heart Transplant Study and with data from a hospital‐based study on invasive breast cancer. Advantages of modelling time‐dependent covariates in relative survival analysis are discussed. Copyright © 2005 John Wiley & Sons, Ltd.
A case study in comparing therapies involving informative drop‐out, non‐ignorable non‐compliance and repeated measurements
Authors: Härkänen T. , Knekt P. , Virtala E. , Lindfors O. 
Published: November 2005

AbstractVirtually no comparisons of different psychotherapies with long follow‐up times have been carried out until now. The Helsinki Psychotherapy Study is a randomized clinical trial, where patients were monitored for 12 months after the onset of study treatments, of which each lasted approximately 6 months. The patients' psychiatric status was measured at five pre‐determined time points during the follow‐up period. In general, the analyses of trials are complicated in cases where compliance with the given treatment is incomplete or the drop‐out from the follow‐up is non‐ignorable. In the present study, the quality of the treatment deviated from the protocol for some patients and some patients took auxiliary treatments which had similar effects to the study treatment during the study treatment or follow‐up period. This might have resulted in standard intention‐to‐treat analyses providing excessively conservative or liberal conclusions. Non‐compliance may have been non‐ignorable in some cases, so subject‐specific latent factors may have influenced the outcome both directly and indirectly via compliance behaviour. The most and least healthy patients are the most likely to dropout from the follow‐up a priori, so the missing data process is informative. The missing data can partly be augmented with surrogate information collected during interviews with patients who dropped out. A Bayesian hierarchical as‐treated model, which uses random‐effects‐based selection models to account for non‐ignorable missing data and non‐compliance, was compared with different mixed effects models. Copyright © 2005 John Wiley & Sons, Ltd.
Goodness of fit of relative survival models
Authors: Stare Janez , Pohar Maja , Henderson Robin 
Published: November 2005

AbstractAdditive regression models are preferred over multiplicative models in the analysis of relative survival data. Such preferences are mainly grounded in practical experience with mostly cancer registries data, where the basic assumption of the additivity of hazards is more likely to be met. Also, the interpretation of coefficients is more meaningful in additive than in multiplicative models. Nonetheless, the question of goodness of fit of the assumed model must still be addressed, and while there is an abundance of methods to check the goodness of fit of multiplicative models, the respective arsenal for additive models is almost empty. We propose here a variety of procedures for testing the null hypothesis of a good fit. These are based on partial residuals defined similarly to Schoenfeld residuals familiar for Cox model diagnostics. The tests have appropriate sizes under the null hypothesis, and good power under different alternatives. We investigate their performance through simulations and apply the methods to data from a study into survival of colon cancer patients. Copyright © 2005 John Wiley & Sons, Ltd.
Meta‐analysis of heterogeneously reported trials assessing change from baseline
Authors: Abrams Keith R. , Gillies Clare L. , Lambert Paul C. 
Published: November 2005

AbstractThis paper considers the quantitative synthesis of published comparative study results when the outcome measures used in the individual studies and the way in which they are reported varies between studies. Whilst the former difficulty may be overcome, at least to a limited extent, by the use of standardized effects, the latter is often more problematic. Two potential solutions to this problem are; sensitivity analyses and a fully Bayesian approach, in which pertinent external information is included. Both approaches are illustrated using the results of two systematic reviews and meta‐analyses which consider the difference in mean change in systolic blood pressure and the difference in physical functioning between an intervention and control group. The two examples illustrate that by adopting a fully Bayesian approach, as opposed to undertaking sensitivity analyses assuming fixed values for unknown parameters, the overall intervention effect can be estimated with greater uncertainty, but that assessing the sensitivity of results to choice of prior distributions in such analyses is crucial. Copyright © 2005 John Wiley & Sons, Ltd.