bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inference for a family of survival models encompassing the proportional hazards and proportional odds models
Authors: Zucker David M. , Yang Song 
Published: October 2005

AbstractFor survival data regression, the Cox proportional hazards model is the most popular model, but in certain situations the Cox model is inappropriate. Various authors have proposed the proportional odds model as an alternative. Yang and Prentice recently presented a number of easily implemented estimators for the proportional odds model. Here we show how to extend the methods of Yang and Prentice to a family of survival models that includes the proportional hazards model and proportional odds model as special cases. The model is defined in terms of a Box–Cox transformation of the survival function, indexed by a transformation parameter ρ. This model has been discussed by other authors, and is related to the Harrington–FlemingGρfamily of tests and to frailty models. We discuss inference for the case where ρ is known and the case where ρ must be estimated. We present a simulation study of a pseudo‐likelihood estimator and a martingale residual estimator. We find that the methods perform reasonably. We apply our model to a real data set. Copyright © 2005 John Wiley & Sons, Ltd.
Partial questionnaire designs, questionnaire non‐response, and attributable fraction: applications to adult onset asthma
Authors: Andrés Houseman E. , Milton Donald K. 
Published: October 2005

AbstractThe attributable fraction (AF) is often used to explore the policy implications of an association between a disease and an exposure. To date, there have been no proposed estimators of AF in the context of partial questionnaire designs (PQD). The PQD, first proposed in a public health context by Wacholder is often used to enhance response rates in questionnaires. It involves eliciting responses from each subject on preassigned subsets of questions, thereby reducing the burden of response. We propose a computationally efficient method of estimating logistic (or more generally, binary) regression parameters from a PQD model where there is non‐response to the questionnaire and the rates of non‐response differ between sub‐populations. Assuming a log‐linear model for the distribution of missing covariates, we employ the methods of Wacholder to motivate consistent estimating equations, and weight each subject's contribution to the estimating function by the inverse probability of responding to the questionnaire. We also propose techniques for goodness‐of‐fit to assist in model selection. We then use the PQD regression estimates to derive an estimate of AF similar to that proposed by Bruzzi. Finally, we demonstrate our methods using data obtained from a study on adult occupational asthma, conducted within a Massachusetts HMO. Although we concentrate on a particular type of missing data mechanism, other missing data techniques can be incorporated into AF estimation in a similar manner. Copyright © 2005 John Wiley & Sons, Ltd.
Interval estimation for treatment effects using propensity score matching
Authors: Hill Jennifer , Reiter Jerome P. 
Published: October 2005

AbstractIn causal studies without random assignment of treatment, causal effects can be estimated using matched treated and control samples, where matches are obtained using estimated propensity scores. Propensity score matching can reduce bias in treatment effect estimators in cases where the matched samples have overlapping covariate distributions. Despite its application in many applied problems, there is no universally employed approach to interval estimation when using propensity score matching. In this article, we present and evaluate approaches to interval estimation when using propensity score matching. Copyright © 2005 John Wiley & Sons, Ltd.
Estimation of time‐dependent area under the ROC curve for long‐term risk prediction
Authors: Chambless Lloyd E. , Diao Guoqing 
Published: October 2005

AbstractSensitivity, specificity, and area under the ROC curve (AUC) are often used to measure the ability of survival models to predict future risk. Estimation of these parameters is complicated by the fact that these parameters are time‐dependent and by the fact that censoring affects their estimation just as it affects estimation of survival curves or coefficients of survival regression models. The authors present several estimators that overcome these complications. One approach is a recursive calculation over the ordered times of events, analogous to the Kaplan–Meier approach to survival function estimation. Another is to first apply Bayes' theorem to write the parameters of interest in terms of conditional survival functions that are then estimated by survival analysis methods. Simulation studies demonstrate that the proposed estimators perform well in practical situations, when compared with an estimator (c‐statistic, from logistic regression) that ignores time. An illustration with data from a cardiovascular follow‐up study is provided. Copyright © 2005 John Wiley & Sons, Ltd.
Forecasting age‐specific breast cancer mortality using functional data models
Authors: Erbas Bircan , Hyndman Rob J. , Gertig Dorota M. 
Published: October 2005

AbstractAccurate estimates of future age‐specific incidence and mortality are critical for allocation of resources to breast cancer control programmes and evaluation of screening programmes. The purpose of this study is to apply functional data analysis techniques to model age‐specific breast cancer mortality time trends, and forecast entire age‐specific mortality functions using a state‐space approach.We use annual unadjusted breast cancer mortality rates in Australia, from 1921 to 2001 in 5 year age groups (45 to 85+). We use functional data analysis techniques where mortality and incidence are modelled as curves with age as a functional covariate varying by time. Data are smoothed using non‐parametric smoothing methods then decomposed (using principal components analysis) to estimate basis functions that represent the functional curve. Period effects from the fitted coefficients are forecast then multiplied by the basis functions, resulting in a forecast mortality curve with prediction intervals. To forecast, we adopt a state‐space approach and an automatic modelling framework for selecting among exponential smoothing methods.Overall, breast cancer mortality rates in Australia remained relatively stable from 1960 to the late 1990s, but have declined over the last few years. A set of four basis functions minimized the mean integrated squared forecasting error and account for 99.3 per cent of variation around the mean mortality curve. Twenty year forecasts suggest a continuing decline, but at a slower rate, and stabilizing beyond 2010. Forecasts show a decline in all age groups with the greatest decline in older women.The proposed methods have the potential to incorporate important covariates such as hormone replacement therapy and interventions to represent mammographic screening. This would be particularly useful for evaluating the impact of screening on mortality and incidence from breast cancer. Copyright © 2005 John Wiley & Sons, Ltd.
Random effects logistic models for analysing efficacy of a longitudinal randomized treatment with non‐adherence
Authors: Small Dylan S. , Ten Have Thomas R. , Joffe Marshall M. , Cheng Jing 
Published: October 2005

AbstractWe present a random effects logistic approach for estimating the efficacy of treatment for compliers in a randomized trial with treatment non‐adherence and longitudinal binary outcomes. We use our approach to analyse a primary care depression intervention trial. The use of a random effects model to estimate efficacy supplements intent‐to‐treat longitudinal analyses based on random effects logistic models that are commonly used in primary care depression research. Our estimation approach is an extension of Nagelkerkeet al.'s instrumental variables approximation for cross‐sectional binary outcomes. Our approach is easily implementable with standard random effects logistic regression software. We show through a simulation study that our approach provides reasonably accurate inferences for the setting of the depression trial under model assumptions. We also evaluate the sensitivity of our approach to model assumptions for the depression trial. Copyright © 2005 John Wiley & Sons, Ltd.
Comparison of case‐deletion diagnostic methods for Cox regression
Authors: Wang Hsiao‐Mei , Jones Michael P. , Storer Barry E. 
Published: October 2005

AbstractCase‐deletion diagnostics are a routine component of regression analysis since they identify unusual observations that substantially affect parameter estimates. The exact approach is to compute the change in each regression parameter by dropping that individual and refitting the model. Repeating a Cox regression for the removal of each individual is very time consuming and therefore not done in practice. The two methods commonly used to approximate the exact case‐deletion change for Cox regression are the empirical influence function approach and the covariate‐vector augmentation approach. This paper reports the results of a simulation study on how well these methods estimate the exact change in a parameter estimate when deleting a known outlier or a known non‐outlier. Additionally, we investigate how well these methods correctly identify outliers and non‐outliers. The covariate augmentation approach clearly outperformed the influence function approach in these simulations. Copyright © 2005 John Wiley & Sons, Ltd.
A new classification rule for incomplete doubly multivariate data using mixed effects model with performance comparisons on the imputed data
Authors: Roy Anuradha 
Published: October 2005

AbstractA mixed effects model, enhanced by a Kronecker product structure for the residual variance–covariance matrix, is used in conjunction with a discriminant analysis technique, to devise a new statistical classification method on incomplete doubly multivariate data. The proposed method is efficient in small scale clinical trials that use relatively few patients. The new classification method is also applied to multiply imputed data sets. The misclassification error rates (MERs) are compared in order to investigate the effectiveness of the new classification rule on an incomplete data set. The classification method is applied to a real data set. The error rates on the incomplete data set are found to be much less than the median error rate on the multiply imputed data sets. Non‐parametric methods, such as kernel method and k‐nearest neighbourhood method, are also applied to multiply imputed data sets. Results illustrating the advantages of the new classification method over classic non‐parametric classification methods are presented. Copyright © 2005 John Wiley & Sons, Ltd.
Assessment on homogeneity tests for kappa statistics under equal prevalence across studies in reliability
Authors: Nam Jun‐mo 
Published: October 2005

AbstractIn this paper, we assess the performance of homogeneity tests for two or more kappa statistics when prevalence rates across reliability studies are assumed to be equal. The likelihood score method and the chi‐square goodness‐of‐fit (GOF) test provide type 1 error rates that are satisfactorily close to the nominal level, but a Fleiss‐like test is not satisfactory for small or moderate sample sizes. Simulations show that the score test is more powerful than the chi‐square GOF test and the approximate sample size required for a specific power of the former is substantially smaller than the latter. In addition, the score test is robust to deviations from the equal prevalence assumption, while the GOF test is highly sensitive and it may give a grossly misleading type 1 error rate when the assumption of equal prevalence is violated. We conclude that the homogeneity score test is the preferred method. Published in 2005 by John Wiley & Sons, Ltd.
Decision‐theoretic designs for dose‐finding clinical trials with multiple outcomes
Authors: Fan Shenghua K. , Wang You‐Gan 
Published: October 2005

AbstractA decision‐theoretic framework is proposed for designing sequential dose‐finding trials with multiple outcomes. The optimal strategy is solvable theoretically via backward induction. However, for dose‐finding studies involvingkdoses, the computational complexity is the same as the bandit problem withk‐dependent arms, which is computationally prohibitive. We therefore provide two computationally compromised strategies, which is of practical interest as the computational complexity is greatly reduced: one is closely related to the continual reassessment method (CRM), and the other improves CRM and approximates to the optimal strategy better. In particular, we present the framework for phase I/II trials with multiple outcomes. Applications to a pediatric HIV trial and a cancer chemotherapy trial are given to illustrate the proposed approach. Simulation results for the two trials show that the computationally compromised strategy can perform well and appear to be ethical for allocating patients. The proposed framework can provide better approximation to the optimal strategy if more extensive computing is available. Copyright © 2005 John Wiley & Sons, Ltd.
Confidence intervals for an effect size measure based on the Mann–Whitney statistic. Part 2: asymptotic methods and evaluation
Authors: Newcombe Robert G. 
Published: October 2005

AbstractSeveral asymptotic confidence interval methods for U/mn, the Mann–Whitney U statistic divided by the product of the two sample sizes, are developed and evaluated alongside published methods. Novel methods derived from Gaussian and beta models perform well, though overall a modification of the Hanley–McNeil approach (method 5 in this evaluation) performed better and is recommended for calculating confidence intervals for U/mn for continuously distributed data. Copyright © 2005 John Wiley & Sons, Ltd.
Checking stationarity of the incidence rate using prevalent cohort survival data
Authors: Asgharian Masoud , Wolfson David B. , Zhang Xun 
Published: October 2005

AbstractWhen survival data are collected as part of a prevalent cohort study with follow‐up, the recruited cases have already experienced their initiating event, say onset of a disease, and consequently the incidence process is only partially observed. Nevertheless, there are good reasons for interest in certain features of the underlying incidence process, for example whether or not it is stationary. Indeed, the well known relationship between incidence and prevalence, often used by epidemiologists, requires stationarity of the incidence rate for its validity. Also, the statistician can exploit stationarity of the incidence process by improving the efficiency of estimators in a prevalent cohort survival analysis. In addition, whether the incident rate is stationary is often in itself of central importance to medical and other researchers. We present here a necessary and sufficient condition for stationarity of the underlying incidence process, which uses only survival observations, possibly right censored, from a prevalent cohort study with follow‐up. This leads to a simple graphical means of checking for the stationarity of the underlying incidence times by comparing the plots of two Kaplan–Meier estimates that are based on partially observed incidence times and follow‐up survival data. We use our method to discuss the incidence rate of dementia in Canada between 1971 and 1991. Copyright © 2005 John Wiley & Sons, Ltd.
Dichotomizing continuous predictors in multiple regression: a bad idea
Authors: Royston Patrick , Altman Douglas G. , Sauerbrei Willi 
Published: October 2005

AbstractIn medical research, continuous variables are often converted into categorical variables by grouping values into two or more categories. We consider in detail issues pertaining to creating just two groups, a common approach in clinical research. We argue that the simplicity achieved is gained at a cost; dichotomization may create rather than avoid problems, notably a considerable loss of power and residual confounding. In addition, the use of a data‐derived ‘optimal’ cutpoint leads to serious bias. We illustrate the impact of dichotomization of continuous predictor variables using as a detailed case study a randomized trial in primary biliary cirrhosis. Dichotomization of continuous data is unnecessary for statistical analysis and in particular should not be applied to explanatory variables in regression models. Copyright © 2005 John Wiley & Sons, Ltd.
Comparison of predictive values of two diagnostic tests from the same sample of subjects using weighted least squares
Authors: Wang Wenquan , Davis Charles S. , Soong Seng‐Jaw 
Published: October 2005

AbstractScreening and diagnostic tests are important in disease prevention or control. The predictive values of positive and negative (PPV and NPV) test results are two of four operational characteristics of a screening test. We review an existing method based on the generalized estimating equation (GEE) methodology for comparing predictive values from the same sample of subjects and propose two Wald test statistics derived from the weighted least squares (WLS) method for the analysis of categorical data. Using these results, we propose sample size calculation formulae for this problem. Simulation studies are conducted to compare the performances of the two Wald test statistics (one based on the difference of two PPVs or NPVs, another based on the logarithm of the ratio of two PPVs or NPVs) and the score/Wald test statistic derived from GEE. We recommend using the difference‐based WLS approach. Copyright © 2005 John Wiley & Sons, Ltd.
An efficient design for a study comparing two drugs, their combination and placebo
Authors: Wei Lynn 
Published: October 2005

AbstractA novel design for a study comparing two drugs (A and B), their combination (AB) and placebo (P) in a stable disease such as chronic asthma is proposed. The primary objectives of the trial are to compare A (a new drug) with placebo and AB with B (an old drug). Other between‐treatment comparisons are secondary. The new design consists of the first 2 sequences of a single 4 × 4 Latin square: (A, AB, P, B|P, B, A, AB|AB, A, B, P|B, P, AB, A). The washout period between A and AB and that between P and B are eliminated based on the assumptions that there is no carry‐over effect from placebo and that the effect of the combination would be approximately same at steady state either starting the 2 drugs at the same time or adding one later. Equal period effect for Periods 1 and 2, and for Periods 3 and 4 are also assumed for all treatment effects being estimable. Advantages and disadvantages of this design were evaluated and compared with other possible designs. Compared with other possible designs, the new design is more efficient. For example, the new design requires less than ¼ of the sample size a parallel design would need and it requires ⅔ of the sample size a 4‐treatment, 3‐period incomplete block design would need for the same power at the same detectable difference. It has a similar efficiency to a 4‐treatment, 4‐period cross‐over design but with a shorter duration. In this design, all between‐treatment comparisons are based on direct within‐subject information. The new design could also potentially decrease the probability of patient's drop‐outs and use of rescue medication due to lack of efficacy during the placebo periods by reducing the number of placebo washout periods. The benefits reaped from the new design seem to outweigh its risks, which include potential bias for the secondary between‐treatment comparisons if the additional assumption about period effects are not satisfied and the incapability of directly assessing the onset of action of the combination of 2 drugs. Copyright © 2005 John Wiley & Sons, Ltd.
Generalizing the TITE‐CRM to adapt for early‐ and late‐onset toxicities
Authors: Braun Thomas M. 
Published: October 2005

AbstractDue to the staggered entry of subjects in phase I trials, some subjects will only be partially through the study when others are ready to be enrolled. Nonetheless, many phase I designs focus solely upon whether or not subjects experience toxicity, thereby determining the maximum tolerated dose (MTD) with a binomial likelihood using data from fully observed subjects. The time‐to‐event continual reassessment method (TITE‐CRM) was the first attempt to incorporate information from partially observed subjects by using a weighted binomial likelihood in which the weights are based upon the actual toxicity time distribution. Unfortunately, it is difficult to accurately estimate the toxicity time distribution because only a small proportion of enrolled subjects will experience toxicity. Creators of the TITE‐CRM propose the simple alternative of weighting subjects by the proportion of time observed, as well as two adaptive weights to adjust for late‐onset toxicities. As a alternative to these approaches, we suggest assuming the toxicity times, as a proportion of the total time under observation, have a Beta distribution with parameters 1.0 and θ; we also allow θ to vary by dose. The value of θ allows us to reflect the occurrence of early‐ or late‐onset toxicities without correctly specifying the actual distribution of toxicity times. Through this model, we do not necessarily expect to improve identification of the MTD, but rather hope to reduce the exposure of subjects to overly toxic doses. Through simulation, we examine how well our model identifies the MTD and allocates dose assignments in three scenarios investigated by previous publications. Copyright © 2005 John Wiley & Sons, Ltd.
The sequential analysis of repeated binary responses: a score test for the case of three time points
Authors: Sooriyarachchi Marina Roshini , Whitehead John , Whitehead Anne , Bolland Kim 
Published: October 2005

AbstractIn this paper a robust method is developed for the analysis of data consisting of repeated binary observations taken at up to three fixed time points on each subject. The primary objective is to compare outcomes at the last time point, using earlier observations to predict this for subjects with incomplete records. A score test is derived. The method is developed for application to sequential clinical trials, as at interim analyses there will be many incomplete records occurring in non‐informative patterns. Motivation for the methodology comes from experience with clinical trials in stroke and head injury, and data from one such trial is used to illustrate the approach. Extensions to more than three time points and to allow for stratification are discussed. Copyright © 2005 John Wiley & Sons, Ltd.
Likelihood approaches to the non‐parametric two‐sample problem for right‐censored data
Authors: Troendle James F. , Yu Kai F. 
Published: October 2005

AbstractThe classical two‐sample problem with random right‐censoring is considered. We show that non‐ parametric likelihood techniques can be used to obtain tests for either the identity hypothesis or the non‐parametric Behrens–Fisher hypothesis (NBFH). In the case of the identity hypothesis, a special imputed permutation distribution is used to estimate the distribution under the null hypothesis. In the case of the NBFH, simulation from the constrained non‐parametric maximum likelihood estimate is used. Simulation shows that the tests using either approximation have excellent control of the type I error rate, even with quite small sample sizes. Further, for Lehmann‐type alternatives the likelihood‐based methods have similar power to the logrank test, while for the non‐Lehmann‐type alternatives tried here the likelihood‐based methods have superior power. Published in 2005 by John Wiley & Sons, Ltd.