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Sample Properties (sample + property)
Kinds of Sample Properties Selected AbstractsLarge Sample Properties of Parameter Estimates for Periodic ARMA ModelsJOURNAL OF TIME SERIES ANALYSIS, Issue 6 2001I. V. Basawa This paper studies the asymptotic properties of parameter estimates for causal and invertible periodic autoregressive moving-average (PARMA) time series models. A general limit result for PARMA parameter estimates with a moving-average component is derived. The paper presents examples that explicitly identify the limiting covariance matrix for parameter estimates from a general periodic autoregression (PAR), a first-order periodic moving average (PMA(1)), and the mixed PARMA(1,1) model. Some comparisons and contrasts to univariate and vector autoregressive moving-average sequences are made. [source] Identification and Estimation of Regression Models with MisclassificationECONOMETRICA, Issue 3 2006Aprajit Mahajan This paper studies the problem of identification and estimation in nonparametric regression models with a misclassified binary regressor where the measurement error may be correlated with the regressors. We show that the regression function is nonparametrically identified in the presence of an additional random variable that is correlated with the unobserved true underlying variable but unrelated to the measurement error. Identification for semiparametric and parametric regression functions follows straightforwardly from the basic identification result. We propose a kernel estimator based on the identification strategy, derive its large sample properties, and discuss alternative estimation procedures. We also propose a test for misclassification in the model based on an exclusion restriction that is straightforward to implement. [source] Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo ApproachEUROPEAN FINANCIAL MANAGEMENT, Issue 3 2008Manuel Ammann G12 Abstract We use Markov Chain Monte Carlo (MCMC) methods for the parameter estimation and the testing of conditional asset pricing models. In contrast to traditional approaches, it is truly conditional because the assumption that time variation in betas is driven by a set of conditioning variables is not necessary. Moreover, the approach has exact finite sample properties and accounts for errors-in-variables. Using S&P 500 panel data, we analyse the empirical performance of the CAPM and theFama and French (1993)three-factor model. We find that time-variation of betas in the CAPM and the time variation of the coefficients for the size factor (SMB) and the distress factor (HML) in the three-factor model improve the empirical performance. Therefore, our findings are consistent with time variation of firm-specific exposure to market risk, systematic credit risk and systematic size effects. However, a Bayesian model comparison trading off goodness of fit and model complexity indicates that the conditional CAPM performs best, followed by the conditional three-factor model, the unconditional CAPM, and the unconditional three-factor model. [source] Genetic association tests with age at onsetGENETIC EPIDEMIOLOGY, Issue 2 2003L. Hsu Abstract Many diseases or traits exhibit a varying age at onset. Recent data examples of prostate cancer and childhood diabetes show that compared to simply treating the disease outcome as affected vs. unaffected, incorporation of age-at-onset information into the transmission/disequilibrium type of test (TDT) does not appear to change the results much. In this paper, we evaluate the power of TDT as a function of age at onset, and show that age-at-onset information is most useful when the disease is common, or the relative risk associated with the high-risk genotype varies with age. Moreover, an extremely old unaffected subject can contribute substantially to the power of the TDT, sometimes as much as old-onset subjects. We propose a modified test statistic for testing no association between the marker at the candidate locus and age at onset. The simulation study was conducted to evaluate the finite sample properties of proposed and the TDT test statistics under various sampling schemes for trios of parents and offspring, as well as for sibling clusters where unaffected siblings were used as controls. Genet Epidemiol 24:118,127, 2003. © 2003 Wiley-Liss, Inc. [source] NONPARAMETRIC BOOTSTRAP PROCEDURES FOR PREDICTIVE INFERENCE BASED ON RECURSIVE ESTIMATION SCHEMES,INTERNATIONAL ECONOMIC REVIEW, Issue 1 2007Valentina Corradi We introduce block bootstrap techniques that are (first order) valid in recursive estimation frameworks. Thereafter, we present two examples where predictive accuracy tests are made operational using our new bootstrap procedures. In one application, we outline a consistent test for out-of-sample nonlinear Granger causality, and in the other we outline a test for selecting among multiple alternative forecasting models, all of which are possibly misspecified. In a Monte Carlo investigation, we compare the finite sample properties of our block bootstrap procedures with the parametric bootstrap due to Kilian (Journal of Applied Econometrics 14 (1999), 491,510), within the context of encompassing and predictive accuracy tests. In the empirical illustration, it is found that unemployment has nonlinear marginal predictive content for inflation. [source] The resolution function in neutron spin-echo spectroscopy with three-axis spectrometersJOURNAL OF APPLIED CRYSTALLOGRAPHY, Issue 6 2003Klaus Habicht A resolution function for inelastic neutron spin-echo spectroscopy on a three-axis spectrometer is derived. Inelastic dispersive excitations where the tilted field technique applies are being considered. Using a Gaussian approximation of the transmission function of the three-axis spectrometer and a second-order expansion of the total Larmor phase, the instrumental resolution function of an idealized spin-echo instrument is obtained. Furthermore, the resolution function is extended to include the effects of sample properties, such as mosaicity, spread in lattice spacings and the curvature of the four-dimensional dispersion surface in a line-width measurement. [source] Asymmetric power distribution: Theory and applications to risk measurementJOURNAL OF APPLIED ECONOMETRICS, Issue 5 2007Ivana Komunjer Theoretical literature in finance has shown that the risk of financial time series can be well quantified by their expected shortfall, also known as the tail value-at-risk. In this paper, I construct a parametric estimator for the expected shortfall based on a flexible family of densities, called the asymmetric power distribution (APD). The APD family extends the generalized power distribution to cases where the data exhibits asymmetry. The first contribution of the paper is to provide a detailed description of the properties of an APD random variable, such as its quantiles and expected shortfall. The second contribution of the paper is to derive the asymptotic distribution of the APD maximum likelihood estimator (MLE) and construct a consistent estimator for its asymptotic covariance matrix. The latter is based on the APD score whose analytic expression is also provided. A small Monte Carlo experiment examines the small sample properties of the MLE and the empirical coverage of its confidence intervals. An empirical application to four daily financial market series reveals that returns tend to be asymmetric, with innovations which cannot be modeled by either Laplace (double-exponential) or Gaussian distribution, even if we allow the latter to be asymmetric. In an out-of-sample exercise, I compare the performances of the expected shortfall forecasts based on the APD-GARCH, Skew- t -GARCH and GPD-EGARCH models. While the GPD-EGARCH 1% expected shortfall forecasts seem to outperform the competitors, all three models perform equally well at forecasting the 5% and 10% expected shortfall. Copyright © 2007 John Wiley & Sons, Ltd. [source] On variable bandwidth selection in local polynomial regressionJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 3 2000Kjell Doksum The performances of data-driven bandwidth selection procedures in local polynomial regression are investigated by using asymptotic methods and simulation. The bandwidth selection procedures considered are based on minimizing ,prelimit' approximations to the (conditional) mean-squared error (MSE) when the MSE is considered as a function of the bandwidth h. We first consider approximations to the MSE that are based on Taylor expansions around h=0 of the bias part of the MSE. These approximations lead to estimators of the MSE that are accurate only for small bandwidths h. We also consider a bias estimator which instead of using small h approximations to bias naïvely estimates bias as the difference of two local polynomial estimators of different order and we show that this estimator performs well only for moderate to large h. We next define a hybrid bias estimator which equals the Taylor-expansion-based estimator for small h and the difference estimator for moderate to large h. We find that the MSE estimator based on this hybrid bias estimator leads to a bandwidth selection procedure with good asymptotic and, for our Monte Carlo examples, finite sample properties. [source] Maximum likelihood estimation of higher-order integer-valued autoregressive processesJOURNAL OF TIME SERIES ANALYSIS, Issue 6 2008Ruijun Bu Abstract., In this article, we extend the earlier work of Freeland and McCabe [Journal of time Series Analysis (2004) Vol. 25, pp. 701,722] and develop a general framework for maximum likelihood (ML) analysis of higher-order integer-valued autoregressive processes. Our exposition includes the case where the innovation sequence has a Poisson distribution and the thinning is binomial. A recursive representation of the transition probability of the model is proposed. Based on this transition probability, we derive expressions for the score function and the Fisher information matrix, which form the basis for ML estimation and inference. Similar to the results in Freeland and McCabe (2004), we show that the score function and the Fisher information matrix can be neatly represented as conditional expectations. Using the INAR(2) specification with binomial thinning and Poisson innovations, we examine both the asymptotic efficiency and finite sample properties of the ML estimator in relation to the widely used conditional least squares (CLS) and Yule,Walker (YW) estimators. We conclude that, if the Poisson assumption can be justified, there are substantial gains to be had from using ML especially when the thinning parameters are large. [source] Testing for Neglected Nonlinearity in Cointegrating Relationships,JOURNAL OF TIME SERIES ANALYSIS, Issue 6 2007Andrew P. Blake C32; C45 Abstract., This article proposes pure significance tests for the absence of nonlinearity in cointegrating relationships. No assumption of the functional form of the nonlinearity is made. It is envisaged that the application of such tests could form the first step towards specifying a nonlinear cointegrating relationship for empirical modelling. The asymptotic and small sample properties of our tests are investigated, where special attention is paid to the role of nuisance parameters and a potential resolution using the bootstrap. [source] Explosive Random-Coefficient AR(1) Processes and Related Asymptotics for Least-Squares EstimationJOURNAL OF TIME SERIES ANALYSIS, Issue 6 2005S. Y. Hwang Abstract., Large sample properties of the least-squares and weighted least-squares estimates of the autoregressive parameter of the explosive random-coefficient AR(1) process are discussed. It is shown that, contrary to the standard AR(1) case, the least-squares estimator is inconsistent whereas the weighted least-squares estimator is consistent and asymptotically normal even when the error process is not necessarily Gaussian. Conditional asymptotics on the event that a certain limiting random variable is non-zero is also discussed. [source] Seasonal Unit Root Tests Under Structural Breaks,JOURNAL OF TIME SERIES ANALYSIS, Issue 1 2004Uwe Hassler C12; C22 Abstract., In this paper, several seasonal unit root tests are analysed in the context of structural breaks at known time and a new break corrected test is suggested. We show that the widely used HEGY test, as well as an LM variant thereof, are asymptotically robust to seasonal mean shifts of finite magnitude. In finite samples, however, experiments reveal that such tests suffer from severe size distortions and power reductions when breaks are present. Hence, a new break corrected LM test is proposed to overcome this problem. Importantly, the correction for seasonal mean shifts bears no consequence on the limiting distributions, thereby maintaining the legitimacy of canonical critical values. Moreover, although this test assumes a breakpoint a priori, it is robust in terms of misspecification of the time of the break. This asymptotic property is well reproduced in finite samples. Based on a Monte-Carlo study, our new test is compared with other procedures suggested in the literature and shown to hold superior finite sample properties. [source] Estimation of the Dominating Frequency for Stationary and Nonstationary Fractional Autoregressive ModelsJOURNAL OF TIME SERIES ANALYSIS, Issue 5 2000Jan Beran This paper was motivated by the investigation of certain physiological series for premature infants. The question was whether the series exhibit periodic fluctuations with a certain dominating period. The observed series are nonstationary and/or have long-range dependence. The assumed model is a Gaussian process Xt whose mth difference Yt = (1 ,B)mXt is stationary with a spectral density f that may have a pole (or a zero) at the origin. the problem addressed in this paper is the estimation of the frequency ,max where f achieves the largest local maximum in the open interval (0, ,). The process Xt is assumed to belong to a class of parametric models, characterized by a parameter vector ,, defined in Beran (1995). An estimator of ,max is proposed and its asymptotic distribution is derived, with , being estimated by maximum likelihood. In particular, m and a fractional differencing parameter that models long memory are estimated from the data. Model choice is also incorporated. Thus, within the proposed framework, a data driven procedure is obtained that can be applied in situations where the primary interest is in estimating a dominating frequency. A simulation study illustrates the finite sample properties of the method. In particular, for short series, estimation of ,max is difficult, if the local maximum occurs close to the origin. The results are illustrated by two of the data examples that motivated this research. [source] Radio frequency magnetic field mapping of a 3 Tesla birdcage coil: Experimental and theoretical dependence on sample properties ,MAGNETIC RESONANCE IN MEDICINE, Issue 2 2001Marcello Alecci Abstract The RF B1 distribution was studied, theoretically and experimentally, in phantoms and in the head of volunteers using a 3 T MRI system equipped with a birdcage coil. Agreement between numerical simulation and experiment demonstrates that B1 distortion at high field can be explained with 3D full-Maxwell calculations. It was found that the B1 distribution in the transverse plane is strongly dependent on the dielectric properties of the sample. We show that this is a consequence of RF penetration effects combined with RF standing wave effects. In contrast, along the birdcage coil z-axis the B1 distribution is determined mainly by the coil geometry. In the transverse plane, the region of B1 uniformity (within 10% of the maximum) was 15 cm with oil, 6 cm with distilled water, 11 cm with saline, and 10 cm in the head. Along z the B1 uniformity was 9 cm with phantoms and 7 cm in the head. Magn Reson Med 46:379,385, 2001. © 2001 Wiley-Liss, Inc. [source] IS THERE UNIT ROOT IN THE NITROGEN OXIDES EMISSIONS: A MONTE CARLO INVESTIGATION?NATURAL RESOURCE MODELING, Issue 1 2010NINA S. JONES Abstract Use of the time-series econometric techniques to investigate issues about environmental regulation requires knowing whether air pollution emissions are trend stationary or difference stationary. It has been shown that results regarding trend stationarity of the pollution data are sensitive to the methods used. I conduct a Monte Carlo experiment to study the size and power of two unit root tests that allow for a structural change in the trend at a known time using the data-generating process calibrated to the actual pollution series. I find that finite sample properties of the Perron test are better than the Park and Sung Phillips-Perron (PP) type test. Severe size distortions in the Park and Sung PP type test can explain the rejection of a unit root in air pollution emissions reported in some environmental regulation analyses. [source] Optically and electrically induced dissipation in quantum Hall systemsPHYSICA STATUS SOLIDI (B) BASIC SOLID STATE PHYSICS, Issue 2 2008G. Nachtwei Abstract We have studied the onset of dissipation in quantum Hall systems (QHSs), patterned in various geometries (Hall bars, meanders and Corbino rings) from wafers with heterojunctions (GaAs/GaAlAs) and HgTe quantum wells with adjacent HgCdTe barriers. The QH samples were excited by electrical pulses with pulse durations tP of 0.5 ns , tP , 180 ns or by illumination with electromagnetic waves of 1.7 THz , f , 2.5 THz. These waves were either emitted coherently by a pulsed p-Ge laser system or by a thermal source. In the case of excitation by electric pulses, it is necessary to exceed a certain critical pulse length which is a function of various extrinsic parameters and sample properties. For no dissipation occurs inside the QHSs. Also, using THz illumination, the QHSs can be driven to dissipation. We found different mechanisms to be responsible for the photoresponse (PR) of the QHSs: non-resonant (bolometric) and resonant (cyclotron resonance) contributions to the PR of the QHSs. First attempts to develop a quantitative model for the observed data are made. We are able to describe a part of the observations by either a drift model or a two-level model. The quantitative agreement of these calculations with the measured data is, however, limited. This is due to the simplicity of the models applied so far and to the complex behaviour of QHSs when nonlinearly excited. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Small area estimation of poverty indicatorsTHE CANADIAN JOURNAL OF STATISTICS, Issue 3 2010Isabel Molina Abstract The authors propose to estimate nonlinear small area population parameters by using the empirical Bayes (best) method, based on a nested error model. They focus on poverty indicators as particular nonlinear parameters of interest, but the proposed methodology is applicable to general nonlinear parameters. They use a parametric bootstrap method to estimate the mean squared error of the empirical best estimators. They also study small sample properties of these estimators by model-based and design-based simulation studies. Results show large reductions in mean squared error relative to direct area-specific estimators and other estimators obtained by "simulated" censuses. The authors also apply the proposed method to estimate poverty incidences and poverty gaps in Spanish provinces by gender with mean squared errors estimated by the mentioned parametric bootstrap method. For the Spanish data, results show a significant reduction in coefficient of variation of the proposed empirical best estimators over direct estimators for practically all domains. The Canadian Journal of Statistics 38: 369,385; 2010 © 2010 Statistical Society of Canada Les auteurs proposent d'estimer les paramètres non linéaires d'une population de petits domaines en utilisant une méthode bayésienne empirique. L'emphase est mise sur les indicateurs de pauvreté comme paramètres non linéaires d'intérêt particuliers, mais ils proposent une méthodologie qui s'applique à des paramètres non linéaires plus généraux. Ils utilisent une méthode de rééchantillonnage paramétrique pour estimer l'erreur quadratique moyenne du meilleur estimateur empirique. À l'aide de simulations basées sur le modèle et sur le plan de sondage, ils étudient les propriétés de ces estimateurs pour les petits échantillons. Les résultats obtenus montrent une grande réduction de l'erreur quadratique moyenne par rapport aux estimateurs propres aux régions et les autres estimateurs obtenus par recensements « simulés». Les auteurs ont aussi appliqué la méthodologie proposée à l'estimation des incidences de pauvreté et des disparités, en fonction du sexe, du niveau de la pauvreté des provinces espagnoles. Les erreurs quadratiques moyennes sont estimées en utilisant la méthode de rééchantillonnage paramétrique citée auparavant. Pour les données espagnoles, les résultats montrent une réduction substantielle du coefficient de variation des meilleurs estimateurs empiriques proposés par rapport aux estimateurs spécifiques pour pratiquement tous les domaines. La revue canadienne de statistique 38: 369,385; 2010 © 2010 Société statistique du Canada [source] Heterogeneity in dynamic discrete choice modelsTHE ECONOMETRICS JOURNAL, Issue 1 2010Martin Browning Summary, We consider dynamic discrete choice models with heterogeneity in both the levels parameter and the state dependence parameter. We first present an empirical analysis that motivates the theoretical analysis which follows. The theoretical analysis considers a simple two-state, first-order Markov chain model without covariates in which both transition probabilities are heterogeneous. Using such a model we are able to derive exact small sample results for bias and mean squared error (MSE). We discuss the maximum likelihood approach and derive two novel estimators. The first is a bias corrected version of the Maximum Likelihood Estimator (MLE) although the second, which we term MIMSE, minimizes the integrated mean square error. The MIMSE estimator is always well defined, has a closed-form expression and inherits the desirable large sample properties of the MLE. Our main finding is that in almost all short panel contexts the MIMSE significantly outperforms the other two estimators in terms of MSE. A final section extends the MIMSE estimator to allow for exogenous covariates. [source] Blockwise generalized empirical likelihood inference for non-linear dynamic moment conditions modelsTHE ECONOMETRICS JOURNAL, Issue 2 2009Francesco Bravo Summary, This paper shows how the blockwise generalized empirical likelihood method can be used to obtain valid asymptotic inference in non-linear dynamic moment conditions models for possibly non-stationary weakly dependent stochastic processes. The results of this paper can be used to construct test statistics for overidentifying moment restrictions, for additional moments, and for parametric restrictions expressed in mixed implicit and constraint form. Monte Carlo simulations seem to suggest that some of the proposed test statistics have competitive finite sample properties. [source] Least squares estimation and tests of breaks in mean and variance under misspecificationTHE ECONOMETRICS JOURNAL, Issue 1 2004Jean-Yves Pitarakis Summary In this paper we investigate the consequences of misspecification on the large sample properties of change-point estimators and the validity of tests of the null hypothesis of linearity versus the alternative of a structural break. Specifically this paper concentrates on the interaction of structural breaks in the mean and variance of a time series when either of the two is omitted from the estimation and inference procedures. Our analysis considers the case of a break in mean under omitted-regime-dependent heteroscedasticity and that of a break in variance under an omitted mean shift. The large and finite sample properties of the resulting least-squares-based estimators are investigated and the impact of the two types of misspecification on inferences about the presence or absence of a structural break subsequently analysed. [source] Alternative tilts for nonparametric option pricingTHE JOURNAL OF FUTURES MARKETS, Issue 10 2010M. Ryan Haley This study generalizes the nonparametric approach to option pricing of Stutzer, M. (1996) by demonstrating that the canonical valuation methodology introduced therein is one member of the Cressie,Read family of divergence measures. Alhough the limiting distribution of the alternative measures is identical to the canonical measure, the finite sample properties are quite different. We assess the ability of the alternative divergence measures to price European call options by approximating the risk-neutral, equivalent martingale measure from an empirical distribution of the underlying asset. A simulation study of the finite sample properties of the alternative measure changes reveals that the optimal divergence measure depends upon how accurately the empirical distribution of the underlying asset is estimated. In a simple Black,Scholes model, the optimal measure change is contingent upon the number of outliers observed, whereas the optimal measure change is a function of time to expiration in the stochastic volatility model of Heston, S. L. (1993). Our extension of Stutzer's technique preserves the clean analytic structure of imposing moment restrictions to price options, yet demonstrates that the nonparametric approach is even more general in pricing options than originally believed. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:983,1006, 2010 [source] The finite sample properties of the GARCH option pricing modelTHE JOURNAL OF FUTURES MARKETS, Issue 6 2007George Dotsis The authors explore the finite sample properties of the generalized autoregressive conditional heteroscedasticity (GARCH) option pricing model proposed by S. L. Heston and S. Nandi (2000). Simulation results show that the maximum likelihood estimators of the GARCH process may contain substantial estimation biases, even when samples as large as 3,000 observations are used. However, it was found that these biases cause significant mispricings only for short-term, out-of-the-money options. It is shown that, given an adequate estimation sample, this bias can be reduced considerably by employing the jackknife resampling method. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:599,615, 2007 [source] Dried blood spot sampling in combination with LC-MS/MS for quantitative analysis of small moleculesBIOMEDICAL CHROMATOGRAPHY, Issue 1 2010Wenkui Li Abstract The collection of whole blood samples on paper, known as dried blood spot (DBS), dates back to the early 1960s in newborn screening for inherited metabolic disorders. DBS offers a number of advantages over conventional blood collection. As a less invasive sampling method, DBS offers simpler sample collection and storage and easier transfer, with reduced infection risk of various pathogens, and requires a smaller blood volume. To date, DBS-LC-MS/MS has emerged as an important method for quantitative analysis of small molecules. Despite the increasing popularity of DBS-LC-MS/MS, the method has its limitations in assay sensitivity due to the small sample size. Sample quality is often a concern. Systematic assessment on the potential impact of various blood sample properties on accurate quantification of analyte of interest is necessary. Whereas most analytes may be stable on DBS, unstable compounds present another challenge for DBS as enzyme inhibitors cannot be conveniently mixed during sample collection. Improvements on the chemistry of DBS card are desirable. In addition to capturing many representative DBS-LS-MS/MS applications, this review highlights some important aspects of developing and validating a rugged DBS-LC-MS/MS method for quantitative analysis of small molecules along with DBS sample collection, processing and storage. Copyright © 2009 John Wiley & Sons, Ltd. [source] Estimation of the ROC Curve under Verification BiasBIOMETRICAL JOURNAL, Issue 3 2009Ronen Fluss Abstract The ROC (receiver operating characteristic) curve is the most commonly used statistical tool for describing the discriminatory accuracy of a diagnostic test. Classical estimation of the ROC curve relies on data from a simple random sample from the target population. In practice, estimation is often complicated due to not all subjects undergoing a definitive assessment of disease status (verification). Estimation of the ROC curve based on data only from subjects with verified disease status may be badly biased. In this work we investigate the properties of the doubly robust (DR) method for estimating the ROC curve under verification bias originally developed by Rotnitzky, Faraggi and Schisterman (2006) for estimating the area under the ROC curve. The DR method can be applied for continuous scaled tests and allows for a non-ignorable process of selection to verification. We develop the estimator's asymptotic distribution and examine its finite sample properties via a simulation study. We exemplify the DR procedure for estimation of ROC curves with data collected on patients undergoing electron beam computer tomography, a diagnostic test for calcification of the arteries. [source] Design and Inference for Cancer Biomarker Study with an Outcome and Auxiliary-Dependent SubsamplingBIOMETRICS, Issue 2 2010Xiaofei Wang Summary In cancer research, it is important to evaluate the performance of a biomarker (e.g., molecular, genetic, or imaging) that correlates patients' prognosis or predicts patients' response to treatment in a large prospective study. Due to overall budget constraint and high cost associated with bioassays, investigators often have to select a subset from all registered patients for biomarker assessment. To detect a potentially moderate association between the biomarker and the outcome, investigators need to decide how to select the subset of a fixed size such that the study efficiency can be enhanced. We show that, instead of drawing a simple random sample from the study cohort, greater efficiency can be achieved by allowing the selection probability to depend on the outcome and an auxiliary variable; we refer to such a sampling scheme as,outcome and auxiliary-dependent subsampling,(OADS). This article is motivated by the need to analyze data from a lung cancer biomarker study that adopts the OADS design to assess epidermal growth factor receptor (EGFR) mutations as a predictive biomarker for whether a subject responds to a greater extent to EGFR inhibitor drugs. We propose an estimated maximum-likelihood method that accommodates the OADS design and utilizes all observed information, especially those contained in the likelihood score of EGFR mutations (an auxiliary variable of EGFR mutations) that is available to all patients. We derive the asymptotic properties of the proposed estimator and evaluate its finite sample properties via simulation. We illustrate the proposed method with a data example. [source] A Generalized Estimator of the Attributable Benefit of an Optimal Treatment RegimeBIOMETRICS, Issue 2 2010Jason Brinkley Summary For many diseases where there are several treatment options often there is no consensus on the best treatment to give individual patients. In such cases, it may be necessary to define a strategy for treatment assignment; that is, an algorithm that dictates the treatment an individual should receive based on their measured characteristics. Such a strategy or algorithm is also referred to as a treatment regime. The optimal treatment regime is the strategy that would provide the most public health benefit by minimizing as many poor outcomes as possible. Using a measure that is a generalization of attributable risk (AR) and notions of potential outcomes, we derive an estimator for the proportion of events that could have been prevented had the optimal treatment regime been implemented. Traditional AR studies look at the added risk that can be attributed to exposure of some contaminant; here we will instead study the benefit that can be attributed to using the optimal treatment strategy. We will show how regression models can be used to estimate the optimal treatment strategy and the attributable benefit of that strategy. We also derive the large sample properties of this estimator. As a motivating example, we will apply our methods to an observational study of 3856 patients treated at the Duke University Medical Center with prior coronary artery bypass graft surgery and further heart-related problems requiring a catheterization. The patients may be treated with either medical therapy alone or a combination of medical therapy and percutaneous coronary intervention without a general consensus on which is the best treatment for individual patients. [source] Semiparametric Analysis for Recurrent Event Data with Time-Dependent Covariates and Informative CensoringBIOMETRICS, Issue 1 2010C.-Y. Huang Summary Recurrent event data analyses are usually conducted under the assumption that the censoring time is independent of the recurrent event process. In many applications the censoring time can be informative about the underlying recurrent event process, especially in situations where a correlated failure event could potentially terminate the observation of recurrent events. In this article, we consider a semiparametric model of recurrent event data that allows correlations between censoring times and recurrent event process via frailty. This flexible framework incorporates both time-dependent and time-independent covariates in the formulation, while leaving the distributions of frailty and censoring times unspecified. We propose a novel semiparametric inference procedure that depends on neither the frailty nor the censoring time distribution. Large sample properties of the regression parameter estimates and the estimated baseline cumulative intensity functions are studied. Numerical studies demonstrate that the proposed methodology performs well for realistic sample sizes. An analysis of hospitalization data for patients in an AIDS cohort study is presented to illustrate the proposed method. [source] Marginal Hazards Regression for Retrospective Studies within Cohort with Possibly Correlated Failure Time DataBIOMETRICS, Issue 2 2009Sangwook Kang Summary A retrospective dental study was conducted to evaluate the degree to which pulpal involvement affects tooth survival. Due to the clustering of teeth, the survival times within each subject could be correlated and thus the conventional method for the case,control studies cannot be directly applied. In this article, we propose a marginal model approach for this type of correlated case,control within cohort data. Weighted estimating equations are proposed for the estimation of the regression parameters. Different types of weights are also considered for improving the efficiency. Asymptotic properties of the proposed estimators are investigated and their finite sample properties are assessed via simulations studies. The proposed method is applied to the aforementioned dental study. [source] Multiple Imputation Methods for Treatment Noncompliance and Nonresponse in Randomized Clinical TrialsBIOMETRICS, Issue 1 2009L. Taylor Summary Randomized clinical trials are a powerful tool for investigating causal treatment effects, but in human trials there are oftentimes problems of noncompliance which standard analyses, such as the intention-to-treat or as-treated analysis, either ignore or incorporate in such a way that the resulting estimand is no longer a causal effect. One alternative to these analyses is the complier average causal effect (CACE) which estimates the average causal treatment effect among a subpopulation that would comply under any treatment assigned. We focus on the setting of a randomized clinical trial with crossover treatment noncompliance (e.g., control subjects could receive the intervention and intervention subjects could receive the control) and outcome nonresponse. In this article, we develop estimators for the CACE using multiple imputation methods, which have been successfully applied to a wide variety of missing data problems, but have not yet been applied to the potential outcomes setting of causal inference. Using simulated data we investigate the finite sample properties of these estimators as well as of competing procedures in a simple setting. Finally we illustrate our methods using a real randomized encouragement design study on the effectiveness of the influenza vaccine. [source] Cox Regression Methods for Two-Stage Randomization DesignsBIOMETRICS, Issue 2 2007Yuliya Lokhnygina Summary Two-stage randomization designs (TSRD) are becoming increasingly common in oncology and AIDS clinical trials as they make more efficient use of study participants to examine therapeutic regimens. In these designs patients are initially randomized to an induction treatment, followed by randomization to a maintenance treatment conditional on their induction response and consent to further study treatment. Broader acceptance of TSRDs in drug development may hinge on the ability to make appropriate intent-to-treat type inference within this design framework as to whether an experimental induction regimen is better than a standard induction regimen when maintenance treatment is fixed. Recently Lunceford, Davidian, and Tsiatis (2002, Biometrics58, 48,57) introduced an inverse probability weighting based analytical framework for estimating survival distributions and mean restricted survival times, as well as for comparing treatment policies at landmarks in the TSRD setting. In practice Cox regression is widely used and in this article we extend the analytical framework of Lunceford et al. (2002) to derive a consistent estimator for the log hazard in the Cox model and a robust score test to compare treatment policies. Large sample properties of these methods are derived, illustrated via a simulation study, and applied to a TSRD clinical trial. [source] | ||||||||||||||||||||||