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Pulmonary function is an important measure of disease severity and prognosis in cystic fibrosis (CF). It is generally expressed as a percentage of a predicted value, calculated using regression equations derived from a reference population. A number of reference equations are in widespread use. The purposes of this study were to determine: 1) the extent to which, for a given absolute FEV(1) value, percent of predicted (PPFEV(1)) values vary when derived by different reference equations; and 2) whether these differences affect conclusions of longitudinal and cross-sectional analyses. Subjects were all Caucasians 6-18 years old in the 1990 Cystic Fibrosis Foundation Registry. We found clinically important discrepancies in PPFEV(1) when calculated by the methods of Dockery et al. [Am Rev Respir Dis 1983;128:405-412] and Wang et al. [Pediatr Pulmonol 1993;15:75-78] as compared to Knudson et al. [Am Rev Respir Dis 1983;127:725-734] or Polgar and Promadhat [Pulmonary Function Testing in Children 1971; Philadelphia: W.B. Saunders]. In longitudinal analyses, the choice of reference equation resulted in varying apparent rates of decline in FEV(1). For example, among subjects ages 12-14 years in 1990, the decline in PPFEV(1) from 1990-1995 varied between 2-11%, depending on the choice of reference equation. In cross-sectional analyses, the choice of reference equation affected the distribution of subjects classified as having mild, moderate, or severe lung disease. CF physicians should be aware of the impact of choice of reference equation in both clinical care and research.

Ordinal categorical assessments are common in medical practice and in research. Variability in such measurements amongst raters making the assessments can be problematic. In this paper we consider how such variability can be described statistically. We review three current approaches, including kappa-type statistics, loglinear models for agreement, and latent class agreement models, and discuss their limitations. We present a new graphical approach to describing interrater variability that involves a simple frequency distribution display of the category probabilities. The method enables description of interrater variability when raters are a random sample from some population as opposed to the traditional setting in which only a few selected raters provide assessments. Advantages of this approach relative to current approaches include the following: (1) it provides a simple visual summary of the rating data, (2) description is closely linked to familiar methods for describing variability in continuous measurements, (3) interpretation is straightforward, and (4) a large sample of raters can be accommodated with ease. We illustrate the method on simulated ordinal data representing radiologists' ratings of mammography images and on rating data from a national image reading study of mammography screening.

Summary Estimating equations which are not necessarily likelihood-based score equations are becoming increasingly popular for estimating regression model parameters. This paper is concerned with estimation based on general estimating equations when true covariate data are missing for all the study subjects, but surrogate or mismeasured covariates are available instead. The method is motivated by the covariate measurement error problem in marginal or partly conditional regression of longitudinal data. We propose to base estimation on the expectation of the complete data estimating equation conditioned on available data. The regression parameters and other nuisance parameters are estimated simultaneously by solving the resulting estimating equations. The expected estimating equation (EEE) estimator is equal to the maximum likelihood estimator if the complete data scores are likelihood scores and conditioning is with respect to all the available data. A pseudo-EEE estimator, which requires less computation, is also investigated. Asymptotic distribution theory is derived. Small sample simulations are conducted when the error process is an order 1 autoregressive model. Regression calibration is extended to this setting and compared with the EEE approach. We demonstrate the methods on data from a longitudinal study of the relationship between childhood growth and adult obesity.

The accuracy of a medical diagnostic test is often summarized in a receiver operating characteristic (ROC) curve. This paper puts forth an interpretation for each point on the ROC curve as being a conditional probability of a test result from a random diseased subject exceeding that from a random nondiseased subject. This interpretation gives rise to new methods for making inference about ROC curves. It is shown that inference can be achieved with binary regression techniques applied to indicator variables constructed from pairs of test results, one component of the pair being from a diseased subject and the other from a nondiseased subject. Within the generalized linear model (GLM) binary regression framework, ROC curves can be estimated, and we highlight a new semiparametric estimator. Covariate effects can also be evaluated with the GLM models. The methodology is applied to a pancreatic cancer dataset where we use the regression framework to compare two different serum biomarkers. Asymptotic distribution theory is developed to facilitate inference and to provide insight into factors influencing variability of estimated model parameters.

Positive and negative predictive values of a diagnostic test are key clinically relevant measures of test accuracy. Surprisingly, statistical methods for comparing tests with regard to these parameters have not been available for the most common study design in which each test is applied to each study individual. In this paper, we propose a statistic for comparing the predictive values of two diagnostic tests using this paired study design. The proposed statistic is a score statistic derived from a marginal regression model and bears some relation to McNemar's statistic. As McNemar's statistic can be used to compare sensitivities and specificities of diagnostic tests, parameters that condition on disease status, our statistic can be considered as an analog of McNemar's test for the problem of comparing predictive values, parameters that condition on test outcome. We report on the results of a simulation study designed to examine the properties of this test under a variety of conditions. The method is illustrated with data from a study of methods for diagnosis of coronary artery disease.

(2000). Receiver Operating Characteristic Methodology. Journal of the American Statistical Association: Vol. 95, No. 449, pp. 308-311.

SUMMARY The appropriate interpretation of measurements often requires standardization for concomitant factors. For example, standardization of weight for both height and age is important in obesity research and in failure-to-thrive research in children. Regression quantiles from a reference population afford one intuitive and popular approach to standardization. Current methods for the estimation of regression quantiles can be classified as nonparametric with respect to distributional assumptions or as fully parametric. We propose a semiparametric method where we model the mean and variance as flexible regression spline functions and allow the unspecified distribution to vary smoothly as a function of covariates. Similarly to Cole and Green, our approach provides separate estimates and summaries for location, scale and distribution. However, similarly to Koenker and Bassett, we do not assume any parametric form for the distribution. Estimation for either cross-sectional or longitudinal samples is obtained by using estimating equations for the location and scale functions and through local kernel smoothing of the empirical distribution function for standardized residuals. Using this technique with data on weight, height and age for females under 3 years of age, we find that there is a close relationship between quantiles of weight for height and age and quantiles of body mass index (BMI=weight/height2) for age in this cohort.

Often the accuracy of a new diagnostic test must be assessed when a perfect gold standard does not exist. Use of an imperfect reference test biases accuracy estimates of the new test. This paper reviews existing approaches to this problem including discrepant resolution and latent class analysis. Deficiencies with these approaches are identified. A new approach is proposed that combines the results of several imperfect reference tests to define a better reference standard. We call this the composite reference standard (CRS). Using the CRS, accuracy can be assessed using multi-stage sampling designs. Maximum likelihood estimates of accuracy and expressions for the variance of sensitivity and specificity are provided. Data from clinical literature on the detection of Chlamydia trachomatis are used to illustrate and compare the different approaches. Advantages of the CRS relative to other approaches include that the CRS is explicitly defined, does not depend on the results of the new test under investigation, and is easy to interpret.

Early diagnosis of disease has potential to reduce morbidity and mortality. Biomarkers may be useful for detecting disease at early stages before it becomes clinically ap parent. Prostate-specific antigen (PSA) is one such marker for prostate cancer. This article is concerned with modeling receiver operating characteristic (ROC) curves as sociated with biomarkers at various times prior to the time at which the disease is detected clinically, by two methods. The first models the biomarkers statistically using mixed-effects regression models, and uses parameter estimates from these models to estimate the time-specific ROC curves. The second directly models the ROC curves as a function of time prior to diagnosis and may be implemented using software pack ages with binary regression or generalized linear model routines. The approaches are applied to data from 71 prostate cancer cases and 71 controls who participated in a lung cancer prevention trial. Two biomarkers for prostate cancer were considered: total serum PSA and the ratio of free to total PSA. Not surprisingly, both markers performed better as the interval between PSA measurement and clinical diagnosis decreased. Although the two markers performed similarly eight years prior to diagnosis, it appears that total PSA performed better than the ratio measure at times closer to diagnosis. The area under the ROC curve was consistently greater for total PSA than for the ratio four and two years prior to diagnosis and at the time of diagnosis. Key words: time- dependent ROC curves; biomarkers; diagnosis; prostate-specific antigen. (Med Decis Making 1999; 19:242-251)

Studies examining the association between an outcome variable and multiple predictors are common in medical research. Examples include epidemiologic studies of risk factors for disease and clinical studies of prognostic indicators for diseased subjects. This paper is concerned with the assessment of the associations between the outcome and each predictor separately, the so-called univariate associations. Comparisons between predictors in regards to the strengths of their association with the outcome are considered. We show that though such comparisons cannot be made with standard techniques, they can be made using an algorithm which performs all of the univariate analyses simultaneously. This is accomplished with a non-standard application of generalized estimating equation methods. Comparisons of univariate associations are shown to be the key analyses of interest in a retrospective longitudinal study of childhood predictors of adult obesity. We illustrate the methodology on data from this study.

Despite the heightened interest in developing biomarkers predicting treatment response that are used to optimize patient treatment decisions, there has been relatively little development of statistical methodology to evaluate these markers. There is currently no unified statistical framework for marker evaluation. This paper proposes a suite of descriptive and inferential methods designed to evaluate individual markers and to compare candidate markers. An R software package has been developed which implements these methods. Their utility is illustrated in the breast cancer treatment context, where candidate markers are evaluated for their ability to identify a subset of women who do not benefit from adjuvant chemotherapy and can therefore avoid its toxicity.

We propose a general modeling approach to longitudinal data that is a hybrid of the marginal regression models of Zeger and Liang and of the classical transition models such as used in time series analyses. Rather than conditioning at time t only on covariate values, as is typical with the marginal approach, or on the entire history of the process up to t, as is typical with the transition model approach, we suggest models that condition on a subset of the process history. Estimation proceeds using generalized estimating equation methodology but with the restriction that the working covariance matrix is diagonal. The proposed regression models share common features with Cox regression models for failure time data in that they are composed of a nuisance baseline function of time and a simple parametric function of the covariates. Two illustrative examples are presented.

SUMMARY Receiver operating characteristic curves (ROC's) are used to evaluate diagnostic tests when test results are not binary. They describe the inherent capacity of the test for distinguishing between truly diseased and nondiseased subjects. Although methodology for estimating and for comparing ROC'S is well developed, to date no general framework exists for evaluating covariate effects on ROC'S. We formulate a general regression model which allows the effects of covariates on test accuracy to be succinctly summarised. Such covariates might include, for example, characteristics of the patient or test environment, test type or severity of disease. The regression models are shown to arise naturally from some classic models for continuous or ordinal test data. Regression parameters are fitted using an estimating equation approach. The method is illustrated on data from a study of multiformat photographic images used for scintigraphy.

Technological advances continue to develop for early detection of disease. Research studies are required to define the statistical properties of such screening or diagnostic tests. However, statistical methodology currently used to evaluate diagnostic tests is limited. We propose the use of marginal regression models with robust sandwich variance estimators to make inference about the sensitivity and specificity of diagnostic tests. This method is more flexible than standard methods in that it allows comparison of sensitivity between two or more tests even if all tests are not carried out on all subjects, it can accommodate correlated data, and the effect of covariates can be evaluated. This last feature is important since it allows researchers to understand the effects on sensitivity and specificity of various environmental and patient characteristics. If such factors are under the control of the clinician, it provides the opportunity to modify the diagnostic testing program to maximize sensitivity and/or specificity. We show that the marginal regression modelling methods generalize standard statistical methods. In particular, when we compare two screening tests and we test each subject with both screens, the method corresponds to McNemar's test. We describe data from an ongoing audiology screening study and we analyse a simulated version of the data to illustrate the methodology. We also analyse data from a longitudinal study of PCR as a diagnostic test for cytomegalovirus.

Hot-deck imputation is an intuitively simple and popular method of accommodating incomplete data. Users of the method will often use the usual multiple imputation variance estimator which is not appropriate in this case. However, no variance expression has yet been derived for this easily implemented method applied to missing covariates in regression models. The simple hot-deck method is in fact asymptotically equivalent to the mean-score method for the estimation of a regression model parameter, so that hot-deck can be understood in the context of likelihood methods. Both of these methods accommodate data where missingness may depend on the observed variables but not on the unobserved value of the incomplete covariate, that is, missing at random (MAR). The asymptotic properties of hot-deck are derived here for the case where the fully observed variables are categorical, though the incomplete covariate(s) may be continuous. Simulation studies indicate that the two methods compare well in small samples and for small numbers of imputations. Current users of hot-deck may now conduct their analysis using mean-score, which is a weighted likelihood method and can thus be implemented by a single pass through the data using any standard package which accommodates weighted regression models. Valid inference is now straightforward using the variance expression provided here. The equivalence of mean-score and hot-deck is illustrated using three clinical data sets where an important covariate is missing for a large number of study subjects. © 1997 by John Wiley & Sons, Ltd.

Two different approaches to analysis of data from diagnostic biomarker studies are commonly employed. Logistic regression is used to fit models for probability of disease given marker values, while ROC curves and risk distributions are used to evaluate classification performance. In this paper we present a method that simultaneously accomplishes both tasks. The key step is to standardize markers relative to the nondiseased population before including them in the logistic regression model. Among the advantages of this method are the following: (i) ensuring that results from regression and performance assessments are consistent with each other; (ii) allowing covariate adjustment and covariate effects on ROC curves to be handled in a familiar way, and (iii) providing a mechanism to incorporate important assumptions about structure in the ROC curve into the fitted risk model. We develop the method in detail for the problem of combining biomarker data sets derived from multiple studies, populations or biomarker measurement platforms, when ROC curves are similar across data sources. The methods are applicable to both cohort and case-control sampling designs. The data set motivating this application concerns Prostate Cancer Antigen 3 (PCA3) for diagnosis of prostate cancer in patients with or without previous negative biopsy where the ROC curves for PCA3 are found to be the same in the two populations. The estimated constrained maximum likelihood and empirical likelihood estimators are derived. The estimators are compared in simulation studies and the methods are illustrated with the PCA3 data set., Published in at http://dx.doi.org/10.1214/13-AOAS634 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Two different approaches to analysis of data from diagnostic biomarker studies are commonly employed. Logistic regression is used to fit models for probability of disease given marker values while ROC curves and risk distributions are used to evaluate classification performance. In this paper we present a method that simultaneously accomplishes both tasks. The key step is to standardize markers relative to the non-diseased population before including them in the logistic regression model. Among the advantages of this method are: (i) ensuring that results from regression and performance assessments are consistent with each other; (ii) allowing covariate adjustment and covariate effects on ROC curves to be handled in a familiar way, and (iii) providing a mechanism to incorporate important assumptions about structure in the ROC curve into the fitted risk model. We develop the method in detail for the problem of combining biomarker datasets derived from multiple studies, populations or biomarker measurement platforms, when ROC curves are similar across data sources. The methods are applicable to both cohort and case-control sampling designs. The dataset motivating this application concerns Prostate Cancer Antigen 3 (PCA3) for diagnosis of prostate cancer in patients with or without previous negative biopsy where the ROC curves for PCA3 are found to be the same in the two populations. Estimated constrained maximum likelihood and empirical likelihood estimators are derived. The estimators are compared in simulation studies and the methods are illustrated with the PCA3 dataset.

There is growing interest in markers that can be used to identify which patients are most likely to benefit from a treatment. For example, the Gail breast cancer risk prediction model may be useful for identifying a subset of older women for whom the benefit of tamoxifen for breast cancer prevention is likely to outweigh the harm. Two general classes of approaches to evaluating treatment selection markers have been developed. The first uses data on a cohort of untreated subjects to develop a risk prediction model, such as the Gail model, which is used to identify a high-risk subset of subjects. This model is paired with a measure of treatment effect to assess the impact of identifying and treating the high-risk subset. The second approach uses data from a randomized trial to model the treatment effect on a composite outcome that includes all effects of treatment (positive and negative). The treatment effect model is used to identify a subset of subjects with positive treatment effects and to assess the impact of identifying and treating this subset. We describe a framework that includes both existing approaches as special cases. In doing so, we review the existing approaches, clarify their underlying assumptions, and facilitate the evaluation of markers under less restrictive assumptions.

This chapter covers material presented in a short course at the 2011 International Conference on Risk Assessment and Evaluation of Predictions. Methods for evaluating the performance of markers to predict risk of a current or future clinical outcome are reviewed. Specifically, we discuss criteria for evaluating a risk model including: calibration, accurate classification and benefit for decision making using the model. Measures for making comparisons between models are described. The role of risk reclassification techniques is discussed. We present a detailed example.

SUMMARY We consider the problem of missing covariate data in the context of censored failure time relative risk regression. Auxiliary covariate data, which are considered informative about the missing data but which are not explicitly part of the relative risk regression model, may be available. Full covariate information is available for a validation set. An estimated partial likelihood method is proposed for estimating relative risk parameters. This method is an extension of the estimated likelihood regression analysis method for uncensored data (Pepe, 1992; Pepe & Fleming, 1991). A key feature of the method is that it is nonparametric with respect to the association between the missing and observed, including auxiliary, covariate components. Asymptotic distribution theory is derived for the proposed estimated partial likelihood estimator in the case where the auxiliary or mismeasured covariates are categorical. Asymptotic efficiencies are calculated for exponential failure times using an exponential relative risk model. The estimated partial likelihood estimator compares favourably with a fully parametric maximum likelihood analysis. Comparisons are also made with a standard partial likelihood analysis which ignores the incomplete observations. Important efficiency gains can be made with the estimated partial likelihood method. Small sample properties are investigated through simulation studies.

The effect of physician diagnostic variability on accuracy at a population level depends on the prevalence of diagnoses.To estimate how diagnostic variability affects accuracy from the perspective of a U.S. woman aged 50 to 59 years having a breast biopsy.Applied probability using Bayes' theorem.B-Path (Breast Pathology) Study comparing pathologists' interpretations of a single biopsy slide versus a reference consensus interpretation from 3 experts.115 practicing pathologists (6900 total interpretations from 240 distinct cases).A single representative slide from each of the 240 cases was used to estimate the proportion of biopsies with a diagnosis that would be verified if the same slide were interpreted by a reference group of 3 expert pathologists. Probabilities of confirmation (predictive values) were estimated using B-Path Study results and prevalence of biopsy diagnoses for women aged 50 to 59 years in the Breast Cancer Surveillance Consortium.Overall, if 1 representative slide were used per case, 92.3% (95% CI, 91.4% to 93.1%) of breast biopsy diagnoses would be verified by reference consensus diagnoses, with 4.6% (CI, 3.9% to 5.3%) overinterpreted and 3.2% (CI, 2.7% to 3.6%) underinterpreted. Verification of invasive breast cancer and benign without atypia diagnoses is highly probable; estimated predictive values were 97.7% (CI, 96.5% to 98.7%) and 97.1% (CI, 96.7% to 97.4%), respectively. Verification is less probable for atypia (53.6% overinterpreted and 8.6% underinterpreted) and ductal carcinoma in situ (DCIS) (18.5% overinterpreted and 11.8% underinterpreted).Estimates are based on a testing situation with 1 slide used per case and without access to second opinions. Population-adjusted estimates may differ for women from other age groups, unscreened women, or women in different practice settings.This analysis, based on interpretation of a single breast biopsy slide per case, predicts a low likelihood that a diagnosis of atypia or DCIS would be verified by a reference consensus diagnosis. This diagnostic grey zone should be considered in clinical management decisions in patients with these diagnoses.National Cancer Institute.

SUMMARY We consider regression analysis when incomplete or auxiliary covariate data are available for all study subjects and, in addition, for a subset called the validation sample, true covariate data of interest have been ascertained. The term auxiliary data refers to data not in the regression model, but thought to be informative about the true missing covariate data of interest. We discuss a method which is nonparametric with respect to the association between available and missing data, allows missingness to depend on available response and covariate values, and is applicable to both cohort and case-control study designs. The method previously proposed by Flanders & Greenland (1991) and by Zhao & Lipsitz (1992) is generalised and asymptotic theory is derived. Our expression for the asymptotic variance of the estimator provides intuition regarding performance of the method. Optimal sampling strategies for the validation set are also suggested by the asymptotic results.

Optimal triage of patients at risk for critical illness requires accurate risk prediction, yet few data on the performance criteria required of a potential biomarker to be clinically useful exists.We studied an adult cohort of nonarrest, nontrauma emergency medical services encounters transported to a hospital from 2002 to 2006. We simulated hypothetical biomarkers increasingly associated with critical illness during hospitalization and determined the biomarker strength and sample size necessary to improve risk classification beyond a best clinical model.Of 57,647 encounters, 3121 (5.4%) were hospitalized with critical illness and 54,526 (94.6%) without critical illness. The addition of a moderate-strength biomarker (odds ratio, 3.0, for critical illness) to a clinical model improved discrimination (c statistic, 0.85 vs 0.8; P.01) and reclassification (net reclassification improvement, 0.15; 95% confidence interval, 0.13-0.18) and increased the proportion of cases in the highest-risk category by +8.6% (95% confidence interval, 7.5%-10.8%). Introducing correlation between the biomarker and physiological variables in the clinical risk score did not modify the results. Statistically significant changes in net reclassification required a sample size of at least 1000 subjects.Clinical models for triage of critical illness could be significantly improved by incorporating biomarkers, yet substantial sample sizes and biomarker strength may be required.

In this issue of the Journal, Pencina and et al. (Am J Epidemiol. 2012;176(6):492-494) examine the operating characteristics of measures of incremental value. Their goal is to provide benchmarks for the measures that can help identify the most promising markers among multiple candidates. They consider a setting in which new predictors are conditionally independent of established predictors. In the present article, the authors consider more general settings. Their results indicate that some of the conclusions made by Pencina et al. are limited to the specific scenarios the authors considered. For example, Pencina et al. observed that continuous net reclassification improvement was invariant to the strength of the baseline model, but the authors of the present study show this invariance does not hold generally. Further, they disagree with the suggestion that such invariance would be desirable for a measure of incremental value. They also do not see evidence to support the claim that the measures provide complementary information. In addition, they show that correlation with baseline predictors can lead to much bigger gains in performance than the conditional independence scenario studied by Pencina et al. Finally, the authors note that the motivation of providing benchmarks actually reinforces previous observations that the problem with these measures is they do not have useful clinical interpretations. If they did, researchers could use the measures directly and benchmarks would not be needed.

BACKGROUND Selecting controls that match cases on risk factors for the outcome is a pervasive practice in biomarker research studies. Such matching, however, biases estimates of biomarker prediction performance. The magnitudes of these biases are unknown. METHODS We examined the prediction performance of biomarkers and improvements in prediction gained by adding biomarkers to risk factor information. Data simulated from bivariate normal statistical models and data from a study to identify critically ill patients were used. We compared true performance with that estimated from case control studies that do or do not use matching. ROC curves were used to quantify performance. We propose a new statistical method to estimate prediction performance from matched studies for which data on the matching factors are available for subjects in the population. RESULTS Performance estimated with standard analyses can be grossly biased by matching, especially when biomarkers are highly correlated with matching risk factors. In our studies, the performance of the biomarker alone was underestimated whereas the improvement in performance gained by adding the marker to risk factors was overestimated by 2–10-fold. We found examples for which the relative ranking of 2 biomarkers for prediction was inappropriately reversed by use of a matched design. The new approach to estimation corrected for bias in matched studies. CONCLUSIONS To properly gauge prediction performance in the population or the improvement gained by adding a biomarker to known risk factors, matched case control studies must be supplemented with risk factor information from the population and must be analyzed with nonstandard statistical methods.

Many biomarkers identified in marker discovery are shown to have inadequate performance in validation studies. This motivates the use of group sequential designs that allow early termination for futility. However, an option for early termination will lead to biased estimates for studies that reach full enrollment. We propose conditional estimators and confidence intervals that correct for this bias assuming that an unadjusted estimator exists that has an independent increments covariance structure. The proposed estimators and confidence intervals are applied to conditional estimation of the receiver operating characteristic (ROC) curve and the positive predictive value (PPV) curve after a two-stage study that allows early termination for futility and their performance is evaluated by simulation.

Volcano deformation monitoring is crucial to understand how magma emplaces, propagates and erupts. Therefore, volcano deformation research projects are particularly important opportunities to improve our understanding of volcano dynamics. SAR4Volcanoes is a 2-year research project funded by the Italian Space Agency (ASI) within the framework of a cooperation agreement with the Japan Aerospace Exploration Agency (JAXA). It focuses on volcano deformation analysis through Differential SAR Interferometry (DInSAR) techniques by means of COSMOSkyMed and ALOS data, through the joint use of L-band and X-band SAR data. It also aims to the identification of methods and techniques to support decision making in emergency cases. Main target volcanoes in the projects are Etna, Vesuvio, Campi Flegrei and Stromboli (Italy) and Sakurajima and Kirishima (Japan). Secondary target volcanoes include recently or currently erupting volcanoes, as El Hierro (Spain), Nabro (Ethiopia) and Galapagos volcanoes (Ecuador). Since the project kickoff (July 2011) a large number of COSMO-SkyMed data has been acquired at these volcanoes; in some cases, the acquisitions are available almost at every satellite orbit, with an average interval down to 4 days. On these premises, the project represents an important opportunity to: (1) collect a significant amount of X-band data on active and erupting volcanoes and (2) study surface deformation to understand magma dynamics in different volcanic settings. We will present preliminary results on the ground deformation analysis of the main and secondary target volcanoes. In particular, target volcanoes without a pre-project archive are analyzed using single deformation maps, while those with archives are analysed through a time series approach, based on the SBAS technique.

18-years (September 1994 - October 2011) gravity and ground deformation sequences, recorded at Etna volcano along an East-West trending profile of 19 stations on the southern flank at a quasi-monthly sampling rate, are presented. Over the last two decades, frequent fountaining events and several flank eruptions occurred at Mt Etna. We use the SBAS DInSAR technique to analyze the temporal evolution of surface displacements by inverting a sequence of interferograms to form a deformation time series. Height changes, evaluated by DInSAR data during the entire period, show modest vertical variations unable to produce significant gravity changes. However, the gravity data set was corrected for the small height variations using the experimental free-air gravity gradients measured at two stations of the profile. The residual space-time gravity image displayed some gravity increase/decrease cycles, mostly affecting the central and eastern stations of the East-West profile.We attributed these gravity cycles to mass redistribution processes mainly located at a depth of 2-4 km bsl in a region recognized to be a preferential pathway of magma rising and an intermediate zone of magma storage/withdrawal. In the latter period 2008-2011, when several paroxysmal events occurred from the South East crater, the gravity and height deformation patterns show many similarities with the previous period 1995-2000 encompassing a long series of paroxysmal episodes that preceded the violent and dramatic explosive/effusive eruptions of 2001 and 2002-2003.

In medical research outcomes of interest, Y , are often difficult to ascertain on a sufficiently large number of study subjects. Cost is frequently an issue for example. A more feasible approach might to be ascertain an easily measured but less accurate surrogate outcome variable, A , and to supplement the study with a validation sample of observations for whom both Y and A have been measured. In the context of a regression model P β ( Y∣X ) with X a covariate vector, we propose a method called mean score to make inference about β using such data. This method does not require specification of the association between Y and A and is semiparametric in this sense. More-over, in contrast to previous work by Espeland and Odoroff ( J. Amer. Statist. Assoc. 80 (1985), 663–670), and Buonaceorsi ( J. Amer. Statist. Assoc. 85 (1990), 1075–1082), sampling of the true outcome can depend on both covariate and auxiliary data. Two illustrations in real medical contexts demonstrate that auxiliary data can substantially improve efficiency over standard statistical designs. Designs which incorporate auxiliary data may become increasingly useful as budgetary restrictions and health care management play a larger role in medical research. p]A third illustration demonstrates that the mean score method can be useful in the classical setting when observational datasets contain missing outcome data. Data need not be missing at random in the usual sense (Rubin ( Multiple Imputation for Non-Response in Surveys (1987) Wiley, New York). Indeed the mean score method can adjust for biases induced by violation of the missing at random assumption in certain settings. We contend that the mean score method will be particularly useful in observational studies where it is possible, although perhaps inconvenient, to retrieve missing data.

One hundred forty-seven patients with hematologic diseases and treated by allogeneic marrow transplants received graft-versus-host disease (GVHD) prevention with methotrexate and cyclosporine. In addition, 73 of the 147 patients were randomized to receive methylprednisolone during the first 35 days after transplant to improve GVHD prevention, whereas 74 patients were randomized not to receive methylprednisolone. The randomized trial enabled us to examine whether methylprednisolone increased the risk of infection after marrow grafting. Charts of study patients were analyzed retrospectively for infection events including bacteremia, septicemia, and fungemia. The randomization was stratified by diagnosis, patient age, genotypic HLA identity, and assignment to laminar airflow room isolation. All patients were given a short course of methotrexate (no longer than 11 days) and cyclosporine for no longer than 180 days after marrow transplantation. Methylprednisolone was begun on the day of marrow grafting at a dose of 1 mg/kg body weight intravenously in divided AM and PM doses through day 22. Methylprednisolone was administered at a dose of 0.5 mg/kg in divided doses from days 22 through 35, and then discontinued. Infections were analyzed for the time interval ending on day 65 after transplantation, which included the period of methylprednisolone administration and 1 month thereafter. Seventy-one episodes of first infection events were observed in patients receiving methylprednisolone compared with 47 episodes in patients not receiving the drug. Predominant infections were bacteremias, followed in descending order by fungemias and septicemias. The most prevalent organisms cultured were gram-positive bacteria, especially coagulase-negative Staphylococcus and Streptococcus species. Pseudomonas species were the most common gram negative bacteria, and the most prevalent fungus was Candida albicans. Multivariable Cox regression analysis showed that patients receiving methylprednisolone had a 1.5 times higher risk of infection (P = .03), with acute GVHD being another independent risk factor for infections (P = .005). Methylprednisolone, when added to GVHD prevention by methotrexate and cyclosporine, increases the risk of infection during the early posttransplantation period.

Surrogate endpoints have been defined by Prentice as response variables that can substitute for a 'true' endpoint for the purpose of comparing specific interventions or treatments in a clinical trial. The applicability of this definition, and of related surrogate endpoint criteria, is discussed, with emphasis on cancer and AIDS research settings. Auxiliary endpoints are defined as response variables, or covariates, that can strengthen true endpoint analyses. Specifically, such response variables provide some additional information on true endpoint occurrence times for study subjects having censored values for such times. Auxiliary variables will very frequently be available, and they may be able to be used without making additional strong assumptions. Approaches to the use of auxiliary variables using ideas based on augmented score and augmented likelihood methods are described.

Inference for cross-sectional models using longitudinal data, can be accomplished with generalized estimating equations (Zeger and Liang, 1992). We show that either a diagonal working covariance matrix should be used or a key assumption should be verified. The assumption is non-trivial when covariates vary over time. The validity of this assumption is explored for some broad classes of correlation structures. Similar considerations are shown to be relevant for the more general problem of correlated response data and marginal regression analysis with individual level covariates.

An interesting and potentially useful approach to biomarker identification was recently reported by Reddy et al. in the journal Cell (1). Using a combinatorial library of synthetic “shape” molecules, they screened for ligands that bind antibodies in the serum of individuals with the target condition but that do not bind antibodies in the serum of individuals without the target condition. When this approach was investigated with 2 mice immunized with myelin oligodendrocyte glycoprotein (MOG)3 (which then developed a syndrome resembling human multiple sclerosis) and 2 mice not immunized with MOG, the investigators identified 3 peptoids that subsequently discriminated perfectly between new sets of mice—7 immunized with MOG and 7 not immunized with MOG. This result was an elegant and convincing proof-of-principle experiment. The authors then used the same protocol to determine if the approach “is capable of identifying potentially useful diagnostic antibody–peptoid pairs for a human disease state.” They compared the antibody responses to 15 000 peptoids of 6 patients with Alzheimer disease (AD) to the antibody responses of 6 age-matched nondemented control individuals and 6 patients with Parkinson disease. The authors report sensitivities ≥93.7%, specificities ≥93.7%, and areas under the ROC curve of 0.99 for the 3 most discriminatory peptoids when those peptoids were tested with serum samples from 16 different patients with AD and 16 nondemented control individuals. The purpose of this Perspective is to highlight the strengths of this study and, by contrast, the weaknesses of the study design used to develop and evaluate the biomarkers for AD. To the authors' credit, some of these weaknesses were acknowledged in their Cell report. Weak study designs are pervasive in the field of biomarker identification, however, and this flaw may be partly responsible for the slow pace of real progress in the development of clinically useful biomarkers. A …

SummaryTo assess the value of a continuous marker in predicting the risk of a disease, a graphical tool called the predictiveness curve has been proposed. It characterizes the marker’s predictiveness, or capacity to stratify risk for the population, by displaying the distribution of risk endowed by the marker. Methods for making inference about the curve and for comparing curves in a general population have been developed. However, knowledge about a marker’s performance in the general population only is not enough. Since a marker’s effect on the risk model and its distribution can both differ across subpopulations, its predictiveness may vary when applied to different subpopulations. Moreover, information about the predictiveness of a marker conditional on baseline covariates is valuable for individual decision-making about having the marker measured or not. Therefore, to realize the usefulness of a risk prediction marker fully, it is important to study its performance conditional on covariates. We propose semiparametric methods for estimating covariate-specific predictiveness curves for a continuous marker. Unmatched and matched case–control study designs are accommodated. We illustrate application of the methodology by evaluating serum creatinine as a predictor of risk of renal artery stenosis.

For comparing the performance of a baseline risk prediction model with one that includes an additional predictor, a risk reclassification analysis strategy has been proposed. The first step is to cross-classify risks calculated according to the 2 models for all study subjects. Summary measures including the percentage of reclassification and the percentage of correct reclassification are calculated, along with 2 reclassification calibration statistics. The author shows that interpretations of the proposed summary measures and P values are problematic. The author's recommendation is to display the reclassification table, because it shows interesting information, but to use alternative methods for summarizing and comparing model performance. The Net Reclassification Index has been suggested as one alternative method. The author argues for reporting components of the Net Reclassification Index because they are more clinically relevant than is the single numerical summary measure.

Treatment selection markers, sometimes called predictive markers, are factors that help clinicians select therapies that maximize good outcomes and minimize adverse outcomes for patients. Existing statistical methods for evaluating a treatment selection marker include assessing its prognostic value, evaluating treatment effects in patients with a restricted range of marker values, and testing for a statistical interaction between marker value and treatment. These methods are inadequate, because they give misleading measures of performance that do not answer key clinical questions about how the marker might help patients choose treatment, how treatment decisions should be made on the basis of a continuous marker measurement, what effect using the marker to select treatment would have on the population, or what proportion of patients would have treatment changes on the basis of marker measurement. Marker-by-treatment predictiveness curves are proposed as a more useful aid to answering these clinically relevant questions, because they illustrate treatment effects as a function of marker value, outcomes when using or not using the marker to select treatment, and the proportion of patients for whom treatment recommendations change after marker measurement. Randomized therapeutic clinical trials, in which entry criteria and treatment regimens are not restricted by the marker, are also proposed as the basis for constructing the curves and evaluating and comparing markers.

Recurrent event time data are common in medical research; examples include infections in AIDS patients and seizures in epilepsy patients. In this context, as well as in the more usual context of a single failure time variable, time-dependent covariates are frequently of interest. We suggest some rate functions that might be displayed when analyzing recurrent failure time data or when the effect of a categorical time-dependent covariate is of interest. Estimators of these functions are provided along with two-sample test statistics. A new approach to regression modeling of these data is suggested and contrasted with existing methods. Our methods do not require that an explicit model be formulated for the probabilistic association between failure times within an individual. This is in line with the currently popular generalized estimating equation approach to longitudinal data. If the nature of such associations is known or is of particular interest, then alternative methods may be appropriate.

In the context of competing risks the Kaplan-Meier estimator is often unsuitable for summarizing failure time data. We discuss some alternative descriptive methods including marginal probability and conditional probability estimators. Two-sample test statistics are also presented.

One hundred twelve patients less than 36 years old received marrow grafts from unrelated donors as treatment for hematologic malignancy. Seventy donor/recipient pairs were phenotypically identical for HLA-A, - B, and -D, while 42 had a “minor” disparity at one HLA locus. There was an increase in the risk of acute graft-versus-host disease (GVHD) in patients receiving HLA-partially matched grafts compared with those receiving HLA-matched grafts (51% v 36% probability of grades III-IV acute GVHD). However, in this cohort of patients, there was no significant difference in survival (at 1.5 years, 46% v 51% for good- risk patients, 44% v 30% for poor-risk patients). This finding suggests that some degree of HLA disparity can be tolerated in young patients transplanted from unrelated donors for malignant disease, thus making transplantation an option available to larger numbers of patients.

The diagnostic likelihood ratio function, DLR, is a statistical measure used to evaluate risk prediction markers. The goal of this paper is to develop new methods to estimate the DLR function. Furthermore, we show how risk prediction markers can be compared using rank-invariant DLR functions. Various estimators are proposed that accommodate cohort or case–control study designs. Performances of the estimators are compared using simulation studies. The methods are illustrated by comparing a lung function measure and a nutritional status measure for predicting subsequent onset of major pulmonary infection in children suffering from cystic fibrosis. For continuous markers, the DLR function is mathematically related to the slope of the receiver operating characteristic (ROC) curve, an entity used to evaluate diagnostic markers. We show that our methodology can be used to estimate the slope of the ROC curve and illustrate use of the estimated ROC derivative in variance and sample size calculations for a diagnostic biomarker study.