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PURPOSE: To determine the prevalence of clinically relevant bacteremia after upper endoscopy in patients undergoing bone marrow transplantation. PATIENTS AND METHODS: We retrospectively reviewed the records of 151 patients who received an HLA-identical allogeneic bone marrow transplant (BMT) at the Seattle Veterans Affairs Medical Center between September 1983 and December 1988. Forty-seven patients who required esophago-gastroduodenoscopy (EGD) during their first 100 days after transplant were selected for evaluation. Clinically relevant bacteremia was dermed as the development of hypotension, temperature greater than 38.5 [degrees] C, and a positive blood culture occurring within 24 hours after endoscopy. The presence of acute graft-versus-host disease (GVHD) at the time of endoscopy and the use of prednisone prior to endoscopy were considered possible risk factors for the development of bacteremia. The proportion of subjects who became bacteremic were compared using Fisher's exact test. RESULTS: Within 24 hours following endoscopy, nine patients (19%) developed clinically evident bacteremia (hypotension, temperature greater than 38.5 [degrees] C, and a positive blood culture). Eight of 14 patients receiving prednisone at the time of endoscopy developed bacteremia, compared to one of 33 not receiving prednisone (p 0.9), the risk of bacteremia was particularly high in patients with acute GVHD treated with prednisone at the time of EGD (six of seven). CONCLUSION: Allogeneic BMT recipients receiving prednisone for immunoprophylaxis after grafting or for treatment of acute GVHD are at high risk for clinically relevant bacteremia following EGD. Such patients are candidates for antibiotic prophylaxis prior to endoscopy., The prevalence of bacteremia, or bacterial infection of the blood, after upper endoscopy was assessed in 47 patients undergoing bone marrow transplantation. Endoscopy is the examination of a body cavity by use of a tube-like device fitted with an optical system. The patients required esophagastroduodenoscopy (EGD), or endoscopic examination of the esophagus, stomach, and first part of the small intestine, within 100 days after transplantation. The criteria for diagnosis of bacteremia included the development of hypotension (low blood pressure), temperature greater than 38.5 degrees Centigrade (101.3 deg F), and evidence of bacterial infection of the blood within 24 hours after endoscopy. The risk of bacteremia is increased by the development of graft-versus-host disease (GVHD), a pathological reaction between the transplant recipient's immune system and the transplanted tissue, and by treatment with the steroid prednisone to suppress the immune response. Bacteremia developed within 24 hours after endoscopy in nine patients, including eight of 14 patients treated with prednisone and one of 33 patients not receiving this drug. Six of 19 patients with GVHD at endoscopy also developed bacteremia. These findings show that prednisone treatment of GVHD after bone marrow transplantation increases the risk of developing bacteremia. Antibiotics should be given to prevent bacterial infection of the blood in bone marrow transplant recipients with GVHD who will be undergoing endoscopy. (Consumer Summary produced by Reliance Medical Information, Inc.)

The statistical literature on assessing the accuracy of risk factors or disease markers as diagnostic tests deals almost exclusively with settings where the test, Y, is measured concurrently with disease status D. In practice, however, disease status may vary over time and there is often a time lag between when the marker is measured and the occurrence of disease. One example concerns the Framingham risk score (FR-score) as a marker for the future risk of cardiovascular events, events that occur after the score is ascertained. To evaluate such a marker, one needs to take the time lag into account since the predictive accuracy may be higher when the marker is measured closer to the time of disease occurrence. We therefore consider inference for sensitivity and specificity functions that are defined as functions of time. Semiparametric regression models are proposed. Data from a cohort study are used to estimate model parameters. One issue that arises in practice is that event times may be censored. In this research, we extend in several respects the work by Leisenring et al. (1997) that dealt only with parametric models for binary tests and uncensored data. We propose semiparametric models that accommodate continuous tests and censoring. Asymptotic distribution theory for parameter estimates is developed and procedures for making statistical inference are evaluated with simulation studies. We illustrate our methods with data from the Cardiovascular Health Study, relating the FR-score measured at enrollment to subsequent risk of cardiovascular events.

Markers that purport to distinguish subjects with a condition from those without a condition must be evaluated rigorously for their classification accuracy. A single approach for statistical evaluation and comparison of markers is not yet established.

Disease prevalence is ideally estimated using a 'gold standard' to ascertain true disease status on all subjects in a population of interest. In practice, however, the gold standard may be too costly or invasive to be applied to all subjects, in which case a two-phase design is often employed. Phase 1 data consisting of inexpensive and non-invasive screening tests on all study subjects are used to determine the subjects that receive the gold standard in the second phase. Naive estimates of prevalence in two-phase studies can be biased (verification bias). Imputation and re-weighting estimators are often used to avoid this bias. We contrast the forms and attributes of the various prevalence estimators. Distribution theory and simulation studies are used to investigate their bias and efficiency. We conclude that the semiparametric efficient approach is the preferred method for prevalence estimation in two-phase studies. It is more robust and comparable in its efficiency to imputation and other re-weighting estimators. It is also easy to implement. We use this approach to examine the prevalence of depression in adolescents with data from the Great Smoky Mountain Study.

Receiver operating characteristic (ROC) regression methodology is used to identify factors that affect the accuracy of medical diagnostic tests. In this paper, we consider a ROC model for which the ROC curve is a parametric function of covariates but distributions of the diagnostic test results are not specified. Covariates can be either common to all subjects or specific to those with disease. We propose a new estimation procedure based on binary indicators defined by the test result for a diseased subject exceeding various specified quantiles of the distribution of test results from non‐diseased subjects with the same covariate values. This procedure is conceptually and computationally simplified relative to existing procedures. Simulation study results indicate that the approach has fairly high statistical efficiency. The new ROC regression methodology is used to evaluate childhood measurements of body mass index as a predictive marker of adult obesity.

Summary. The development of biomarkers for cancer screening is an active area of research. While several biomarkers exist, none is sufficiently sensitive and specific on its own for population screening. It is likely that successful screening programs will require combinations of multiple markers. We consider how to combine multiple disease markers for optimal performance of a screening program. We show that the risk score, defined as the probability of disease given data on multiple markers, is the optimal function in the sense that the receiver operating characteristic (ROC) curve is maximized at every point. Arguments draw on the Neyman‐Pearson lemma. This contrasts with the corresponding optimality result of classic decision theory, which is set in a Bayesian framework and is based on minimizing an expected loss function associated with decision errors. Ours is an optimality result denned from a strictly frequentist point of view and does not rely on the notion of associating costs with misclassifications. The implication for data analysis is that binary regression methods can be used to yield appropriate relative weightings of different biomarkers, at least in large samples. We propose some modifications to standard binary regression methods for application to the disease screening problem. A flexible biologically motivated simulation model for cancer biomarkers is presented and we evaluate our methods by application to it. An application to real data concerning two ovarian cancer biomarkers is also presented. Our results are equally relevant to the more general medical diagnostic testing problem, where results of multiple tests or predictors are combined to yield a composite diagnostic test. Moreover, our methods justify the development of clinical prediction scores based on binary regression.

Summary. 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 non diseased 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 non diseased 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.

Summary. 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.

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. Copyright © 1999 John Wiley & Sons, Ltd.

Summary. Data collected longitudinally in time provide the opportunity to develop predictive models of future observations given current data for an individual. Such models may be of particular value in defining individuals at high risk and thereby in suggesting subgroups for targeting of prevention intervention research efforts. In this paper, we propose a method for estimating predictive functions. The method uses an extension of the marginal regression analysis methods of Liang and Zeger (1986, Biometrika73, 13–22) and is implemented using simple estimating equations. A key feature of the models is that regression coefficients are modelled as smooth functions of the times both at and for prediction. Data from a study of obesity in childhood and early adulthood is used to demonstrate the methodology. Criteria for defining individuals to be at high risk can be defined on the basis of estimated predictive functions. We suggest methods for evaluating the diagnostic accuracy (sensitivity and specificity) of such rules using cross‐validation. The method holds promise as a robust and technically easy way of evaluating information about future prognosis that may be gleaned from a patient's current and past clinical status.

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.

Estimation of the residual noise in the auditory brain‐stem response waveform is considered. The residual noise measures one aspect of waveform quality. Moreover, it is an important component of signal detection algorithms used for automatic termination of the test. It is shown that the most commonly used method for estimating residual noise can be severely biased. Reasons for this bias are explored and two alternative estimators are presented. © 1995 Acoustical Society of America.

A variety of estimators have been proposed for modelling how the proportion of patients with a transient condition varies over time. In this paper we compare these estimators qualitatively and quantitatively and introduce a new estimator based on a generalized additive model (GAM). The assumptions on which the various estimators are based are discussed. Using simulations we compare their performances and investigate their robustness to departures from the assumptions. The GAM estimator is the only one which can incorporate covariate information. Even when estimating a single prevalence function without any covariate information the GAM estimator is seen to be preferable as long as the censoring mechanism is random censoring. The GAM prevalence estimator is applied to data on chronic graft‐versus‐host disease following bone marrow transplantation. © 1997 John Wiley & Sons, Ltd.

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. © 1997 by John Wiley & Sons, Ltd.

No Abstract

In the context of estimating β from the regression model Pβ(Y|X), relating response Y to covariates X, suppose that only a surrogate response S is available for most study subjects. Suppose that for a random subsample of the study cohort, termed the validation sample, the true outcome Y is available in addition to S. We consider maximum likelihood estimation of β from such data and show that it is nonrobust to misspecification of the distribution relating the surrogate to the true outcome, P(S|Y, X). An alternative semi-parametric method is also considered, which is nonparametric with respect to P(S|Y, X), Large-sample distribution theory for maximum estimated likelihood estimates is developed. An illustrative example is presented.

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.

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.