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Studies of clinical characteristics frequently measure covariates with a single observation. This may be a mismeasured version of the "true" phenomenon due to sources of variability like biological fluctuations and device error. Descriptive analyses and outcome models that are based on mismeasured data generally will not reflect the corresponding analyses based on the "true" covariate. Many statistical methods are available to adjust for measurement error. Imputation methods like regression calibration and moment reconstruction are easily implemented but are not always adequate. Sophisticated methods have been proposed for specific applications like density estimation, logistic regression, and survival analysis. However, it is frequently infeasible for an analyst to adjust each analysis separately, especially in preliminary studies where resources are limited. We propose an imputation approach called moment-adjusted imputation that is flexible and relatively automatic. Like other imputation methods, it can be used to adjust a variety of analyses quickly, and it performs well under a broad range of circumstances. We illustrate the method via simulation and apply it to a study of systolic blood pressure and health outcomes in patients hospitalized with acute heart failure., (© 2011, The International Biometric Society.)

A routine challenge is that of making inference on parameters in a statistical model of interest from longitudinal data subject to dropout, which are a special case of the more general setting of monotonely coarsened data. Considerable recent attention has focused on doubly robust (DR) estimators, which in this context involve positing models for both the missingness (more generally, coarsening) mechanism and aspects of the distribution of the full data, that have the appealing property of yielding consistent inferences if only one of these models is correctly specified. DR estimators have been criticized for potentially disastrous performance when both of these models are even only mildly misspecified. We propose a DR estimator applicable in general monotone coarsening problems that achieves comparable or improved performance relative to existing DR methods, which we demonstrate via simulation studies and by application to data from an AIDS clinical trial., (© 2010, The International Biometric Society.)

The Superior Yield of the New Strategy of Enoxaparin, Revascularization, and GlYcoprotein IIb/IIIa inhibitors (SYNERGY) was a randomized, open-label, multicenter clinical trial comparing 2 anticoagulant drugs on the basis of time-to-event endpoints. In contrast to other studies of these agents, the primary, intent-to-treat analysis did not find evidence of a difference, leading to speculation that premature discontinuation of the study agents by some subjects may have attenuated the apparent treatment effect and thus to interest in inference on the difference in survival distributions were all subjects in the population to follow the assigned regimens, with no discontinuation. Such inference is often attempted via ad hoc analyses that are not based on a formal definition of this treatment effect. We use SYNERGY as a context in which to describe how this effect may be conceptualized and to present a statistical framework in which it may be precisely identified, which leads naturally to inferential methods based on inverse probability weighting.

Doubly robust estimation combines a form of outcome regression with a model for the exposure (i.e., the propensity score) to estimate the causal effect of an exposure on an outcome. When used individually to estimate a causal effect, both outcome regression and propensity score methods are unbiased only if the statistical model is correctly specified. The doubly robust estimator combines these 2 approaches such that only 1 of the 2 models need be correctly specified to obtain an unbiased effect estimator. In this introduction to doubly robust estimators, the authors present a conceptual overview of doubly robust estimation, a simple worked example, results from a simulation study examining performance of estimated and bootstrapped standard errors, and a discussion of the potential advantages and limitations of this method. The supplementary material for this paper, which is posted on the Journal's Web site (http://aje.oupjournals.org/), includes a demonstration of the doubly robust property (Web Appendix 1) and a description of a SAS macro (SAS Institute, Inc., Cary, North Carolina) for doubly robust estimation, available for download at http://www.unc.edu/~mfunk/dr/.

In this chapter, we cover basic and fundamental principles and methods in statistics - from "What are Data and Statistics?" to "ANOVA and linear regression," which are the basis of any statistical thinking and undertaking. Readers can easily find the selected topics in most introductory statistics textbooks, but we have tried to assemble and structure them in a succinct and reader-friendly manner in a stand-alone chapter. This text has long been used in real classroom settings for both undergraduate and graduate students who do or do not major in statistical sciences. We hope that from this chapter, readers would understand the key statistical concepts and terminologies, how to design a study (experimental or observational), how to analyze the data (e.g., describe the data and/or estimate the parameter(s) and make inference), and how to interpret the results. This text would be most useful if it is used as a supplemental material, while the readers take their own statistical courses or it would serve as a great reference text associated with a manual for any statistical software as a self-teaching guide.

While marginal models, random-effects models, and conditional models are routinely considered to be the three main modeling families for continuous and discrete repeated measures with linear and generalized linear mean structures, respectively, it is less common to consider non-linear models, let alone frame them within the above taxonomy. In the latter situation, indeed, when considered at all, the focus is often exclusively on random-effects models. In this paper, we consider all three families, exemplify their great flexibility and relative ease of use, and apply them to a simple but illustrative set of data on tree circumference growth of orange trees.

Considerable recent interest has focused on doubly robust estimators for a population mean response in the presence of incomplete data, which involve models for both the propensity score and the regression of outcome on covariates. The usual doubly robust estimator may yield severely biased inferences if neither of these models is correctly specified and can exhibit nonnegligible bias if the estimated propensity score is close to zero for some observations. We propose alternative doubly robust estimators that achieve comparable or improved performance relative to existing methods, even with some estimated propensity scores close to zero.

We propose a similarity-based regression method to detect associations between traits and multimarker genotypes. The model regresses similarity in traits for pairs of "unrelated" individuals on their haplotype similarities, and detects the significance by a score test for which the limiting distribution is derived. The proposed method allows for covariates, uses phase-independent similarity measures to bypass the needs to impute phase information, and is applicable to traits of general types (e.g., quantitative and qualitative traits). We also show that the gene-trait similarity regression is closely connected with random effects haplotype analysis, although commonly they are considered as separate modeling tools. This connection unites the classic haplotype sharing methods with the variance-component approaches, which enables direct derivation of analytical properties of the sharing statistics even when the similarity regression model becomes analytically challenging.

Joint modeling of a primary response and a longitudinal process via shared random effects is widely used in many areas of application. Likelihood-based inference on joint models requires model specification of the random effects. Inappropriate model specification of random effects can compromise inference. We present methods to diagnose random effect model misspecification of the type that leads to biased inference on joint models. The methods are illustrated via application to simulated data, and by application to data from a study of bone mineral density in perimenopausal women and data from an HIV clinical trial.

We propose a procedure for estimating the survival function of a time-to-event random variable under arbitrary patterns of censoring. The method is predicated on the mild assumption that the distribution of the random variable, and hence the survival function, has a density that lies in a class of 'smooth' densities whose elements can be represented by an infinite Hermite series. Truncation of the series yields a 'parametric' expression that can well approximate any plausible survival density, and hence survival function, provided the degree of truncation is suitably chosen. The representation admits a convenient expression for the likelihood for the 'parameters' in the approximation under arbitrary censoring/truncation that is straightforward to compute and maximize. A test statistic for comparing two survival functions, which is based on an integrated weighted difference of estimates of each under this representation, is proposed. Via simulation studies and application to a number of data sets, we demonstrate that the approach yields reliable inferences and can result in gains in efficiency over traditional nonparametric methods.

There is considerable debate regarding whether and how covariate-adjusted analyses should be used in the comparison of treatments in randomized clinical trials. Substantial baseline covariate information is routinely collected in such trials, and one goal of adjustment is to exploit covariates associated with outcome to increase precision of estimation of the treatment effect. However, concerns are routinely raised over the potential for bias when the covariates used are selected post hoc and the potential for adjustment based on a model of the relationship between outcome, covariates, and treatment to invite a 'fishing expedition' for that leading to the most dramatic effect estimate. By appealing to the theory of semiparametrics, we are led naturally to a characterization of all treatment effect estimators and to principled, practically feasible methods for covariate adjustment that yield the desired gains in efficiency and that allow covariate relationships to be identified and exploited while circumventing the usual concerns. The methods and strategies for their implementation in practice are presented. Simulation studies and an application to data from an HIV clinical trial demonstrate the performance of the techniques relative to the existing methods.

The primary goal of a randomized clinical trial is to make comparisons among two or more treatments. For example, in a two-arm trial with continuous response, the focus may be on the difference in treatment means; with more than two treatments, the comparison may be based on pairwise differences. With binary outcomes, pairwise odds ratios or log odds ratios may be used. In general, comparisons may be based on meaningful parameters in a relevant statistical model. Standard analyses for estimation and testing in this context typically are based on the data collected on response and treatment assignment only. In many trials, auxiliary baseline covariate information may also be available, and it is of interest to exploit these data to improve the efficiency of inferences. Taking a semiparametric theory perspective, we propose a broadly applicable approach to adjustment for auxiliary covariates to achieve more efficient estimators and tests for treatment parameters in the analysis of randomized clinical trials. Simulations and applications demonstrate the performance of the methods.

A general framework for regression analysis of time-to-event data subject to arbitrary patterns of censoring is proposed. The approach is relevant when the analyst is willing to assume that distributions governing model components that are ordinarily left unspecified in popular semiparametric regression models, such as the baseline hazard function in the proportional hazards model, have densities satisfying mild "smoothness" conditions. Densities are approximated by a truncated series expansion that, for fixed degree of truncation, results in a "parametric" representation, which makes likelihood-based inference coupled with adaptive choice of the degree of truncation, and hence flexibility of the model, computationally and conceptually straightforward with data subject to any pattern of censoring. The formulation allows popular models, such as the proportional hazards, proportional odds, and accelerated failure time models, to be placed in a common framework; provides a principled basis for choosing among them; and renders useful extensions of the models straightforward. The utility and performance of the methods are demonstrated via simulations and by application to data from time-to-event studies.

Inference on the association between a primary endpoint and features of longitudinal profiles of a continuous response is of central interest in medical and public health research. Joint models that represent the association through shared dependence of the primary and longitudinal data on random effects are increasingly popular; however, existing inferential methods may be inefficient or sensitive to assumptions on the random effects distribution. We consider a semiparametric joint model that makes only mild assumptions on this distribution and develop likelihood-based inference on the association and distribution, which offers improved performance relative to existing methods that is insensitive to the true random effects distribution. Moreover, the estimated distribution can reveal interesting population features, as we demonstrate for a study of the association between longitudinal hormone levels and bone status in peri-menopausal women.

We propose "score-type" tests for the proportional hazards assumption and for covariate effects in the Cox model using the natural smoothing spline representation of the corresponding nonparametric functions of time or covariate. The tests are based on the penalized partial likelihood and are derived by viewing the inverse of the smoothing parameter as a variance component and testing an equivalent null hypothesis that the variance component is zero. We show that the tests have a size close to the nominal level and good power against general alternatives, and we apply them to data from a cancer clinical trial.

The pretest-posttest study is commonplace in numerous applications. Typically, subjects are randomized to two treatments, and response is measured at baseline, prior to intervention with the randomized treatment (pretest), and at prespecified follow-up time (posttest). Interest focuses on the effect of treatments on the change between mean baseline and follow-up response. Missing posttest response for some subjects is routine, and disregarding missing cases can lead to invalid inference. Despite the popularity of this design, a consensus on an appropriate analysis when no data are missing, let alone for taking into account missing follow-up, does not exist. Under a semiparametric perspective on the pretest-posttest model, in which limited distributional assumptions on pretest or posttest response are made, we show how the theory of Robins, Rotnitzky and Zhao may be used to characterize a class of consistent treatment effect estimators and to identify the efficient estimator in the class. We then describe how the theoretical results translate into practice. The development not only shows how a unified framework for inference in this setting emerges from the Robins, Rotnitzky and Zhao theory, but also provides a review and demonstration of the key aspects of this theory in a familiar context. The results are also relevant to the problem of comparing two treatment means with adjustment for baseline covariates.

Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score, the probability of treatment exposure conditional on covariates, is the basis for two approaches to adjusting for confounding: methods based on stratification of observations by quantiles of estimated propensity scores and methods based on weighting observations by the inverse of estimated propensity scores. We review popular versions of these approaches and related methods offering improved precision, describe theoretical properties and highlight their implications for practice, and present extensive comparisons of performance that provide guidance for practical use., (2004 John Wiley & Sons, Ltd.)

Marginal structural models (MSMs) are causal models designed to adjust for time-dependent confounding in observational studies of time-varying treatments. MSMs are powerful tools for assessing causality with complicated, longitudinal data sets but have not been widely used by practitioners. The objective of this paper is to illustrate the fitting of an MSM for the causal effect of iron supplement use during pregnancy (time-varying treatment) on odds of anemia at delivery in the presence of time-dependent confounding. Data from pregnant women enrolled in the Iron Supplementation Study (Raleigh, North Carolina, 1997-1999) were used. The authors highlight complexities of MSMs and key issues epidemiologists should recognize before and while undertaking an analysis with these methods and show how such methods can be readily interpreted in existing software packages, including SAS and Stata. The authors emphasize that if a data set with rich information on confounders is available, MSMs can be used straightforwardly to make robust inferences about causal effects of time-dependent treatments/exposures in epidemiologic research.

The relationship between a primary endpoint and features of longitudinal profiles of a continuous response is often of interest, and a relevant framework is that of a generalized linear model with covariates that are subject-specific random effects in a linear mixed model for the longitudinal measurements. Naive implementation by imputing subject-specific effects from individual regression fits yields biased inference, and several methods for reducing this bias have been proposed. These require a parametric (normality) assumption on the random effects, which may be unrealistic. Adapting a strategy of Stefanski and Carroll (1987, Biometrika74, 703-716), we propose estimators for the generalized linear model parameters that require no assumptions on the random effects and yield consistent inference regardless of the true distribution. The methods are illustrated via simulation and by application to a study of bone mineral density in women transitioning to menopause.

Background: Although many believe that platelet glycoprotein IIb/IIIa inhibitors should be used only in acute coronary syndrome patients undergoing percutaneous coronary intervention, supporting data from randomized clinical trials are tenuous. The assumption that these agents are useful only in conjunction with percutaneous coronary intervention is based primarily on inappropriate subgroup analyses performed across the glycoprotein IIb/IIIa inhibitor trials., Methods and Results: We describe the problems with these analytical techniques and demonstrate that different approaches to the question can result in opposing answers., Conclusions: Clinical-practice decisions and practice guidelines should be based on overall trial results and not analyses of post-randomization subgroups.

Inference on treatment effects in a pretest-posttest study is a routine objective in medicine, public health, and other fields. A number of approaches have been advocated. We take a semiparametric perspective, making no assumptions about the distributions of baseline and posttest responses. By representing the situation in terms of counterfactual random variables, we exploit recent developments in the literature on missing data and causal inference, to derive the class of all consistent treatment effect estimators, identify the most efficient such estimator, and outline strategies for implementation of estimators that may improve on popular methods. We demonstrate the methods and their properties via simulation and by application to a data set from an HIV clinical trial.

Joint models for a time-to-event (e.g., survival) and a longitudinal response have generated considerable recent interest. The longitudinal data are assumed to follow a mixed effects model, and a proportional hazards model depending on the longitudinal random effects and other covariates is assumed for the survival endpoint. Interest may focus on inference on the longitudinal data process, which is informatively censored, or on the hazard relationship. Several methods for fitting such models have been proposed, most requiring a parametric distributional assumption (normality) on the random effects. A natural concern is sensitivity to violation of this assumption; moreover, a restrictive distributional assumption may obscure key features in the data. We investigate these issues through our proposal of a likelihood-based approach that requires only the assumption that the random effects have a smooth density. Implementation via the EM algorithm is described, and performance and the benefits for uncovering noteworthy features are illustrated by application to data from an HIV clinical trial and by simulation.

In many longitudinal studies, it is of interest to characterize the relationship between a time-to-event (e.g. survival) and several time-dependent and time-independent covariates. Time-dependent covariates are generally observed intermittently and with error. For a single time-dependent covariate, a popular approach is to assume a joint longitudinal data-survival model, where the time-dependent covariate follows a linear mixed effects model and the hazard of failure depends on random effects and time-independent covariates via a proportional hazards relationship. Regression calibration and likelihood or Bayesian methods have been advocated for implementation; however, generalization to more than one time-dependent covariate may become prohibitive. For a single time-dependent covariate, Tsiatis and Davidian (2001) have proposed an approach that is easily implemented and does not require an assumption on the distribution of the random effects. This technique may be generalized to multiple, possibly correlated, time-dependent covariates, as we demonstrate. We illustrate the approach via simulation and by application to data from an HIV clinical trial.

Background: The relative anti-aggregatory effects of currently prescribed platelet glycoprotein IIb/IIIa receptor antagonists during and after percutaneous coronary intervention for acute coronary syndromes have not been established., Methods and Results: We randomized 70 acute coronary syndrome patients undergoing percutaneous coronary intervention to receive abciximab, eptifibatide, or tirofiban at doses used in the Evaluation of Platelet IIb/IIIa Inhibitor for STENTing (EPISTENT), Platelet glycoprotein IIb/IIIa in Unstable angina Receptor Suppression Using Integrilin Therapy (PURSUIT), and Platelet Receptor Inhibition in ischemic Syndrome Management in Patients Limited by Unstable Signs and symptoms (PRISM-PLUS)/Randomized Efficacy Study of Tirofiban for Outcomes and Restenosis (RESTORE) trials, respectively. Platelet aggregation (PA) in response to 20 micro mol/L of adenosine diphosphate was measured with turbidimetric aggregometry in both D-phenylalanyl-L-prolyl-L-arginine chloromethylketone and citrate-anticoagulated blood early (15 and 30 minutes) and late (4, 12, and 18 to 24 hours) after drug initiation. At 15 and 30 minutes, PA was significantly less inhibited by the tirofiban-RESTORE regimen compared with abciximab (P=0.028) and eptifibatide regimens (P=0.0001). The abciximab regimen, however, showed increasingly varied anti-aggregatory effects during continued infusion for > or =4 hours. Citrate exaggerated ex vivo platelet inhibition after eptifibatide and tirofiban, but had the opposite effect on abciximab. Of all regimens evaluated, the eptifibatide regimen inhibited PA most consistently throughout both the early and late periods., Conclusions: Currently recommended drug regimens to inhibit the platelet glycoprotein IIb/IIIa receptor have distinct pharmacodynamic profiles that might affect their relative efficacy in acute coronary syndromes and percutaneous coronary intervention.

A popular way to represent clustered binary, count, or other data is via the generalized linear mixed model framework, which accommodates correlation through incorporation of random effects. A standard assumption is that the random effects follow a parametric family such as the normal distribution; however, this may be unrealistic or too restrictive to represent the data. We relax this assumption and require only that the distribution of random effects belong to a class of 'smooth' densities and approximate the density by the seminonparametric (SNP) approach of Gallant and Nychka (1987). This representation allows the density to be skewed, multi-modal, fat- or thin-tailed relative to the normal and includes the normal as a special case. Because an efficient algorithm to sample from an SNP density is available, we propose a Monte Carlo EM algorithm using a rejection sampling scheme to estimate the fixed parameters of the linear predictor, variance components and the SNP density. The approach is illustrated by application to a data set and via simulation.

Some clinical trials follow a design where patients are randomized to a primary therapy at entry followed by another randomization to maintenance therapy contingent upon disease remission. Ideally, analysis would allow different treatment policies, i.e., combinations of primary and maintenance therapy if specified up-front, to be compared. Standard practice is to conduct separate analyses for the primary and follow-up treatments, which does not address this issue directly. We propose consistent estimators for the survival distribution and mean restricted survival time for each treatment policy in such two-stage studies and derive large-sample properties. The methods are demonstrated on a leukemia clinical trial data set and through simulation.

The constrained four parameter logistic model has found wide application in describing dose response relationships across many assay systems. This discussion examines the basic model and its practical application to potency testing in the context of the 96 well plate. A two step procedure is recommended for the analysis: (i) the constrained logistic model to generate potency estimates, (ii) a linear mixed-effects model to account for within-plate and between plate variability for producing the final combined estimate of potency. The method is outlined in a case study. Design issues related to possible location effects on the plate may be ameliorated by use of a Latin square design.

Normality of random effects is a routine assumption for the linear mixed model, but it may be unrealistic, obscuring important features of among-individual variation. We relax this assumption by approximating the random effects density by the seminonparameteric (SNP) representation of Gallant and Nychka (1987, Econometrics 55, 363-390), which includes normality as a special case and provides flexibility in capturing a broad range of nonnormal behavior, controlled by a user-chosen tuning parameter. An advantage is that the marginal likelihood may be expressed in closed form, so inference may be carried out using standard optimization techniques. We demonstrate that standard information criteria may be used to choose the tuning parameter and detect departures from normality, and we illustrate the approach via simulation and using longitudinal data from the Framingham study.

Hierarchical models encompass two sources of variation, namely within and among individuals in the population; thus, it is important to identify outliers that may arise at each sampling level. A two-stage approach to analyzing nonlinear repeated measurements naturally allows parametric modeling of the respective variance structure for the intraindividual random errors and interindividual random effects. We propose a robust two-stage procedure based on Huber's (1981, Robust Statistics) theory of M-estimation to accommodate separately aberrant responses within an experimental unit and subjects deviating from the study population when the usual assumptions of normality are violated. A toxicology study of chronic ozone exposure in rats illustrates the impact of outliers on the population inference and hence the advantage of adopting the robust methodology. The robust weights generated by the two-stage M-estimation process also serve as diagnostics for gauging the relative influence of outliers at each level of the hierarchical model. A practical appeal of our proposal is the computational simplicity since the estimation algorithm may be implemented using standard statistical software with a nonlinear least squares routine and iterative capability.

Many studies have shown that patients infected with hepatitis C virus (HCV) of genotype 2 have better response to interferon (IFN)-alpha treatment than genotype 1 patients; however, the mechanisms responsible for this difference are not understood. In this study, viral dynamics during high-dose IFN induction treatment were compared between the genotypes. Patients in each group received 10 MU of IFN-alpha2b for 14 days, and HCV RNA levels were frequently determined. Nonlinear fitting, both individually for each patient and using a mixed-effects approach, of the viral kinetic data to a mathematical model of the IFN effect on HCV infection was performed. The antiviral effectiveness of IFN in blocking virus production, the free virion clearance rate, and the HCV-infected cell death rate were all significantly higher for genotype 2 patients than for genotype 1 patients. Thus, the better response rate of patients infected with HCV genotype 2 is multifactorial. This is the first finding of a difference in viral dynamics between subtypes of the same virus and demonstrates the importance of subtype-specific virus-host-drug interactions.

The nonlinear mixed effects model is used to represent data in pharmacokinetics, viral dynamics, and other areas where an objective is to elucidate associations among individual-specific model parameters and covariates; however, covariates may be measured with error. For additive measurement error, we show substitution of mismeasured covariates for true covariates may lead to biased estimators for fixed effects and random effects covariance parameters, while regression calibration may eliminate bias in fixed effects but fail to correct that in covariance parameters. We develop methods to take account of measurement error that correct this bias and may be implemented with standard software, and we demonstrate their utility via simulation and application to data from a study of HIV dynamics.

Purpose: To assess the feasibility and efficacy of reteplase in transcatheter arterial thrombolysis for lower extremity occlusive disease., Materials and Methods: Fifteen consecutive patients with acute lower extremity ischemia due to occlusive disease were treated by means of catheter-directed thrombolysis with reteplase. Following diagnostic angiography, thrombolysis was started either from an antegrade puncture site in cases of femoropopliteal occlusions, or from the contralateral groin in cases of thrombosis of the iliac artery, common femoral artery, or infrainguinal bypass grafts. Reteplase was infused at a rate of either 0.5 U/h (six patients) or 1.0 U/h (nine patients)., Results: Complete thrombolysis was achieved in all of the nine (100%) patients in the 1.0 U/h group and in four of six (66%) patients in the 0.5 U/h group for a combined success rate of 13 of 15 (87%). Clinical success was achieved in 11 of 15 patients overall (73%). Major bleeding complications occurred in none of the 9 patients in the 1.0 U/h group and in one (16%) of the six patients in the 0.5 U/h group for an overall rate of 6%., Conclusions: Reteplase shows promise as an alternative to urokinase in the treatment of lower extremity ischemia due to arterial occlusive disease.

A routine practice in the analysis of repeated measurement data is to represent individual responses by a mixed effects model on some transformed scale. For example, for pharmacokinetic, growth, and other data, both the response and the regression model are typically transformed to achieve approximate within-individual normality and constant variance on the new scale; however, the choice of transformation is often made subjectively or by default, with adoption of a standard choice such as the log. We propose a mixed effects framework based on the transform-both-sides model, where the transformation is represented by a monotone parametric function and is estimated from the data. For this model, we describe a practical fitting strategy based on approximation of the marginal likelihood. Inference is complicated by the fact that estimation of the transformation requires modification of the usual standard errors for estimators of fixed effects; however, we show that, under conditions relevant to common applications, this complication is asymptotically negligible, allowing straightforward implementation via standard software.

HIV-1-specific cytotoxic T cell (CTL) activity has been suggested to correlate with protection from progression to AIDS. We have examined the relationship between HIV-specific CTL activity and maintenance of peripheral blood CD4+ T lymphocyte counts and control of viral load in 17 long-term survivors (LTSs) of HIV-1 infection. Longitudinal analysis indicated that the LTS cohort demonstrated a decreased rate of CD4+ T cell loss (18 cells/mm3/year) compared with typical normal progressors (approximately 60 cells/mm3/year). The majority of the LTSs had detectable, variable, and in some individuals, quite high (>10(4) RNA copies/ml) plasma viral load during the study period. In a cross-sectional analysis, HIV-specific CTL activity to HIV Gag, Pol, and Env proteins was detectable in all 17 LTSs. Simultaneous analysis of HIV-1 Gag-Pol, and Env-specific CTLs and virus load in protease inhibitor-naive individuals showed a significant inverse correlation between Pol-specific CTL activity and plasma HIV-1 RNA levels (p = 0.001). Furthermore, using a mixed linear effects model the combined effects of HIV-1 Pol- and Env-specific CTL activity on the viral load were significantly stronger than the effects of HIV-1 Pol-specific CTL activity alone on predicted virus load. These data suggest that the presence of HIV-1-specific CTL activity in HIV-1-infected long-term survivors is an important component in the effective control of HIV-1 replication.

The Cox proportional hazards model is commonly used to model survival data as a function of covariates. Because of the measuring mechanism or the nature of the environment, covariates are often measured with error and are not directly observable. A naive approach is to use the observed values of the covariates in the Cox model, which usually produces biased estimates of the true association of interest. An alternative strategy is to take into account the error in measurement, which may be carried out for the Cox model in a number of ways. We examine several such approaches and compare and contrast them through several simulation studies. We introduce a likelihood-based approach, which we refer to as the semiparametric method, and show that this method is an appealing alternative. The methods are applied to analyze the relationship between survival and CD4 count in patients with AIDS.

Objective: To describe the laser scanning cytometry (LSC) processing and analysis developed for the quantitative analysis of estrogen receptor (ER) content in routine paraffin sections of breast carcinomas., Study Design: Histologic sections of archival, paraffin-embedded tissues from 30 breast carcinomas were labeled for ER with fluoresceinated monoclonal antibody. ER expression was quantified by LSC and expressed as percent positive tumor cells and as histogram distributions of receptor expression per cell. Duplicate sections of the same tumors were stained for ER by a conventional immunoperoxidase reaction and percent positive tumor cells counted visually., Results: Percent ER-positive tumor cells by LSC of immunofluorescence-stained sections correlated well with conventional (visual) counts of immunoperoxidase-stained duplicate sections when the latter were categorized as low, intermediate or high percent of positive cells. In addition, the marked variation in relative number of ER binding sites per cell could be quantified by LSC and displayed in histogram distribution., Conclusion: LSC measurements are fast and objective and can be carried out on sections of paraffin-embedded tissue after routine processing in the pathology laboratory. In addition, LSC data provide the relative number of ER binding sites per unit of DNA; that may reveal clinically significant skewed distributions or subpopulations of tumor cells.

A common practice in immunoassay is the use of sequential dilutions of an initial stock solution of the antigen of interest to obtain standard samples in a desired concentration range. Nonlinear, heteroscedastic regression models are a common framework for analysis, and the usual methods for fitting the model assume that measured responses on the standards are independent. However, the dilution procedure introduces a propagation of random measurement error that may invalidate this assumption. We demonstrate that failure to account for serial dilution error in calibration inference on unknown samples leads to serious inaccuracy of assessments of assay precision such as confidence intervals and precision profiles. Techniques for taking serial dilution error into account based on data from multiple assay runs are discussed and are shown to yield valid calibration inferences.

Several authors have documented the poor performance of usual large-sample, individual calibration confidence intervals based on a single run of an immunoassay. Inaccuracy of these intervals may be attributed to the paucity of information on model parameters available in a single run. Methods for combining information from multiple runs to estimate assay response variance parameters and to refine characterization of the standard curve for the current run via empirical Bayes techniques have been proposed. We investigate formally the utility of these techniques for improving the quality of routine individual calibration inference.

A 16-year-old female with cyclic neutropenia (CN) was incidentally found to have a thoracic vertebral mass during the preoperative work-up for maxillary sinus surgery. An exploratory thoracotomy revealed a very vascular tumor involving T-10 and T-11 vertebral bodies. Gross total resection of the tumor was achieved. Pathology revealed a solid variant of aneurysmal bone cyst. This is a rare benign neoplasm of bone more commonly seen in the mandible and facial bones as well as involving the small tubular bones of the hands and feet. Because of its rarity, location, and an unknown association with CN, we found this case worthwhile to publish.

Background: Adenocarcinoma of the retinal pigment epithelium (RPE) is an uncommon intraocular tumor which has been rarely if ever diagnosed prior to enucleation. Our review of the literature suggests that when a melanotic and tuberous-shaped tumor presents in a woman with signs of uveitis one should consider the diagnosis of adenocarcinoma of the RPE., Methods: We report a case in which a 4.9-mm-tall, mushroom-shaped, moderately reflective melanotic tumor found to be present in a 50-year-old woman. Other findings included a 12 x 10-mm dense transillumination shadow and 270 deg of posterior iris synechiae. With a presumptive diagnosis of uveal melanoma the patient was treated with palladium-103 plaque radiotherapy. Within 6 months she underwent enucleation due to uncontrollable ocular pain and progressive tumor growth. Histopathologic evaluation revealed an adenocarcinoma of the (RPE)., Results: Two months after radiotherapy the eye developed acute angle-closure glaucoma, secondary retinal detachment, and a painful scleritis (a result similar to that of Greer, who treated an intraocular adenoma with radiation). Our diagnosis of adenocarcinoma of the RPE was made after enucleation., Conclusion: Adenocarcinomas of the RPE are rarely if ever diagnosed on clinical evaluation, should be suspected in women with a melanotic intraocular tumor and uveitis, and may respond poorly to ophthalmic plaque radiotherapy. All reported cases presenting without extrascleral extension have not metastasized.

Often with data from immunoassays, the concentration-response relationship is nonlinear and intra-assay response variance is heterogeneous. Estimation of the standard curve is usually based on a nonlinear heteroscedastic regression model for concentration-response, where variance is modeled as a function of mean response and additional variance parameters. This paper discusses calibration inference for immunoassay data which exhibit this nonlinear heteroscedastic mean-variance relationship. An assessment of the effect of variance function estimation in three types of approximate large-sample confidence intervals for unknown concentrations is given by theoretical and empirical investigation and application to two examples. A major finding is that the accuracy of such calibration intervals depends critically on the nature of response variance and the quality with which variance parameters are estimated.

Purpose: Iron deficiency anemia (IDA) causes benign intracranial hypertension (BIH)., Patients and Methods: A case of an 11-year-old girl with severe IDA and benign intracranial hypertension is presented., Results: Treatment of iron deficiency resulted in the resolution of BIH. Recurrence of BIH is observed with the recurrence of IDA. Chronic intermittent blood loss from the intestinal polyps was the etiology of recurrent IDA., Conclusions: A thorough search to determine the etiology of IDA is essential for proper treatment. Although BIH is a rare complication of IDA, its association with IDA should be recognized for prompt treatment before an extensive and costly workup for BIH is done.

This bibliography lists all published methodological works (statistical methodology, implementation methodology, review papers, descriptions of software) in the area of population pharmacokinetics/pharmacodynamics up to 1993 and all published works describing applications of population pharmacokinetics/pharmacodynamics up to 1992.

The gross and microscopic effects of four common modes of ureteral dilation and ureteroscopy were examined in 26 renoureteral units in 13 minipigs. Acutely, ureters subjected to mechanical (bougie, Teflon, or balloon) ureteral dilation and ureteropyeloscopy (UPS) demonstrated active mucosal bleeding with multiple sites of perforation, whereas ureters subjected to hydraulic dilation and UPS were significantly less traumatized. Two weeks after mechanical ureteral dilation and UPS, 3 of 6 ureters were obstructed radiographically, whereas all 7 hydraulically dilated ureters were unobstructed. By 6 weeks, all radiographic evidence of obstruction had resolved in the mechanically dilated group. While 5 of 6 mechanically dilated ureters showed extensive scarring with muscle loss 4 to 6 weeks after dilation, no scarring was seen in those ureters dilated hydraulically. Renal pelvic pressure (RPP) was measured continuously with a nephrostomy catheter in vivo during (bougie, Teflon, balloon and hydraulic) ureteral dilation and UPS. Renal pelvic pressure during rigid ureteroscopy approximated the resting pelvic pressure plus the irrigant height above the kidney or set pressure on a hydraulic pump, plus a "scope effect" which was characterized by a 20 to 25 mm. Hg increase in RPP produced by moving the endoscope in the ureter without flow. The effects on RPP of continuous bladder drainage with a uretheral catheter and renal pelvic decompression with an open-ended ureteral catheter passed into the renal pelvis through the ureteroscope working channel were also examined. The maximum RPP was evaluated in vitro in a separate group of 16 freshly harvested pig kidneys of similar weight examined immediately after sacrifice and was found to be 439 mm. Hg. We also studied the immediate and long-term effects of low (< 120 cm. H2O or 90 mm. Hg) versus high (> 200 cm. H2O or 150 mm. Hg) RPP on renal histology. Acutely, high pressure caused diffuse denudation and flattening of the caliceal urothelium, submucosal edema and congestion not seen in calyces subjected to low irrigant pressure. Four to six weeks later, there was a higher incidence of columnar metaplasia, subepithelial nests and pericalyceal vasculitis in calyces subjected to high pressure as compared with those subjected to low irrigant pressure. Acutely, renal tubules subjected to high irrigant pressure demonstrated marked vacuolization and degeneration, whereas tubules subjected to low pressure appeared normal. At 4 to 6 weeks, focal scarring was seen in 5 of 7 kidneys subjected to high irrigant pressure, whereas no scarring was noted in all 6 kidneys subjected to low irrigant pressure.(ABSTRACT TRUNCATED AT 400 WORDS)

A nonlinear mixed-effects model suitable for characterizing repeated measurement data is described. The model allows dependence of random coefficients on covariate information and accommodates general specifications of a common intraindividual covariance structure, such as models for variance within individuals that depend on individual mean response and autocorrelation. Two classes of procedures for estimation in this model are described, which incorporate estimation of unknown parameters in the assumed intraindividual covariance structure. The procedures are straightforward to implement using standard statistical software. The techniques are illustrated by examples in growth analysis and assay development.

The seminonparametric (SNP) method, popular in the econometrics literature, is proposed for use in population pharmacokinetic analysis. For data that can be described by the nonlinear mixed effects model, the method produces smooth nonparametric estimates of the entire random effects density and simultaneous estimates of fixed effects by maximum likelihood. A graphical model-building strategy based on the SNP method is described. The methods are illustrated by a population analysis of plasma levels in 136 patients undergoing oral quinidine therapy.

We have reported that short-term treatment of spontaneously hypertensive rats (SHR) and normotensive Wistar-Kyoto (WKY) rats with stannous chloride (SnCl2), which selectively depletes renal cytochrome P450, restores blood pressure to normal in young but not in adult SHR, and is without effect on blood pressure of either young or adult WKY rats. We report in the present study that chronic treatment with SnCl2, begun at age 5 weeks, prevented the development of hypertension in SHR over a period of 15 weeks at which time they were killed. Suspension of SnCl2 treatment after 8 weeks (i.e., at age 13 weeks) did not result in return of blood pressure to hypertensive levels in SHR. Age-matched WKY rats were not affected by tin treatment. These findings provide additional evidence that administration of tin, which stimulates heme oxygenase, thereby producing depletion of cytochrome P450, restores blood pressure to normal levels in SHR.