Primary Neural Degeneration in the Human Cochlea: Evidence for Hidden Hearing Loss in the Aging Ear.

The noise-induced and age-related loss of synaptic connections between auditory-nerve fibers and cochlear hair cells is well-established from histopathology in several mammalian species; however, its prevalence in humans, as inferred from electrophysiological measures, remains controversial. Here we look for cochlear neuropathy in a temporal-bone study of "normal-aging" humans, using autopsy material from 20 subjects aged 0-89 yrs, with no history of otologic disease. Cochleas were immunostained to allow accurate quantification of surviving hair cells in the organ Corti and peripheral axons of auditory-nerve fibers. Mean loss of outer hair cells was 30-40% throughout the audiometric frequency range (0.25-8.0 kHz) in subjects over 60 yrs, with even greater losses at both apical (low-frequency) and basal (high-frequency) ends. In contrast, mean inner hair cell loss across audiometric frequencies was rarely >15%, at any age. Neural loss greatly exceeded inner hair cell loss, with 7/11 subjects over 60 yrs showing >60% loss of peripheral axons re the youngest subjects, and with the age-related slope of axonal loss outstripping the age-related loss of inner hair cells by almost 3:1. The results suggest that a large number of auditory neurons in the aging ear are disconnected from their hair cell targets. This primary neural degeneration would not affect the audiogram, but likely contributes to age-related hearing impairment, especially in noisy environments. Thus, therapies designed to regrow peripheral axons could provide clinically meaningful improvement in the aged ear.

Characterizing the relationship between HIV-1 genotype and phenotype: prediction-based classification.

This paper establishes a framework for understanding the complex relationships between HIV-1 genotypic markers of resistance to antiretroviral drugs and clinical measures of disease progression. A new classification scheme based on the probabilities of how new patients will respond to antiretroviral therapy given the available data is proposed as a method for distinguishing among groups of viral sequences. This approach draws from existing cluster analysis, discriminant analysis, and recursive partitioning techniques and requires a model relating genotypic characteristics to phenotypic response. A data set of 2,746 sequences and the corresponding Indinavir 50% inhibitory concentrations are described and used for illustrative purposes.

Considerations in the evaluation of surrogate endpoints in clinical trials. summary of a National Institutes of Health workshop.

We report on recommendations from a National Institutes of Health Workshop on methods for evaluating the use of surrogate endpoints in clinical trials, which was attended by experts in biostatistics and clinical trials from a broad array of disease areas. Recent advances in biosciences and technology have increased the ability to understand, measure, and model biological mechanisms; appropriate application of these advances in clinical research settings requires collaboration of quantitative and laboratory scientists. Biomarkers, new examples of which arise rapidly from new technologies, are used frequently in such areas as early detection of disease and identification of patients most likely to benefit from new therapies. There is also scientific interest in exploring whether, and under what conditions, biomarkers may substitute for clinical endpoints of phase III trials, although workshop participants agreed that these considerations apply primarily to situations where trials using clinical endpoints are not feasible. Evaluating candidate biomarkers in the exploratory phases of drug development and investigating surrogate endpoints in confirmatory trials require the establishment of a statistical and inferential framework. As a first step, participants reviewed methods for investigating the degree to which biomarkers can explain or predict the effect of treatments on clinical endpoints measured in clinical trials. They also suggested new approaches appropriate in settings where biomarkers reflect only indirectly the important processes on the causal path to clinical disease and where biomarker measurement errors are of concern. Participants emphasized the need for further research on development of such models, whether they are empirical in nature or attempt to describe mechanisms in mathematical terms. Of special interest were meta-analytic models for combining information from multiple studies involving interventions for the same condition. Recommendations also included considerations for design and conduct of trials and for assemblage of databases needed for such research. Finally, there was a strong recommendation for increased training of quantitative scientists in biologic research as well as in statistical methods and modeling to ensure that there will be an adequate workforce to meet future research needs.

Patterns of plasma human immunodeficiency virus type 1 RNA response to antiretroviral therapy.

Early identification of treatment failure among human immunodeficiency virus (HIV) type 1--infected patients receiving antiretroviral therapy could enable clinicians to modify inadequate regimens and to improve treatment response. Clinical definitions of treatment failure, however, may not be ideally suited for this purpose. This study empirically characterizes the patterns of HIV-1 RNA response to antiretroviral therapy in patients in 4 AIDS clinical trials. The approach assumed 2 patterns of HIV-1 response: "on track," for eventual suppression to HIV-1 RNA levels below the limit of quantification, and "off track," for deviation from this response. The results of this on- or off-track classification generally agreed with the protocol-defined outcomes of virologic success and failure, thus validating these commonly used definitions. Overall, only a minority of patients went off track because of suboptimal HIV-1 RNA response by the first follow-up visit. Most patients who went off track did so at later time points and had sharp unexpected rebounds without prior indication of a suboptimal response.

Comparative analysis of HIV type 1 genotypic resistance across antiretroviral trial treatment regimens.

From data on HIV-1 genotypes collected from antiretroviral trial participants who fail virologically, we describe methods for comparing distributions of acquired HIV-1 mutations across different treatment regimens. Given a definition of a "mutational distance" that summarizes the genetic change of a subject's virus in a way that captures the resistance cost of exposure to an antiretroviral regimen, these comparative analyses inform about the relative treatability of emergent virus by next-line therapy directed to the same viral target. The utility of the methods is illustrated by application to data from AIDS Clinical Trials Group (ACTG) Study 241. We find that patients failing zidovudine/didanosine/nevirapine accumulated a 2.41-fold greater nonnucleoside reverse transcriptase inhibitor (RTI) mutational distance than patients failing zidovudine/didanosine [95% confidence interval (1.55, 5.26), p < 0.000001], quantitating expectations that adding a nonnucleoside RTI to a double nucleoside regimen may attenuate future effectiveness of nonnucleoside RTI therapy for nucleoside-experienced patients if viremia is not suppressed. We also find that persons with extensive prior experience with suboptimal nucleoside therapy who were virologically failing zidovudine/didanosine/nevirapine or zidovudine/didanosine accumulated a similar nucleoside RTI mutational distance, implying that the addition of the nonnucleoside RTI did not preserve future nucleoside options.

Assessment of non-response bias in a probability household survey of male same-gender sexual behavior.

OBJECTIVE: To assess non-participation bias in a survey of male sexual behavior. MATERIAL AND METHODS: A household survey was carried out in 1992-1993 using a probability sampling frame in Mexico City. Demographic variables were available for all eligible men. The extent of non-participation bias was estimated using a version of the Heckman method, which utilizes two equations, one to predict participation and the other to predict reports of same-gender sexual behavior. RESULTS: A total of 8,068 of the 13,713 eligible men completed a face-to-face questionnaire (response rate 59%); 173 men (2.1%) reported bisexual behavior in their lifetime, and 37 (0.4%) reported only male partners. Survey participation was predicted using demographic variables: 67% of the observations were correctly predicted by a probit regression model: 82% of participants and 53% of non-participants (pseudo-r2 = 0.13). Same-gender sexual behavior was predicted by variables indicating attachment to gay/bisexual social networks, history of sexually transmitted diseases, positive attitudes towards gay and bisexual males, and lack of support from male relatives. Ninety-seven per cent of the cases was correctly predicted by the probit model (pseudo-r2 = 0.14). The correlation between these two equations was not statistically significant. CONCLUSIONS: These results indicate that prevalence estimates of same-gender sexual behavior among Mexico City men were not biased by selective survey participation. Careful selection and training of household interviewers may have assisted in minimizing potential bias.

Recombinant gp160 as a therapeutic vaccine for HIV-infection: results of a large randomized, controlled trial. European Multinational IMMUNO AIDS Vaccine Study Group.

OBJECTIVES: The primary objective of this study was to expand the safety and immunogenicity database of recombinant gp160 as a therapeutic vaccine in the treatment of HIV-infection. Preliminary efficacy data was also sought. DESIGN: This trial was a randomized, double-blind, placebo-controlled study. Two-hundred and eight volunteers, 96 therapy-naive with CD4 cell count >500x10(6)/l (group A) and 112 with CD4 cell count of 200-500x10(6)/l (group B, 51 out of 112 on treatment with one or two nucleoside analogues), received monthly injections of rgp160 IIIB vaccine or placebo for the first 6 months of the study; booster immunizations with rgp160 MN or placebo were given at times 15, 18, and 21 months. METHODS: Safety and immunogenicity data were obtained and measurements of CD4 cell count, plasma viral RNA, and proviral DNA were performed. Clinical outcome was recorded for the 24 months of study. RESULTS: The vaccine was safe and well tolerated. Despite the induction of new rgp160-specific lymphoproliferative responses and the presence of positive delayed type hypersensitivity skin tests to rgp160 at the end of the 24 month study, no effect on the natural history of HIV infection was detected. Within 24 months, AIDS-defining illnesses had occurred in 19 of the vaccinated volunteers and in 18 of the placebo recipients. Persons with higher plasma viral RNA levels and higher proviral DNA had a more rapid decline in CD4 cell count when compared to persons with lower values. Vaccine did not alter viral RNA or proviral DNA levels. CONCLUSION: There was no clinical benefit to therapeutic immunizations with rgp160, despite the induction of new lymphoproliferative responses.

Strategies for cohort sampling under the Cox proportional hazards model, application to an AIDS clinical trial.

In some studies that relate covariates to times of failure it is not feasible to observe all covariates for all subjects. For example, some covariates may be too costly in terms of time, money, or effect on the subject to record for all subjects. This paper considers the relative efficiencies of several designs for sampling a portion of the cohort on which the costly covariates will be observed. Such designs typically measure all covariates for each failure and control for covariates of lesser interest. Control subjects are sampled either from "risk sets" at times of observed failures or from the entire cohort. A new design in which the sampling probability for each individual depends on the amount of information that the individual can contribute to estimated coefficients is shown to be superior to other sampling designs under certain conditions. Primary focus of our designs is on time-invariant covariates, but some methods easily generalize to the time-varying setting. Data from a study conducted by the AIDS Clinical Trials Group are used to illustrate the new sampling procedure and to explore the relative efficiency of several sampling schemes.

Estimation of HIV dynamic parameters.

Investigation of HIV viral dynamics is important for understanding the HIV pathogenesis and for development of treatment strategies. Perelson et al. demonstrated that simple viral dynamic models fit to data on viral load as measured by plasma HIV-RNA could produce estimates of rates of clearance of virus and of infected CD4+ T-lymphocytes. In this paper we extend the work of Perelson et al. by proposing models with less restrictive assumptions about drug activity. Our models take into account the fact that infectious and non-infectious virions are produced by infected T-cells both before and after the treatment. We also show that direct measurement of infectious virus load provides sufficient information for estimation of antiretroviral drug efficacy parameter. For characterizing viral dynamics of populations and estimation of dynamic parameters, we propose a hierarchical non-linear model. Compared to other methods such as the non-linear least square method used by Perelson et al., we show that the proposed approach has the following advantages: (i) it is more appropriate for modelling within-patient and between-patient variation and to characterize the population dynamics; (ii) it is flexible enough to deal with both rich and sparse individual data; (iii) it has more power to detect model misspecification; (iv) it allows incorporation of covariates for viral dynamic parameters; (v) it makes more efficient use of between-subject information to get better parameter estimates. We give two simulation examples to illustrate the proposed approach and its advantages. Finally, we discuss practical issues regarding the clinical trial design for viral dynamic studies.

Perspective: human immunodeficiency virus type 1 (HIV-1) RNA end points in HIV clinical trials: issues in interim monitoring and early stopping.

Due to the desire to both shorten the length and reduce the size of clinical trials in human immunodeficiency virus (HIV) disease, the use of surrogate end points such as HIV-1 RNA is becoming increasingly standard. While these end points may be reasonable surrogates for the clinical effectiveness of drugs, a key point in their use as trial end points is the definition of a relevant duration of antiviral response. This definition is often complicated by the desire to perform interim reviews of ongoing laboratory end point trials. Unlike clinical end point trials, in which early clinical response is generally indicative of longer-term follow-up, it is yet to be determined whether short-term viral response adequately predicts the long-term durability of that response.

Estimating the proportion of treatment effect explained by a surrogate marker.

In this paper, we measure the extent to which a biological marker is a surrogate endpoint for a clinical event by the proportional reduction in the regression coefficient for the treatment indicator due to the inclusion of the marker in the Cox regression model. We estimate this proportion by applying the partial likelihood function to two Cox models postulated on the same failure time variable. We show that the resultant estimator is asymptotically normal with a simple variance estimator. One can construct confidence intervals for the proportion by using the direct normal approximation to the point estimator or by using Fieller's theorem. Extensive simulation studies demonstrate that the proposed methods are appropriate for practical use. We provide applications to HIV/AIDS clinical trials.

An approach to the validation of markers for use in AIDS clinical trials.

Dr. Mildvan and coauthors have thoroughly reviewed and documented what is known about the validation of surrogate markers for use in clinical trials. They have proposed a classification system based on the usefulness of available immunologic and virological assays as measures of prognosis, drug activity, and therapeutic efficacy. The latter, a type II marker in the proposed classification, should estimate the proportion of treatment effect explained by change in the marker induced by therapy and, if complete, can substitute for clinical endpoints. HIV clinical trialists have had a long-standing interest in using surrogates for clinical endpoints to facilitate conduct of experimental protocols and to decrease the time and effort required to develop new treatment strategies. The approach outlined in this review by experienced clinicians, biostatisticians, and immunologists provides a framework to evaluate currently available and potential surrogate markers.

Perspective: validating surrogate markers--are we being naive?

Because of the difficulties in conducting studies of clinical efficacy of new therapies for human immunodeficiency virus infection and other diseases, there is increasing interest in using measures of biologic activity as surrogates for clinical end points. A widely used criterion for evaluating whether such measures are reliable as surrogates requires that the putative surrogate fully captures the "net effect"-the effect aggregated over all mechanisms of action-of the treatment on the clinical end point. The variety of proposed metrics for evaluating the degree to which this criterion is met are subject to misinterpretation because of the multiplicity of mechanisms by which drugs operate. Without detailed understanding of these mechanisms, metrics of "surrogacy" are not directly interpretable. Even when all of the mechanisms are understood, these metrics are associated with a high degree of uncertainty unless either treatment effects are large in moderate-size studies or sample sizes are large in studies of moderately effective treatments.

The predictive value of changes in serologic and cell markers of HIV activity for subsequent clinical outcome in patients with asymptomatic HIV disease treated with zidovudine.

OBJECTIVE: To determine if serologic marker responses to zidovudine treatment during the first year of antiretroviral therapy could predict subsequent HIV disease progression independently of absolute CD4 lymphocyte responses. METHODS: We conducted a case-control study in patients with asymptomatic HIV disease, who were initiating zidovudine therapy in a randomized, prospective trial. A total of 102 patients who progressed to AIDS or advanced AIDS-related complex and 177 randomly selected controls matched by baseline CD4 cell count and duration of follow-up had serum samples (from prior to and at 8, 16, 32 and 48 weeks of zidovudine treatment) assayed for acid-disassociated HIV p24 antigen, beta 2-microglobulin (beta 2M), neopterin, soluble interleukin (IL)-2 receptor, soluble CD4 protein and soluble CD8 protein. RESULTS: Median time to event for cases was 20.2 months; median follow-up on study was 35.4 months for controls. After controlling for absolute CD4 count at baseline, increased baseline serum concentrations of HIV p24 antigen, beta 2M, neopterin, and soluble IL-2 receptor were highly predictive of increased risk of HIV disease progression. In a multiple logistic regression model, controlling for baseline marker values, change in beta 2M consistently added independent value to change in CD4 count in predicting subsequent risk of disease progression. CONCLUSIONS: Monitoring serum immunologic markers, in particular beta 2M, in addition to absolute CD4 lymphocyte counts prior to and within the first 4 months after initiating dideoxynucleoside therapy can increase the accuracy of estimations of subsequent long-term risk of clinical HIV disease progression. This information may be useful to clinicians and patients who are making decisions about initiating or changing antiretroviral therapy.

Modelling progression of CD4-lymphocyte count and its relationship to survival time.

The purpose of this article is to model the progression of CD4-lymphocyte count and the relationship between different features of this progression and survival time. The complicating factors in this analysis are that the CD4-lymphocyte count is observed only at certain fixed times and with a high degree of measurement error, and that the length of the vector of observations is determined, in part, by the length of survival. If probability of death depends on the true, unobserved CD4-lymphocyte count, then the survival process must be modelled. Wu and Carroll (1988, Biometrics 44, 175-188) proposed a random effects model for two-sample longitudinal data in the presence of informative censoring, in which the individual effects included only slopes and intercepts. We propose methods for fitting a broad class of models of this type, in which both the repeated CD4-lymphocyte counts and the survival time are modelled using random effects. These methods permit us to estimate parameters describing the progression of CD4-lymphocyte count as well as the effect of differences in the CD4 trajectory on survival. We apply these methods to results of AIDS clinical trials.

Regression analysis of censored and truncated data: estimating reporting-delay distributions and AIDS incidence from surveillance data.

AIDS surveillance provides a vital source of information for health departments to assess the AIDS epidemic and to plan for future health-care needs. However, the use of surveillance data requires proper adjustments for the underreporting of AIDS cases caused by the delay in reporting diagnosed AIDS cases to the surveillance system. The statistical problem of adjusting for this underreporting concerns making inferences about an unobservable random sample of which only a portion is observed in a chronologic time interval defined by the analysis. Most regression methods for making inferences using right-truncated data employ a reverse-time hazard function, which requires that the observed data be transformed so that methods for left-truncated data can be applied. In this paper, we discuss fitting regression models to data that can be truncated and even censored in arbitrary intervals. The proposed methodology was applied to the national AIDS surveillance data provided by the Centers for Disease Control to analyze the trend of delays over chronologic time and variation among different geographic regions as well as across risk groups.

Modeling the relationship between survival and CD4 lymphocytes in patients with AIDS and AIDS-related complex.

CD4 lymphocyte and survival data from two completed trials, a double-blind placebo-controlled trial of zidovudine in patients with advanced human immunodeficiency virus type 1 (HIV) disease (BW-02 study) and a randomized trial of two different doses of zidovudine in patients with advanced HIV disease (ACTG-002 study) were used to determine the degree to which CD4 lymphocyte counts reflect zidovudine-associated survival benefit. Proportional hazards models were used, and CD4 lymphocyte counts were smoothed by using empirical Bayes estimates. The geometric mean of the CD4 lymphocyte counts increased by 71 and 46 cells/mm3 for patients in the BW-02 and ACTG-002 studies, respectively, followed by a progressive decline. Higher pretreatment CD4 lymphocyte counts (p = 0.001), greater increases in CD4 lymphocytes at 8 weeks (p = 0.1), and smaller declines in the slope (p = 0.001) were associated with a lower risk of death. The most current CD4 lymphocyte count was most prognostic of death (p = 0.001). The risk of death was greater for patients with lower CD4 lymphocytes and this risk increased sharply when the CD4 lymphocyte counts fell below 50 cells/mm3. The hazard of death was higher for placebo recipients at all levels of CD4 lymphocytes compared with zidovudine recipients. Although higher CD4 lymphocyte counts are associated with improved survival, these increases account for only a small proportion of the survival benefit of zidovudine in these two studies.