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A potential difficulty in the analysis of biomarker data occurs when data are subject to a detection limit. This detection limit is often defined as the point at which the true values cannot be measured reliably. Multiple, regression-type models designed to analyze such data exist. Studies have compared the bias among such models, but few have compared their statistical power. This simulation study provides a comparison of approaches for analyzing two-group, cross-sectional data with a Gaussian-distributed outcome by exploring statistical power and effect size confidence interval coverage of four models able to be implemented in standard software. We found using a Tobit model fit by maximum likelihood provides the best power and coverage. An example using human immunodeficiency virus type 1 ribonucleic acid data is used to illustrate the inferential differences in these models. [ABSTRACT FROM AUTHOR]

Laboratory data with a lower quantification limit (censored data) are sometimes analyzed by replacing non-quantifiable values with a single value equal to or less than the quantification limit, yielding possibly biased point estimates and variance estimates that are too small. Motivated by a three-period, three-treatment crossover study of a candidate vaginal microbicide in human immunodeficiency virus (HIV)-infected women, we consider four analysis methods for censored Gaussian data with a single follow-up measurement: nonparametric methods, mixed models, mixture models, and dichotomous measures of a treatment effect. We apply these methods to the crossover study data and use simulation to evaluate the statistical properties of these methods in analyzing the treatment effect in a two-treatment parallel-arm or crossover study with censored Gaussian data. Our simulated data and our mixed and mixture models consider treated follow-up data with the same variance as the baseline data or with an inflated variance. Mixed models have the correct type I error, the best power, the least biased Gaussian parameter treatment-effect estimates, and appropriate confidence interval coverage for these estimates. A crossover study analysis with a period effect can greatly increase the required study sample size. For both designs and both variance assumptions, published sample-size estimation methods do not yield a good estimate of the sample size to obtain the stated power. [ABSTRACT FROM AUTHOR]

Time-location sampling (TLS) is useful for collecting information on a hard-to-reach population (such as men who have sex with men [MSM]) by sampling locations where persons of interest can be found, and then sampling those who attend. These studies have typically been analyzed as a simple random sample (SRS) from the population of interest. If this population is the source population, as we assume here, such an analysis is likely to be biased, because it ignores possible associations between outcomes of interest and frequency of attendance at the locations sampled, and is likely to underestimate the uncertainty in the estimates, as a result of ignoring both the clustering within locations and the variation in the probability of sampling among members of the population who attend sampling locations. We propose that TLS data be analyzed as a two-stage sample survey using a simple weighting procedure based on the inverse of the approximate probability that a person was sampled and using sample survey analysis software to estimate the standard errors of estimates (to account for the effects of clustering within the first stage [locations] and variation in the weights). We use data from the Young Men's Survey Phase II, a study of MSM, to show that, compared with an analysis assuming a SRS, weighting can affect point prevalence estimates and estimates of associations and that weighting and clustering can substantially increase estimates of standard errors. We describe data on location attendance that would yield improved estimates of weights. We comment on the advantages and disadvantages of TLS and respondent-driven sampling. [ABSTRACT FROM AUTHOR]

The development of an human immunodeficiency virus (HIV) test that detects recent infection has enabled the U.S. Centers for Disease Control and Prevention (CDC) to estimate annual HIV incidence (number of new infections per year, not per person at risk) in the United States from data on new HIV and acquired immunodeficiency syndrome (AIDS) diagnoses reported to HIV/AIDS surveillance. We developed statistical procedures to estimate the probability that an infected person will be detected as recently infected, accounting for individuals choosing whether and how frequently to seek HIV testing, variation of testing frequency, the reporting of test results only for infected persons, and infected persons who never had an HIV-negative test. The incidence estimate is the number of persons detected as recently infected divided by the estimated probability of detection. We used simulation to show that, under the assumptions we make, our procedures have acceptable bias and correct confidence interval coverage. Because data on the biomarker for recent infection or on testing history were missing for many persons, we used multiple imputation to apply our models to surveillance data. CDC has used these procedures to estimate HIV incidence in the United States. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The analysis of length-biased data has been mostly limited to the interarrival interval of a renewal process covering a specific time point. Motivated by a surveillance problem, we consider a more general situation where this time point is random and related to a specific event, for example, status change or onset of a disease. We also consider the problem when additional information is available on whether the event intervals (interarrival intervals covering the random event) end within or after a random time period (which we call a window period) following the random event. Under the assumptions that the occurrence rate of the random event is low and the renewal process is independent of the random event, we provide formulae for the estimation of the distribution of interarrival times based on the observed event intervals. Procedures for testing the required assumptions are also furnished. We apply our results to human immunodeficiency virus (HIV) test data from public test sites in Seattle, Washington, where the random event is HIV infection and the window period is from the onset of HIV infection to the time at which a less sensitive HIV test becomes positive. Results show that the estimator of the intertest interval length distribution from event intervals ending within the window period is less biased than the estimator from all event intervals; the latter estimator is affected by right truncation. Finally, we discuss possible applications to estimating HIV incidence and analyzing length-biased samples with right or left truncated data. [ABSTRACT FROM AUTHOR]

Motivated by a problem in estimating the incidence of a disease with delayed onset of symptoms, the current, excess, and total life length distributions of a renewal process at a random event time point are considered. Distributions of these renewal variables at a random time point can be easily derived if we know their corresponding distributions at a fixed time point. Unfortunately, the distribution conditional on a fixed time point is usually not available since it requires solving a renewal equation and an explicit solution of the renewal equation rarely exists. In this article, we consider the situation where the time to a specific event is random and independent of the renewal process. We assume that the occurrence rate of the event is constant, in other words, the time to the event for each individual follows an exponential distribution. Under these assumptions, we derive the distributions for the renewal variables considered. We also derive the distribution of the total life when excess life is bounded by an independent random variable. Using results developed in this article, one can derive the distributions of the renewal variables considered from the distribution of the renewal process and vice versa. Our results show that when the incidence rate is small, the renewal distributions can be approximated by the corresponding asymptotic distributions under equilibrium. [ABSTRACT FROM AUTHOR]

Objectives. The goals of this study were two-fold: (1) to describe methods for drawing a population-based sample of individuals in care for HIV infection and (2) to compare data from the sample with data from existing surveillance systems that describe care for HIV. Methods. The authors implemented a two-stage sampling method, using local HIV/AIDS surveillance data as a sampling frame of HIV care providers in three states. At selected providers, medical records of a random sample of patients were abstracted. Results. The medical records of a number of patients, ranging from 253 to 374 individuals per state, were abstracted. The demographics of sampled individuals and of individuals reported to the local HIV/AIDS surveillance program were similar; however, differences existed in the proportion of individuals receiving HIV care consistent with treatment guidelines between the sample and a contemporary facility-based supplemental surveillance project. The median design effect for outcomes collected in the sample was 1.8 (range=0.5-29.6). Conclusions. This survey method is feasible for collecting population-based data on patients in care for HIV. Sample size and some design elements should be changed in future studies to increase precision of estimates and usefulness of data for local planning and evaluation. [ABSTRACT FROM AUTHOR]

Conclusions. The epidemic increasingly affects women, minorities, persons infected through heterosexual contact, and the poor. Renewed interest and investment in HIV and AIDS surveillance and surveillance of behaviors associated with HIV transmission are essential to direct resources for prevention to populations with greatest need and to evaluate intervention programs. (Am J Public Health. 2001:91:1060-1068) [ABSTRACT FROM AUTHOR]

Presents information on a study which described methods of making statistical adjustments to the human immunodeficiency virus infection surveillance data for cases reported without exposure risk information. Methodology of the study; Results and discussion on the study; Conclusions.

Reports on an examination of national trends in survival time among persons diagnosed as having AIDS in 1984-1997 in the United States. Design; Setting; Participants; Main outcome measure; Results; Conclusion that as AIDS incidence declines, improved survival times that result in a large number of persons living with chronic HIV/AIDS are a growing public health concern.

Presents a study which investigates the state of the HIV epidemic among adolescent and young adult men who have sex with men in the United States by assessing the prevalence of HIV infection and associated risks in this population in metropolitan areas. Methods; Results; Conclusion.

The emergence of a new infectious disease, AIDS, in the early 1980s resulted in the development of a national AIDS surveillance system. AIDS surveillance data provided an understanding of transmission risks and characterized communities affected by the epidemic. Later, these data provided the basis for allocating resources for prevention and treatment programs. New treatments have dramatically improved survival. Resulting declines in AIDS incidence and deaths offer hope that HIV disease can be successfully managed. However, to prevent and control HIV/AIDS in the coming decades, the public health community must address new challenges. These include the defining of the role of treatment in reducing infectiousness; the potential for an epidemic of treatment-resistant HIV; side effects of treatment; complacency that leads to relapses to high-risk behaviors; and inadequate surveillance and research capacity at state and local levels to guide the development of health interventions. Meeting these challenges will require reinvesting in the public health capacity of state and local health departments, restructuring HIV/AIDS surveillance programs to collect the data needed to guide the response to the epidemic, and providing timely answers to emerging epidemiologic questions. (Am d Public Health. 2000; 90:1037-1041) [ABSTRACT FROM AUTHOR]

Of 88,510 cases of acquired immunodeficiency syndrome (AIDS) reported in adults in the United States from 1983 through 1988, the percentage attributed to reported heterosexual contact with persons known to be infected or at increased risk of infection with human immunodeficiency virus (HIV) has increased steadily from 0.9 percent in the first quarter of 1983 to 4.0 percent in the fourth quarter of 1988, from 0.1(in 1983) to 1.4 percent (in 1988) among men, and from 13 (in 1983) to 28 percent (in 1988) among women. Among women, the cumulative incidence of AIDS attributable to heterosexual contact per million population is over 11 times greater for Blacks and Hispanics than for Whites. Among men, this incidence is over 10 times greater for Blacks and four times greater for Hispanics than for Whites. The pattern of distribution of heterosexually acquired AIDS parallels the distribution of other heterosexually transmitted diseases, which are also more frequent in Black and Hispanic inner-city populations. Drug use, exchange of sex for drugs or money, and early onset of sexual activity in adolescents are increasingly associated with heterosexually transmitted infections and are likely to be very important in heterosexual transmission of HIV in inner-city US populations. [ABSTRACT FROM AUTHOR]

Presents a study regarding the prevalence of infection with the human immunodeficiency virus (HIV) in the United States from 1984 to 1992. Number of persons infected with the HIV virus; Change in HIV infection prevalence since 1984; Three data sources from which HIV prevalence was estimated; Results; Conclusions.

To measure the effect of the human immunodeficiency virus (HIV) epidemic on mortality from opportunistic infections (OIs) in 1993, national multiple-cause death certificate data were examined using two approaches. First, for each OI, the percentage of deaths with HIV infection reported as the underlying cause was calculated. Second, the age-adjusted rate of death per million population was compared with the rate predicted from a model of rates in 1970–1980 or 1979–1981, as available. The percentage of deaths with HIV as the underlying cause and the ratio of observed to predicted death rates were as follows: toxoplasmosis, 91% and 86 (5.24/0.06); cryptosporidiosis/isosporiasis, 90% and infinite (1.61/0.00); progressive multifocal leukoencephalopathy, 87% and 19 (2.58/0.13); pneumocystosis, 82% and 18 (15.44/0.87); cytomegalovirus disease, 82% and 17 (12.60/0.74); nontuberculous mycobacteriosis, 79% and 18 (15.51/0.84); cryptococcosis, 76% and 4 (5.80/1.35); and histoplasmosis, 68% and 6 (1.36/0.23). Thus, the HIV epidemic has greatly increased mortality from several OIs. [ABSTRACT FROM PUBLISHER]

Responds to comments on the paper 'Serum 2,3,7,8-Tetrachlorodibenzo-p-dioxin Levels in US Army Vietnam-Era Veterans,' prepared by the Centers for Disease Control Veterans Health Studies and published in a 1988 issue of 'Journal of the American Medical Association.' Purpose of the study; Questions regarding the metabolism of Agent Orange.