Bronchoalveolar lavage cell immunophenotyping facilitates diagnosis of lung allograft rejection.

Supplementary methods to identify acute rejection and to distinguish rejection from infection may improve clinical outcomes for lung allograft recipients. We hypothesized that distinct bronchoalveolar lavage (BAL) cell profiles are associated with rejection and infection. We retrospectively compared 2939 BAL cell counts and immunophenotypes against concomitantly obtained transbronchial biopsies and microbiologic studies. We randomly assigned 317 subjects to a derivation or validation cohort. BAL samples were classified into four groups: infection, rejection grade ≥A1, both or neither. We employed generalized estimating equation and survival modeling to identify clinical predictors of rejection and infection. We found that CD25(+) and natural killer cell percentages identified a twofold increased odds of rejection compared to either the infection or the neither infection nor rejection groups. Also, monocytes, lymphocytes and eosinophil percentages were independently associated with rejection. A four-predictor scoring system had high negative predictive value (96-98%) for grade ≥A2 rejection, predicted future rejection in the validation cohort and predicted increased risk of bronchiolitis obliterans syndrome in otherwise benign samples. In conclusion, BAL cell immunophenotyping discriminates between infection and acute rejection and predicts future outcomes in lung transplant recipients. Although it cannot replace histopathology, immunophenotyping may be a clinically useful adjunct.

Misclassified group-tested current status data.

Group testing, introduced by Dorfman (1943), has been used to reduce costs when estimating the prevalence of a binary characteristic based on a screening test of [Formula: see text] groups that include [Formula: see text] independent individuals in total. If the unknown prevalence is low and the screening test suffers from misclassification, it is also possible to obtain more precise prevalence estimates than those obtained from testing all [Formula: see text] samples separately (Tu et al., 1994). In some applications, the individual binary response corresponds to whether an underlying time-to-event variable [Formula: see text] is less than an observed screening time [Formula: see text], a data structure known as current status data. Given sufficient variation in the observed [Formula: see text] values, it is possible to estimate the distribution function [Formula: see text] of [Formula: see text] nonparametrically, at least at some points in its support, using the pool-adjacent-violators algorithm (Ayer et al., 1955). Here, we consider nonparametric estimation of [Formula: see text] based on group-tested current status data for groups of size [Formula: see text] where the group tests positive if and only if any individual's unobserved [Formula: see text] is less than the corresponding observed [Formula: see text]. We investigate the performance of the group-based estimator as compared to the individual test nonparametric maximum likelihood estimator, and show that the former can be more precise in the presence of misclassification for low values of [Formula: see text]. Potential applications include testing for the presence of various diseases in pooled samples where interest focuses on the age-at-incidence distribution rather than overall prevalence. We apply this estimator to the age-at-incidence curve for hepatitis C infection in a sample of U.S. women who gave birth to a child in 2014, where group assignment is done at random and based on maternal age. We discuss connections to other work in the literature, as well as potential extensions.

CD8+ T lymphocytes and progression to AIDS in HIV-infected men: some observations.

The relationship between CD8+ lymphocyte counts and progression to AIDS was studied in 340 HIV-1-seropositive men participating in a population-based prospective study. Overall, the relative hazard for developing AIDS during 60 months of observation was slightly elevated (1.08, P = 0.003), indicating an 8% increase in risk of progression for every 100 CD8+ cell count increment. When the data were analyzed in relation to date of diagnosis, the relative hazard was depressed (0.90, P less than 0.001) for the period 6 months prior to diagnosis, but was close to 1.0 for the period 6-36 months prior to diagnosis. These findings suggest a complex relationship between CD8+ cell counts and progression to AIDS, with the possibility that various subsets of the CD8+ compartment play different roles.

Benefits associated with WIC supplemental feeding during the interpregnancy interval.

Interpregnancy WIC supplementation was evaluated by comparing maternal nutritional status indicators and subsequent birth outcomes of 703 WIC participants divided into two groups. Study group women received postpartum benefits for 5-7 mo while control group women received postpartum benefits for only 0-2 mo. Both groups received prenatal benefits during each of two study pregnancies. Infants born to study group women had a higher mean birthweight (131 g) and birthlength (0.3 cm) and a lower risk of being less than or equal to 2500 g. Additionally, at the onset of the second pregnancy study group women had higher mean hemoglobin levels and lower risk of maternal obesity. These results suggest that postpartum WIC supplementation has positive benefits for both the mother and her subsequent infants.

PIP: Nutritional depletion is often found in women of high parity and short intervals between births. Food supplementation for the postpartum woman may be a desirable intervention. Interpregnancy WIC (special supplemental food program for women, infants and children) supplementation was evaluated by comparing maternal nutritional status indicators and subsequent birth outcomes of 703 California WIC participants divided into 2 groups. Study group women received postpartum benefits for 5-7 months white control group women received postpartum benefits for only 0-2 months. Both groups received prenatal benefits during each of 2 study pregnancies. Infants born to study group women had a higher mean birthweight and birthlength and a lower risk of being less than 2500 grams. Additionally, at the onset of the 2nd pregnancy study group women had higher mean hemoglobin levels and lower risk of maternal obesity. Since many women do not enter the WIC program until 3 ro 4 months following conception, they miss the opportunity to receive supplemental nutrients when the need is most critical. From the observations in this study, increases in birthweight are not operating through increased gestation. For underweight women these results are compatible with the belief of most nutritionists that supplementation will increase energy reserves which are beneficial for normal fetal growth. Extended feeding during the interpregnancy interval improves both infant and maternal nutritional status by enhancing birthweight and birthlength and altering the prepregnancy weight of women to a more optimal level. Women receiving extended postpartum benefits compared to those receiving limited benefits had a lowered risk o fdelivering infants of low birthweight and 1/2 the odds of maternal obesity at the onset of their subsequent pregnancy. The magnitude of birthweight effects demonstrated in this study emphasize the potentially important role of interpregnancy nutrition in human reproduction.

Discriminant analysis of data in enzyme immunoassay.

A critical step in the development of both qualitative and quantitative enzyme immunoassays is establishing the positive/negative discrimination, or cut-off, value. Data derived from an indirect immunofluorescence assay, hemagglutination inhibition, and enzyme immunoassay to detect IgG antibodies to measles virus were applied to a discriminant analysis program to determine the positive/negative cut-off value. Application of the discriminant analysis demonstrated a greater utilization of the sensitivity of the enzyme immunoassay than the most commonly used methods. This method also illustrates the importance of examining both antibody positive and negative sera, rather than negative sera alone, in determining the cut-off value. In addition, probability of membership in the antibody positive or negative group is included in the determination. This increases the information base for risk assessment and clinical evaluation.

Characterization of the receptor mediating the nicotine discriminative stimulus.

The discriminative stimulus (cue) property of nicotine was studied in a T-maze paradigm, and the results were analyzed by a new statistical method. For rats trained on 0.4 mg/kg, the ED50 was 0.11 mg/kg. The enantinomer of natural nicotine (+)nicotine was much less potent, and both position isomers of nicotine were inactive. Anabasine, which is active at nicotinic cholinergic receptors, provided the nicotine cue. Cytisine, a potent nicotinic agonist in vitro, was ineffective after SC administration and this was shown to be due to its inability to enter the brain in adequate amounts. High doses of cytisine by the intracerebroventricular route partially provided the cue. The cue was blocked by low doses of mecamylamine and pempidine and by high doses of hexamethonium. The data indicate that the cue receptor is pharmacologically similar to the nicotinic cholinergic receptor in autonomic ganglia.

The glyceryl [14C]tripalmitate breath test: a reassessment.

Several reports have been published commending the use of 14C-labelled triglyceride breath tests in the assessment of fat malabsorption. We report further studies using gyceryl [14C]tripalmitate. Corrections for age, weight or metabolic rate failed to improve the test's ability to discriminate between malabsorbers and control subjects. A correction for respiratory quotient improved the linear correlation observed between the breath test results and daily faecal fat excretion. The significance of these findings is discussed and a number of problems identified which, at present, are preventing the introduction of breath tests for fat malabsorption into routine clinical practice.

Demand for outpatient mental health services in a heavily insured population: the case of the Blue Cross and Blue Shield Association's Federal Employees Health Benefits Program.

This article presents the results of a study of the impact of an increase in coinsurance on the demand for outpatient mental health services. The study population was a set of fully employed subscribers enrolled in the Blue Cross and Blue Shield Association's Federal Employees Health Benefits Program at some time during the period 1979 through 1981. A two-part model was used to examine the determinants of both the probability of mental health service use and the level of use. Our results indicate little price sensitivity in either part of the model, but substantial and significant income elasticities. Our results concerning the role of various sociodemographic and environmental variables are also reported.

Semiparametric estimation of proportional mean residual life model in presence of censoring.

A mean residual life function is the average remaining life of a surviving subject, as it varies with time. The proportional mean residual life model was proposed by Oakes and Dasu (1990, Biometrika77, 409-410) in regression analysis to study its association with related covariates in absence of censoring. In this article, we develop some semiparametric estimation procedures to take censoring into account. The proposed methodology is evaluated via simulation studies, and further applied to a clinical trial of chemotherapy in postoperative radiotherapy of lung cancer patients.

Non-parametric estimation and doubly-censored data: general ideas and applications to AIDS.

In many epidemiologic studies of human immunodeficiency virus (HIV) disease, interest focuses on the distribution of the length of the interval of time between two events. In many such cases, statistical estimation of properties of this distribution is complicated by the fact that observation of the times of both events is subject to intervalcensoring so that the length of time between the events is never observed exactly. Following DeGruttola and Lagakos, we call such data doubly-censored. Jewell, Malani and Vittinghoff showed that, with certain assumptions and for a particular doubly-censored data structure, non-parametric maximum likelihood estimation of the interval length distribution is equivalent to non-parametric estimation of a mixing distribution. Here, we extend these ideas to various other kinds of doubly-censored data. We consider application of the methods to various studies generated by investigations into the natural history of HIV disease with particular attention given to estimation of the distribution of time between infection of an individual (an index case) and transmission of HIV to their sexual partner.

Some statistical issues in studies of the epidemiology of AIDS.

Analysis of studies of the epidemiology and natural history of infection with the Human Immunodeficiency Virus and subsequent onset of AIDS are complicated by many statistical issues. Several such problems are associated with the nature of data collection which is often incomplete. Here we briefly survey some of the statistical methods that have been developed to meet the needs of analysis of AIDS data. In particular, we consider projection of the number of future cases, and estimation and identification of two key epidemiological unknowns, namely the properties of the incubation distribution and those of the infectivity associated with transmission.

Small-sample bias of point estimators of the odds ratio from matched sets.

The bias of several point estimators of the odds ratio arising from matched-pair data is investigated for small samples. Simple alternatives to the traditional maximum likelihood estimator are suggested, on both the original scale and the logarithm scale. In each case the suggested estimators possess a superior performance in terms of mean square error. Generalizations are given for 1:R matched data sets.

Generalizations of current status data with applications.

In estimation of a survival function, current status data arises when the only information available on individuals is their survival status at a single monitoring time. Here, we briefly review extensions of this form of data structure in two directions: (i) doubly censored current status data, where there is incomplete information on the origin of the failure time random variable, and (ii) current status information on more complicated stochastic processes. Simple examples of these data forms are presented for motivation.

Nonparametric estimation of the incubation period of AIDS based on a prevalent cohort with unknown infection times.

Estimation of the incubation period distribution of human immunodeficiency virus based on prevalent cohorts of subjects, already infected at the time of recruitment, is complicated by the absence of information on the original times of infection. Here, we overcome this difficulty by using a prior distribution for the infection times, based on external data. Our estimate is nonparametric, but uses smoothness assumptions to avoid instability. The method is illustrated on two prevalent cohorts from San Francisco, separately and combined. The estimates produced agree with other published estimates of the incubation period distribution.

Marker processes in survival analysis.

In the development of many diseases there are often associated variables which continuously measure the progress of an individual towards the final expression of the disease (failure). Such variables are stochastic processes, here called marker processes, and, at a given point in time, they may provide information about the current hazard and subsequently on the remaining time to failure. Here we consider a simple additive model for the relationship between the hazard function at time t and the history of the marker process up until time t. We develop some basic calculations based on this model. Interest is focused on statistical applications for markers related to estimation of the survival distribution of time to failure, including (i) the use of markers as surrogate responses for failure with censored data, and (ii) the use of markers as predictors of the time elapsed since onset of a survival process in prevalent individuals. Particular attention is directed to potential gains in efficiency incurred by using marker process information.

Statistical analysis of HIV infectivity based on partner studies.

Partner studies produce data on the infection status of partners of individuals known or assumed to be infected with the human immunodeficiency virus (HIV) after a known or estimated number of contacts. Previous studies have assumed a constant probability of transmission (infectivity) of the virus at each contact. Recently, interest has focused on the possibility of heterogeneity of infectivity across partnerships. This paper develops parametric and nonparametric procedures based on partner data in order to examine the risk of infection after a given number of contacts. Graphical methods and inference techniques are presented that allow the investigator to evaluate the constant infectivity model and consider the impact of heterogeneity of infectivity, error in measurement of the number of contacts, and regression effects of other covariates. The majority of the methods can be computationally implemented easily with use of software to fit generalized linear models. The concepts and techniques are closely related to ideas from discrete survival analysis. A data set on heterosexual transmission is used to illustrate the methods.

The effect of retrospective sampling on binary regression models for clustered data.

Recently a great deal of attention has been given to binary regression models for clustered or correlated observations. The data of interest are of the form of a binary dependent or response variable, together with independent variables X1,...., Xk, where sets of observations are grouped together into clusters. A number of models and methods of analysis have been suggested to study such data. Many of these are extensions in some way of the familiar logistic regression model for binary data that are not grouped (i.e., each cluster is of size 1). In general, the analyses of these clustered data models proceed by assuming that the observed clusters are a simple random sample of clusters selected from a population of clusters. In this paper, we consider the application of these procedures to the case where the clusters are selected randomly in a manner that depends on the pattern of responses in the cluster. For example, we show that ignoring the retrospective nature of the sample design, by fitting standard logistic regression models for clustered binary data, may result in misleading estimates of the effects of covariates and the precision of estimated regression coefficients.

Female-to-male transmission of human immunodeficiency virus.

OBJECTIVE: --To examine rates of heterosexual transmission of human immunodeficiency virus (HIV) and associated risk factors and to determine the relative efficiency of female-to-male and male-to-female transmission. DESIGN: --Survey of infected individuals and their heterosexual partners recruited since 1985. SETTING: --Participants were recruited from various HIV counseling and testing sites throughout California but were generally interviewed and tested in their homes. PARTICIPANTS: --Data from 379 couples at entry to the study are reported: 72 male partners of infected women and 307 female partners of infected men. The infected index case had a well-established source of risk; couples were eliminated if the direction of transmission could not be established. The majority of couples were monogamous since 1978, white, and in their 30s. Most partners did not know their serostatus at entry into the study. MAIN OUTCOME MEASURE: --HIV serostatus in the exposed sexual partner. RESULTS: --We observed one probable instance (1%) of female-to-male transmission compared with 20% transmission rates in the female partners of infected men. All couples were sampled in the same way. Male index cases were more likely to be symptomatic than female index cases. CONCLUSION: --The odds of male-to-female transmission were significantly greater than female-to-male transmission. The one case of female-to-male transmission was unique in that the couple reported numerous unprotected sexual contacts and noted several instances of vaginal and penile bleeding during intercourse.

Estimating patterns of CD4 lymphocyte decline using data from a prevalent cohort of HIV infected individuals.

In natural history studies of chronic disease, it is of interest to understand the evolution of key variables that measure aspects of disease progression. This is particularly true for immunological variables among persons infected with the human immunodeficiency virus (HIV). The natural time scale for such studies is time since infection. Most data available for analysis, however, arise from prevalent cohorts, where the date of infection is unknown for most or all individuals. As a result, standard curve fitting algorithms are not immediately applicable. Here we propose two methods to circumvent this difficulty. The first uses repeated measurement data to provide information not only on the level of the variable of interest, but also on its rate of change, and is based on the principal curves algorithm of Hastie and Stuetzle. The second uses an external estimate of the expected time since infection. Both methods use locally-weighted linear smoothers, and are applied to data from a prevalent cohort of HIV-infected homosexual men, giving estimates of the average pattern of CD4 lymphocyte decline. These methods apply to natural history studies that use data from prevalent cohorts where the time of disease origin is uncertain, provided availability of certain information from external sources.

Statistical models for prevalent cohort data.

In prospective cohort studies individuals are sometimes recruited according to a certain cross-sectional sampling criterion. A prevalent cohort is defined as a group of individuals who have a certain disease at enrollment into the study. Statistical models for the analysis of prevalent cohort data are considered when the onset or diagnosis time of the disease is known. The incident proportional hazards model, where the time scale is duration with disease, is compared to the prevalent proportional hazards model, where the fundamental time scale is follow-up time. In certain cases the time of enrollment may coincide with another event (such as the initiation of treatment). This situation is also considered and its limitations highlighted. To illustrate the methodological ideas discussed in the paper, the analysis of data from an observational study of zidovudine (ZVD) in patients with the acquired immunodeficiency syndrome (AIDS) is presented.