Influence of noninjecting and injecting drug use on mortality, retention in the cohort, and antiretroviral therapy, in participants in the Swiss HIV Cohort Study.

OBJECTIVES We studied the influence of noninjecting and injecting drug use on mortality, dropout rate, and the course of antiretroviral therapy (ART), in the Swiss HIV Cohort Study (SHCS). METHODS Cohort participants, registered prior to April 2007 and with at least one drug use questionnaire completed until May 2013, were categorized according to their self-reported drug use behaviour. The probabilities of death and dropout were separately analysed using multivariable competing risks proportional hazards regression models with mutual correction for the other endpoint. Furthermore, we describe the influence of drug use on the course of ART. RESULTS A total of 6529 participants (including 31% women) were followed during 31 215 person-years; 5.1% participants died; 10.5% were lost to follow-up. Among persons with homosexual or heterosexual HIV transmission, noninjecting drug use was associated with higher all-cause mortality [subhazard rate (SHR) 1.73; 95% confidence interval (CI) 1.07-2.83], compared with no drug use. Also, mortality was increased among former injecting drug users (IDUs) who reported noninjecting drug use (SHR 2.34; 95% CI 1.49-3.69). Noninjecting drug use was associated with higher dropout rates. The mean proportion of time with suppressed viral replication was 82.2% in all participants, irrespective of ART status, and 91.2% in those on ART. Drug use lowered adherence, and increased rates of ART change and ART interruptions. Virological failure on ART was more frequent in participants who reported concomitant drug injections while on opiate substitution, and in current IDUs, but not among noninjecting drug users. CONCLUSIONS Noninjecting drug use and injecting drug use are modifiable risks for death, and they lower retention in a cohort and complicate ART.

Age in antiretroviral therapy programmes in South Africa: a retrospective, multicentre, observational cohort study.

BACKGROUND As access to antiretroviral therapy (ART) expands, increasing numbers of older patients will start treatment and need specialised long-term care. However, the effect of age in ART programmes in resource-constrained settings is poorly understood. The HIV epidemic is ageing rapidly and South Africa has one of the highest HIV population prevalences worldwide. We explored the effect of age on mortality of patients on ART in South Africa and whether this effect is mediated by baseline immunological status. METHODS In this retrospective cohort analysis, we studied HIV-positive patients aged 16-80 years who started ART for the first time in six large South African cohorts of the International Epidemiologic Databases to Evaluate AIDS-Southern Africa collaboration, in KwaZulu-Natal, Gauteng, and Western Cape (two primary care clinics, three hospitals, and a large rural cohort). The primary outcome was mortality. We ascertained patients' vital status through linkage to the National Population Register. We used inverse probability weighting to correct mortality for loss to follow-up. We estimated mortality using Cox's proportional hazards and competing risks regression. We tested the interaction between baseline CD4 cell count and age. FINDINGS Between Jan 1, 2004, and Dec 31, 2013, 84,078 eligible adults started ART. Of these, we followed up 83,566 patients for 174,640 patient-years. 8% (1817 of 23,258) of patients aged 16-29 years died compared with 19% (93 of 492) of patients aged 65 years or older. The age adjusted mortality hazard ratio was 2·52 (95% CI 2·01-3·17) for people aged 65 years or older compared with those 16-29 years of age. In patients starting ART with a CD4 count of less than 50 cells per μL, the adjusted mortality hazard ratio was 2·52 (2·04-3·11) for people aged 50 years or older compared with those 16-39 years old. Mortality was highest in patients with CD4 counts of less than 50 cells per μL, and 15% (1103 of 7295) of all patients aged 50 years or older starting ART were in this group. The proportion of patients aged 50 years or older enrolling in ART increased with successive years, from 6% (290 of 4999) in 2004 to 10% (961 of 9657) in 2012-13, comprising 9% of total enrolment (7295 of 83 566). At the end of the study, 6304 (14%) of 44,909 patients still alive and in care were aged 50 years or older. INTERPRETATION Health services need reorientation towards HIV diagnosis and starting of ART in older individuals. Policies are needed for long-term care of older people with HIV. FUNDING National Institutes of Health (National Institute of Allergy and Infectious Diseases), US Agency for International Development, and South African Centre for Epidemiological Modelling and Analysis.

THE RELATIONSHIP BETWEEN SERUM CHOLESTEROL AND CANCER INCIDENCE IN THE POPULATION OF THE HYPERTENSION DETECTION AND FOLLOW-UP PROGRAM

The relationship between serum cholesterol and cancer incidence was investigated in the population of the Hypertension Detection and Follow-up Program (HDFP). The HDFP was a multi-center trial designed to test the effectiveness of a stepped program of medication in reducing mortality associated with hypertension. Over 10,000 participants, ages 30-69, were followed with clinic and home visits for a minimum of five years. Cancer incidence was ascertained from existing study documents, which included hospitalization records, autopsy reports and death certificates. During the five years of follow-up, 286 new cancer cases were documented. The distribution of sites and total number of cases were similar to those predicted using rates from the Third National Cancer Survey. A non-fasting baseline serum cholesterol level was available for most participants. Age, sex, and race specific five-year cancer incidence rates were computed for each cholesterol quartile. Rates were also computed by smoking status, education status, and percent ideal weight quartiles. In addition, these and other factors were investigated with the use of the multiple logistic model.^ For all cancers combined, a significant inverse relationship existed between baseline serum cholesterol levels and cancer incidence. Previously documented associations between smoking, education and cancer were also demonstrated but did not account for the relationship between serum cholesterol and cancer. The relationship was more evident in males than females but this was felt to represent the different distribution of occurrence of specific cancer sites in the two sexes. The inverse relationship existed for all specific sites investigated (except breast) although a level of statistical significance was reached only for prostate carcinoma. Analyses after exclusion of cases diagnosed during the first two years of follow-up still yielded an inverse relationship. Life table analysis indicated that competing risks during the period of follow-up did not account for the existence of an inverse relationship. It is concluded that a weak inverse relationship does exist between serum cholesterol for many but not all cancer sites. This relationship is not due to confounding by other known cancer risk factors, competing risks or persons entering the study with undiagnosed cancer. Not enough information is available at the present time to determine whether this relationship is causal and further research is suggested. ^

Estimation du paramètre d'une copule archimedienne en présence de censure dépendante

Les méthodes classiques d’analyse de survie notamment la méthode non paramétrique de Kaplan et Meier (1958) supposent l’indépendance entre les variables d’intérêt et de censure. Mais, cette hypothèse d’indépendance n’étant pas toujours soutenable, plusieurs auteurs ont élaboré des méthodes pour prendre en compte la dépendance. La plupart de ces méthodes émettent des hypothèses sur cette dépendance. Dans ce mémoire, nous avons proposé une méthode d’estimation de la dépendance en présence de censure dépendante qui utilise le copula-graphic estimator pour les copules archimédiennes (Rivest etWells, 2001) et suppose la connaissance de la distribution de la variable de censure. Nous avons ensuite étudié la consistance de cet estimateur à travers des simulations avant de l’appliquer sur un jeu de données réelles.

The big data upgrade : new dimensions in data analysis - risks and opportunities [das big data upgrade : neue dimensionen der datennutzung – chancen und risiken]

Summary: More than ever before contemporary societies are characterised by the huge amounts of data being transferred. Authorities, companies, academia and other stakeholders refer to Big Data when discussing the importance of large and complex datasets and developing possible solutions for their use. Big Data promises to be the next frontier of innovation for institutions and individuals, yet it also offers possibilities to predict and influence human behaviour with ever-greater precision

Maximum likelihood analysis of multi-stress accelerated life test data of series systems with competing log-normal causes of failure

This article presents frequentist inference of accelerated life test data of series systems with independent log-normal component lifetimes. The means of the component log-lifetimes are assumed to depend on the stress variables through a linear stress translation function that can accommodate the standard stress translation functions in the literature. An expectation-maximization algorithm is developed to obtain the maximum likelihood estimates of model parameters. The maximum likelihood estimates are then further refined by bootstrap, which is also used to infer about the component and system reliability metrics at usage stresses. The developed methodology is illustrated by analyzing a real as well as a simulated dataset. A simulation study is also carried out to judge the effectiveness of the bootstrap. It is found that in this model, application of bootstrap results in significant improvement over the simple maximum likelihood estimates.

Bivariate distribution modeling with tree diameter and height data

The Logit-Logistic (LL), Johnson's SB, and the Beta (GBD) are flexible four-parameter probability distribution models in terms of the (skewness-kurtosis) region covered, and each has been used for modeling tree diameter distributions in forest stands. This article compares bivariate forms of these models in terms of their adequacy in representing empirical diameter-height distributions from 102 sample plots. Four bivariate models are compared: SBB, the natural, well-known, and much-used bivariate generalization of SB; the bivariate distributions with LL, SB, and Beta as marginals, constructed using Plackett's method (LL-2P, etc.). All models are fitted using maximum likelihood, and their goodness-of-fits are compared using minus log-likelihood (equivalent to Akaike's Information Criterion, the AIC). The performance ranking in this case study was SBB, LL-2P, GBD-2P, and SB-2P

An analysis of the masking of speech by competing speech using self-report data (L)

Many of the items in the “Speech, Spatial, and Qualities of Hearing” scale questionnaire [S. Gatehouse and W. Noble, Int. J. Audiol.43, 85–99 (2004)] are concerned with speech understanding in a variety of backgrounds, both speech and nonspeech. To study if this self-report data reflected informational masking, previously collected data on 414 people were analyzed. The lowest scores (greatest difficulties) were found for the two items in which there were two speech targets, with successively higher scores for competing speech (six items), energetic masking (one item), and no masking (three items). The results suggest significant masking by competing speech in everyday listening situations.

Modelling and Analysis of Bivariate Lifetime Data using Reversed Hazard Rates

Department of Statistics, Cochin University of Science and Technology

A bivariate regression model for matched paired survival data: local influence and residual analysis

The use of bivariate distributions plays a fundamental role in survival and reliability studies. In this paper, we consider a location scale model for bivariate survival times based on the proposal of a copula to model the dependence of bivariate survival data. For the proposed model, we consider inferential procedures based on maximum likelihood. Gains in efficiency from bivariate models are also examined in the censored data setting. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the bivariate regression model for matched paired survival data. Sensitivity analysis methods such as local and total influence are presented and derived under three perturbation schemes. The martingale marginal and the deviance marginal residual measures are used to check the adequacy of the model. Furthermore, we propose a new measure which we call modified deviance component residual. The methodology in the paper is illustrated on a lifetime data set for kidney patients.

Improved Regression Estimation of a Multivariate Relationship with Population Data on the Bivariate Relationship

Regression coefficients specify the partial effect of a regressor on the dependent variable. Sometimes the bivariate or limited multivariate relationship of that regressor variable with the dependent variable is known from population-level data. We show here that such population- level data can be used to reduce variance and bias about estimates of those regression coefficients from sample survey data. The method of constrained MLE is used to achieve these improvements. Its statistical properties are first described. The method constrains the weighted sum of all the covariate-specific associations (partial effects) of the regressors on the dependent variable to equal the overall association of one or more regressors, where the latter is known exactly from the population data. We refer to those regressors whose bivariate or limited multivariate relationships with the dependent variable are constrained by population data as being ‘‘directly constrained.’’ Our study investigates the improvements in the estimation of directly constrained variables as well as the improvements in the estimation of other regressor variables that may be correlated with the directly constrained variables, and thus ‘‘indirectly constrained’’ by the population data. The example application is to the marital fertility of black versus white women. The difference between white and black women’s rates of marital fertility, available from population-level data, gives the overall association of race with fertility. We show that the constrained MLE technique both provides a far more powerful statistical test of the partial effect of being black and purges the test of a bias that would otherwise distort the estimated magnitude of this effect. We find only trivial reductions, however, in the standard errors of the parameters for indirectly constrained regressors.

Competing regression models for longitudinal data

The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing this type of data via a practical example involving pretestposttest longitudinal data. In particular, we consider log-normal linear mixed models (LNLMM), generalized linear mixed models (GLMM), and models based on generalized estimating equations (GEE). We show how some special features of the data, like a nonconstant coefficient of variation, may be handled in the three approaches and evaluate their performance with respect to the magnitude of standard errors of interpretable and comparable parameters. We also show how different diagnostic tools may be employed to identify outliers and comment on available software. We conclude by noting that the results are similar, but that GEE-based models may be preferable when the goal is to compare the marginal expected responses.