A composite likelihood approach for estimating HIV prevalence in the presence of spatial variation.

Since 1990, the World Health Organization has recommended HIV surveillance among pregnant women as an essential surveillance activity for countries with generalized HIV epidemics. Despite the widespread availability and potential usefulness of antenatal HIV surveillance, analyses of such data present important challenges. Within an individual clinic, the HIV status of its attendees may be correlated because of similarities in HIV risk among women close in age. Between-clinic correlation may also arise as women often seek antenatal care at clinics located close to their home, and individuals living in nearby communities may share important characteristics or behaviours related to susceptibility. A general estimating equation-based approach for spatially-correlated, binary data such as that antenatal HIV surveillance based on a pairwise composite likelihood has been described. We present an extended version of this model that can accommodate penalized spline estimators and apply it to antenatal HIV surveillance data collected in 2011 in Botswana to estimate the effects of proximity to the 'hotspot' of the country's HIV epidemic and age on HIV prevalence. Finally, we compare the results with a logistic regression analysis, which ignores potential correlation of responses.

Eliminating bias due to censoring in Kendall's tau estimators for quasi-independence of truncation and failure.

While the currently available estimators for the conditional Kendall's tau measure of association between truncation and failure are valid for testing the null hypothesis of quasi-independence, they are biased when the null does not hold. This is because they converge to quantities that depend on the censoring distribution. The magnitude of the bias relative to the theoretical Kendall's tau measure of association between truncation and failure due to censoring has not been studied, and so its importance in real problems is not known. We quantify this bias in order to assess the practical usefulness of the estimators. Furthermore, we propose inverse probability weighted versions of the conditional Kendall's tau estimators to remove the effects of censoring and provide asymptotic results for the estimators. In simulations, we demonstrate the decrease in bias achieved by these inverse probability weighted estimators. We apply the estimators to the Channing House data set and an AIDS incubation data set.

Computationally simple estimation and improved efficiency for special cases of double truncation.

Doubly truncated survival data arise when event times are observed only if they occur within subject specific intervals of times. Existing iterative estimation procedures for doubly truncated data are computationally intensive (Turnbull 38:290-295, 1976; Efron and Petrosian 94:824-825, 1999; Shen 62:835-853, 2010a). These procedures assume that the event time is independent of the truncation times, in the sample space that conforms to their requisite ordering. This type of independence is referred to as quasi-independence. In this paper we identify and consider two special cases of quasi-independence: complete quasi-independence and complete truncation dependence. For the case of complete quasi-independence, we derive the nonparametric maximum likelihood estimator in closed-form. For the case of complete truncation dependence, we derive a closed-form nonparametric estimator that requires some external information, and a semi-parametric maximum likelihood estimator that achieves improved efficiency relative to the standard nonparametric maximum likelihood estimator, in the absence of external information. We demonstrate the consistency and potentially improved efficiency of the estimators in simulation studies, and illustrate their use in application to studies of AIDS incubation and Parkinson's disease age of onset.

Transformation model estimation of survival under dependent truncation and independent censoring.

Truncation is a mechanism that permits observation of selected subjects from a source population; subjects are excluded if their event times are not contained within subject-specific intervals. Standard survival analysis methods for estimation of the distribution of the event time require quasi-independence of failure and truncation. When quasi-independence does not hold, alternative estimation procedures are required; currently, there is a copula model approach that makes strong modeling assumptions, and a transformation model approach that does not allow for right censoring. We extend the transformation model approach to accommodate right censoring. We propose a regression diagnostic for assessment of model fit. We evaluate the proposed transformation model in simulations and apply it to the National Alzheimer's Coordinating Centers autopsy cohort study, and an AIDS incubation study. Our methods are publicly available in an R package, tranSurv.

Hemoglobin increase is associated with improved health-related quality of life in patients with cancer not receiving chemotherapy.

Anemia is becoming recognized as a substantial problem for patients with cancer who are not receiving chemotherapy. Recently, darbepoetin alfa has been shown to provide significant clinical benefits in this patient population. This analysis assesses the effect of change in hemoglobin levels on health-related quality of life (HRQOL) in these patients. Eligible patients had anemia (hemoglobin /= 3 points) were significantly (P < 0.05) associated with improvements in functional, physical, and emotional well-being, depression, anxiety, and global ratings of energy, activity, and overall health. In summary, an increase in hemoglobin was associated with a decrease in fatigue in patients with anemia associated with nonmyeloid malignancies who were not receiving chemotherapy.