Tree-based subgroup discovery using electronic health record data: heterogeneity of treatment effects for DTG-containing therapies.

The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the subgroup discovery for longitudinal data algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment effects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation.

A pseudo-likelihood method for estimating misclassification probabilities in competing-risks settings when true-event data are partially observed.

Outcome misclassification occurs frequently in binary-outcome studies and can result in biased estimation of quantities such as the incidence, prevalence, cause-specific hazards, cumulative incidence functions, and so forth. A number of remedies have been proposed to address the potential misclassification of the outcomes in such data. The majority of these remedies lie in the estimation of misclassification probabilities, which are in turn used to adjust analyses for outcome misclassification.

Modeling the causal effect of treatment initiation time on survival: Application to HIV/TB co-infection.

The timing of antiretroviral therapy (ART) initiation for HIV and tuberculosis (TB) co-infected patients needs to be considered carefully. CD4 cell count can be used to guide decision making about when to initiate ART. Evidence from recent randomized trials and observational studies generally supports early initiation but does not provide information about effects of initiation time on a continuous scale.

Factors Associated with Late Engagement to HIV Care in Western Kenya: A Cross-Sectional Study.

Background: Late presentation of patients contributes significantly to the high mortality reported in HIV -care and treatment programs in sub-Saharan Africa.

Methods: A cross-sectional study was conducted to assess factors associated with late engagement to HIV care at the Academic Model Providing Access to Healthcare in western Kenya. Late engagement was defined as baseline CD4 ≤100 cells/mm.

Active tuberculosis is associated with worse clinical outcomes in HIV-infected African patients on antiretroviral therapy.

Objective: This cohort study utilized data from a large HIV treatment program in western Kenya to describe the impact of active tuberculosis (TB) on clinical outcomes among African patients on antiretroviral therapy (ART).

Design: We included all patients initiating ART between March 2004 and November 2007. Clinical (signs and symptoms), radiological (chest radiographs) and laboratory (mycobacterial smears, culture and tissue histology) criteria were used to record the diagnosis of TB disease in the program's electronic medical record system.