Estimating mono- and bi-phasic regression parameters using a mixture piecewise linear Bayesian hierarchical model

The dynamics of tumor burden, secreted proteins or other biomarkers over time, is often used to evaluate the effectiveness of therapy and to predict outcomes for patients. Many methods have been proposed to investigate longitudinal trends to better characterize patients and to understand disease progression. However, most approaches assume a homogeneous patient population and a uniform response trajectory over time and across patients. Here, we present a mixture piecewise linear Bayesian hierarchical model, which takes into account both population heterogeneity and nonlinear relationships between biomarkers and time. Simulation results show that our method was able to classify subjects according to their patterns of treatment response with greater than 80% accuracy in the three scenarios tested. We then applied our model to a large randomized controlled phase III clinical trial of multiple myeloma patients. Analysis results suggest that the longitudinal tumor burden trajectories in multiple myeloma patients are heterogeneous and nonlinear, even among patients assigned to the same treatment cohort. In addition, between cohorts, there are distinct differences in terms of the regression parameters and the distributions among categories in the mixture. Those results imply that longitudinal data from clinical trials may harbor unobserved subgroups and nonlinear relationships; accounting for both may be important for analyzing longitudinal data

Comparison of patient comprehension of rapid HIV pre-test fundamentals by information delivery format in an emergency department setting

<p>Abstract</p> <p>Background</p> <p>Two trials were conducted to compare emergency department patient comprehension of rapid HIV pre-test information using different methods to deliver this information.</p> <p>Methods</p> <p>Patients were enrolled for these two trials at a US emergency department between February 2005 and January 2006. In Trial One, patients were randomized to a no pre-test information or an in-person discussion arm. In Trial Two, a separate group of patients were randomized to an in-person discussion arm or a Tablet PC-based video arm. The video, "Do you know about rapid HIV testing?", and the in-person discussion contained identical Centers for Disease Control and Prevention-suggested pre-test information components as well as information on rapid HIV testing with OraQuick^®. Participants were compared by information arm on their comprehension of the pre-test information by their score on a 26-item questionnaire using the Wilcoxon rank-sum test.</p> <p>Results</p> <p>In Trial One, 38 patients completed the no-information arm and 31 completed the in-person discussion arm. Of these 69 patients, 63.8% had twelve years or fewer of formal education and 66.7% had previously been tested for HIV. The mean score on the questionnaire for the in-person discussion arm was higher than for the no information arm (18.7 vs. 13.3, p ≤ 0.0001). In Trial Two, 59 patients completed the in-person discussion and 55 completed the video arms. Of these 114 patients, 50.9% had twelve years or fewer of formal education and 68.4% had previously been tested for HIV. The mean score on the questionnaire for the video arm was similar to the in-person discussion arm (20.0 vs. 19.2; p ≤ 0.33).</p> <p>Conclusion</p> <p>The video "Do you know about rapid HIV testing?" appears to be an acceptable substitute for an in-person pre-test discussion on rapid HIV testing with OraQuick^®. In terms of adequately informing ED patients about rapid HIV testing, either form of pre-test information is preferable than for patients to receive no pre-test information.</p

A Resampling-Based Approach to Multiple Testing with Uncertainty in Phase

Characterizing the genetic correlates to complex diseases requires consideration of a large number of potentially informative biological markers. In addition, attention to alignment of alleles within or across chromosomal pairs, commonly referred to as phase, may be essential for uncovering true biological associations. In the context of population based association studies, phase is generally unobservable. Preservation of type-1 error in a setting with multiple testing presents a further analytical challenge. This manuscript combines a likelihood-based approach to handling missing-ness in phase with a resampling method to adjust for multiple testing. Through simulations we demonstrate preservation of the family-wise error rate and reasonable power for detecting associations. The method is applied to a cohort of 626 HIV-1 infected individuals receiving highly active anti-retroviral therapies, to ascertain potential genetic contributions to abnormalities in lipid profiles. The haplotypic effects of 2 genes, hepatic lipase (HL) and endothelial lipase (EL), on high-density lipoprotein cholesterol (HDL-C) are tested

A Resampling-Based Approach to Multiple Testing with Uncertainty in Phase

Characterizing the genetic correlates to complex diseases requires consideration of a large number of potentially informative biological markers. In addition, attention to alignment of alleles within or across chromosomal pairs, commonly referred to as phase, may be essential for uncovering true biological associations. In the context of population based association studies, phase is generally unobservable. Preservation of type-1 error in a setting with multiple testing presents a further analytical challenge. This manuscript combines a likelihood-based approach to handling missing-ness in phase with a resampling method to adjust for multiple testing. Through simulations we demonstrate preservation of the family-wise error rate and reasonable power for detecting associations. The method is applied to a cohort of 626 HIV-1 infected individuals receiving highly active anti-retroviral therapies, to ascertain potential genetic contributions to abnormalities in lipid profiles. The haplotypic effects of 2 genes, hepatic lipase (HL) and endothelial lipase (EL), on high-density lipoprotein cholesterol (HDL-C) are tested.

Accommodating Uncertainty in a Tree Set for Function Estimation

Multiple branching trees have been used to model the acquisition of HIV drug resistance mutations, and several different algorithms have been developed to construct the tree set that best describes the data. These algorithms have mainly focused on the structure of the tree set. The focal point of this paper is estimation of functions of the tree set parameters that incorporate uncertainty in the tree set. The functions of interest are the state probabilities, the co-occurrence of mutations and the order of acquisition. Such functions are of interest because they help characterize the genetic pathways that lead to multi-drug resistance. We propose a bootstrap technique to account for the additional variability in estimates due to uncertainty in the tree set. The methods are applied to genetic sequences of patients from a database compiled by the Forum for Collaborative HIV Research in an effort to characterize genetic pathways to resistance to drugs from the nucleoside reverse transcriptase inhibitor (NRTI) class. The main results were that patients with a 211K mutation in the RT region of the viral genome were more likely to have a 215Y mutation and less likely to have a 70R mutation compared to patients without a 211K mutation.

Longitudinal trajectories for patients in the VISTA trial separated by treatment cohorts.

<p>The mono-phasic (blue) and bi-phasic (red) lines indicate the population-mean trajectories based on the maximum likelihood estimates from the EM algorithm.</p

Specificity as a function of the numbers of observations per patient and the total numbers of patients per simulated data set.

<p>Parameter values are identical to those used in the three scenarios. The numbers of observations per patient vary in the figure on the left; these observations are evenly distributed between day 0 and day 357. The total numbers of patients, <i>N</i>, vary in the figure on the right; the number of observations per patient is kept at 18.</p

Specificity as a function of true bi-phasic slopes.

<p>True mono-phasic slope is kept at −0.25; bi-phasic first slopes vary between −0.45 and −0.26; bi-phasic second slopes vary between −0.24 and −0.05. Population-level mono-phasic slope and bi-phasic first intercepts are 90 and 91 respectively; the second slopes for bi-phasic patients are selected such that the population-level phase transition occurs at 178 days, which is in the middle of 357-day trial period. Each graph is generated based on the averages of 10 simulations. Sensitivity is omitted since it is at 100% for all given scenarios; please refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180756#pone.0180756.t003" target="_blank">Table 3</a>.</p