Assessing intra-lab precision and inter-lab repeatability of outgrowth assays of HIV-1 latent reservoir size.

Quantitative viral outgrowth assays (QVOA) use limiting dilutions of CD4+ T cells to measure the size of the latent HIV-1 reservoir, a major obstacle to curing HIV-1. Efforts to reduce the reservoir require assays that can reliably quantify its size in blood and tissues. Although QVOA is regarded as a "gold standard" for reservoir measurement, little is known about its accuracy and precision or about how cell storage conditions or laboratory-specific practices affect results. Owing to this lack of knowledge, confidence intervals around reservoir size estimates-as well as judgments of the ability of therapeutic interventions to alter the size of the replication-competent but transcriptionally inactive latent reservoir-rely on theoretical statistical assumptions about dilution assays. To address this gap, we have carried out a Bayesian statistical analysis of QVOA reliability on 75 split samples of peripheral blood mononuclear cells (PBMC) from 5 antiretroviral therapy (ART)-suppressed participants, measured using four different QVOAs at separate labs, estimating assay precision and the effect of frozen cell storage on estimated reservoir size. We found that typical assay results are expected to differ from the true value by a factor of 1.6 to 1.9 up or down. Systematic assay differences comprised a 24-fold range between the assays with highest and lowest scales, likely reflecting differences in viral outgrowth readout and input cell stimulation protocols. We also found that controlled-rate freezing and storage of samples did not cause substantial differences in QVOA compared to use of fresh cells (95% probability of < 2-fold change), supporting continued use of frozen storage to allow transport and batched analysis of samples. Finally, we simulated an early-phase clinical trial to demonstrate that batched analysis of pre- and post-therapy samples may increase power to detect a three-fold reservoir reduction by 15 to 24 percentage points.

Effect of Maternal Vitamin B12 Supplementation on Cognitive Outcomes in South Indian Children: A Randomized Controlled Clinical Trial.

Objectives To examine the effects of oral maternal vitamin B12 supplementation during pregnancy and early lactation on cognitive development in children. Method We studied 218 children born to mothers enrolled in a placebo-controlled, randomized trial of vitamin B12 supplementation during pregnancy through 6 weeks post-partum. Cognitive functions were assessed at 30 months using the Bayley Scales of Infant Development- 3rd edition (BSID III). The association of maternal sociodemographic characteristics, maternal biochemical status during pregnancy, birth weight and home environment with each sub-domain of BSID-III was examined using linear regression analysis. Separate multiple linear regression analyses for each of the BSID-III sub-domains with maternal trimester specific nutritional biomarker status was conducted. Results Children of mothers who received oral vitamin B12 supplementation had significantly higher scores on expressive language compared to children of mothers who received placebo (ß = 0.14, P = 0.03). Children of mothers with elevated serum total homocysteine (tHcy) in the second and third trimesters of pregnancy had significantly lower scores on expressive language (ß = - 0.18, P = 0.03 and ß = - 0.19, P = 0.02, respectively) and gross motor domains (ß = - 0.23, P = 0.008 and ß = - 0.30, P = 0.001, respectively) of BSID-III adjusted for treatment arm and multiple confounders, compared with children whose mothers did not have elevated tHcy. Conclusions for practice Maternal B12 supplementation during pregnancy was associated with higher expressive language scores in children at 30 months. Elevated maternal tHcy levels during pregnancy had negative associations with expressive language and gross motor domains of BSID-III. Larger trials of maternal B12 supplementation are needed to confirm these findings.

Optimal treatment of replicate measurements in anthropometric studies.

BACKGROUND: Anthropometric studies often include replicates of each measurement to decrease error. The optimal method to combine these measurements is uncertain. AIM: To identify the optimal method to combine replicate measures for analysis. METHODS: The authors carried out 10 000 Monte Carlo simulations to explore the effect of six approaches to combine replicate measurements in a hypothetical two-group intervention study (n = 100 per arm) in which the outcome, infant length at age 1 year, was measured two or three times. One group had a true value with a normal distribution N (mean = 76, SD = 2.4 cm). Statistical power was estimated to detect a 1 cm difference between the groups, based on a t-test. RESULTS: Under a realistic scenario with a measurement error distribution N (0, 0.8), highest power was reached by use of the mean and the median of pairwise averages. However, when a portion of the data (≥2%) were contaminated by greater error (e.g. due to data entry), the median of three measurements outperformed all other methods while the mean had the lowest performance. CONCLUSION: Obtaining three rather than two measures and using the median of the three replicates is a safe and robust approach to combine participants' raw data values for use in subsequent analyses.

A reporting tool for real-time assessment of study data availability.

A simple framework for assessing and reporting data availability in an ongoing clinical trial is described. Protocol requirements, visit schedules and data availability are combined into a simple report to track the progress of a study.

Sensitivity analysis for the assessment of causal vaccine effects on viral load in HIV vaccine trials.

Vaccines with limited ability to prevent HIV infection may positively impact the HIV/AIDS pandemic by preventing secondary transmission and disease in vaccine recipients who become infected. To evaluate the impact of vaccination on secondary transmission and disease, efficacy trials assess vaccine effects on HIV viral load and other surrogate endpoints measured after infection. A standard test that compares the distribution of viral load between the infected subgroups of vaccine and placebo recipients does not assess a causal effect of vaccine, because the comparison groups are selected after randomization. To address this problem, we formulate clinically relevant causal estimands using the principal stratification framework developed by Frangakis and Rubin (2002, Biometrics 58, 21-29), and propose a class of logistic selection bias models whose members identify the estimands. Given a selection model in the class, procedures are developed for testing and estimation of the causal effect of vaccination on viral load in the principal stratum of subjects who would be infected regardless of randomization assignment. We show how the procedures can be used for a sensitivity analysis that quantifies how the causal effect of vaccination varies with the presumed magnitude of selection bias.

Assessment of diagnostic markers by goodness-of-fit tests.

Receiver operating characteristic (ROC) curves are useful statistical tools used to assess the precision of diagnostic markers or to compare new diagnostic markers with old ones. The most common index employed for these purposes is the area under the ROC curve (theta) and several statistical tests exist that test the null hypotheses H(0): theta= 0.5 or H(0): theta1=theta2, in the case of two-marker comparisons, against alternatives of interest. In this paper we show that goodness-of-fit of uniformity of the distribution of the false positive (true positive) rates can be used instead of tests based on the area index. A semi-parametric approach is based on a completely specified distribution of marker measurements for either the healthy (F) or diseased (G) subjects, and this is extended to the two-marker case. We then extend to the one- and two-marker case when neither distribution is specified (the non-parametric case). In general, ROC-based tests are more powerful than goodness-of-fit tests for location differences between the distributions of healthy and diseased subjects. However ROC-based tests are less powerful when location-scale differences exist (producing ROC curves that cross the diagonal) and are incapable of discriminating between healthy and diseased samples when theta=0.5 but F not equal G. In these cases, goodness-of-fit tests have a distinct advantage over ROC-based tests. In conclusion, ROC methodology should be used with recognition of its potential limitations and should be replaced by goodness-of-fit tests when appropriate. The latter are a viable alternative and can be used as a 'black box' or as an exploratory first step in the evaluation of novel diagnostic markers.