The optimal CD4 count threshold for initiating therapy.

<p>The optimal CD4 count threshold for initiating therapy.</p

Sensitivity analysis of effect percent change in LE and QALE from the presence of pipeline drugs by age, viral load, and toxicity.

<p>The graphs on the left depict the percent change in life expectancy assuming new drugs have a lower toxicity than existing drugs (top), identical toxicity to existing drugs (middle) or a higher toxicity that existing drugs (bottom). The graphs on the right depict the percent change in quality-adjusted life expectancy for the same toxicity levels.</p

Inter-arrival time distributions.

<p>A Poisson process produces exponential inter-arrival distribution.</p

Arrival process of pipeline drugs.

<p>The arrival process of HIV pipeline drugs follows a split Poisson process. See text for details.</p

Percent change in outcomes from the presence of pipeline drugs by age, viral load, and CD4 count at initiation of therapy.

<p>The graphs on the left depict the percent change in life expectancy from the presence of pipeline drugs, the graphs on the right the percent change in quality-adjusted life expectancy.</p

Sensitivity analysis of pipeline effect with varying different parameter assumptions.

<p>The numbers are the QALYs gain percentage due to pipeline drugs. Starting CD4 count for all categories is 500 cells/mL</p

Resistance distributions for existing drug classes.

<p>* p-value is for the for Kolmogorov-Smirnov goodness of fit test.</p

Clinical trial interventions and associated costs and effects considered in HIV transmission simulation model.

<p>Uniform distributions were used for all costs and lognormal distribution for all effects in probabilistic analyses. Intervention costs were derived from India-specific sources.[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184179#pone.0184179.ref035" target="_blank">35</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184179#pone.0184179.ref049" target="_blank">49</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184179#pone.0184179.ref051" target="_blank">51</a>] Cost in 2012 USD.</p

Betting on the fastest horse: Using computer simulation to design a combination HIV intervention for future projects in Maharashtra, India

<div><p>Objective</p><p>To inform the design of a combination intervention strategy targeting HIV-infected unhealthy alcohol users in Maharashtra, India, that could be tested in future randomized control trials.</p><p>Methods</p><p>Using probabilistic compartmental simulation modeling we compared intervention strategies targeting HIV-infected unhealthy alcohol users on antiretroviral therapy (ART) in Maharashtra, India. We tested interventions targeting four behaviors (unhealthy alcohol consumption, risky sexual behavior, depression and antiretroviral adherence), in three formats (individual, group based, community) and two durations (shorter versus longer). A total of 5,386 possible intervention combinations were tested across the population for a 20-year time horizon and intervention bundles were narrowed down based on incremental cost-effectiveness analysis using a two-step probabilistic uncertainty analysis approach.</p><p>Results</p><p>Taking into account uncertainty in transmission variables and intervention cost and effectiveness values, we were able to reduce the number of possible intervention combinations to be used in a randomized control trial from over 5,000 to less than 5. The most robust intervention bundle identified was a combination of three interventions: long individual alcohol counseling; weekly Short Message Service (SMS) adherence counseling; and brief sex risk group counseling.</p><p>Conclusions</p><p>In addition to guiding policy design, simulation modeling of HIV transmission can be used as a preparatory step to trial design, offering a method for intervention pre-selection at a reduced cost.</p></div

Analyses methodology.

<p>Pipeline workflow for intervention bundle prioritization. a, Creation of efficient frontier for all combinations of 15 interventions and filtering out 8 interventions that were never found on the frontier. b, For the remaining 7 interventions, completion of 100 probabilistic runs varying intervention costs and effects and filtering out intervention bundles that were never found on the frontier. c, Completion of a full probabilistic analyses (run N = 1000) varying intervention cost and effect as well as 96 input variables. All analysis was run for a 20-year simulation.</p