Wordclouds for the categorized immune response outcomes from SSA models.

<p><i>Figure 2A</i>: Covariates adjusted for in the final slope models; <i>Figure 2B</i>: Covariates adjusted for in the final Survival models; and <i>figure 2C</i>: Covariates adjusted for in the final Asymptote models. The word size and color represents the frequency of covariates, hence the larger the size of the covariate, the higher its frequency in the list of adjusted covariates. <b>Site</b>—location of the study; <b>KSincid</b>—Kaposis’ sarcoma diagnosed after ART start; <b>HBVprev</b>—Hepatitis B virus diagnosed at ART start; TBprev—History of TB at ART start; <b>TDFbl</b>—treated with tenofovir at ART start; <b>3TCbl</b>—treated with lamivudine at ART start; <b>DistanceHC</b>—distance from health center; <b>Maritstatus</b>—marital status of the subject; <b>Season</b>—season of the tear when patient was initiated on ART; <b>ALTbl</b>—alanine aminotransferase at ART start; <b>sdNVP</b>—history of single does nevirapine; <b>Parity</b>—number of children; <b>CD8bl</b>—CD8 count at ART start; <b>CONSULTratio</b>—cadre levels at health center; <b>Hhassets</b>—possession of any household assets; <b>OralCandida</b>—Oral candidiasis at ART start; <b>ChronDiarrhea</b>—Chronic diarrhea at ART start; <b>VLsupress</b>—ever had viral suppression; <b>NNRTIcr</b>—time-updated exposure to either nevirapine or efavirenz; <b>NRTI</b>_<b>cr</b>—time-updated exposure to <b>d4T</b>_<b>cr</b> (stavudine) or <b>AZT</b>_<b>cr</b> (zidovudine) or <b>TDF</b>_<b>cr</b> (tenofovir) or <b>3TC</b>_<b>cr</b> (lamivudine); <b>CD4preART</b>—pre-ART start CD4 count; <b>VLpreART</b>—pre-ART start viral load; <b>PreARTexp</b>—pre-ART exposure; <b>AlcoholCons</b>—consumption of alcohol; <b>DurapreART</b>—duration between ART start and diagnosis; <b>duraCD4<200</b>—duration while CD4 <200 cells/μL before ART start; and <b>antiTBstart</b>—patient initiated on anti-tuberculosis medicine. <i>For other variable definitions, please refer to the notes below</i> Tables <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171658#pone.0171658.t002" target="_blank">2</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171658#pone.0171658.t003" target="_blank">3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171658#pone.0171658.t004" target="_blank">4</a>.</p

Summary of different multivariate immune response modeling methods in SSA.

<p>Summary of different multivariate immune response modeling methods in SSA.</p

‘Survival’, or time-to immune response, models in SSA.

<p>‘Survival’, or time-to immune response, models in SSA.</p

Systematic review of statistically-derived models of immunological response in HIV-infected adults on antiretroviral therapy in Sub-Saharan Africa

<div><p>Introduction</p><p>In Sub-Saharan African (SSA) resource limited settings, Cluster of Differentiation 4 (CD4) counts continue to be used for clinical decision making in antiretroviral therapy (ART). Here, HIV-infected people often remain with CD4 counts <350 cells/μL even after 5 years of viral load suppression. Ongoing immunological monitoring is necessary. Due to varying statistical modeling methods comparing immune response to ART across different cohorts is difficult. We systematically review such models and detail the similarities, differences and problems.</p><p>Methods</p><p>‘Preferred Reporting Items for Systematic Review and Meta-Analyses’ guidelines were used. Only studies of immune-response after ART initiation from SSA in adults were included. Data was extracted from each study and tabulated. Outcomes were categorized into 3 groups: ‘slope’, ‘survival’, and ‘asymptote’ models. Wordclouds were drawn wherein the frequency of variables occurring in the reviewed models is indicated by their size and color.</p><p>Results</p><p>69 covariates were identified in the final models of 35 studies. Effect sizes of covariates were not directly quantitatively comparable in view of the combination of differing variables and scale transformation methods across models. Wordclouds enabled the identification of qualitative and semi-quantitative covariate sets for each outcome category. Comparison across categories identified sex, baseline age, baseline log viral load, baseline CD4, ART initiation regimen and ART duration as a minimal consensus set.</p><p>Conclusion</p><p>Most models were different with respect to covariates included, variable transformations and scales, model assumptions, modelling strategies and reporting methods, even for the same outcomes. To enable comparison across cohorts, statistical models would benefit from the application of more uniform modelling techniques. Historic efforts have produced results that are anecdotal to individual cohorts only. This study was able to define ‘prior’ knowledge in the Bayesian sense. Such information has value for prospective modelling efforts.</p></div

The high frequency (≥3) covariates adjusted for in multivariate models.

<p>The high frequency (≥3) covariates adjusted for in multivariate models.</p

Predicted posterior median CD4 counts trajectory by covariate strata for the slope of CD4 count model, with 95% prediction intervals.

A. baseline CD4 count (cells/μL), B. sex, C. baseline age (years), D. baseline log10 VL (copies/mL). Median predicted CD4 counts from model 1, is plotted. The red, black or blue dots represent predicted median CD4 count at different time points, and the bars are the whiskers.</p

The effect of changing from a Gaussian to a skew-normal on the estimated regression coefficients, with 95% credible intervals, in the slope model.

The effect of changing from a Gaussian to a skew-normal on the estimated regression coefficients, with 95% credible intervals, in the slope model.</p

‘Asymptote’ models in SSA.

<p>‘Asymptote’ models in SSA.</p

Systematic review flow chart.

<p>Systematic review flow chart.</p

‘Slope’ models of CD4 count trajectory in SSA.

<p>‘Slope’ models of CD4 count trajectory in SSA.</p