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      No change in health-related quality of life for at-risk U.S. women and men starting HIV pre-exposure prophylaxis (PrEP): Findings from HPTN 069/ACTG A5305

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          Abstract

          Introduction

          Tenofovir (TDF)-containing PrEP is effective for HIV prevention, but its effect on health-related quality of life (QOL) is unknown. Using data from HPTN 069/ACTG A5305, a randomized study of potential PrEP regimens comparing maraviroc alone, or together with TDF or emtricitabine (FTC), to TDF + FTC (control), we evaluated the impact of these regimens on QOL in at-risk HIV-uninfected U.S. women and men.

          Methods

          QOL was measured at baseline (before starting medications) and every 8 weeks through week 48 using the EQ-5D-3L. Responses were converted to a scale from 0.0 (death) to 1.0 (perfect health), using published valuation weights. Mean scores were compared between groups at each time point using nonparametric testing. Multivariable linear regression was used to adjust for potential confounders.

          Results

          We analyzed 186 women (median age 35 years, 65% black, 17% Hispanic) and 405 men (median age 30 years, 28% black, 22% Hispanic), including 9 transgender participants analyzed based on sex-at-birth. Mean baseline QOL was 0.91 for women and 0.95 for men. There were minimal changes in mean QOL over time for any regimen (women: p = 0.29; men: p = 0.14). There were no significant differences between participants who continued the regimen compared to participants who discontinued early (women: p = 0.61; men: p = 0.1). Mean QOL did not differ significantly by regimen at any time point, both unadjusted and after adjustment for age, race/ethnicity, adherence, and use of alcohol, marijuana, opiates, and other substances.

          Conclusions

          QOL in at-risk individuals starting candidate PrEP regimens in a clinical trial is similar to the general population and maintained over time. This finding did not vary among regimens or when adjusted for demographics, adherence, and substance use. Our findings are the first to show that starting a candidate PrEP regimen in at-risk HIV-uninfected U.S. women and men was not associated with significant changes in QOL.

          Trial registration

          Clinicaltrials.gov NCT01505114.

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          Most cited references7

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          US valuation of the EQ-5D health states: development and testing of the D1 valuation model.

          The EQ-5D is a brief, multiattribute, preference-based health status measure. This article describes the development of a statistical model for generating US population-based EQ-5D preference weights. A multistage probability sample was selected from the US adult civilian noninstitutional population. Respondents valued 13 of 243 EQ-5D health states using the time trade-off (TTO) method. Data for 12 states were used in econometric modeling. The TTO valuations were linearly transformed to lie on the interval [-1, 1]. Methods were investigated to account for interaction effects caused by having problems in multiple EQ-5D dimensions. Several alternative model specifications (eg, pooled least squares, random effects) also were considered. A modified split-sample approach was used to evaluate the predictive accuracy of the models. All statistical analyses took into account the clustering and disproportionate selection probabilities inherent in our sampling design. Our D1 model for the EQ-5D included ordinal terms to capture the effect of departures from perfect health as well as interaction effects. A random effects specification of the D1 model yielded a good fit for the observed TTO data, with an overall R of 0.38, a mean absolute error of 0.025, and 7 prediction errors exceeding 0.05 in absolute magnitude. The D1 model best predicts the values for observed health states. The resulting preference weight estimates represent a significant enhancement of the EQ-5D's utility for health status assessment and economic analysis in the US.
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            Using instrument-defined health state transitions to estimate minimally important differences for four preference-based health-related quality of life instruments.

            To estimate minimally important differences (MIDs) for the EQ-5D, Health Utilities Index Mark II (HUI2), HUI3, and SF-6D health index scores using health-state transitions defined by each instrument's multiattribute health classification (MAHC) system. We assume that changes in preference scores associated with the smallest health transitions defined by an MAHC system are minimally important. Any transitions between 2 health states defined by an MAHC system which differ in only one health dimension or attribute and by only one functional level are considered "smallest health transitions." Thus, each such health transition provides 1 MID estimate. The MID for each of the 4 instruments was estimated using all the hypothetical smallest health transitions defined by its MAHC system. Based on our definitions, the total number of smallest health transitions was 405 for the EQ-5D, 127,600 for the HUI2, 6,382,800 for the HUI3, and 86,700 for the SF-6D. The mean (standard deviation) MID estimate was 0.040 (0.026) for the EQ-5D (US algorithm), 0.082 (0.032) for the EQ-5D (UK algorithm), 0.045 (0.039) for the HUI2, 0.032 (0.027) for the HUI3, and 0.027 (0.028) for the SF-6D. The effect sizes of these MID estimates ranged from 0.11 to 0.37. These MID estimates are quite comparable to published values estimated from empirical data using anchor-based methods. It is possible to use health transitions defined by the MAHC system to estimate the MIDs for preference-based health index scores. This study provides new information regarding MID estimates for the 4 health indices examined.
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              Analysis of health utility data when some subjects attain the upper bound of 1: are Tobit and CLAD models appropriate?

              Health utility data often show an apparent truncation effect, where a proportion of individuals achieve the upper bound of 1. The Tobit model and censored least absolute deviations (CLAD) have both been used as analytic solutions to this apparent truncation effect. These models assume that the observed utilities are censored at 1, and hence that the true utility can be greater than 1.We aimed to examine whether the Tobit and CLAD models yielded acceptable results when this censoring assumption was not appropriate. Using health utility (captured through EQ5D) data from a diabetes study, we conducted a simulation to compare the performance of the Tobit, CLAD, ordinary least squares (OLS), two-part and latent class estimators in terms of their bias and estimated confidence intervals. We also illustrate the performance of semiparametric and nonparametric bootstrap methods. When the true utility was conceptually bounded above at 1, the Tobit and CLAD estimators were both biased. The OLS estimator was asymptotically unbiased and, while the model-based and semiparametric bootstrap confidence intervals were too narrow, confidence intervals based on the robust standard errors or the nonparametric bootstrap were acceptable for sample sizes of 100 and larger. Two-part and latent class models also yielded unbiased estimates. When the intention of the analysis is to inform an economic evaluation, and the utilities should be bounded above at 1, CLAD, and Tobit methods were biased. OLS coupled with robust standard errors or the nonparametric bootstrap is recommended as a simple and valid approach.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: Project administrationRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                26 December 2018
                2018
                : 13
                : 12
                : e0206577
                Affiliations
                [1 ] Division of Infectious Diseases, Weill Cornell Medicine, New York, New York, United States of America
                [2 ] Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, United States of America
                [3 ] Statistical Center for HIV/AIDS Research and Prevention, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
                [4 ] Fenway Health, Department of Medicine, Beth Israel Deaconess Medical Center / Harvard Medical School, Boston, Massachusetts, United States of America
                [5 ] Department of Health Behavior and Health Education, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
                [6 ] UCLA Center for Clinical AIDS Research & Education, University of California Los Angeles, Los Angeles, California, United States of America
                [7 ] Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
                [8 ] Division of AIDS, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland, United States of America
                [9 ] FHI 360, Washington, DC, United States of America
                Janssen Research and Development, UNITED STATES
                Author notes

                Competing Interests: TJW has received research grants (to Weill Cornell) from Bristol Myers-Squibb, Gilead Sciences, and GlaxoSmithKline/Viiv Healthcare and has served as an ad hoc consultant to GlaxoSmithKline/ViiV Healthcare. KRA has received an educational grant (to the University of Michigan) and has served as an ad hoc consultant to Gilead Sciences. RJL has received drug supplies, consulting fees, and travel costs from Gilead Sciences. AA received an honorarium from Bristol Meyers Squibb for a continuing medical education program and research grants from Gilead Sciences and GlaxoSmithKline for investigatorinitiated studies. KHM has received unrestricted research grants (to Fenway Health) from Gilead Sciences and GlaxoSmithKline/ViiV Healthcare. All other authors report no potential conflicts. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                Author information
                http://orcid.org/0000-0001-6363-2839
                Article
                PONE-D-17-43167
                10.1371/journal.pone.0206577
                6306196
                30586364
                260e8e66-bada-45d9-bed9-47bd846421d2

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 11 December 2017
                : 16 October 2018
                Page count
                Figures: 2, Tables: 3, Pages: 9
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: UM1-AI068619; UM1-AI068613; UM1-AI068617
                Funded by: funder-id http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: UM1-AI-068636
                Funded by: funder-id http://dx.doi.org/10.13039/100005564, Gilead Sciences;
                Funded by: funder-id http://dx.doi.org/10.13039/100010877, ViiV Healthcare;
                This work was supported by DAIDS, National Institute of Allergy and Infectious Diseases, National Institutes of Health through the HPTN (grants UM1-AI068619, UM1-AI068613, and UM1-AI068617) and the AIDS Clinical Trials Group (grant UM1-AI-068636). Gilead Sciences and ViiV Healthcare provided study drugs.
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