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Benitez A, Petersen ML, van der Laan MJ, Santos N, Butrick E, Walker D, Ghosh R, Otieno P, Waiswa P, Balzer LB. Defining and estimating effects in cluster randomized trials: A methods comparison. Stat Med 2023; 42:3443-3466. [PMID: 37308115 PMCID: PMC10898620 DOI: 10.1002/sim.9813] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 04/27/2023] [Accepted: 05/21/2023] [Indexed: 06/14/2023]
Abstract
Across research disciplines, cluster randomized trials (CRTs) are commonly implemented to evaluate interventions delivered to groups of participants, such as communities and clinics. Despite advances in the design and analysis of CRTs, several challenges remain. First, there are many possible ways to specify the causal effect of interest (eg, at the individual-level or at the cluster-level). Second, the theoretical and practical performance of common methods for CRT analysis remain poorly understood. Here, we present a general framework to formally define an array of causal effects in terms of summary measures of counterfactual outcomes. Next, we provide a comprehensive overview of CRT estimators, including the t-test, generalized estimating equations (GEE), augmented-GEE, and targeted maximum likelihood estimation (TMLE). Using finite sample simulations, we illustrate the practical performance of these estimators for different causal effects and when, as commonly occurs, there are limited numbers of clusters of different sizes. Finally, our application to data from the Preterm Birth Initiative (PTBi) study demonstrates the real-world impact of varying cluster sizes and targeting effects at the cluster-level or at the individual-level. Specifically, the relative effect of the PTBi intervention was 0.81 at the cluster-level, corresponding to a 19% reduction in outcome incidence, and was 0.66 at the individual-level, corresponding to a 34% reduction in outcome risk. Given its flexibility to estimate a variety of user-specified effects and ability to adaptively adjust for covariates for precision gains while maintaining Type-I error control, we conclude TMLE is a promising tool for CRT analysis.
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Affiliation(s)
| | - Maya L. Petersen
- School of Public Health, Biostatistics, University of California Berkeley, Berkeley, California
| | - Mark J. van der Laan
- School of Public Health, Biostatistics, University of California Berkeley, Berkeley, California
| | - Nicole Santos
- Institute for Global Health Sciences, University of California San Francisco, San Francisco, California
| | - Elizabeth Butrick
- Institute for Global Health Sciences, University of California San Francisco, San Francisco, California
| | - Dilys Walker
- Institute for Global Health Sciences, University of California San Francisco, San Francisco, California
| | - Rakesh Ghosh
- Institute for Global Health Sciences, University of California San Francisco, San Francisco, California
| | - Phelgona Otieno
- Center for Clinical Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - Peter Waiswa
- Centre of Excellence for Maternal, Newborn and Child Health, Makerere University College of Health Sciences, Kampala, Uganda
| | - Laura B. Balzer
- School of Public Health, Biostatistics, University of California Berkeley, Berkeley, California
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Larmarange J, Bachanas P, Skalland T, Balzer LB, Iwuji C, Floyd S, Mills LA, Pillay D, Havlir D, Kamya MR, Ayles H, Wirth K, Dabis F, Hayes R, Petersen M. Population-level viremia predicts HIV incidence at the community level across the Universal Testing and Treatment Trials in eastern and southern Africa. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0002157. [PMID: 37450436 PMCID: PMC10348573 DOI: 10.1371/journal.pgph.0002157] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 06/20/2023] [Indexed: 07/18/2023]
Abstract
Universal HIV testing and treatment (UTT) strategies aim to optimize population-level benefits of antiretroviral treatment. Between 2012 and 2018, four large community randomized trials were conducted in eastern and southern Africa. While their results were broadly consistent showing decreased population-level viremia reduces HIV incidence, it remains unclear how much HIV incidence can be reduced by increasing suppression among people living with HIV (PLHIV). We conducted a pooled analysis across the four UTT trials. Leveraging data from 105 communities in five countries, we evaluated the linear relationship between i) population-level viremia (prevalence of non-suppression-defined as plasma HIV RNA >500 or >400 copies/mL-among all adults, irrespective of HIV status) and HIV incidence; and ii) prevalence of non-suppression among PLHIV and HIV incidence, using parametric g-computation. HIV prevalence, measured in 257 929 persons, varied from 2 to 41% across the communities; prevalence of non-suppression among PLHIV, measured in 31 377 persons, from 3 to 70%; population-level viremia, derived from HIV prevalence and non-suppression, from < 1% to 25%; and HIV incidence, measured over 345 844 person-years (PY), from 0.03/100PY to 3.46/100PY. Decreases in population-level viremia were strongly associated with decreased HIV incidence in all trials (between 0.45/100PY and 1.88/100PY decline in HIV incidence per 10 percentage points decline in viremia). Decreases in non-suppression among PLHIV were also associated with decreased HIV incidence in all trials (between 0.06/100PY and 0.17/100PY decline in HIV incidence per 10 percentage points decline in non-suppression). Our results support both the utility of population-level viremia as a predictor of incidence, and thus a tool for targeting prevention interventions, and the ability of UTT approaches to reduce HIV incidence by increasing viral suppression. Implementation of universal HIV testing approaches, coupled with interventions to leverage linkage to treatment, adapted to local contexts, can reduce HIV acquisition at population level.
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Affiliation(s)
- Joseph Larmarange
- Centre Population et Développement, Université Paris Cité, IRD, Inserm, Paris, France
| | - Pamela Bachanas
- Division of Global HIV/AIDS and TB, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
| | - Timothy Skalland
- Fred Hutchinson Cancer Center, Seattle, WA, United States of America
| | - Laura B. Balzer
- Division of Biostatistics, School of Public Health, University of California, Berkeley, California, United States of America
| | - Collins Iwuji
- Department of Global Health and Infection, Brighton and Sussex Medical School, University of Sussex, Falmer, United Kingdom
| | - Sian Floyd
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Lisa A. Mills
- Division of Global HIV and TB, Centers for Disease Control and Prevention, Gaborone, Botswana
| | - Deenan Pillay
- Division of Infection & Immunity, University College London, London, United Kingdom
| | - Diane Havlir
- Department of Medicine, University of California San Francisco, San Francisco, CA, United States of America
| | - Moses R. Kamya
- Department of Medicine, Makerere University Kampala, Uganda and the Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Helen Ayles
- Clinical Research Department London School of Hygiene & Tropical Medicine, London, United Kingdom
- Zambart, University of Zambia School of Public Health, Lusaka, Zambia
| | - Kathleen Wirth
- Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - François Dabis
- Université Bordeaux, ISPED, Centre INSERM U1219-Bordeaux Population Health, Bordeaux, France
| | - Richard Hayes
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Maya Petersen
- Division of Biostatistics, School of Public Health, University of California, Berkeley, California, United States of America
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Achieving the UNAIDS 90-90-90 targets: a comparative analysis of four large community randomised trials delivering universal testing and treatment to reduce HIV transmission in sub-Saharan Africa. BMC Public Health 2022; 22:2333. [PMID: 36514036 PMCID: PMC9746009 DOI: 10.1186/s12889-022-14713-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 11/23/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Four large community-randomized trials examining universal testing and treatment (UTT) to reduce HIV transmission were conducted between 2012-2018 in Botswana, Kenya, Uganda, Zambia and South Africa. In 2014, the UNAIDS 90-90-90 targets were adopted as a useful metric to monitor coverage. We systematically review the approaches used by the trials to measure intervention delivery, and estimate coverage against the 90-90-90 targets. We aim to provide in-depth understanding of the background contexts and complexities that affect estimation of population-level coverage related to the 90-90-90 targets. METHODS Estimates were based predominantly on "process" data obtained during delivery of the interventions which included a combination of home-based and community-based services. Cascade coverage data included routine electronic health records, self-reported data, survey data, and active ascertainment of HIV viral load measurements in the field. RESULTS The estimated total adult populations of trial intervention communities included in this study ranged from 4,290 (TasP) to 142,250 (Zambian PopART Arm-B). The estimated total numbers of PLHIV ranged from 1,283 (TasP) to 20,541 (Zambian PopART Arm-B). By the end of intervention delivery, the first-90 target (knowledge of HIV status among all PLHIV) was met by all the trials (89.2%-94.0%). Three of the four trials also achieved the second- and third-90 targets, and viral suppression in BCPP and SEARCH exceeded the UNAIDS target of 73%, while viral suppression in the Zambian PopART Arm-A and B communities was within a small margin (~ 3%) of the target. CONCLUSIONS All four UTT trials aimed to implement wide-scale testing and treatment for HIV prevention at population level and showed substantial increases in testing and treatment for HIV in the intervention communities. This study has not uncovered any one estimation approach which is superior, rather that several approaches are available and researchers or policy makers seeking to measure coverage should reflect on background contexts and complexities that affect estimation of population-level coverage in their specific settings. All four trials surpassed UNAIDS targets for universal testing in their intervention communities ahead of the 2020 milestone. All but one of the trials also achieved the 90-90 targets for treatment and viral suppression. UTT is a realistic option to achieve 95-95-95 by 2030 and fast-track the end of the HIV epidemic.
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Balzer LB, van der Laan M, Ayieko J, Kamya M, Chamie G, Schwab J, Havlir DV, Petersen ML. Two-Stage TMLE to reduce bias and improve efficiency in cluster randomized trials. Biostatistics 2021; 24:502-517. [PMID: 34939083 PMCID: PMC10102904 DOI: 10.1093/biostatistics/kxab043] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 10/19/2021] [Accepted: 11/15/2021] [Indexed: 11/14/2022] Open
Abstract
Cluster randomized trials (CRTs) randomly assign an intervention to groups of individuals (e.g., clinics or communities) and measure outcomes on individuals in those groups. While offering many advantages, this experimental design introduces challenges that are only partially addressed by existing analytic approaches. First, outcomes are often missing for some individuals within clusters. Failing to appropriately adjust for differential outcome measurement can result in biased estimates and inference. Second, CRTs often randomize limited numbers of clusters, resulting in chance imbalances on baseline outcome predictors between arms. Failing to adaptively adjust for these imbalances and other predictive covariates can result in efficiency losses. To address these methodological gaps, we propose and evaluate a novel two-stage targeted minimum loss-based estimator to adjust for baseline covariates in a manner that optimizes precision, after controlling for baseline and postbaseline causes of missing outcomes. Finite sample simulations illustrate that our approach can nearly eliminate bias due to differential outcome measurement, while existing CRT estimators yield misleading results and inferences. Application to real data from the SEARCH community randomized trial demonstrates the gains in efficiency afforded through adaptive adjustment for baseline covariates, after controlling for missingness on individual-level outcomes.
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Affiliation(s)
- Laura B Balzer
- Department of Biostatistics & Epidemiology, University of Massachusetts Amherst, 715 North Pleasant St, Amherst, MA, USA
| | - Mark van der Laan
- Division of Biostatistics, University of California Berkeley, 2121 Berkeley Way, Berkeley, CA, USA
| | - James Ayieko
- Center for Microbiology Research, Kenya Medical Research Institute, P.O. BOX 54840 00200 Off Raila Odinga Way, Nairobi, Kenya
| | - Moses Kamya
- Department of Medicine, Makerere University and the Infectious Diseases Research Collaboration, P.O Box 7475, Kampala, Uganda
| | - Gabriel Chamie
- Department of Medicine, University of California San Francisco, 995 Potrero Ave, San Francisco, CA, USA
| | - Joshua Schwab
- Division of Biostatistics, University of California Berkeley, 2121 Berkeley Way, Berkeley, CA, USA
| | - Diane V Havlir
- Department of Medicine, University of California San Francisco, 995 Potrero Ave, San Francisco, CA, USA
| | - Maya L Petersen
- Division of Biostatistics, University of California Berkeley, 2121 Berkeley Way, Berkeley, CA, USA
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Hickey MD, Ayieko J, Owaraganise A, Sim N, Balzer LB, Kabami J, Atukunda M, Opel FJ, Wafula E, Nyabuti M, Brown L, Chamie G, Jain V, Peng J, Kwarisiima D, Camlin CS, Charlebois ED, Cohen CR, Bukusi EA, Kamya MR, Petersen ML, Havlir DV. Effect of a patient-centered hypertension delivery strategy on all-cause mortality: Secondary analysis of SEARCH, a community-randomized trial in rural Kenya and Uganda. PLoS Med 2021; 18:e1003803. [PMID: 34543267 PMCID: PMC8489716 DOI: 10.1371/journal.pmed.1003803] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 10/04/2021] [Accepted: 09/09/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Hypertension treatment reduces morbidity and mortality yet has not been broadly implemented in many low-resource settings, including sub-Saharan Africa (SSA). We hypothesized that a patient-centered integrated chronic disease model that included hypertension treatment and leveraged the HIV care system would reduce mortality among adults with uncontrolled hypertension in rural Kenya and Uganda. METHODS AND FINDINGS This is a secondary analysis of the SEARCH trial (NCT:01864603), in which 32 communities underwent baseline population-based multidisease testing, including hypertension screening, and were randomized to standard country-guided treatment or to a patient-centered integrated chronic care model including treatment for hypertension, diabetes, and HIV. Patient-centered care included on-site introduction to clinic staff at screening, nursing triage to expedite visits, reduced visit frequency, flexible clinic hours, and a welcoming clinic environment. The analytic population included nonpregnant adults (≥18 years) with baseline uncontrolled hypertension (blood pressure ≥140/90 mm Hg). The primary outcome was 3-year all-cause mortality with comprehensive population-level assessment. Secondary outcomes included hypertension control assessed at a population level at year 3 (defined per country guidelines as at least 1 blood pressure measure <140/90 mm Hg on 3 repeated measures). Between-arm comparisons used cluster-level targeted maximum likelihood estimation. Among 86,078 adults screened at study baseline (June 2013 to July 2014), 10,928 (13%) had uncontrolled hypertension. Median age was 53 years (25th to 75th percentile 40 to 66); 6,058 (55%) were female; 677 (6%) were HIV infected; and 477 (4%) had diabetes mellitus. Overall, 174 participants (3.2%) in the intervention group and 225 participants (4.1%) in the control group died during 3 years of follow-up (adjusted relative risk (aRR) 0.79, 95% confidence interval (CI) 0.64 to 0.97, p = 0.028). Among those with baseline grade 3 hypertension (≥180/110 mm Hg), 22 (4.9%) in the intervention group and 42 (7.9%) in the control group died during 3 years of follow-up (aRR 0.62, 95% CI 0.39 to 0.97, p = 0.038). Estimated population-level hypertension control at year 3 was 53% in intervention and 44% in control communities (aRR 1.22, 95% CI 1.12 to 1.33, p < 0.001). Study limitations include inability to identify specific causes of death and control conditions that exceeded current standard hypertension care. CONCLUSIONS In this cluster randomized comparison where both arms received population-level hypertension screening, implementation of a patient-centered hypertension care model was associated with a 21% reduction in all-cause mortality and a 22% improvement in hypertension control compared to standard care among adults with baseline uncontrolled hypertension. Patient-centered chronic care programs for HIV can be leveraged to reduce the overall burden of cardiovascular mortality in SSA. TRIAL REGISTRATION ClinicalTrials.gov NCT01864603.
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Affiliation(s)
- Matthew D. Hickey
- Division of HIV, ID, & Global Medicine, Department of Medicine, UCSF, San Francisco, California, United States of America
| | - James Ayieko
- Centre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya
| | | | - Nicholas Sim
- School of Public Health, University of California Berkeley, Berkeley, California, United States of America
| | - Laura B. Balzer
- School of Public Health & Health Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
| | - Jane Kabami
- Infectious Disease Research Collaboration, Kampala, Uganda
| | | | - Fredrick J. Opel
- Centre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - Erick Wafula
- Centre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - Marilyn Nyabuti
- Centre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - Lillian Brown
- Division of HIV, ID, & Global Medicine, Department of Medicine, UCSF, San Francisco, California, United States of America
| | - Gabriel Chamie
- Division of HIV, ID, & Global Medicine, Department of Medicine, UCSF, San Francisco, California, United States of America
| | - Vivek Jain
- Division of HIV, ID, & Global Medicine, Department of Medicine, UCSF, San Francisco, California, United States of America
| | - James Peng
- Division of HIV, ID, & Global Medicine, Department of Medicine, UCSF, San Francisco, California, United States of America
| | | | - Carol S. Camlin
- Center for AIDS Prevention Studies & Department of Medicine, UCSF, San Francisco, California, United States of America
| | - Edwin D. Charlebois
- Center for AIDS Prevention Studies & Department of Medicine, UCSF, San Francisco, California, United States of America
| | - Craig R. Cohen
- Department of Obstetrics, Gynecology & Reproductive Sciences, UCSF, San Francisco, California, United States of America
| | - Elizabeth A. Bukusi
- Centre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - Moses R. Kamya
- Infectious Disease Research Collaboration, Kampala, Uganda
- Department of Medicine, Makerere University, Kampala, Uganda
| | - Maya L. Petersen
- School of Public Health, University of California Berkeley, Berkeley, California, United States of America
| | - Diane V. Havlir
- Division of HIV, ID, & Global Medicine, Department of Medicine, UCSF, San Francisco, California, United States of America
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Erratum: Far from MCAR: Obtaining Population-level Estimates of HIV Viral Suppression. Epidemiology 2021; 32:e25. [PMID: 34348398 DOI: 10.1097/ede.0000000000001398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Population HIV viral load metrics for community health. Lancet HIV 2021; 8:e523-e524. [PMID: 34331861 DOI: 10.1016/s2352-3018(21)00182-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/23/2021] [Accepted: 07/23/2021] [Indexed: 01/09/2023]
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Marcus JL, Sewell WC, Balzer LB, Krakower DS. Artificial Intelligence and Machine Learning for HIV Prevention: Emerging Approaches to Ending the Epidemic. Curr HIV/AIDS Rep 2020; 17:171-179. [PMID: 32347446 PMCID: PMC7260108 DOI: 10.1007/s11904-020-00490-6] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW We review applications of artificial intelligence (AI), including machine learning (ML), in the field of HIV prevention. RECENT FINDINGS ML approaches have been used to identify potential candidates for preexposure prophylaxis (PrEP) in healthcare settings in the USA and Denmark and in a population-based research setting in Eastern Africa. Although still in the proof-of-concept stage, other applications include ML with smartphone-collected and social media data to promote real-time HIV risk reduction, virtual reality tools to facilitate HIV serodisclosure, and chatbots for HIV education. ML has also been used for causal inference in HIV prevention studies. ML has strong potential to improve delivery of PrEP, with this approach moving from development to implementation. Development and evaluation of AI and ML strategies for HIV prevention may benefit from an implementation science approach, including qualitative assessments with end users, and should be developed and evaluated with attention to equity.
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Affiliation(s)
- Julia L Marcus
- Harvard Medical School and Harvard Pilgrim Health Care Institute, 401 Park Dr, Ste 401, Boston, MA, 02215, USA.
| | - Whitney C Sewell
- Harvard Medical School and Harvard Pilgrim Health Care Institute, 401 Park Dr, Ste 401, Boston, MA, 02215, USA
| | - Laura B Balzer
- University of Massachusetts Amherst, 715 North Pleasant St, Amherst, MA, 01003, USA
| | - Douglas S Krakower
- Beth Israel Deaconess Medical Center, Division of Infectious Diseases, 110 Francis St., W/LMOB Suite GB, Boston, MA, 02215, USA
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