This competitive renewal will develop general methods for evaluating the comparative effectiveness of alter- native strategies for HIV prevention, treatment and care in Southern and Eastern Africa. Large cluster random- ized trials and global cohort collaborations generate longitudinal data on hundreds of thousands of patients in real world settings. These provide a tremendous resource for developing the "practice-based evidence" needed to maximize the impact of HIV prevention strategies and to improve healthcare delivery systems. Realizing this potential, however, demands innovations to the field of Targeted Learning for maximally unbiased and efficient estimation of statistical parameters, best approximating the causal effects of interest. First, improved methods for estimating the effects of patient responsive monitoring and treatment strategies must be developed. In the common settings of strong confounding and rare outcomes, current estimators suffer from bias, lack efficiency and have unreliable measures of uncertainty. Second, general causal models and identifiability assumptions must be developed for the joint effects of cluster and individual-level interventions over multiple time points. These models will account for interactions between individuals within clusters and potential contamination between clusters. This work will inform the optimal design for sampling clusters and measuring individuals within communities or clinics. Third, efficient and maximally unbiased estimators must be developed to evaluate the impact of cluster and individual-level interventions over multiple time points. Current methods are highly susceptible to bias and misleading inference due to model misspecification and due to the often incorrect assumption that the observed data represent n independent, identically distributed (i.i.d.) repetitions of an experiment. The developed methods will elucidate the pathways by which cluster- based interventions impact health, while remaining robust to the common challenges of sparsity, irregular and informative missingness, and few truly independent units (clusters) but potentially hundreds of thousands of conditionally independent units. These innovations are motivated by our collaborations with the International epidemiologic Databases to Evaluate AIDS (IeDEA) in Southern (PI Dr. Egger) and Eastern Africa (PI Dr. Yiannoutsos) and the Sustain- able East Africa Research in Community Health (SEARCH) consortium (PI Dr. Havlir), a cluster randomized trial to evaluate the community-wide benefits of ART initiation at all CD4 counts. The developed methods will be applied to these data sources to investigate (i) strategies for monitoring antiretroviral therapy (ART) and guiding switches to second line regimens, (ii) the direct and indirect effects of a community-based HIV prevention strategy and (iii) the impact of clinic-based programs for delivering HIV care. Finally, the resulting estimators will be implemented as publicly available software packages and teaching papers written to explain the methodology in a clear and rigorous manner.

Public Health Relevance

There is a pressing need to evaluate the optimal strategies for delivering HIV prevention, treatment and care in resource-limited settings. We will advance HIV implementation science by formally evaluating which study designs best address our causal questions, developing statistical methodology to learn as much as possible from the complex data generated, implementing the resulting methodology in publicly available software, and ap- plying it to two large clinical cohort consortiums in Southern and Eastern Africa and a large cluster randomized trial of antiretroviral-based HIV prevention in Kenya and Uganda.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
2R01AI074345-06A1
Application #
8659000
Study Section
AIDS Clinical Studies and Epidemiology Study Section (ACE)
Program Officer
Gezmu, Misrak
Project Start
2007-04-01
Project End
2018-06-30
Budget Start
2014-07-30
Budget End
2015-06-30
Support Year
6
Fiscal Year
2014
Total Cost
$461,878
Indirect Cost
$141,195
Name
University of California Berkeley
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
124726725
City
Berkeley
State
CA
Country
United States
Zip Code
94704
Balzer, Laura B; Petersen, Maya L; van der Laan, Mark J et al. (2015) Adaptive pair-matching in randomized trials with unbiased and efficient effect estimation. Stat Med 34:999-1011
Schnitzer, Mireille E; van der Laan, Mark J; Moodie, Erica E M et al. (2014) EFFECT OF BREASTFEEDING ON GASTROINTESTINAL INFECTION IN INFANTS: A TARGETED MAXIMUM LIKELIHOOD APPROACH FOR CLUSTERED LONGITUDINAL DATA. Ann Appl Stat 8:703-725
van der Laan, Mark J (2014) Targeted estimation of nuisance parameters to obtain valid statistical inference. Int J Biostat 10:29-57
Sapp, Stephanie; van der Laan, Mark J; Canny, John (2014) Subsemble: an ensemble method for combining subset-specific algorithm fits. J Appl Stat 41:1247-1259
Sapp, Stephanie; van der Laan, Mark J; Page, Kimberly (2014) Targeted estimation of binary variable importance measures with interval-censored outcomes. Int J Biostat 10:77-97
Schnitzer, Mireille E; Moodie, Erica E M; van der Laan, Mark J et al. (2014) Modeling the impact of hepatitis C viral clearance on end-stage liver disease in an HIV co-infected cohort with targeted maximum likelihood estimation. Biometrics 70:144-52
Petersen, Maya L; van der Laan, Mark J (2014) Causal models and learning from data: integrating causal modeling and statistical estimation. Epidemiology 25:418-26
Lendle, Samuel D; Subbaraman, Meenakshi S; van der Laan, Mark J (2013) Identification and efficient estimation of the natural direct effect among the untreated. Biometrics 69:310-7
Lendle, Samuel D; Fireman, Bruce; van der Laan, Mark J (2013) Targeted maximum likelihood estimation in safety analysis. J Clin Epidemiol 66:S91-8
Wang, Hui; van der Laan, Mark J (2011) Dimension reduction with gene expression data using targeted variable importance measurement. BMC Bioinformatics 12:312

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