arXiv subjects

Jane Kabami

Publications and source records attributed to Jane Kabami.

3 recordsLinked to original sources

arXiv2025

A causal framework for evaluating the total effect of strategies aiming to expand screening and to improve outcomes

For many health conditions, there are highly efficacious treatment and prevention products. Maximizing their impact requires strategies that improve the reach of health screening in order to establish who could benefit. For example, HIV prevention strategies aim to expand risk screening and to improve uptake of pre-exposure prophylaxis (PrEP) among those experiencing risk. Often, these strategies induce changes at the group-level (e.g., health clinics or communities) and are evaluated through cluster randomized trials. This scenario creates a complex, multilevel-mediation-missing data problem for the following reasons. First, the strategy is delivered at the cluster-level, while health screening and outcomes are at the individual-level. Second, the strategy improves health outcomes directly and indirectly through improved health screening. Third, everyone has an underlying status, which is only observed among those screened. To formally define the total effect in such settings, we use Counterfactual Strata Effects: causal estimands where the outcome is only relevant for a group whose membership is subject to missingness and/ or impacted by the exposure of interest. To identify and estimate the corresponding statistical estimand, we propose a novel extension of Two-Stage targeted minimum loss-based estimation (TMLE). Simulations demonstrate the practical performance of our approach as well as the limitations of existing approaches.

stat.ME↗

arXiv2024

Causal Inference in Randomized Trials with Partial Clustering

Clustering and dependence are common in trials. For example, in some cluster randomized trials (CRTs), pre-existing clusters are enrolled, randomized, and serve as the basis of intervention delivery. Such CRTs are "fully clustered": participants are dependent within clusters. In contrast, "partially clustered" trials contain a mix of participants that are dependent within clusters and participants that are completely independent. One example of this design is a trial where participants are artificially grouped together for the purposes of randomization only; then, for intervention participants, the groups are the basis for intervention delivery, while control participants are un-grouped. Another example is an individually randomized group treatment trial (IRGTT) where participants are individually randomized and, post-randomization, intervention participants are grouped for intervention delivery, while the control participants remain un-grouped. For the three trial designs, we use causal models to non-parametrically describe the data generating process and formalize the observed data dependence structure. We show that despite the different randomization approach, both designs can be represented with the same dependence structure, enabling the use of the same statistical methods for estimation and inference of causal effects. We propose a novel implementation of targeted minimum loss-based estimation (TMLE) for these trials. TMLE is model-robust, leverages covariate adjustment and machine learning, and estimates many causal effects. In simulations, TMLE achieved comparable higher statistical power than alternatives for partially clustered designs. Finally, application to real data from the SEARCH-IPT trial resulted in 20-57% efficiency gains, demonstrating the consequences of our proposed approach.

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arXiv2019

Semi-Supervised Record Linkage for Construction of Large-Scale Sociocentric Networks in Resource-limited Settings: An application to the SEARCH Study in Rural Uganda and Kenya

This paper presents a novel semi-supervised algorithmic approach to creating large scale sociocentric networks in rural East Africa. We describe the construction of 32 large-scale sociocentric social networks in rural Sub-Saharan Africa. Networks were constructed by applying a semi-supervised record-linkage algorithm to data from census-enumerated residents of the 32 communities included in the SEARCH study (NCT01864603), a community-cluster randomized HIV prevention trial in Uganda and Kenya. Contacts were solicited using a five question name generator in the domains of emotional support, food sharing, free time, health issues and money issues. The fully constructed networks include 170; 028 nodes and 362; 965 edges aggregated across communities (ranging from 4449 to 6829 nodes and from 2349 to 31,779 edges per community). Our algorithm matched on average 30% of named contacts in Kenyan communities and 50% of named contacts in Ugandan communities to residents named in census enumeration. Assortative mixing measures for eight different covariates reveal that residents in the network have a very strong tendency to associate with others who are similar to them in age, sex, and especially village. The networks in the SEARCH Study will provide a platform for improved understanding of health outcomes in rural East Africa. The network construction algorithm we present may facilitate future social network research in resource-limited settings.

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