In the nineteenth century, homosexuality shifted from being understood as an act to an essentialized category of human, and then in the twentieth century, homosexuality shifted again to become an identity. Now in the twenty-first century, a groundswell of sexual identities beyond those based on gender attraction have sprung forth. Despite the socially constructed history of homosexuality and the proliferation of new sexual identities in psychological research literature, little empirical work has interrogated racialized sexual attractions and experiences, what I call ethnosexuality. Across the research literature, there is considerable evidence that race factors into human sexuality. Racialized sexual stereotypes, such as the Jezebel or China doll, have been documented in psychology, while historians have traced their roots to slavery or colonialism. Research on sexual preferences for racial groups reveals a clear privileging of White people, fetishization of Asian women and Black men, and marginalization of Asian men and Black women. Scholarship on sexual racism, erotic capital, and gender-race prototypicality informs these trends in desirability. Research on interracial dating and marriage shows that it is correlated with diversity in one’s milieu and demands engaged racework to maintain. Finally, the literature on White privilege shows that racial difference is often invisible to White people, and discussions of it provoke distress. I brought this cross-disciplinary scholarship to bear in order to investigate how 21 participants considered their ethnosexuality in their sexual lives and identities. My thematic analysis of in-depth, two- to three-hour interviews revealed several consistent themes: essentialism, capital, culture, and authenticity. Codes for ethnosexual essentialism revealed that people drew from stereotypes of racial groups to interpret and explain their own sexual preferences or their perceptions of other people. Participants also recognized and endorsed a consistent hierarchy of racialized (and gendered) desirability, and they worked their ethnosexual capital within this stratification. Everyone in the study emphasized finding common ground and shared experience, and although White participants rarely thought of this in ethnic terms, most people of color saw the importance of ethnosexual culture. Similarly, participants of color often spoke with ethnosexual authenticity, whereas White participants struggled and shut down the conversation. I connected these themes to an array of real-world issues, such as creating a family, developing self-esteem, and perpetuating de facto eugenics. I also applied essentialism, capital, culture, and authenticity themes to the study of homosexuality. Instead of conceptualizing sexual attraction as an intrinsic drive toward an essentialized target, researchers could consider how all dimensions of sexuality are socially constructed through essentialized stereotypes about different groups, the capital afforded different bodies and ways of being, the cultural connections that love and passion rely on, and the savvy and awareness that people need to make sense and meaning out of all of it.

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This dissertation is focused on the development of the optimal design and analysis for cluster randomized trials. Specifically, we tackle three common questions: whether or not to pair-match clusters, which causal parameter best captures the intervention effect, and how to select the adjustment set for the analysis. We begin by introducing a formal framework for causal inference in Chapter 1. Throughout, the Sustainable East Africa Research in Community Health (SEARCH) trial serves as the motivating example (NCT01864603). SEARCH is an ongoing community randomized trial to evaluate the impact of immediate and streamlined antiretroviral therapy on HIV incidence in rural East Africa.

In Chapter 2, we consider pair-matching, an intuitive design strategy to protect study validity and to potentially increase power in randomized trials. In a common design, candidate units are identified, and their baseline characteristics are used to create the best n/2 matched pairs. Within the resulting pairs, the intervention is randomized, and the outcomes are measured at the end of follow-up. We consider this design to be adaptive, because the construction of the matched pairs depends on the baseline covariates of all candidate units. As a consequence, the observed data cannot be considered as n/2 independent, identically distributed (i.i.d.) pairs of units, as common practice assumes. Instead, the observed data consist of n dependent units. Chapter 2 explores the consequences of adaptive pair-matching in randomized trials for estimation of the conditional average treatment effect (CATE): the intervention effect, given the measured covariates of the n study units. We contrast the unadjusted estimator with TMLE and show substantial efficiency gains from matching and further gains with adjustment.

In Chapter 3, we compare three causal parameters: the population, conditional and sample average treatment effects. Using a structural causal model, we explicitly define each parameter, discuss interpretation, and formally examine identifiability. To the best of our knowledge, Chapter 3 is the first to propose using TMLE for estimation and inference of the sample effect. In most settings, the sample parameter will be estimated more efficiently than the conditional parameter, which will, in turn, be estimated more efficiently than the population parameter. Finite sample simulations illustrate the potential gains in precision and power from selecting the sample effect as the target of inference.

Finally in Chapter 4, we discuss adjustment for measured covariates during the analysis to reduce variance and increase power in randomized trials. To avoid misleading inference, the analysis plan must be pre-specified. However, it is often unclear a priori which baseline covariates (if any) should be included in the analysis. In the SEARCH trial, for example, there are 16 matched pairs of communities and many potential adjustment variables, including region, HIV prevalence, male circumcision coverage and measures of community-level viral load. In Chapter 4, we propose a rigorous procedure to data-adaptively select the adjustment set, which maximizes the efficiency of the analysis. Specifically, we use cross-validation to select from a pre-specified library the candidate TMLE that minimizes the estimated variance. For further gains in precision, we also propose a collaborative procedure for estimating the known exposure mechanism. Our small sample simulations demonstrate the promise of the methodology to maximize study power, while maintaining nominal confidence interval coverage. Our procedure is tailored to the scientific question (sample vs. population treatment effect) and study design (pair-matched or not) and alleviates many of the common concerns.