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There is an extensive literature on the estimation and evaluation of optimal individualized treatment rules in settings where all confounders of the effect of treatment on outcome are observed. We study the development of individualized decision rules in settings where some of these confounders may not have been measured but a valid binary instrument is available for a binary treatment. We first consider individualized treatment rules, which will naturally be most interesting in settings where it is feasible to intervene directly on treatment. We then consider a setting where intervening on treatment is infeasible, but intervening to encourage treatment is feasible. In both of these settings, we also handle the case that the treatment is a limited resource so that optimal interventions focus the available resources on those individuals who will benefit most from treatment. Given a reference rule, we evaluate an optimal individualized rule by its average causal effect relative to a prespecified reference rule. We develop methods to estimate optimal individualized rules and construct asymptotically efficient plug-in estimators of the corresponding average causal effect relative to a prespecified reference rule.

Estimation and evaluation of individualized treatment rules have been studied extensively, but real-world treatment resource constraints have received limited attention in existing methods. We investigate a setting in which treatment is intervened upon based on covariates to optimize the mean counterfactual outcome under treatment cost constraints when the treatment cost is random. In a particularly interesting special case, an instrumental variable corresponding to encouragement to treatment is intervened upon with constraints on the proportion receiving treatment. For such settings, we first develop a method to estimate optimal individualized treatment rules. We further construct an asymptotically efficient plug-in estimator of the corresponding average treatment effect relative to a given reference rule.

There is an extensive literature on the estimation and evaluation of optimal individualized treatment rules in settings where all confounders of the effect of treatment on outcome are observed. We study the development of individualized decision rules in settings where some of these confounders may not have been measured but a valid binary instrument is available for a binary treatment. We first consider individualized treatment rules, which will naturally be most interesting in settings where it is feasible to intervene directly on treatment. We then consider a setting where intervening on treatment is infeasible, but intervening to encourage treatment is feasible. In both of these settings, we also handle the case that the treatment is a limited resource so that optimal interventions focus the available resources on those individuals who will benefit most from treatment. Given a reference rule, we evaluate an optimal individualized rule by its average causal effect relative to a prespecified reference rule. We develop methods to estimate optimal individualized rules and construct asymptotically efficient plug-in estimators of the corresponding average causal effect relative to a prespecified reference rule.

Suppose that we wish to estimate a finite-dimensional summary of one or more function-valued features of an underlying data-generating mechanism under a nonparametric model. One approach to estimation is by plugging in flexible estimates of these features. Unfortunately, in general, such estimators may not be asymptotically efficient, which often makes these estimators difficult to use as a basis for inference. Though there are several existing methods to construct asymptotically efficient plug-in estimators, each such method either can only be derived using knowledge of efficiency theory or is only valid under stringent smoothness assumptions. Among existing methods, sieve estimators stand out as particularly convenient because efficiency theory is not required in their construction, their tuning parameters can be selected data adaptively, and they are universal in the sense that the same fits lead to efficient plug-in estimators for a rich class of estimands. Inspired by these desirable properties, we propose two novel universal approaches for estimating function-valued features that can be analyzed using sieve estimation theory. Compared to traditional sieve estimators, these approaches are valid under more general conditions on the smoothness of the function-valued features by utilizing flexible estimates that can be obtained, for example, using machine learning., 46 pages, 6 figures, submitted to Bernoulli

Additional file 1. Details of the power evaluation for selecting an unbiased analytic sample for the PROUD effectiveness analysis.

PURPOSE: To characterize people with epilepsy (PWE) presenting to a free neurology consultation and antiepileptic drug (AED) service in the Republic of Guinea. METHODS: Guinea is a low-income country in West Africa that recently experienced an Ebola Virus Disease epidemic. Community-dwelling PWE were seen at a public referral hospital in Conakry, the capital city. During two visits in 2017, an African-U.S. team performed structured interviews and electroencephalograms and provided AEDs. RESULTS: Of 257 participants (143 children, 122 female), 25% had untreated epilepsy and 72% met our criteria for poorly controlled epilepsy. 59% had >100 lifetime seizures, and 58% reported a history consistent with status epilepticus. 38 school-aged children were not in school and 26 adults were unemployed. 115 were not currently taking an AED, including 50 participants who had previously taken an AED and stopped. Commonly cited reasons for AED discontinuation were perceived side effects, unaffordability, and unavailability of AEDs. Traditional medicine use was more frequent among children versus adults (92/143 vs. 60/114, p=0.048). 57 participants had head injuries, 29 had burns, and 18 had fractures. In a multivariable regression analysis, >100 lifetime seizure count was strongly associated with seizure-related injury (p

One of the major issues in signed networks is to use network structure to predict the missing sign of an edge. In this paper, we introduce a novel probabilistic approach for the sign prediction problem. The main characteristic of the proposed models is their ability to adapt to the sparsity level of an input network. The sparsity of networks is one of the major reasons for the poor performance of many link prediction algorithms, in general, and sign prediction algorithms, in particular. Building a model that has an ability to adapt to the sparsity of the data has not yet been considered in the previous related works. We suggest that there exists a dilemma between local and global structures and attempt to build sparsity adaptive models by resolving this dilemma. To this end, we propose probabilistic prediction models based on local and global structures and integrate them based on the concept of smoothing. The model relies more on the global structures when the sparsity increases, whereas it gives more weights to the information obtained from local structures for low levels of the sparsity. The proposed model is assessed on three real-world signed networks, and the experiments reveal its consistent superiority over the state of the art methods. As compared to the previous methods, the proposed model not only better handles the sparsity problem, but also has lower computational complexity and can be updated using real-time data streams.

Recent migrations and inter-ethnic mating of long isolated populations have resulted in genetically admixed populations. To understand the complex population admixture process, which is critical to both evolutionary and medical studies, here we used admixture induced linkage disequilibrium (LD) to infer continuous admixture events, which is common for most existing admixed populations. Unlike previous studies, we expanded the typical continuous admixture model to a more general scenario with isolation after a certain duration of continuous gene flow. Based on the new models, we developed a method, CAMer, to infer the admixture history considering continuous and complex demographic process of gene flow between populations. We evaluated the performance of CAMer by computer simulation and further applied our method to real data analysis of a few well-known admixed populations.

Human migration and human isolation serve as the driving forces of modern human civilization. Recent migrations of long isolated populations has resulted in genetically admixed populations. The history of population admixture is generally complex; however, understanding the admixture process is critical to both evolutionary and medical studies. Here, we utilized admixture induced linkage disequilibrium (LD) to infer occurrence of continuous admixture events, which is common for most existing admixed populations. Unlike previous studies, we expanded the typical continuous admixture model to a more general admixture scenario with isolation after a certain duration of continuous gene flow, and we demonstrated that such treatment significantly improved the accuracy of inference under complex admixture scenarios. Based on the extended models, we developed a method based on weighted LD to infer the admixture history considering continuous and complex demographic process of gene flow between populations. We evaluated the performance of the method by computer simulation and applied our method to real data analysis of a few well-known admixed populations.