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Abstract In this article, we develop a weighted approach to estimation for right-censored time to event data in the presence of external predictions available from a prediction model. There are several advantages to the proposed approach. First, the method allows for arbitrary forms for the external prediction model. Second, the methodology can be fit easily using standard software packages that allow for subject-specific weights. Third, all that is needed from the external models are access to predictions and not the actually prediction equation. A complication is that inference becomes challenging, so we develop new theoretical results along with a perturbation-based method for inference. The methodology is applied to three publicly available datasets. 

For many rare diseases with no approved preventive interventions, promising interventions exist. However, it has proven difficult to conduct a pivotal phase 3 trial that could provide direct evidence demonstrating a beneficial effect of the intervention on the target disease outcome. When a promising putative surrogate endpoint(s) for the target outcome is available, surrogate-based provisional approval of an intervention may be pursued. Following the general Causal Roadmap rubric, we describe a surrogate endpoint-based provisional approval causal roadmap. Based on an observational study data set and a phase 3 randomized trial data set, this roadmap defines an approach to analyze the combined data set to draw a conservative inference about the treatment effect (TE) on the target outcome in the phase 3 study population. The observational study enrolls untreated individuals and collects baseline covariates, surrogate endpoints, and the target outcome, and is used to estimate the surrogate index—the regression of the target outcome on the surrogate endpoints and baseline covariates. The phase 3 trial randomizes participants to treated vs. untreated and collects the same data but is much smaller and hence very underpowered to directly assess TE, such that inference on TE is based on the surrogate index. This inference is made conservative by specifying 2 bias functions: one that expresses an imperfection of the surrogate index as a surrogate endpoint in the phase 3 study, and the other that expresses imperfect transport of the surrogate index in the untreated from the observational to the phase 3 study. Plug-in and nonparametric efficient one-step estimators of TE, with inferential procedures, are developed. The finite-sample performance of the estimators is evaluated in simulation studies. The causal roadmap is motivated by and illustrated with contemporary Group B Streptococcus vaccine development. 

The widespread use of machine learning algorithms in radiomics has led to a proliferation of flexible prognostic models for clinical outcomes. However, a limitation of these techniques is their black-box nature, which prevents the ability for increased mechanistic phenomenological understanding. In this article, we develop an inferential framework for estimating causal effects with radiomics data. A new challenge is that the exposure of interest is latent so that new estimation procedures are needed. We leverage a multivariate version of partial least squares for causal effect estimation. The methodology is illustrated with applications to two radiomics datasets, one in osteosarcoma and one in glioblastoma. 

Abstract Background Evidence to guide type 2 diabetes treatment individualization is limited. We evaluated heterogeneous treatment effects (HTE) of intensive glycemic control in type 2 diabetes patients on major adverse cardiovascular events (MACE) in the Action to Control Cardiovascular Risk in Diabetes Study (ACCORD) and the Veterans Affairs Diabetes Trial (VADT). Methods Causal forests machine learning analysis was performed using pooled individual data from two randomized trials (n = 12,042) to identify HTE of intensive versus standard glycemic control on MACE in patients with type 2 diabetes. We used variable prioritization from causal forests to build a summary decision tree and examined the risk difference of MACE between treatment arms in the resulting subgroups. Results A summary decision tree used five variables (hemoglobin glycation index, estimated glomerular filtration rate, fasting glucose, age, and body mass index) to define eight subgroups in which risk differences of MACE ranged from − 5.1% (95% CI − 8.7, − 1.5) to 3.1% (95% CI 0.2, 6.0) (negative values represent lower MACE associated with intensive glycemic control). Intensive glycemic control was associated with lower MACE in pooled study data in subgroups with low (− 4.2% [95% CI − 8.1, − 1.0]), intermediate (− 5.1% [95% CI − 8.7, − 1.5]), and high (− 4.3% [95% CI − 7.7, − 1.0]) MACE rates with consistent directions of effect in ACCORD and VADT alone. Conclusions This data-driven analysis provides evidence supporting the diabetes treatment guideline recommendation of intensive glucose lowering in diabetes patients with low cardiovascular risk and additionally suggests potential benefits of intensive glycemic control in some individuals at higher cardiovascular risk. 

In many medical and scientific settings, the choice of treatment or intervention may be de-termined by a covariate threshold. For example, elderly men may receive more thoroughdiagnosis if their prostate-specific antigen (PSA) level is high. In these cases, the causaltreatment effect is often of great interest, especially when there is a lack of evidence fromrandomized clinical trials. From the social science literature, a class of methods known asregression discontinuity (RD) designs can be used to estimate the treatment effect in thissituation. Under certain assumptions, such an estimand enjoys a causal interpretation. Weshow how to estimate causal effects under the regression discontinuity design for censoreddata. The proposed estimation procedure employs a class of censoring unbiased transfor-mations that includes inverse probability censored weighting and doubly robust transfor-mation schemes. Simulation studies are used to evaluate the finite-sample properties of theproposed estimator. We also illustrate the proposed method by evaluating the causal effectof PSA-dependent screening strategies 

A common goal in observational research is to estimate marginal causal effects in the presence of confounding variables. One solution to this problem is to use the covariate distribution to weight the outcomes such that the data appear randomized. The propensity score is a natural quantity that arises in this setting. Propensity score weights have desirable asymptotic properties, but they often fail to adequately balance covariate data in finite samples. Empirical covariate balancing methods pose as an appealing alternative by exactly balancing the sample moments of the covariate distribution. With this objective in mind, we propose a framework for estimating balancing weights by solving a constrained convex program, where the criterion function to be optimized is a Bregman distance. We then show that the different distances in this class render identical weights to those of other covariate balancing methods. A series of numerical studies are presented to demonstrate these similarities.