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Bowel preparation is inadequate in a large proportion of colonoscopies, leading to multiple clinical and economic harms. While most patients receive some form of education before colonoscopy, there is no consensus on the best approach. This systematic review aimed to evaluate the efficacy of patient education interventions to improve bowel preparation. We searched the Cochrane Database, CINAHL, EMBASE, Ovid, and Web of Science. Inclusion criteria were: (1) a patient education intervention; (2) a primary aim of improving bowel preparation; (3) a validated bowel preparation scale; (4) a prospective design; (5) a concurrent control group; and, (6) adult participants. Study validity was assessed using a modified Downs and Black scale. 1,080 abstracts were screened. Seven full text studies met inclusion criteria, including 2,660 patients. These studies evaluated multiple delivery platforms, including paper-based interventions (three studies), videos (two studies), re-education telephone calls the day before colonoscopy (one study), and in-person education by physicians (one study). Bowel preparation significantly improved with the intervention in all but one study. All but one study were done in a single center. Validity scores ranged from 13 to 24 (maximum 27). Four of five abstracts and research letters that met inclusion criteria also showed improvements in bowel preparation. Statistical and clinical heterogeneity precluded meta-analysis. Compared to usual care, patient education interventions appear efficacious in improving the quality of bowel preparation. However, because of the small scale of the studies and individualized nature of the interventions, results of these studies may not be generalizable to other settings. Healthcare practices should consider systematically evaluating their current bowel preparation education methods before undertaking new interventions.

Pancreatic ductal adenocarcinoma (PDA) is characterized by an immune-suppressive tumor microenvironment that renders it largely refractory to immunotherapy. We implemented a multimodal analysis approach to elucidate the immune landscape in PDA. Using a combination of CyTOF, single-cell RNA sequencing, and multiplex immunohistochemistry on patient tumors, matched blood, and non-malignant samples, we uncovered a complex network of immune-suppressive cellular interactions. These experiments revealed heterogeneous expression of immune checkpoint receptors in individual patient's T cells and increased markers of CD8 T cell dysfunction in advanced disease stage. Tumor-infiltrating CD8 T cells had an increased proportion of cells expressing an exhausted expression profile that included upregulation of the immune checkpoint , a finding that we validated at the protein level. Our findings point to a profound alteration of the immune landscape of tumors, and to patient-specific immune changes that should be taken into account as combination immunotherapy becomes available for pancreatic cancer.

Background Statistical inference based on small datasets, commonly found in precision oncology, is subject to low power and high uncertainty. In these settings, drawing strong conclusions about future research utility is difficult when using standard inferential measures. It is therefore important to better quantify the uncertainty associated with both significant and non-significant results based on small sample sizes. Methods We developed a new method, Bayesian Additional Evidence (BAE), that determines (1) how much additional supportive evidence is needed for a non-significant result to reach Bayesian posterior credibility, or (2) how much additional opposing evidence is needed to render a significant result non-credible. Although based in Bayesian analysis, a prior distribution is not needed; instead, the tipping point output is compared to reasonable effect ranges to draw conclusions. We demonstrate our approach in a comparative effectiveness analysis comparing two treatments in a real world biomarker-defined cohort, and provide guidelines for how to apply BAE in practice. Results Our initial comparative effectiveness analysis results in a hazard ratio of 0.31 with 95% confidence interval (0.09, 1.1). Applying BAE to this result yields a tipping point of 0.54; thus, an observed hazard ratio of 0.54 or smaller in a replication study would result in posterior credibility for the treatment association. Given that effect sizes in this range are not extreme, and that supportive evidence exists from a similar published study, we conclude that this problem is worthy of further research. Conclusions Our proposed method provides a useful framework for interpreting analytic results from small datasets. This can assist researchers in deciding how to interpret and continue their investigations based on an initial analysis that has high uncertainty. Although we illustrated its use in estimating parameters based on time-to-event outcomes, BAE easily applies to any normally-distributed estimator, such as those used for analyzing binary or continuous outcomes.

Real‐world data derived from electronic health records often exhibit high levels of missingness in variables, such as laboratory results, presenting a challenge for statistical analyses. We developed a systematic workflow for gathering evidence of different missingness mechanisms and performing subsequent statistical analyses. We quantify evidence for missing completely at random (MCAR) or missing at random (MAR), mechanisms using Hotelling's multivariate t ‐test, and random forest classifiers, respectively. We further illustrate how to apply sensitivity analyses using the not at random fully conditional specification procedure to examine changes in parameter estimates under missing not at random (MNAR) mechanisms. In simulation studies, we validated these diagnostics and compared analytic bias under different mechanisms. To demonstrate the application of this workflow, we applied it to two exemplary case studies with an advanced non‐small cell lung cancer and a multiple myeloma cohort derived from a real‐world oncology database. Here, we found strong evidence against MCAR, and some evidence of MAR, implying that imputation approaches that attempt to predict missing values by fitting a model to observed data may be suitable for use. Sensitivity analyses did not suggest meaningful departures of our analytic results under potential MNAR mechanisms; these results were also in line with results reported in clinical trials.

Meaningful real-world evidence (RWE) generation requires unstructured data found in electronic health records (EHRs) which are often missing from administrative claims; however, obtaining relevant data from unstructured EHR sources is resource-intensive. In response, researchers are using natural language processing (NLP) with machine learning (ML) techniques (i.e., ML extraction) to extract real-world data (RWD) at scale. This study assessed the quality and fitness-for-use of EHR-derived oncology data curated using NLP with ML as compared to the reference standard of expert abstraction. Using a sample of 186,313 patients with lung cancer from a nationwide EHR-derived de-identified database, we performed a series of replication analyses demonstrating some common analyses conducted in retrospective observational research with complex EHR-derived data to generate evidence. Eligible patients were selected into biomarker- and treatment-defined cohorts, first with expert-abstracted then with ML-extracted data. We utilized the biomarker- and treatment-defined cohorts to perform analyses related to biomarker-associated survival and treatment comparative effectiveness, respectively. Across all analyses, the results differed by less than 8% between the data curation methods, and similar conclusions were reached. These results highlight that high-performance ML-extracted variables trained on expert-abstracted data can achieve similar results as when using abstracted data, unlocking the ability to perform oncology research at scale.

In this dissertation, we make methodological contributions in three separate areas. In Chapter 2, we introduce a new algorithm for learning high-dimensional causal networks from observational data. Our algorithm, which is a simple modification to the well-known PC-Algorithm, provides reductions in both computational and sample complexity, by leveraging properties of common random graph families. In Chapter 3, we develop a penalized regression framework to integrate known network structure into high-dimensional generalized linear models. Our framework is unique in that it considers two-way structured data, where networks connect both the features and the observation units. We also introduce a statistical inference procedure to provide valid confidence intervals and hypothesis tests. Finally, in Chapter 4, we present an improved estimator for counterfactual policy evaluation in contextual bandit problems. This method is based on classifier-based density ratio estimation, and displays state-of-the-art performance for continuous action spaces. We conclude with a discussion in Chapter 5, describing the limitations of the work, and avenues for future research.

Databases derived from electronic health records (EHRs) are commonly subject to left truncation, a type of selection bias induced due to patients needing to survive long enough to satisfy certain entry criteria. Standard methods to adjust for left truncation bias rely on an assumption of marginal independence between entry and survival times, which may not always be satisfied in practice. In this work, we examine how a weaker assumption of conditional independence can result in unbiased estimation of common statistical parameters. In particular, we show the estimability of conditional parameters in a truncated dataset, and of marginal parameters that leverage reference data containing non-truncated data on confounders. The latter is complementary to observational causal inference methodology applied to real world external comparators, which is a common use case for real world databases. We implement our proposed methods in simulation studies, demonstrating unbiased estimation and valid statistical inference. We also illustrate estimation of a survival distribution under conditionally independent left truncation in a real world clinico-genomic database.