Generalizations to Corrections of Measurement Error Effects for Dynamic Treatment Regimes

Measurement error is a pervasive issue in questions of estimation and inference. Generally, any data which are measured with error will render the results of an analysis which ignores this error unreliable. This is a particular concern in health research, where many quantities of interest are typically subject to measurement error. One particular field of health research, precision medicine, has not yet seen a substantive attempt to account for measurement error. Dynamic treatment regimes (DTRs), which can be used to represent sequences of treatment decisions in a medical setting, have historically been analyzed assuming, implicitly, that all quantities are perfectly observable. We consider the problem of optimal DTR estimation where quantities of interest may be subject to measurement error. The nature of this problem is such that many existing techniques to account for the effects of measurement error need to be expanded in order to accommodate the data which are available in practice. This expansion further highlights theoretical shortcomings in the existing methodologies. This thesis begins by expanding existing methods for correcting for the effects of measurement error to accommodate issues which are frequently observed in real-world data. We expand the most commonly applied measurement error corrections (regression calibration and simulation extrapolation), demonstrating how they are able to be conducted with non-identically distributed replicate measurements. We further expand simulation extrapolation, which typically assumes normality of the underlying error terms, proposing a nonparametric simulation extrapolation. These expansions are conducted generally, separate from the specific context of optimal DTR estimation. Following the expansion of these extant techniques, we consider the problem of errors in covariates within the DTR framework. We apply the aforementioned generalized error correction techniques to this setting, and demonstrate how valid estimation and inference can proceed. Finally, we consider problems which are present when there is treatment misclassification in DTRs, proposing techniques to restore consistency and perform valid inference. To our knowledge this work represents the first substantive attempt to explore these problems. Thus, in addition to proposing methodological solutions, we also elucidate the particular challenges of estimation in this setting. All proposed techniques are explored theoretically, using simulation studies, and through real-world data analyses

Optimal Dynamic Treatment Regime Estimation in the Presence of Nonadherence

Dynamic treatment regimes (DTRs) are sequences of functions that formalize the process of precision medicine. DTRs take as input patient information and output treatment recommendations. A major focus of the DTR literature has been on the estimation of optimal DTRs, the sequences of decision rules that result in the best outcome in expectation, across the complete population were they to be applied. While there is a rich literature on optimal DTR estimation, to date there has been minimal consideration of the impacts of nonadherence on these estimation procedures. Nonadherence refers to any process through that an individual's prescribed treatment does not match their true treatment. We explore the impacts of nonadherence and demonstrate that generally, when nonadherence is ignored, suboptimal regimes will be estimated. In light of these findings we propose a method for estimating optimal DTRs in the presence of nonadherence. The resulting estimators are consistent and asymptotically normal, with a double robustness property. Using simulations we demonstrate the reliability of these results, and illustrate comparable performance between the proposed estimation procedure adjusting for the impacts of nonadherence and estimators that are computed on data without nonadherence

Differentially private projection-depth-based medians

We develop $(ε,δ)$-differentially private projection-depth-based medians using the propose-test-release (PTR) and exponential mechanisms. Under general conditions on the input parameters and the population measure, (e.g. we do not assume any moment bounds), we quantify the probability the test in PTR fails, as well as the cost of privacy via finite sample deviation bounds. Next, we show that when some observations are contaminated, the private projection-depth-based median does not break down, provided its input location and scale estimators do not break down. We demonstrate our main results on the canonical projection-depth-based median, as well as on projection-depth-based medians derived from trimmed estimators. In the Gaussian setting, we show that the resulting deviation bound matches the known lower bound for private Gaussian mean estimation. In the Cauchy setting, we show that the ``outlier error amplification\u27\u27 effect resulting from the heavy tails outweighs the cost of privacy. This result is then verified via numerical simulations. Additionally, we present results on general PTR mechanisms and a uniform concentration result on the projected spacings of order statistics, which may be of general interest.45 pages, 1 figur

How Well Do Executives Trust Their Intuition

In this age of Big Data and analytics, knowledge gained through experiential learning and intuition may be taking a back seat to analytics. However, the use of intuition should not be underestimated and should play an important role in the decision process. How Well Do Executives Trust Their Intuition covers the Fulbright research study conducted by this international team of editors. The main question of their investigation is: How well do executives trust their intuition? In other words, do they typically prefer intuition over analysis and analytics. And equally importantly, what types of intuition may be most favorable looking at different variables? The research utilizes survey and biometrics approaches with C-level executives from Canada, U.S., Poland, and Italy. In addition, the book contains chapters from leading executives in industry, academia, and government. Their insights provide examples of how their intuition enabled key decisions that they made. This book covers such topics as: Using intuition How gender, experience, role, industry, and country affect intuition Trust and intuition in management Trusting intuition It’s a matter of heart Leadership intuition and the future of work Creating an intuitive awareness for executives Improvisation and instinct. The book explores how executives can use intuition to guide decision making. It also explains how to trust intuition-based decisions. How Well Do Executives Trust Their Intuition is a timely and prescient reminder in this age of data-driven analytics that human insight, instinct, and intuition should also play key roles

If numbers could “feel”: How well do executives trust their intuition?

Purpose In the business and data analytics community, intuition has not been discussed widely in terms of its application to executive decision-making. However, the purpose of this paper is to focus on new global research that combines intuition, trust and analytics in terms of how well C-level executives trust their intuition. Design/methodology/approach Our Fulbright research, as described in this paper and performed by colleagues from the United States, Canada, Poland and Italy, examines executives’ as well as other less experienced employees’ preferences for different types of intuition versus data analysis. This study set out to better understand the degree to which executives prefer intuition versus analysis and the relationship between these approaches to decision-making. Our research combines elements of a review, a cross-cultural/cross-company survey study and a biometrics study in interoception. The research team has a multidisciplinary background in business, information technology, strategy, trust management, statistics and neuroscience. Findings Based on our research, the main findings are as follows. The use of and preference for intuition types change as employees gain more experience. However, there may be intuition styles that are more static and trait-like, which are linked to roles, differentiating managers from leaders. Using “inferential intuition” and “seeing the big picture” go hand in hand. Listening to your body signals can promote improved intuition. Cross-cultural differences may impact executive decision-making. Executives often prefer to use their intuition over analysis/analytics. Research limitations/implications This research could be expanded to have a larger sample size of C-level executives. We had 172 responses with 65% C-level executives and 12% directors. However, a recent survey by the Economist Intelligence Unit on intuition used by executives had a sample of 174 executives around the world, which is comparable with our sample size. Practical implications From our research, executives should continue to apply their experiential learning through intuition to complement their use of data in making strategic decisions. We have often discounted the use of intuition in executive decision-making, but our research highlights the importance of making it a critical part of the executive decision-making process. Originality/value Based on the results of our survey and biometrics research, executives apply their intuition to gain greater confidence in their decision-making. Listening to their body signals can also improve their intuitive executive awareness. This complements their use of data and analytics when making executive decisions. </jats:sec

How well do executives trust their intuition

In this age of Big Data and analytics, knowledge gained through experiential learning and intuition may be taking a back seat to analytics. However, the use of intuition should not be underestimated and should play an important role in the decision process. How Well Do Executives Trust Their Intuition covers the Fulbright research study conducted by this international team of editors. The main question of their investigation is: How well do executives trust their intuition? In other words, do they typically prefer intuition over analysis and analytics. And equally importantly, what types of intuition may be most favorable looking at different variables? The research utilizes survey and biometrics approaches with C-level executives from Canada, U.S., Poland, and Italy. In addition, the book contains chapters from leading executives in industry, academia, and government. Their insights provide examples of how their intuition enabled key decisions that they made. This book covers such topics as: Using intuition How gender, experience, role, industry, and country affect intuition Trust and intuition in management Trusting intuition It’s a matter of heart Leadership intuition and the future of work Creating an intuitive awareness for executives Improvisation and instinct. The book explores how executives can use intuition to guide decision making. It also explains how to trust intuition-based decisions. How Well Do Executives Trust Their Intuition is a timely and prescient reminder in this age of data-driven analytics that human insight, instinct, and intuition should also play key roles