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Missing data is a prevailing issue for statisticians and medical practitioners, who must deal with incomplete information in data from many sources, including clinical trials and other randomized experiments. This issue is especially worrisome in critical cases, such as policy making or medical drug and device approval, because it is difficult to determine how missing units might affect conclusions about efficacy. Numerous studies have discussed methods for handling missing data, but there is no universal agreement on how to interpret results. One promising solution comes from tipping point analyses. Liublinska et al. (2014) visualized results from a sensitivity analysis via enhanced tipping point (ETP) displays which aided the process of an FDA drug evaluation. While the use of ETP displays has not been formalized for 2^k factorial experiments, increasingly advanced computational tools have made such a generalization feasible. I therefore propose extending the use of ETP displays to balanced 2^k factorial experiments. Based on the mathematical properties of estimators for factorial experiments, I show how this process works specifically for balanced factorial designs. Then, using a simulated data set, I run a study of a factorial experiment where k=2 and compare the results for cases of high and low "missingness.” From my analysis of a 2^2 design, I then generalize my procedure to 2^k designs. This is a powerful and useful tool for factorial experiments as they, too, are subject to the problems of missing data. It is now clear that these convenient displays can also be used for 2^k experiments.

This is an advanced expository book on statistical methods for the Design and Analysis of Simulation Experiments (DASE). Though the book focuses on DASE for discrete-event simulation (such as queuing and inventory simulations), it also discusses DASE for deterministic simulation (such as engineering and physics simulations). The text presents both classic and modern statistical designs. Classic designs (e.g., fractional factorials) assume only a few factors with a few values per factor. The resulting input/output data of the simulation experiment are analyzed through low-order polynomials, which are linear regression (meta)models. Modern designs allow many more factors, possible with many values per factor. These designs include group screening (e.g., Sequential Bifurcation, SB) and space filling designs (e.g., Latin Hypercube Sampling, LHS).^ The data resulting from these modern designs may be analyzed through low-order polynomials for group screening and various metamodel types (e.g., Kriging) for LHS. In this way, the book provides relatively simple solutions for the problem of which scenarios to simulate and how to analyze the resulting data. The book also includes methods for computationally expensive simulations. It discusses only those tactical issues that are closely related to strategic issues; i.e., the text briefly discusses run-length and variance reduction techniques. The leading textbooks on discrete-event simulation pay little attention to the strategic issues of simulation. The author has been working on strategic issues for approximately forty years, in various scientific disciples--such as operations research, management science, industrial engineering, mathematical statistics, economics, nuclear engineering, computer science, and information systems.^ The intended audience is comprised of researchers, graduate students, and mature practitioners in the simulation area. They are assumed to have a basic knowledge of simulation and mathematical statistics; nevertheless, the book summarizes these basics, for the readers' convenience.

This is an advanced expository book on statistical methods for the Design and Analysis of Simulation Experiments (DASE). Though the book focuses on DASE for discrete-event simulation (such as queuing and inventory simulations), it also discusses DASE for deterministic simulation (such as engineering and physics simulations). The text presents both classic and modern statistical designs. Classic designs (e.g., fractional factorials) assume only a few factors with a few values per factor. The resulting input/output data of the simulation experiment are analyzed through low-order polynomials, which are linear regression (meta)models. Modern designs allow many more factors, possible with many values per factor. These designs include group screening (e.g., Sequential Bifurcation, SB) and space filling designs (e.g., Latin Hypercube Sampling, LHS). The data resulting from these modern designs may be analyzed through low-order polynomials for group screening and various metamodel types (e.g., Kriging) for LHS. In this way, the book provides relatively simple solutions for the problem of which scenarios to simulate and how to analyze the resulting data. The book also includes methods for computationally expensive simulations. It discusses only those tactical issues that are closely related to strategic issues; i.e., the text briefly discusses run-length and variance reduction techniques. The leading textbooks on discrete-event simulation pay little attention to the strategic issues of simulation. The author has been working on strategic issues for approximately forty years, in various scientific disciples--such as operations research, management science, industrial engineering, mathematical statistics, economics, nuclear engineering, computer science, and information systems. The intended audience is comprised of researchers, graduate students, and mature practitioners in the simulation area. They are assumed to have a basic knowledge of simulation and mathematical statistics; nevertheless, the book summarizes these basics, for the readers' convenience.

This is an advanced expository book on statistical methods for the Design and Analysis of Simulation Experiments (DASE). Though the book focuses on DASE for discrete-event simulation (such as queuing and inventory simulations), it also discusses DASE for deterministic simulation (such as engineering and physics simulations). The text presents both classic and modern statistical designs. Classic designs (e.g., fractional factorials) assume only a few factors with a few values per factor. The resulting input/output data of the simulation experiment are analyzed through low-order polynomials, which are linear regression (meta)models. Modern designs allow many more factors, possible with many values per factor. These designs include group screening (e.g., Sequential Bifurcation, SB) and space filling designs (e.g., Latin Hypercube Sampling, LHS). The data resulting from these modern designs may be analyzed through low-order polynomials for group screening and various metamodel types (e.g., Kriging) for LHS. In this way, the book provides relatively simple solutions for the problem of which scenarios to simulate and how to analyze the resulting data. The book also includes methods for computationally expensive simulations. It discusses only those tactical issues that are closely related to strategic issues; i.e., the text briefly discusses run-length and variance reduction techniques. The leading textbooks on discrete-event simulation pay little attention to the strategic issues of simulation. The author has been working on strategic issues for approximately forty years, in various scientific disciples--such as operations research, management science, industrial engineering, mathematical statistics, economics, nuclear engineering, computer science, and information systems. The intended audience is comprised of researchers, graduate students, and mature practitioners in the simulation area. They are assumed to have a basic knowledge of simulation and mathematical statistics; nevertheless, the book summarizes these basics, for the readers' convenience.