Leveraging historical data into oncology development programs: Two case studies of phase 2 Bayesian augmented control trial designs

Leveraging historical data into the design and analysis of phase 2 randomized controlled trials can improve efficiency of drug development programs. Such approaches can reduce sample size without loss of power. Potential issues arise when the current control arm is inconsistent with historical data, which may lead to biased estimates of treatment efficacy, loss of power, or inflated type 1 error. Consideration as to how to borrow historical information is important, and in particular, adjustment for prognostic factors should be considered. This paper will illustrate two motivating case studies of oncology Bayesian augmented control (BAC) trials. In the first example, a glioblastoma study, an informative prior was used for the control arm hazard rate. Sample size savings were 15% to 20% by using a BAC design. In the second example, a pancreatic cancer study, a hierarchical model borrowing method was used, which enabled the extent of borrowing to be determined by consistency of observed study data with historical studies. Supporting Bayesian analyses also adjusted for prognostic factors. Incorporating historical data via Bayesian trial design can provide sample size savings, reduce study duration, and enable a more scientific approach to development of novel therapies by avoiding excess recruitment to a control arm. Various sensitivity analyses are necessary to interpret results. Current industry efforts for data transparency have meaningful implications for access to patient‐level historical data, which, while not critical, is helpful to adjust for potential imbalances in prognostic factors.     

Key considerations for choosing a statistical method to deal with incomplete treatment adherence in pragmatic trials

Pragmatic trials offer practical means of obtaining real-world evidence to help improve decision-making in comparative effectiveness settings. Unfortunately, incomplete adherence is a common problem in pragmatic trials. The commonly used methods in randomized control trials often cannot handle the added complexity imposed by incomplete adherence, resulting in biased estimates. Several naive methods and advanced causal inference methods (e.g., inverse probability weighting and instrumental variable-based approaches) have been used in the literature to deal with incomplete adherence. Practitioners and applied researchers are often confused about which method to consider under a given setting. This current work is aimed to review commonly used statistical methods to deal with non-adherence along with their key assumptions, advantages, and limitations, with a particular focus on pragmatic trials. We have listed the applicable settings for these methods and provided a summary of available software. All methods were applied to two hypothetical datasets to demonstrate how these methods perform in a given scenario, along with the R codes. The key considerations include the type of intervention strategy (point treatment settings, where treatment is administered only once versus sustained treatment settings, where treatment has to be continued over time) and availability of data (e.g., the extent of measured or unmeasured covariates that are associated with adherence, dependent confounding impacted by past treatment, and potential violation of assumptions). This study will guide practitioners and applied researchers to use the appropriate statistical method to address incomplete adherence in pragmatic trial settings for both the point and sustained treatment strategies.     

Relationship between boardroom independence and corporate performance: Reflections and perspectives

The perceived economic value of an independent boardroom configuration has progressively emerged as a matter of considerable importance in the academic and popular literature. The normative research paradigm has fundamentally been dominated by positivists who formulate inferential models populated by large sets of archival data. Regrettably, however, several decades of intense inquiries and passionate debates have invariably failed to ascertain (or dispel) the economic value of an independent boardroom configuration. The lingering boardroom independence–corporate performance saga has reached an impasse with no clear resolution in the foreseeable future. In this study, I provide a diagnosis (through the thematic analysis of semi-structured interviews) of why the economic viability of an independent boardroom remains an elusive phenomenon for positivist researchers. A central reason for the research impasse is attributed to ontological complexities intrinsic to the very nature of the corporation, compounded by multiple layers of methodological complexities. Ultimately, the disentanglement of this enigma would require a pivotal reconceptualization of the corporate governance research agenda.     

Multiple cases deletion measures in linear measurement error models

In this paper, we define a multiple cases deletion model (MCDM) in linear measurement error models (LMEMs). Then, by using the corrected score method of Nakamura (1990), the estimation of parameters is obtained. Furthermore, Based on MCDM, we provide computationally inexpensive deletion diagnostic tools for LMEMs. An example illustrates that our method is useful for diagnosing influential subsets of observations.     

Identification and Identification Failure for Treatment Effects Using Structural Systems

We provide necessary and sufficient conditions for effect identification, thereby characterizing the limits to identification. Our results link the nonstructural potential outcome framework for identifying and estimating treatment effects to structural approaches in economics. This permits economic theory to be built into treatment effect methods. We elucidate the sources and consequences of identification failure by examining the biases arising when the necessary conditions fail, and we clarify the relations between unconfoundedness, conditional exogeneity, and the necessary and sufficient identification conditions. A new quantity, the exogeneity score, plays a central role in this analysis, permitting an omitted variable representation for effect biases. This analysis also provides practical guidance for selecting covariates and insight into the price paid for making various identifying assumptions and the benefits gained.     

Sex Selection for Daughters: Demographic Consequences of Female-Biased Sex Ratios

Population Research and Policy Review - Modern fertility techniques allow parents to carry out preimplantation sex selection. Sex selection for non-medical purposes is legal in many high-income...     

A Simulation of Partial Information Use in Decision Making: Implications for DSS Design

Information matrices are often the output produced by a decision support system. These matrices are a common method for expressing a decision situation under different decision-making scenarios. The decisions involved in designing a decision support system to generate the information matrix are important and involve several cost and benefit components. A designer needs guidance in making effective design decisions in this context. Such guidance can be provided by considering the relationships among specific design decisions, costs, and benefits. The general objective of this study is to provide a comprehensive framework for this purpose. This study is the first to develop and present a comprehensive cost-benefit framework for evaluating design decisions for a variety of scenarios. The specific objective of this research is to provide guidance regarding the number of available information dimensions to incorporate in a computer-based decision aid. Simulation experiments are conducted with a completely specified model based on the cost-benefit framework (including needed assumptions) to evaluate how many information dimensions to include for a specific information matrix size to achieve a balance between information use costs and decision quality. Based upon extensive simulation analyses for a hypothetical decision maker, the practical guideline found for designers is to include only the top half of the relevant information dimensions in any specific decision support system. Over a large number of repeating choice decisions, the savings in cognitive effort and information gathering costs clearly offset relatively minor losses in decision quality.     

“She was a little bit unrealistic”: Choice in healthcare decision making for older people

This paper examines autonomy, choice, options, and power in healthcare decision making for older people. Using discourse analysis and a case study from data gathered as part of an ethnographic field study we critique a common conceptualization of healthcare decision making as patients choosing from an array of options offered by healthcare providers. A discourse of “giving options and being realistic” used by healthcare providers is contrasted with the experience of a single patient's transitional care from hospital to home after hip fracture. This illustrates how a wide variety of actors, institutions, values, and resources take precedence in determining a discharge destination. While the accounts given by healthcare providers cast patient choice in respectful terms, an ethnographic approach illustrates that the “choices” are structured by a discourse which simplifies the complexity of what is offered and who gets to choose. In the case study the patient's choice was subjugated by expertise and institutional concerns; her options were largely illusory; and her autonomy was “at risk” due to her age, poor health, and limited resources. We use Foucault's ideas about discourse and governmentality to question the scope of agency in healthcare decision making. We argue that the conceptualization of informed patients making autonomous choices acts as “misdirection” which deflects problem solving and discussion away from a productive examination of the differences between healthcare system offerings and client needs. We conclude by posing questions to reorient the debate surrounding healthcare decision making for older adults and recommend a more participatory approach to designing social services.     

Approximation and sampling of multivariate probability distributions in the tensor train decomposition

Statistics and Computing - General multivariate distributions are notoriously expensive to sample from, particularly the high-dimensional posterior distributions in PDE-constrained inverse...