The smart use of real-world evidence (RWE) is known to enable more flexible forms of access to novel medicines. This flexibility may be especially promising for targeted cancer medicines, which often do not align with the traditional approach to medicinal development, and for which therapeutic innovation (i.e. favourable and clinically significant benefits at an affordable price) in routine clinical practice is becoming ever more difficult to achieve due to its highly complex nature. Amongst others, RWE should include information on the performance of those medicines for every individual patient. However, the current estimation of this patientlevel performance using conventional methods from pharmaceutical and medical sciences is challenging. This is because these methods are unable to derive causal conclusions on medicinal performance from the complex real-world environment, as opposed to controlled and randomised clinical trial settings. Simultaneously, the increasing emergence of novel medicines, and their promising combined effects are now creating a new combinatorial complexity level on the captured data. At the same time, new and advanced analytical methods within the field of data science are continuously being developed and have recently been applied to pharmaceutical and medical research, including the domain of pharmacoepidemiology. These powerful methods include techniques such as machine learning and Bayesian approaches, both being recognised as having a transformative potential in clinical research and practice. Specifically, they may be used to gain new insights into the patient-level performance of novel medicines in the messy real world, thereby providing a better understanding of RWE. In doing so, studies may generate new hypotheses through the exploration of data sets, or test existing hypotheses prespecified during prior clinical research. In this dissertation, I present the specific methods of advanced analytics to unravel the complexity of RWE , therefore, increasing our understanding of the individual performance of cancer treatments. These methods are investigated for their use in both hypothesis generation (part 1) and hypothesis testing (part 2) studies. A general ntroduction into the field is provided in Chapter 1, followed by the research objectives. In Chapter 2, I validate the use of an advanced modelling technique, i.e. machine learning, as a personal performance prediction model for glioblastoma. Optimisations of this model to be used on novel medicines in more complex situations are proposed in Chapter 3. In Chapter 4, the importance of multi-product RWE assessments is explored, for which hypotheses. Lastly, for these investigated analytics to become useful in healthcare, the need for an insight-providing federated network is introduced in Chapter 6. Chapter 7, the last chapter of this dissertation, presents a general conclusion with discussion of the research contributions.
It is a well-accepted notion that to respond to competitive attacks firms need the necessary resources to do so. However, the presence of resources may not be a sufficient condition to enhance competitive responsiveness. Following a managerial decision-making approach, the present paper investigates how the availability of resources affects decision makers' assessment of a competitor's new product and their subsequent reaction to it. This study posits that competitive reaction follows from a decision maker's assessment of a competitive action. This assessment contains a motivation dimension and an ability dimension. The effect of three types of resources—financial, marketing, and technological—are examined. A quasi-experiment with the Markstrat business game as an empirical setting provided 339 questionnaires containing information on 29 different new product introductions. The motivation and ability dimensions are confirmed as important antecedents explaining reaction behavior. The results show that resources possess a dual, and opposing, role in influencing competitive reaction to new products. On the one hand, resources enhance decision makers' belief that they are able to react effectively to competitive attacks, but the presence of resources also makes them less motivated to react. The paper introduces two explanations for this: the liability-of-wealth hypothesis and the strong-competitor hypothesis. The addition of competitor orientation as a moderator allows us to discern between the two competing rationales for the existence of a negative effect of resources on the expected likelihood of success of a competitive new product introduction, supporting the liability-of-wealth hypothesis. The paper demonstrates the key role of competitor orientation and formulates implications from that.
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