Path Dependence in Corporate Contracting: Increasing Returns, Herd Behavior and Cognitive Biases

Part I of this Article reviews our prior analysis of increasing returns in corporate contract terms. Within the rubric of increasing returns, we discuss learning and network externalities in corporate contracts. Parts II and III examine how agency costs and behavioral biases can lead to standardization

Computerised tomography indices of raised intracranial pressure and traumatic brain injury severity in a New Zealand sample

After traumatic brain injury (TBI) complex cellular and biochemical processes occur¹ including changes in blood flow and oxygenation of the brain; cerebral swelling; and raised intracranial pressure (ICP).² This can dramatically worsen the damage³ and contributes to mortality

Sports-related brain injury in the general population: An epidemiological study

Objectives To determine the incidence, nature and severity of all sports-related brain injuries in the general population. Design Population-based epidemiological incidence study. Methods Data on all traumatic brain injury events sustained during a sports-related activity were extracted from a dataset of all new traumatic brain injury cases (both fatal and non-fatal), identified over a one-year period in the Hamilton and Waikato districts of New Zealand. Prospective and retrospective case ascertainment methods from multiple sources were used. All age groups and levels of traumatic brain injury severity were included. Details of the registering injuries and recurrent injuries sustained over the subsequent year were obtained through medical/accident records and assessment interviews with participants. Results Of 1369 incident traumatic brain injury cases, 291 were identified as being sustained during a sports-related activity (21% of all traumatic brain injuries) equating to an incidence rate of 170 per 100,000 of the general population. Recurrent injuries occurred more frequently in adults (11%) than children (5%). Of the sports-related injuries 46% were classified as mild with a high risk of complications. Injuries were most frequently sustained during rugby, cycling and equestrian activities. It was revealed that up to 19% of traumatic brain injuries were not recorded in medical notes. Conclusions Given the high incidence of new and recurrent traumatic brain injury and the high risk of complications following injury, further sport specific injury prevention strategies are urgently needed to reduce the impact of traumatic brain injury and facilitate safer engagement in sports activities. The high levels of ‘missed’ traumatic brain injuries, highlights the importance in raising awareness of traumatic brain injury during sports-related activity in the general population

Computer analysis, learning and creation of physical arrangements of information

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (leaves 75-76).Humans' ability to arrange the individual pieces of a set of information is paramount to their understanding of the set as a whole. The physical arrangement of pieces of information yields important clues as to how those pieces are related. This thesis focuses on computer analysis of physical arrangements and use of perceived physical relations, such as horizontal and vertical alignment, in determining which pieces of information are most likely related. The computer program described in this thesis demonstrates that once a computer can deduce physical relations between pieces of information, it can learn to order the information as a human would with great accuracy. The information analysis methods presented in this thesis are of benefit to projects that deal with user collaboration and the sorting of data based on relative importance, such as the Electronic Card Wall (EWall) project.by Michael Alan Kahan.M.Eng

Eliminating ambiguous treatment effects using estimands

Most reported treatment effects in medical research studies are ambiguously defined, which can lead to misinterpretation of study results. This is because most studies do not attempt to describe what the treatment effect represents, and instead require readers to deduce this based on the reported statistical methods. However, this approach is fraught, as many methods provide counterintuitive results. For example, some methods include data from all patients, yet the resulting treatment effect applies only to a subset of patients, whereas other methods will exclude certain patients while results will apply to everyone. Additionally, some analyses provide estimates pertaining to hypothetical settings where patients never die or discontinue treatment. Herein we introduce estimands as a solution to the aforementioned problem. An estimand is a clear description of what the treatment effect represents, thus saving readers the necessity of trying to infer this from study methods and potentially getting it wrong. We provide examples of how estimands can remove ambiguity from reported treatment effects and describe their current use in practice. The crux of our argument is that readers should not have to infer what investigators are estimating; they should be told explicitly

Estimands in cluster-randomized trials: choosing analyses that answer the right question

Background Cluster-randomized trials (CRTs) involve randomizing groups of individuals (e.g. hospitals, schools or villages) to different interventions. Various approaches exist for analysing CRTs but there has been little discussion around the treatment effects (estimands) targeted by each. Methods We describe the different estimands that can be addressed through CRTs and demonstrate how choices between different analytic approaches can impact the interpretation of results by fundamentally changing the question being asked, or, equivalently, the target estimand. Results CRTs can address either the participant-average treatment effect (the average treatment effect across participants) or the cluster-average treatment effect (the average treatment effect across clusters). These two estimands can differ when participant outcomes or the treatment effect depends on the cluster size (referred to as ‘informative cluster size’), which can occur for reasons such as differences in staffing levels or types of participants between small and large clusters. Furthermore, common estimators, such as mixed-effects models or generalized estimating equations with an exchangeable working correlation structure, can produce biased estimates for both the participant-average and cluster-average treatment effects when cluster size is informative. We describe alternative estimators (independence estimating equations and cluster-level analyses) that are unbiased for CRTs even when informative cluster size is present. Conclusion We conclude that careful specification of the estimand at the outset can ensure that the study question being addressed is clear and relevant, and, in turn, that the selected estimator provides an unbiased estimate of the desired quantity

A simple principal stratum estimator for failure to initiate treatment

A common intercurrent event affecting many trials is when some participants do not begin their assigned treatment. For example, in a trial comparing two different methods for fluid delivery during surgery, some participants may have their surgery cancelled. Similarly, in a double-blind drug trial, some participants may not receive any dose of study medication. The commonly used intention-to-treat analysis preserves the randomisation structure, thus protecting against biases from post-randomisation exclusions. However, it estimates a treatment policy effect (i.e. addresses the question "what is the effect of the intervention, regardless of whether the participant actually begins treatment?"), which may not be the most clinically relevant estimand. A principal stratum approach, estimating the treatment effect in the subpopulation of participants who would initiate treatment (regardless of treatment arm), may be a more clinically relevant estimand for many trials. We show that a simple principal stratum estimator based on a "modified intention-to-treat" population, where participants who experience the intercurrent event are excluded, is unbiased for the principal stratum estimand under certain assumptions that are likely to be plausible in many trials, namely that participants who initiate the intervention under one treatment condition would also do so under the other treatment condition. We provide several examples of trials where this assumption is plausible, and several instances where it is not. We conclude that this simple principal stratum estimator can be a useful strategy for handling failure to initiate treatment

Using modified intention-to-treat as a principal stratum estimator for failure to initiate treatment

BACKGROUND: A common intercurrent event affecting many trials is when some participants do not begin their assigned treatment. For example, in a double-blind drug trial, some participants may not receive any dose of study medication. Many trials use a 'modified intention-to-treat' approach, whereby participants who do not initiate treatment are excluded from the analysis. However, it is not clear (a) the estimand being targeted by such an approach and (b) the assumptions necessary for such an approach to be unbiased. METHODS: Using potential outcome notation, we demonstrate that a modified intention-to-treat analysis which excludes participants who do not begin treatment is estimating a principal stratum estimand (i.e. the treatment effect in the subpopulation of participants who would begin treatment, regardless of which arm they were assigned to). The modified intention-to-treat estimator is unbiased for the principal stratum estimand under the assumption that the intercurrent event is not affected by the assigned treatment arm, that is, participants who initiate treatment in one arm would also do so in the other arm (i.e. if someone began the intervention, they would also have begun the control, and vice versa). RESULTS: We identify two key criteria in determining whether the modified intention-to-treat estimator is likely to be unbiased: first, we must be able to measure the participants in each treatment arm who experience the intercurrent event, and second, the assumption that treatment allocation will not affect whether the participant begins treatment must be reasonable. Most double-blind trials will satisfy these criteria, as the decision to start treatment cannot be influenced by the allocation, and we provide an example of an open-label trial where these criteria are likely to be satisfied as well, implying that a modified intention-to-treat analysis which excludes participants who do not begin treatment is an unbiased estimator for the principal stratum effect in these settings. We also give two examples where these criteria will not be satisfied (one comparing an active intervention vs usual care, where we cannot identify which usual care participants would have initiated the active intervention, and another comparing two active interventions in an unblinded manner, where knowledge of the assigned treatment arm may affect the participant's choice to begin or not), implying that a modified intention-to-treat estimator will be biased in these settings. CONCLUSION: A modified intention-to-treat analysis which excludes participants who do not begin treatment can be an unbiased estimator for the principal stratum estimand. Our framework can help identify when the assumptions for unbiasedness are likely to hold, and thus whether modified intention-to-treat is appropriate or not

Using modified intention-to-treat as a principal stratum estimator for failure to initiate treatment

Background: A common intercurrent event affecting many trials is when some participants do not begin their assigned treatment. Many trials use a modified intention-to-treat (mITT) approach, whereby participants who do not initiate treatment are excluded from the analysis. However, it is not clear the estimand being targeted by such an approach or the assumptions necessary for it to be unbiased. Methods: We demonstrate that a mITT analysis which excludes participants who do not begin treatment is estimating a principal stratum estimand (i.e. the treatment effect in the subpopulation of participants who would begin treatment, regardless of which arm they were assigned to). The mITT estimator is unbiased for the principal stratum estimand under the assumption that the intercurrent event is not affected by the assigned treatment arm, that is, participants who initiate treatment in one arm would also do so in the other arm. Results: We identify two key criteria in determining whether the mITT estimator is likely to be unbiased: first, we must be able to measure the participants in each treatment arm who experience the intercurrent event, and second, the assumption that treatment allocation will not affect whether the participant begins treatment must be reasonable. Most double-blind trials will satisfy these criteria, and we provide an example of an open-label trial where these criteria are likely to be satisfied as well. Conclusions: A modified intention-to-treat analysis which excludes participants who do not begin treatment can be an unbiased estimator for the principal stratum estimand. Our framework can help identify when the assumptions for unbiasedness are likely to hold, and thus whether modified intention-to-treat is appropriate or not.Comment: Changes to Introduction and Abstract, minor changes to Method