EVPI and Real Option Valuation

A poster discussing the interaction of the expected value of perfect information and real options valuation

Using physician’s prescribing preference as an instrumental variable in comparative effectiveness research

Background: Comparative effectiveness research (CER) studies using non-randomised study designs sometimes employ instrumental variables (IVs) to address the problem of unmeasured confounding. Physician’s prescribing preference (PPP) is a commonly used IV in this context and had been shown to have utility in many CERs. However, these IVs are generally used as a supplementary method rather than the main analytical strategy. In this thesis, I aim to test the validity of PPP IVs, including an evaluation of the different ways they can be constructed to help promote their more widespread use in CER. Methods: This thesis consists of a range of underpinning methodological approaches, including a literature review summarising applied and simulation studies between 2005 and 2020 that use PPP IV in CER; applied CERs using PPP IV in studies utilising routinely-collected health datasets; target trial emulation approaches based on benchmarking from a randomised clinical trial; and simulation studies to test the performance of PPP IV in multiple CER settings. Results: My literature review provides guidance on the further use of physician’s prescribing preference as instrumental variables in comparative effectiveness research. It highlighted that practical use of PPP needs to consider the findings from simulation studies in the area. In my empirical chapters, I provide strong evidence that PPP is a valid IV approach for conducting CERs using non-randomised study designs. I found that constructing PPP using longer prescription histories generally produces stronger instruments, which in turn leads to greater precision in estimation of treatment effects. In practice, validation of assumptions is crucial for the utility of IVs in CER. In my applied research, I found strong real-world evidence that supports diazepam is associated with lower risk of rehospitalisation and mortality due to the alcohol intoxication and harmful than chlordiazepoxide; that disulfiram is superior to acamprosate in terms of preventing alcohol dependence-related hospitalisations; and that sulfonylureas (SU) performs better than dipeptidyl peptidase-4 inhibitor (DPP-4 inhibitor) in reducing HbA1c levels as the second-line treatment for Type-2 diabetes patients. In my simulation studies, I found PPP IV, when unmeasured confounding exists, can produce less biased estimates of treatment effects than conventional multivariable regressions that only adjust for measured confounding variables, albeit with lower statistical power. The simulations also show PPP IV has potential in alleviating noncollapsibility in non-linear IV approaches. Implications: Findings from this thesis indicate that PPP IVs can be valid IVs and reduce unmeasured confounding in observational CER studies. However, I have found that there is room for improvement in the application of PPP IV in CER studies; researchers need to pay more attention on validating IV assumptions and carefully consider how different formulations of PPP IVs can be applied in order to improve the quality of statistical inference. Future applied PPP IV research should consider findings from relevant simulation studies to inform study designs and analysis plans. Conversely, one also needs information on PPP IVs from empirical studies to inform future simulation study design and to gain further knowledge from triangulation between applied and simulation findings. Many of my thesis findings can be generalised to the use of non-PPP IV approaches in CER

Comparing the performance of two-stage residual inclusion methods when using physician's prescribing preference as an instrumental variable: unmeasured confounding and noncollapsibility

Aim: The first objective is to compare the performance of two-stage residual inclusion (2SRI), two-stage least square (2SLS) with the multivariable generalized linear model (GLM) in terms of the reducing unmeasured confounding bias. The second objective is to demonstrate the ability of 2SRI and 2SPS in alleviating unmeasured confounding when noncollapsibility exists. Materials and methods: This study comprises a simulation study and an empirical example from a real-world UK population health dataset (Clinical Practice Research Datalink). The instrumental variable (IV) used is based on physicians' prescribing preferences (defined by prescribing history). Results: The percent bias of 2SRI in terms of treatment effect estimates to be lower than GLM and 2SPS and was less than 15% in most scenarios. Further, 2SRI was found to be robust to mild noncollapsibility with the percent bias less than 50%. As the level of unmeasured confounding increased, the ability to alleviate the noncollapsibility decreased. Strong IVs tended to be more robust to noncollapsibility than weak IVs. Conclusion: 2SRI tends to be less biased than GLM and 2SPS in terms of estimating treatment effect. It can be robust to noncollapsibility in the case of the mild unmeasured confounding effect

Assessing the performance of physician’s prescribing preference as an instrumental variable in comparative effectiveness research with moderate and small sample sizes: a simulation study

Aim: This simulation study is to assess the utility of physician's prescribing preference (PPP) as an instrumental variable for moderate and smaller sample sizes. Materials and methods: We designed a simulation study to imitate a comparative effectiveness research under different sample sizes. We compare the performance of instrumental variable (IV) and non-IV approaches using two-stage least squares (2SLS) and ordinary least squares (OLS) methods, respectively. Further, we test the performance of different forms of proxies for PPP as an IV. Results: The percent bias of 2SLS is around approximately 20%, while the percent bias of OLS is close to 60%. The sample size is not associated with the level of bias for the PPP IV approach. Conclusion: Irrespective of sample size, the PPP IV approach leads to less biased estimates of treatment effectiveness than OLS adjusting for known confounding only. Particularly for smaller sample sizes, we recommend constructing PPP from long prescribing histories to improve statistical power

Towards superior biopolymer gels by enabling interpenetrating network structures:A review on types, applications, and gelation strategies

Gels derived from single networks of natural polymers (biopolymers) typically exhibit limited physical properties and thus have seen constrained applications in areas like food and medicine. In contrast, gels founded on a synergy of multiple biopolymers, specifically polysaccharides and proteins, with intricate interpenetrating polymer network (IPN) structures, represent a promising avenue for the creation of novel gel materials with significantly enhanced properties and combined advantages. This review begins with the scrutiny of newly devised IPN gels formed through a medley of polysaccharides and/or proteins, alongside an introduction of their practical applications in the realm of food, medicine, and environmentally friendly solutions. Finally, based on the fact that the IPN gelation process and mechanism are driven by different inducing factors entwined with a diverse amalgamation of polysaccharides and proteins, our survey underscores the potency of physical, chemical, and enzymatic triggers in orchestrating the construction of crosslinked networks within these biomacromolecules. In these mixed systems, each specific inducer aligns with distinct polysaccharides and proteins, culminating in the generation of semi-IPN or fully-IPN gels through the intricate interpenetration between single networks and polymer chains or between two networks, respectively. The resultant IPN gels stand as paragons of excellence, characterized by their homogeneity, dense network structures, superior textural properties (e.g., hardness, elasticity, adhesion, cohesion, and chewability), outstanding water-holding capacity, and heightened thermal stability, along with guaranteed biosafety (e.g., nontoxicity and biocompatibility) and biodegradability. Therefore, a judicious selection of polymer combinations allows for the development of IPN gels with customized functional properties, adept at meeting precise application requirements.</p

UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training

Magnetic resonance imaging~(MRI) have played a crucial role in brain disease diagnosis, with which a range of computer-aided artificial intelligence methods have been proposed. However, the early explorations usually focus on the limited types of brain diseases in one study and train the model on the data in a small scale, yielding the bottleneck of generalization. Towards a more effective and scalable paradigm, we propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics. Different from previous pre-training techniques for the unitary vision or textual feature, or with the brute-force alignment between vision and language information, we leverage the unique characteristic of report information in different granularity to build a hierarchical alignment mechanism, which strengthens the efficiency in feature learning. Our UniBrain is validated on three real world datasets with severe class imbalance and the public BraTS2019 dataset. It not only consistently outperforms all state-of-the-art diagnostic methods by a large margin and provides a superior grounding performance but also shows comparable performance compared to expert radiologists on certain disease types