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Cost-effectiveness modeling often requires extrapolation of survival data from clinical trials over a long-term horizon. The choice of extrapolation method is often uncertain and can have a profound impact on the results. We propose a novel Bayesian approach towards incorporating external information (e.g., registry data or clinical opinion) into the extrapolation process as a means of reducing this uncertainty.
Methods
Standard parametric survival curves are fitted to immature time-to-event data using maximum likelihood estimation (MLE). Separately, external information on expected cohort-level survival at a future time point is used to specify a prior probability distribution. These are combined to generate posterior distributions of survival curve extrapolations that simultaneously incorporate both observed data and external information. This is done using importance sampling and multivariate normal approximations of the likelihood and posterior distributions; it requires only summary model parameter estimates (and not patient-level data). We apply our method to analyze survival data from the KEYNOTE-426 trial of pembrolizumab+axitinib in advanced renal cell carcinoma.
Results
The method was implemented in R, and outputs survival curve parameters that are compatible with cost-effectiveness models developed in other software (e.g., Microsoft Excel). In all examples considered, our method resulted in extrapolated survival predictions that were more closely aligned with the external information compared with the standard (MLE-based) approach. Incorporation of external information decreased between-distribution variance (reduced structural uncertainty), and generally also decreased within-distribution variance as well (reduced parameter uncertainty). Results were comparable with those obtained from the method of Cooney and White, which uses similar ideas but requires full patient-level data and is more computationally complex.
Conclusions
The extrapolation method we describe can reduce uncertainty when valid external information is available. Only MLE-based parameter estimates are required to implement our method, thus secondary model users such as HTA agencies can adjust survival extrapolations from existing cost-effectiveness models without access to patient-level data. Implementation is straightforward and computationally efficient, and outputs are easily incorporated into existing cost-effectiveness models.
Researchers are increasingly faced with the challenge of producing a robust systematic literature review (SLR) within the confines of time and budget. Semi-automating the title and abstract screening phase has been proposed to reduce the workload burden of SLRs. This research aimed to evaluate the efficacy of text mining and machine learning techniques in the semi-automation of the title and abstract screening phase of SLRs.
Methods
Two SLRs that had been manually screened by one screener (manual SLRs) were examined. The titles and abstracts of these SLRs were tokenized and the datasets were split into training and test sets. Support vector machines (SVM), Naïve Bayes (NB), and k-nearest neighbors (k-NN) classification machine learning models were used to predict whether documents in the test set were classed as relevant during the manual SLR. Diagnostic evaluation was carried out using Shapley Additive explanations and local interpretable model-agnostic explanations to explain the predictions of the optimal model.
Results
SVM achieved a sensitivity of one for both SLRs, successfully identifying all documents classed as relevant in the manual SLR. For one SLR, diagnostic evaluation indicated that the model used relevant features to generate its predictions. For the second SLR, the model had the tendency to predict using less relevant or misinterpreted variables. This may be because certain features (i.e., words) the model was trained on had different meanings depending on the clinical context and were present in both relevant and irrelevant citations. This demonstrates the inability of such models to extract semantic meaning from text.
Conclusions
For the second SLR, domain expertise was required to evaluate the features driving the SVM model predictions. This highlights the importance of using discretion when determining the trustworthiness of results generated by such models. This is important to researchers, who need assurance that the use of such techniques will not compromise the validity of their results.
Evidence synthesis (ES) is often required for economic evaluation (EE) of pharmaceuticals. Commonly used methods are based on the assumption of proportional hazards in trial data, using the hazard ratio (HR). Alternative methods for ES are increasingly used in EE, in situations where the pattern of hazards in the trial data indicates that the proportional hazards assumption may be violated. The impact of these methodological choices on model outcomes is explored.
The model outcomes predicted by each method (HR, FP and AFT) are presented and compared. Both deterministic and probabilistic results are presented, alongside a discussion around how the uncertainty in these structural assumptions may be captured in EE.
Conclusions
Structural assumptions in ES may lead to differences in model outcomes. The impact of these differences may be important in situations where decision uncertainty is high. Methods should be chosen and justified based on patterns of hazard present in the trial data.
Social network analysis is the study of how links between a set of actors are formed. Typically, it is believed that links are formed in a structured manner, which may be due to, for example, political or material incentives, and which often may not be directly observable. The stochastic blockmodel represents this structure using latent groups which exhibit different connective properties, so that conditional on the group membership of two actors, the probability of a link being formed between them is represented by a connectivity matrix. The mixed membership stochastic blockmodel extends this model to allow actors membership to different groups, depending on the interaction in question, providing further flexibility.
Attribute information can also play an important role in explaining network formation. Network models which do not explicitly incorporate covariate information require the analyst to compare fitted network models to additional attributes in a post-hoc manner. We introduce the mixed membership of experts stochastic blockmodel, an extension to the mixed membership stochastic blockmodel which incorporates covariate actor information into the existing model. The method is illustrated with application to the Lazega Lawyers dataset. Model and variable selection methods are also discussed.
To define the pathology in cases of non-Alzheimer primary degenerative dementia (non-AD PDD), we have studied autopsies from four medical centres accessioned in consecutive years since 1976. Neurochemical studies of the basal forebrain-cortical (BF-C) cholinergic system have been conducted in cases from which frozen tissue was available. Twenty-two cases (mean age 70 years, range 47-86) in which the history was consistent with PDD, but which did not meet anatomic criteria for AD, were selected. Approximately 70 cases of PDD, which were accessioned in the same years and met the anatomic criteria for AD, were excluded. The pathologic findings permitted a classification into six groups: Lewy body disease (LBD), 4 cases; Pick's disease, 6 cases; cortical degeneration with motor neuron disease (CDmnd), 2 cases; hippocampal and temporal lobe sclerosis, 3 cases; few or nonspecific abnormalities, 5 cases; other disorders, 2 cases. Our findings suggest that LBD and Pick's disease account for a large proportion of cases of non-AD PDD in the presenile age group, but that a large number of other disorders occasionally present as PDD. Careful examination of the motor systems, as well as cerebral structures relate' to cognitive function, is important in the neuropathologic evaluation. Lesions of the BF-C cholinergic system have been most consistent and severe in LBD, and have not been identified in CDmnd.
These diaries by Ralph Ward (fl.1754–6) and Arthur Jessop (1682–1751) were first published in 1952 and paint a valuable portrait of the trials, tribulations and pleasures of everyday life for the middle classes in rural Yorkshire in the mid-eighteenth century. A transcription of Jessop's diary from 1861 was first discovered in a Huddersfield bookshop in 1927. A local apothecary and pious community man, Jessop depicts the cycles of life in West Yorkshire, displaying a very British preoccupation with the weather. His diary, which covers the period 1730–46, notably discusses the impact of the Jacobite uprising of 1745. Ralph Ward was a fairly wealthy cattle trader, farmer and businessman in North Yorkshire. He was involved in local government, which he describes factually and clearly. His diary, covering the period 1754–6, also discusses business transactions, farming methods and, of course, the weather.
We introduce five probability models for random topological graph theory. For two of these models (I and II), the sample space consists of all labeled orientable 2-cell imbeddings of a fixed connected graph, and the interest centers upon the genus random variable. Exact results are presented for the expected value of this random variable for small-order complete graphs, for closed-end ladders, and for cobblestone paths. The expected genus of the complete graph is asymptotic to the maximum genus. For Model III, the sample space consists of all labeled 2-cell imbeddings (possibly nonorientable) of a fixed connected graph, and for Model IV the sample space consists of all such imbeddings with a rotation scheme also fixed. The event of interest is that the ambient surface is orientable. In both these models the complete graph is almost never orientably imbedded. The probability distribution in Models I and III is uniform; in Models II and IV it depends on a parameter p and is uniform precisely when p = 1/2. Model V combines the features of Models II and IV.
The early composers of change ringing music for English church bells were not mathematicians, yet they developed intricate algebraic ideas more than a century before mathematicians independently discovered them. We introduce the mathematical concepts of permutation group and symmetry group by means of elementary change ringing compositions of the 17th and 18th centuries.
This fracture study evaluates the role of a fiber/matrix interfacial glass on the toughening of two different carbon/carbon (C/C) composites. Both composites incorporate a two-dimensional layup of 8-harness satin weave continuous fiber fabric, but differ in several aspects, the most significant of which is the presence of an oxidation inhibitor in one of these. The fracture behavior of both materials was determined in three-point flexure at 20 through 1650 °C. Microstructural studies indicate that the nonhomogeneous distribution of the oxidation inhibitor within the fiber bundles controls the fracture behavior. Electron microprobe results indicate a high concentration of the glass oxidation inhibitor associated with the inter-bundle matrix, while the intra-bundle matrix is composed primarily of carbon. Accordingly, debonding along the inter-bundle interfaces characterizes the oxidation inhibited composite, whereas the nonoxidation inhibited samples debond by individual fibers. Both materials exhibit strongly rising R-curves throughout the test temperature range. At the higher test temperatures the oxidation inhibited C/C shows the greatest cumulative toughening component, although at a lower value of the fracture toughness. This is consistent with the observed increase in the percentage of fibers that experience individual pullout at the higher temperatures.
Hamiltonian circuits with associated word an n-cycle, in Schreier right coset graphs for symmetric groups Sn mod cyclic groups Zn, correspond to change ringing principles on n bells for which the plain course is the extent; that is, neither bobs nor singles are required. This connection is made explicit for the general case, and then specialized to the cases n = 4 (minimus) and n= 5 (doubles). In particular, all 102 no-call doubles principles on three generators are found and catalogued.
The ancient and continuing art of change ringing, or campanology (how the British ring church bells), is studied from a mathematical viewpoint. An extent on n bells is regarded as a hamiltonian cycle in a Cayley colour graph for the symmetric group Sn, embedded on an appropriate surface. Two methods for variable n (Plain Bob for all n and Grandsire for n =; 3 (mod 4)) are discussed, and a new method for n odd is introduced. All minimus methods (n = 4) and five doubles methods (n = 5) are depicted, one of these being the new No Call Doubles.