Semiparametric Bayesian commensurate survival model for post-market medical device surveillance with non-exchangeable historical data.
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ABSTRACT:
SUBMITTER: Murray TA
PROVIDER: S-EPMC3954409 | biostudies-literature | 2014 Mar
REPOSITORIES: biostudies-literature
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Project description:There is a disparity between low and middle-income countries (LMICs) and high-income countries (HICs) in translating medical device innovations to the market, affecting health care service delivery. Whereas medical technologies developed in HICs face substantial challenges in getting to the bedside, there are at least clear pathways in most of the major markets, such as the UK, the EU, and the USA. Much less is known about the challenges that innovators of medical technologies face in LMICs. The aim of this study was to map out current bottlenecks in medical device innovation in Uganda, a LMIC in Sub-Saharan East Africa.A cross-sectional survey was carried out using a digital questionnaire. Twenty-one individuals completed the questionnaire, with the majority being medical device innovators (n = 12). Only one of these had undertaken all the innovation stages, up to clinical validation. Very few innovators had established companies, and/or acquired intellectual property. It is evident from similar studies that challenges in medical device translations are multidimensional, and hence interdisciplinary collaborations are key to accelerating translation processes, especially for LMICs.
| S-EPMC10563284 | biostudies-literature
Project description:Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements-one of the most common implantable devices-as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8-53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t?=?5.14; p?<?0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance using electronic health records.
| S-EPMC6761113 | biostudies-literature
Project description:High-dimensional and highly correlated data leading to non- or weakly identified effects are commonplace. Maximum likelihood will typically fail in such situations and a variety of shrinkage methods have been proposed. Standard techniques, such as ridge regression or the lasso, shrink estimates toward zero, with some approaches allowing coefficients to be selected out of the model by achieving a value of zero. When substantive information is available, estimates can be shrunk to nonnull values; however, such information may not be available. We propose a Bayesian semiparametric approach that allows shrinkage to multiple locations. Coefficients are given a mixture of heavy-tailed double exponential priors, with location and scale parameters assigned Dirichlet process hyperpriors to allow groups of coefficients to be shrunk toward the same, possibly nonzero, mean. Our approach favors sparse, but flexible, structure by shrinking toward a small number of random locations. The methods are illustrated using a study of genetic polymorphisms and Parkinson's disease.
| S-EPMC3631538 | biostudies-literature
Project description:We consider the problem of multivariate density deconvolution when interest lies in estimating the distribution of a vector valued random variable X but precise measurements on X are not available, observations being contaminated by measurement errors U. The existing sparse literature on the problem assumes the density of the measurement errors to be completely known. We propose robust Bayesian semiparametric multivariate deconvolution approaches when the measurement error density of U is not known but replicated proxies are available for at least some individuals. Additionally, we allow the variability of U to depend on the associated unobserved values of X through unknown relationships, which also automatically includes the case of multivariate multiplicative measurement errors. Basic properties of finite mixture models, multivariate normal kernels and exchangeable priors are exploited in novel ways to meet modeling and computational challenges. Theoretical results showing the flexibility of the proposed methods in capturing a wide variety of data generating processes are provided. We illustrate the efficiency of the proposed methods in recovering the density of X through simulation experiments. The methodology is applied to estimate the joint consumption pattern of different dietary components from contaminated 24 hour recalls. Supplementary Material presents substantive additional details.
| S-EPMC6075844 | biostudies-literature
Project description:BackgroundThe process of assuring the safety of medical devices is constrained by reliance on voluntary reporting of adverse events. We evaluated a strategy of prospective, active surveillance of a national clinical registry to monitor the safety of an implantable vascular-closure device that had a suspected association with increased adverse events after percutaneous coronary intervention (PCI).MethodsWe used an integrated clinical-data surveillance system to conduct a prospective, propensity-matched analysis of the safety of the Mynx vascular-closure device, as compared with alternative approved vascular-closure devices, with data from the CathPCI Registry of the National Cardiovascular Data Registry. The primary outcome was any vascular complication, which was a composite of access-site bleeding, access-site hematoma, retroperitoneal bleeding, or any vascular complication requiring intervention. Secondary safety end points were access-site bleeding requiring treatment and postprocedural blood transfusion.ResultsWe analyzed data from 73,124 patients who had received Mynx devices after PCI procedures with femoral access from January 1, 2011, to September 30, 2013. The Mynx device was associated with a significantly greater risk of any vascular complication than were alternative vascular-closure devices (absolute risk, 1.2% vs. 0.8%; relative risk, 1.59; 95% confidence interval [CI], 1.42 to 1.78; P<0.001); there was also a significantly greater risk of access-site bleeding (absolute risk, 0.4% vs. 0.3%; relative risk, 1.34; 95% CI, 1.10 to 1.62; P=0.001) and transfusion (absolute risk, 1.8% vs. 1.5%; relative risk, 1.23; 95% CI, 1.13 to 1.34; P<0.001). The initial alerts occurred within the first 12 months of monitoring. Relative risks were greater in three prespecified high-risk subgroups: patients with diabetes, those 70 years of age or older, and women. All safety alerts were confirmed in an independent sample of 48,992 patients from April 1, 2014, to September 30, 2015.ConclusionsA strategy of prospective, active surveillance of a clinical registry rapidly identified potential safety signals among recipients of an implantable vascular-closure device, with initial alerts occurring within the first 12 months of monitoring. (Funded by the Food and Drug Administration and others.).
| S-EPMC6145132 | biostudies-literature
Project description:We discuss inference for a human phage display experiment with three stages. The data are tripeptide counts by tissue and stage. The primary aim of the experiment is to identify ligands that bind with high affinity to a given tissue. We formalize the research question as inference about the monotonicity of mean counts over stages. The inference goal is then to identify a list of peptide-tissue pairs with significant increase over stages. We use a semiparametric Dirichlet process mixture of Poisson model. The posterior distribution under this model allows the desired inference about the monotonicity of mean counts. However, the desired inference summary as a list of peptide-tissue pairs with significant increase involves a massive multiplicity problem. We consider two alternative approaches to address this multiplicity issue. First we propose an approach based on the control of the posterior expected false discovery rate. We notice that the implied solution ignores the relative size of the increase. This motivates a second approach based on a utility function that includes explicit weights for the size of the increase.
| S-EPMC3622196 | biostudies-literature
Project description:There has been great interest in developing nonlinear structural equation models and associated statistical inference procedures, including estimation and model selection methods. In this paper a general semiparametric structural equation model (SSEM) is developed in which the structural equation is composed of nonparametric functions of exogenous latent variables and fixed covariates on a set of latent endogenous variables. A basis representation is used to approximate these nonparametric functions in the structural equation and the Bayesian Lasso method coupled with a Markov Chain Monte Carlo (MCMC) algorithm is used for simultaneous estimation and model selection. The proposed method is illustrated using a simulation study and data from the Affective Dynamics and Individual Differences (ADID) study. Results demonstrate that our method can accurately estimate the unknown parameters and correctly identify the true underlying model.
| S-EPMC3371320 | biostudies-literature
Project description:This paper develops Bayesian sample size formulae for experiments comparing two groups, where relevant preexperimental information from multiple sources can be incorporated in a robust prior to support both the design and analysis. We use commensurate predictive priors for borrowing of information and further place Gamma mixture priors on the precisions to account for preliminary belief about the pairwise (in)commensurability between parameters that underpin the historical and new experiments. Averaged over the probability space of the new experimental data, appropriate sample sizes are found according to criteria that control certain aspects of the posterior distribution, such as the coverage probability or length of a defined density region. Our Bayesian methodology can be applied to circumstances that compare two normal means, proportions, or event times. When nuisance parameters (such as variance) in the new experiment are unknown, a prior distribution can further be specified based on preexperimental data. Exact solutions are available based on most of the criteria considered for Bayesian sample size determination, while a search procedure is described in cases for which there are no closed-form expressions. We illustrate the application of our sample size formulae in the design of clinical trials, where pretrial information is available to be leveraged. Hypothetical data examples, motivated by a rare-disease trial with an elicited expert prior opinion, and a comprehensive performance evaluation of the proposed methodology are presented.
| S-EPMC10952893 | biostudies-literature
Project description:The use of sequential statistical analysis for post-market drug safety surveillance is quickly emerging. Both continuous and group sequential analysis have been used, but consensus is lacking as to when to use which approach. We compare the statistical performance of continuous and group sequential analysis in terms of type I error probability; statistical power; expected time to signal when the null hypothesis is rejected; and the sample size required to end surveillance without rejecting the null. We present a mathematical proposition to show that for any group sequential design there always exists a continuous sequential design that is uniformly better. As a consequence, it is shown that more frequent testing is always better. Additionally, for a Poisson based probability model and a flat rejection boundary in terms of the log likelihood ratio, we compare the performance of various continuous and group sequential designs. Using exact calculations, we found that, for the parameter settings used, there is always a continuous design with shorter expected time to signal than the best group design. The two key conclusions from this article are (i) that any post-market safety surveillance system should attempt to obtain data as frequently as possible, and (ii) that sequential testing should always be performed when new data arrives without deliberately waiting for additional data.
| S-EPMC4888780 | biostudies-literature
Project description:ObjectiveThe aim of this study was to describe previously unrecognised or under-recognised adverse events associated with Melody® valve implantation.BackgroundIn rare diseases and conditions, it is typically not feasible to conduct large-scale safety trials before drug or device approval. Therefore, post-market surveillance mechanisms are necessary to detect rare but potentially serious adverse events.MethodsWe reviewed the United States Food and Drug Administration's Manufacturer and User Facility Device Experience (MAUDE) database and conducted a structured literature review to evaluate adverse events associated with on- and off-label Melody® valve implantation. Adverse events were compared with those described in the prospective Investigational Device Exemption and Post-Market Approval Melody® transcatheter pulmonary valve trials.ResultsWe identified 631 adverse events associated with "on-label" Melody® valve implants and 84 adverse events associated with "off-label" implants. The most frequent "on-label" adverse events were similar to those described in the prospective trials including stent fracture (n=210) and endocarditis (n=104). Previously unrecognised or under-recognised adverse events included stent fragment embolisation (n=5), device erosion (n=4), immediate post-implant severe valvar insufficiency (n=2), and late coronary compression (n=2 cases at 5 days and 3 months after implantation). Under-recognised adverse events associated with off-label implantation included early valve failure due to insufficiency when implanted in the tricuspid position (n=7) and embolisation with percutaneous implantation in the mitral position (n=5).ConclusionPost-market passive surveillance does not demonstrate a high frequency of previously unrecognised serious adverse events with "on-label" Melody® valve implantation. Further study is needed to evaluate safety of "off-label" uses.
| S-EPMC5425318 | biostudies-literature