Browsing by Author "Evans, Michael"
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Item A Bayesian Approach to Factor Analysis via Comparing Prior and Posterior Concentration(2010-08-05T19:35:39Z) Cao, Yun ; Evans, Michael ; StatisticsWe consider a factor analysis model that arises as some distribution form known up to first and second moments. We propose a new Bayesian approach to determine if any latent factors exist and the number of factors. As opposed to current Bayesian methodology for factor analysis, our approach only requires the specification of a prior for the mean vector and the variance matrix for the manifest variables. We compare the concentration of the prior and posterior about the various subsets of parameter space specified by the hypothesized factor structures. We consider two priors here, one is conjugate type and the other is based on the correlation factorization of the covariance matrix. A computational problem associated with the use of the second prior is solved by the use of importance sampling for the posterior analysis. If the data does not lead to a substantial increase in the concentration about the relevant subset, of the posterior compared to the prior, then we have evidence against the hypothesized factor structure. The hypothesis is assessed by computing the observed relative surprise. This results in a considerable simplification of the problem, especially with respect to the elicitation of the prior.Item Effects of Rhythmic Sensory Stimulation on Ehlers–Danlos Syndrome: A Pilot Study(2020-04-28) Vuong, Veronica; Mosabbir, Abdullah; Paneduro, Denise; Picard, Larry; Faghfoury, Hanna; Evans, Michael; Gordon, Allan; Bartel, LeeEhlers–Danlos syndrome (EDS) is a connective tissue disorder characterized by joint hypermobility and skin extensibility and is often accompanied by chronic pain. Rhythmic sensory stimulation (RSS) can be defined as the stimulation of the senses in a periodic manner within a range of low frequencies. Music plus sound delivered through a vibroacoustic device is a form of RSS and has demonstrated utility in managing pain. In this current study, we conducted an open-label pilot study of 15 patients with hypermobile EDS using RSS as the intervention. Posttreatment improvements were seen in 11 of the 15 patients (73%), whereas 3 of the 15 patients (20%) experienced worse outcomes. Of the 14 patients that completed the experiment, 6 participants (43%) were classified as “responders” to the device while 8 participants (57%) were classified as “nonresponders.” Responders demonstrated significant improvements in pain interference (51.5 ± 16 preintervention vs. 43.5 ± 16.4 postintervention BPI score) and depression symptoms (34.0 ± 15.9 preintervention vs. 26.8 ± 12.1 postintervention CESD score). Poststudy interviews confirm the improvements of pain interference, mood, and bowel symptoms. Furthermore, analysis of medical conditions within the responder group indicates that the presence of depression, anxiety, irritable bowel syndrome, and fibromyalgia may indicate a greater likelihood for patients to benefit with vibroacoustic applications. These results indicate a possible potential for RSS, delivered using a vibroacoustic device, in managing pain-related symptoms. Further research is necessary to elucidate the exact mechanism behind the physiological benefits of RSS.Item Establishing a national knowledge translation and generation network in kidney disease: the CAnadian KidNey KNowledge TraNslation and GEneration NeTwork(2014-04-07) Manns, Braden; Barrett, Brendan; Evans, Michael; Garg, Amit; Hemmelgarn, Brenda; Kappel, Joanne; Klarenbach, Scott; Madore, Francois; Parfrey, Patrick; Samuel, Susan; Soroka, Steven; Suri, Rita; Tonelli, Marcello; Wald, Ron; Walsh, Michael; Zappitelli, MichaelAbstract Patients with chronic kidney disease (CKD) do not always receive care consistent with guidelines, in part due to complexities in CKD management, lack of randomized trial data to inform care, and a failure to disseminate best practice. At a 2007 conference of key Canadian stakeholders in kidney disease, attendees noted that the impact of Canadian Society of Nephrology (CSN) guidelines was attenuated given limited formal linkages between the CSN Clinical Practice Guidelines Group, kidney researchers, decision makers and knowledge users, and that further knowledge was required to guide care in patients with kidney disease. The idea for the Canadian Kidney Knowledge Translation and Generation Network (CANN-NET) developed from this meeting. CANN-NET is a pan-Canadian network established in partnership with CSN, the Kidney Foundation of Canada and other professional societies to improve the care and outcomes of patients with and at risk for kidney disease. The initial priority areas for knowledge translation include improving optimal timing of dialysis initiation, and increasing the appropriate use of home dialysis. Given the urgent need for new knowledge, CANN-NET has also brought together a national group of experienced Canadian researchers to address knowledge gaps by encouraging and supporting multicentre randomized trials in priority areas, including management of cardiovascular disease in patients with kidney failure.Item Goodness of fit for the logistic regression model using relative belief(2017-08-31) Al-Labadi, Luai; Baskurt, Zeynep; Evans, MichaelAbstract A logistic regression model is a specialized model for product-binomial data. When a proper, noninformative prior is placed on the unrestricted model for the product-binomial model, the hypothesis H 0 of a logistic regression model holding can then be assessed by comparing the concentration of the posterior distribution about H 0 with the concentration of the prior about H 0. This comparison is effected via a relative belief ratio, a measure of the evidence that H 0 is true, together with a measure of the strength of the evidence that H 0 is either true or false. This gives an effective goodness of fit test for logistic regression.Item Invariant Procedures for Model Checking, Checking for Prior-Data Conflict and Bayesian Inference(2010-08-13T14:38:30Z) Jang, Gun Ho ; Evans, Michael ; StatisticsWe consider a statistical theory as being invariant when the results of two statisticians' independent data analyses, based upon the same statistical theory and using effectively the same statistical ingredients, are the same. We discuss three aspects of invariant statistical theories. Both model checking and checking for prior-data conflict are assessments of single null hypothesis without any specific alternative hypothesis. Hence, we conduct these assessments using a measure of surprise based on a discrepancy statistic. For the discrete case, it is natural to use the probability of obtaining a data point that is less probable than the observed data. For the continuous case, the natural analog of this is not invariant under equivalent choices of discrepancies. A new method is developed to obtain an invariant assessment. This approach also allows several discrepancies to be combined into one discrepancy via a single P-value. Second, Bayesians developed many noninformative priors that are supposed to contain no information concerning the true parameter value. Any of these are data dependent or improper which can lead to a variety of difficulties. Gelman (2006) introduced the notion of the weak informativity as a comprimise between informative and noninformative priors without a precise definition. We give a precise definition of weak informativity using a measure of prior-data conflict that assesses whether or not a prior places its mass around the parameter values having relatively high likelihood. In particular, we say a prior Pi_2 is weakly informative relative to another prior Pi_1 whenever Pi_2 leads to fewer prior-data conflicts a priori than Pi_1. This leads to a precise quantitative measure of how much less informative a weakly informative prior is. In Bayesian data analysis, highest posterior density inference is a commonly used method. This approach is not invariant to the choice of dominating measure or reparametrizations. We explore properties of relative surprise inferences suggested by Evans (1997). Relative surprise inferences which compare the belief changes from a priori to a posteriori are invariant under reparametrizations. We mainly focus on the connection of relative surprise inferences to classical Bayesian decision theory as well as important optimalities.Item Prior Elicitation, Assessment and Inference with a Dirichlet Prior(2017-10-22) Evans, Michael; Guttman, Irwin; Li, PeiyingMethods are developed for eliciting a Dirichlet prior based upon stating bounds on the individual probabilities that hold with high prior probability. This approach to selecting a prior is applied to a contingency table problem where it is demonstrated how to assess the prior with respect to the bias it induces as well as how to check for prior-data conflict. It is shown that the assessment of a hypothesis via relative belief can easily take into account what it means for the falsity of the hypothesis to correspond to a difference of practical importance and provide evidence in favor of a hypothesis.Item Relative Belief Inferences from Decision Theory(2024-09-14) Evans, Michael; Jang, Gun HoRelative belief inferences are shown to arise as Bayes rules or limiting Bayes rules. These inferences are invariant under reparameterizations and possess a number of optimal properties. In particular, relative belief inferences are based on a direct measure of statistical evidence.Item ROC Analyses Based on Measuring Evidence Using the Relative Belief Ratio(2022-11-23) Al-Labadi, Luai; Evans, Michael; Liang, QiaoyuROC (Receiver Operating Characteristic) analyses are considered under a variety of assumptions concerning the distributions of a measurement X in two populations. These include the binormal model as well as nonparametric models where little is assumed about the form of distributions. The methodology is based on a characterization of statistical evidence which is dependent on the specification of prior distributions for the unknown population distributions as well as for the relevant prevalence w of the disease in a given population. In all cases, elicitation algorithms are provided to guide the selection of the priors. Inferences are derived for the AUC (Area Under the Curve), the cutoff c used for classification as well as the error characteristics used to assess the quality of the classification.Item Short-Term Effects of Rhythmic Sensory Stimulation in Alzheimer’s Disease: An Exploratory Pilot Study(IOS Press, 2016-05-10) Clements-Cortes, Amy; Ahonen, Heidi; Evans, Michael; Freedman, Morris; Bartel, LeeThis study assessed the effect of stimulating the somatosensory system of Alzheimer’s disease (AD) patients at three stages of their illness with 40 Hz sound. In this AB cross-over study design, 18 participants (6 mild, 6 moderate, 6 severe) each participated in 13 sessions: one intake and 12 treatment. Treatment A consisted of 40 Hz sound stimulation and Treatment B consisted of visual stimulation using DVDs, each provided twice a week over 6 weeks for a total of 6 times per treatment. Outcome measures included: St. Louis University Mental Status Test (SLUMS), Observed Emotion Rating Scale, and behavioral observation by the researcher. Data were submitted to regression analysis for the series of 6 SLUMS scores in treatment A and 6 scores in B with comparison by group. The slopes for the full sample and subgroups in the 40 Hz treatment were all significant beyond alpha = 0.05, while those for the DVD were not. A thematic analysis of qualitative observations supported the statistical findings. 40 Hz treatment appeared to have the strongest impact on persons with mild and moderate AD. Results are promising in terms of a potential new treatment for persons with AD, and further research is needed.Item Statistical Reasoning: Choosing and Checking the Ingredients, Inferences Based on a Measure of Statistical Evidence with Some Applications(2018-04-16) Al-Labadi, Luai; Baskurt, Zeynep; Evans, MichaelThe features of a logically sound approach to a theory of statistical reasoning are discussed. A particular approach that satisfies these criteria is reviewed. This is seen to involve selection of a model, model checking, elicitation of a prior, checking the prior for bias, checking for prior-data conflict and estimation and hypothesis assessment inferences based on a measure of evidence. A long-standing anomalous example is resolved by this approach to inference and an application is made to a practical problem of considerable importance, which, among other novel aspects of the analysis, involves the development of a relevant elicitation algorithm.Item The Concept of Statistical Evidence, Historical Roots and Current Developments(2024-08-02) Evans, MichaelOne can argue that one of the main roles of the subject of statistics is to characterize what the evidence in the collected data says about questions of scientific interest. There are two broad questions that we will refer to as the estimation question and the hypothesis assessment question. For estimation, the evidence in the data should determine a particular value of an object of interest together with a measure of the accuracy of the estimate, while for the hypothesis assessment, the evidence in the data should provide evidence in favor of or against some hypothesized value of the object of interest together with a measure of the strength of the evidence. This will be referred to as the evidential approach to statistical reasoning, which can be contrasted with the behavioristic or decision-theoretic approach where the notion of loss is introduced, and the goal is to minimize expected losses. While the two approaches often lead to similar outcomes, this is not always the case, and it is commonly argued that the evidential approach is more suited to scientific applications. This paper traces the history of the evidential approach and summarizes current developments.Item The Measurement of Statistical Evidence as the Basis for Statistical Reasoning(2020-03-13) Evans, MichaelThere are various approaches to the problem of how one is supposed to conduct a statistical analysis. Different analyses can lead to contradictory conclusions in some problems so this is not a satisfactory state of affairs. It seems that all approaches make reference to the evidence in the data concerning questions of interest as a justification for the methodology employed. It is fair to say, however, that none of the most commonly used methodologies is absolutely explicit about how statistical evidence is to be characterized and measured. We will discuss the general problem of statistical reasoning and the development of a theory for this that is based on being precise about statistical evidence. This will be shown to lead to the resolution of a number of problems.Item The Ontario printed educational message (OPEM) trial to narrow the evidence-practice gap with respect to prescribing practices of general and family physicians: a cluster randomized controlled trial, targeting the care of individuals with diabetes and hypertension in Ontario, Canada(2007-11-26) Zwarenstein, Merrick; Hux, Janet E; Kelsall, Diane; Paterson, Michael; Grimshaw, Jeremy; Davis, Dave; Laupacis, Andreas; Evans, Michael; Austin, Peter C; Slaughter, Pamela M; Shiller, Susan K; Croxford, Ruth; Tu, KarenAbstract Background There are gaps between what family practitioners do in clinical practice and the evidence-based ideal. The most commonly used strategy to narrow these gaps is the printed educational message (PEM); however, the attributes of successful printed educational messages and their overall effectiveness in changing physician practice are not clear. The current endeavor aims to determine whether such messages change prescribing quality in primary care practice, and whether these effects differ with the format of the message. Methods/design The design is a large, simple, factorial, unblinded cluster-randomized controlled trial. PEMs will be distributed with informed, a quarterly evidence-based synopsis of current clinical information produced by the Institute for Clinical Evaluative Sciences, Toronto, Canada, and will be sent to all eligible general and family practitioners in Ontario. There will be three replicates of the trial, with three different educational messages, each aimed at narrowing a specific evidence-practice gap as follows: 1) angiotensin-converting enzyme inhibitors, hypertension treatment, and cholesterol lowering agents for diabetes; 2) retinal screening for diabetes; and 3) diuretics for hypertension. For each of the three replicates there will be three intervention groups. The first group will receive informed with an attached postcard-sized, short, directive "outsert." The second intervention group will receive informed with a two-page explanatory "insert" on the same topic. The third intervention group will receive informed, with both the above-mentioned outsert and insert. The control group will receive informed only, without either an outsert or insert. Routinely collected physician billing, prescription, and hospital data found in Ontario's administrative databases will be used to monitor pre-defined prescribing changes relevant and specific to each replicate, following delivery of the educational messages. Multi-level modeling will be used to study patterns in physician-prescribing quality over four quarters, before and after each of the three interventions. Subgroup analyses will be performed to assess the association between the characteristics of the physician's place of practice and target behaviours. A further analysis of the immediate and delayed impacts of the PEMs will be performed using time-series analysis and interventional, auto-regressive, integrated moving average modeling. Trial registration number Current controlled trial ISRCTN72772651.