Saman Muthukumarana

A generalized p-value approach for assessing noninferiority in a three-arm trial.

The existing generalized p-value approach, from statistical literature, is applied to assess noninferiority of an experimental treatment in a three-arm clinical trial including a placebo. Two generalized test functions (GTFs) are constructed and Monte Carlo simulations are used to compute the p-value. The GTFs perform well in terms of maintaining the Type-I error probabilities, and the power of the tests are shown to increase to 1 as both the sample size and the parameter denoting the fraction of the effect of the reference drug with respect to placebo increase. The generalized confidence intervals are shown to retain the coverage probabilities. A published dataset is re-analysed using the proposed test and the results are in agreement with earlier findings.

Meta-analysis using Dirichlet process

This article develops a Bayesian approach for meta-analysis using the Dirichlet process. The key aspect of the Dirichlet process in meta-analysis is the ability to assess evidence of statistical heterogeneity or variation in the underlying effects across study while relaxing the distributional assumptions. We assume that the study effects are generated from a Dirichlet process. Under a Dirichlet process model, the study effects parameters have support on a discrete space and enable borrowing of information across studies while facilitating clustering among studies. We illustrate the proposed method by applying it to a dataset on the Program for International Student Assessment on 30 countries. Results from the data analysis, simulation studies, and the log pseudo-marginal likelihood model selection procedure indicate that the Dirichlet process model performs better than conventional alternative methods.

Identifying Distinct Healthcare Pathways During Episodes of Chronic Obstructive Pulmonary Disease Exacerbations.

AbstractHealthcare pathways are important to measure because they are expected to affect outcomes. However, they are challenging to define because patients exhibit heterogeneity in their use of healthcare services. The objective of this study was to identify and describe healthcare pathways during episodes of chronic obstructive pulmonary disease (COPD) exacerbations.Linked administrative databases from Saskatchewan, Canada were used to identify a cohort of newly diagnosed COPD patients and their episodes of healthcare use for disease exacerbations. Latent class analysis (LCA) was used to classify the cohort into homogeneous pathways using indicators of respiratory-related hospitalizations, emergency department (ED) visits, general and specialist physician visits, and outpatient prescription drug dispensations. Multinomial logistic regression models tested patients’ demographic and disease characteristics associated with pathway group membership. The most frequent healthcare contact sequences in each pathway were described. Tests of mean costs across groups were conducted using a model-based approach with &khgr; 2 statistics.LCA identified 3 distinct pathways for patients with hospital- (n = 963) and ED-initiated (n = 364) episodes. For the former, pathway group 1 members followed complex pathways in which multiple healthcare services were repeatedly used and incurred substantially higher costs than patients in the other pathway groups. For patients with an ED-initiated episode, pathway group 1 members also had higher costs than other groups. Pathway groups differed with respect to patient demographic and disease characteristics. A minority of patients were discharged from ED or hospital, but did not have any follow-up care during the remainder of their episode.Patients who followed complex pathways could benefit from case management interventions to streamline their journeys through the healthcare system. The minority of patients whose pathways were not consistent with recommended follow-up care should be further investigated to fully align COPD treatment in the province with recommended care practices.

Bayesian inference in two-arm trials using relative belief ratios.

Various methodologies proposed for some inference problems associated with two-arm trails are known to suffer from difficulties, as documented in Senn (2001). We propose an alternative Bayesian approach to these problems that deals with these difficulties through providing an explicit measure of statistical evidence and the strength of this evidence. Bayesian methods are often criticized for their intrinsic subjectivity. We show how these concerns can be dealt with through assessing the bias induced by a prior model checking and checking for prior-data conflict.

Bayesian Analysis of Ordinal Survey Data Using the Dirichlet Process to Account for Respondent Personality Traits

This article presents a Bayesian latent variable model used to analyze ordinal response survey data by taking into account the characteristics of respondents. The ordinal response data are viewed as multivariate responses arising from continuous latent variables with known cut-points. Each respondent is characterized by two parameters that have a Dirichlet process as their joint prior distribution. The proposed mechanism adjusts for classes of personalities. The model is applied to student survey data in course evaluations. Goodness-of-fit (GoF) procedures are developed for assessing the validity of the model. The proposed GoF procedures are simple, intuitive, and do not seem to be a part of current Bayesian practice.

Bayesian Equivalence Testing and Meta-Analysis in Two-Arm Trials with Binary Data

We consider a Bayesian approach for assessing hypotheses of equivalence in two-arm trials with binary Data. We discuss the development of likelihood, the prior, and the posterior distributions of parameters of interest. We then examine the suitability of a normal approximation to the posterior distribution obtained via a Taylor series expansion. The Bayesian inference is carried out using Markov Chain Monte Carlo (MCMC) methods. We illustrate the methods using actual data arising from two-arm clinical trials on preventing mortality after myocardial infarction.

Model-based methods for case definitions from administrative health data: application to rheumatoid arthritis

Objective This research proposes a model-based method to facilitate the selection of disease case definitions from validation studies for administrative health data. The method is demonstrated for a rheumatoid arthritis (RA) validation study. Study design and setting Data were from 148 definitions to ascertain cases of RA in hospital, physician and prescription medication administrative data. We considered: (A) separate univariate models for sensitivity and specificity, (B) univariate model for Youden’s summary index and (C) bivariate (ie, joint) mixed-effects model for sensitivity and specificity. Model covariates included the number of diagnoses in physician, hospital and emergency department records, physician diagnosis observation time, duration of time between physician diagnoses and number of RA-related prescription medication records. Results The most common case definition attributes were: 1+ hospital diagnosis (65%), 2+ physician diagnoses (43%), 1+ specialist physician diagnosis (51%) and 2+ years of physician diagnosis observation time (27%). Statistically significant improvements in sensitivity and/or specificity for separate univariate models were associated with (all p values <0.01): 2+ and 3+ physician diagnoses, unlimited physician diagnosis observation time, 1+ specialist physician diagnosis and 1+ RA-related prescription medication records (65+ years only). The bivariate model produced similar results. Youden’s index was associated with these same case definition criteria, except for the length of the physician diagnosis observation time. Conclusion A model-based method provides valuable empirical evidence to aid in selecting a definition(s) for ascertaining diagnosed disease cases from administrative health data. The choice between univariate and bivariate models depends on the goals of the validation study and number of case definitions.

A semiparametric Bayesian approach for mark-recapture estimation

This paper describes a semiparametric Bayesian approach for modelling mark-recapture data. A main assumption in modelling mark-recapture data is that survival probabilities are homogeneous. We relax this assumption by modelling survival probabilities as a function of two parameters which explain variations due to unknown biological and environmental reasons. The heterogeneity in travel times and survival probabilities is accounted using the Dirichlet process. The Dirichlet process also provides a clustering mechanism which is often suitable for mark-recapture data where groups of animals can be thought of as arising from the same cohort. The approach is highlighted using actual data arising from thed Pacific Ocean Shelf Tracking (POST) project. Log-pseudo marginal likelihood (LPML) model selection procedure indicates that the proposed model performs better over conventional alternative methods.