Statistical methods for multivariate meta-analysis of diagnostic tests: An overview and tutorial

In this article, we present an overview and tutorial of statistical methods for meta-analysis of diagnostic tests under two scenarios: 1) when the reference test can be considered a gold standard; and 2) when the reference test cannot be considered a gold standard. In the first scenario, we first review the conventional summary receiver operating characteristics (ROC) approach and a bivariate approach using linear mixed models (BLMM). Both approaches require direct calculations of study-specific sensitivities and specificities. We next discuss the hierarchical summary ROC curve approach for jointly modeling positivity criteria and accuracy parameters, and the bivariate generalized linear mixed models (GLMM) for jointly modeling sensitivities and specificities. We further discuss the trivariate GLMM for jointly modeling prevalence, sensitivities and specificities, which allows us to assess the correlations among the three parameters. These approaches are based on the exact binomial distribution and thus do not require an ad hoc continuity correction. Last, we discuss a latent class random effects model for meta-analysis of diagnostic tests when the reference test itself is imperfect for the second scenario. A number of case studies with detailed annotated SAS code in procedures MIXED and NLMIXED are presented to facilitate the implementation of these approaches

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Spline smoothing in Bayesian disease mapping

Abstract: In this paper, a class of Bayesian hierarchical disease mapping models with spline smoothing are motivated and developed for sequential disease mapping and for surveillance of disease risk trends and clustering. The methodological development aims to provide reliable information about the patterns (both over space and time) of disease risk and to quantify uncertainty. Bayesian disease mapping models with B-splines, smoothing splines and P-splines are developed respectively and a comparison of the three smoothing methods in the context of risks ensemble prediction is presented. The methods are illustrated through a Bayesian analysis of iatrogenic injuries to hospital in-patients in British Columbia, Canada

On empirical Bayes penalized quasi-likelihood inference in GLMMs and in Bayesian disease mapping and ecological modeling

Penalized quasi-likelihood(PQL) procedure for statistical inference in generalized linear mixed models (GLMMs) and in Bayesian disease mapping and ecological modeling are revisited. In GLMM framework, empirical Bayes PQL (EBPQL) procedure is discussed in the context of approximating posterior point and interval prediction of random effects. An in-depth Monte Carlo assessment on EBPQL point and interval estimation of random effects, fixed effects, and prior parameters in univariate and bivariate (shared component) disease mapping and ecological models is presented, with illustrative examples including spatial and ecological modeling of infant mortality rates (relative uncommon events), suicide hospitalization rates (rare events) and suicide mortality rates (extremely rare events), and associated ecological risk factors in local health areas in British Columbia Canada. In particular, EBPQL interval prediction of random effects is explored by prediction uncertainty attributions with respect to uncertainties associated with estimation of random effects, fixed effects, and prior parameters. Estimation of percent attributions of EBPQL random effects prediction errors to prior uncertainty is developed in the context of GLMMs and explored in Bayesian disease mapping and ecological models, suggesting evidence that uncertainty about prior parameter(s) may have minor and negligible influence on EBPQL interval prediction of random effects in Bayesian hierarchical disease mapping and ecological modeling of moderate Poisson observations. The EBPQL inference procedure may be judiciously and profitably utilized in Bayesian disease mapping and ecological model development.

Editorial

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How to use SAS® Proc Traj and SAS® Proc Glimmix in respiratory epidemiology

This document outlines the use of two procedures capable of modeling repeated respiratory symptom data in the software package SAS®: Proc Traj and Proc Glimmix. SAS® Proc Traj is a discrete mixture model which models the patterns of change over time in multiple subgroups within the population. SAS® Proc Glimmix is a procedure that fits a generalized linear model to non-linear outcome data either with or without random effects.Environmental Health (SOEH), School ofHealth Care and Epidemiology, Department ofOccupational and Environmental Hygiene, School ofMedicine, Faculty ofPopulation and Public Health (SPPH), School ofUnreviewedFacultyGraduat

Does Routine Pain Assessment Result in Better Care?

BACKGROUND: Although a variety of national organizations such as the Canadian Pain Society, the American Pain Society and the Joint Commission on Accreditation of Health Care Organizations have advanced the idea that pain should be assessed on a routine basis, there is little evidence that systematic pain assessment information is used routinely by clinicians even when it is readily available