Allan Donner

Bio: Allan Donner is an academic researcher from University of Western Ontario. The author has contributed to research in topics: Cluster randomised controlled trial & Sample size determination. The author has an hindex of 82, co-authored 284 publications receiving 30079 citations. Previous affiliations of Allan Donner include London Clinic & Robarts Research Institute.

The impact of patient-centered care on outcomes.

Abstract: Background: We designed this observational cohort study to assess the association between patient-centered communication in primary care visits and subsequent health and medical care utilization. Methods: We selected 39 family physicians at random, and 315 of their patients participated. Office visits were audiotaped and scored for patient-centered communication. In addition, patients were asked for their perceptions of the patient-centeredness of the visit. The outcomes were: (1) patients' health, assessed by a visual analogue scale on symptom discomfort and concern; (2) self-report of health, using the Medical Outcomes Study Short Form-36; and (3) medical care utilization variables of diagnostic tests, referrals, and visits to the family physician, assessed by chart review. The 2 measures of patient-centeredness were correlated with the outcomes of visits, adjusting for the clustering of patients by physician and controlling for confounding variables. Results: Patient-centered communication was correlated with the patients' perceptions of finding common ground. In addition, positive perceptions (both the total score and the subscore on finding common ground) were associated with better recovery from their discomfort and concern, better emotional health 2 months later, and fewer diagnostic tests and referrals. Conclusions: Patient-centered communication influences patients' health through perceptions that their visit was patient centered, and especially through perceptions that common ground was achieved with the physician. Patient-centered practice improved health status and increased the efficiency of care by reducing diagnostic tests and referrals.

A Randomized Trial of Arthroscopic Surgery for Osteoarthritis of the Knee

Abstract: Background The efficacy of arthroscopic surgery for the treatment of osteoarthritis of the knee is unknown. Methods We conducted a single-center, randomized, controlled trial of arthroscopic surgery in patients with moderate-to-severe osteoarthritis of the knee. Patients were randomly assigned to surgical lavage and arthroscopic debridement together with optimized physical and medical therapy or to treatment with physical and medical therapy alone. The primary outcome was the total Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score (range, 0 to 2400; higher scores indicate more severe symptoms) at 2 years of follow-up. Secondary outcomes included the Short Form-36 (SF-36) Physical Component Summary score (range, 0 to 100; higher scores indicate better quality of life). Results Of the 92 patients assigned to surgery, 6 did not undergo surgery. Of the 86 patients assigned to control treatment, all received only physical and medical therapy. After 2 years, the mean (±SD) WOMAC score for the surgery group was 874±624, as compared with 897±583 for the control group (absolute difference [surgery-group score minus control-group score], −23±605; 95% confidence interval [CI], −208 to 161; P = 0.22 after adjustment for baseline score and grade of severity). The SF-36 Physical Component Summary scores were 37.0±11.4 and 37.2±10.6, respectively (absolute difference, −0.2±11.1; 95% CI, −3.6 to 3.2; P = 0.93). Analyses of WOMAC scores at interim visits and other secondary outcomes also failed to show superiority of surgery.

Sample size and optimal designs for reliability studies

Abstract: A method is developed to calculate the required number of subjects k in a reliability study, where reliability is measured using the intraclass correlation rho. The method is based on a functional approximation to earlier exact results. The approximation is shown to have excellent agreement with the exact results and one can use it easily without intensive numerical computation. Optimal design configurations are also discussed; for reliability values of about 40 per cent or higher, use of two or three observations per subject will minimize the total number of observations required.

Design and Analysis of Cluster Randomization Trials in Health Research

Abstract: Acknowledgements. Preface. 1. Introduction. 1.1 Why randomize clusters? 1.2 What is the impact of cluster randomization on the design and analysis of a trial? 1.3 Quantifying the effect of clustering. 1.4 Randomized versus non-randomized comparisons. 1.5 The unit of inference. 1.6 Terminology: what's in a name? 2. The historical development of cluster randomized trials. 2.1 Randomized trials before 1950. 2.2 Cluster randomized trials between 1950 and 1978. 2.3 Cluster randomized trails since 1978. 3. Issues arising in the planning of cluster randomization trials. 3.1 Selecting interventions. 3.2 Setting eligibility criteria. 3.3 Measuring subject response. 3.4 The most commonly used experimental designs. 3.5 Factorial and crossover designs. 3.6 Selecting an experimental design. 3.7 The importance of cluster-level replication. 3.8 Strategies for conducting successful trials. 4. The role of informed consent and other ethical issues. 4.1 The risk of harm. 4.2 Informed consent. 4.3 Subject blindness and informed consent. 4.4 Randomized consent designs. 4.5 Ethical issues and trial monitoring. 5. Sample size estimation for cluster randomization designs. 5.1 General issues of sample size estimation. 5.2 The completely randomized design. 5.3 The matched-pair design. 5.4 The stratified design. 5.5 Issues involving losses to follow-up. 5.6 Strategies for achieving desired power. 6. Analysis of binary outcomes. 6.1 Selecting the unit of analysis. 6.2 The completely randomized design. 6.3 The matched-pair design. 6.4 The stratified design. 7. Analysis of quantitative outcomes. 7.1 The completely randomized design. 7.2 The matched-pair design. 7.3 The stratified design. 8. Analysis of count, time to event and categorical outcomes. 8.1 Count and time to event data. 8.2 Categorical data. 9. Reporting of cluster randomization trials. 9.1 Reporting of study design. 9.2 Reporting of study results. References. Index.

Measuring inconsistency in meta-analyses

Abstract: Cochrane Reviews have recently started including the quantity I 2 to help readers assess the consistency of the results of studies in meta-analyses. What does this new quantity mean, and why is assessment of heterogeneity so important to clinical practice? Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care.1 Their value is especially clear when the results of the studies they include show clinically important effects of similar magnitude. However, the conclusions are less clear when the included studies have differing results. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The test seeks to determine whether there are genuine differences underlying the results of the studies (heterogeneity), or whether the variation in findings is compatible with chance alone (homogeneity). However, the test is susceptible to the number of trials included in the meta-analysis. We have developed a new quantity, I 2, which we believe gives a better measure of the consistency between trials in a meta-analysis. Assessment of the consistency of effects across studies is an essential part of meta-analysis. Unless we know how consistent the results of studies are, we cannot determine the generalisability of the findings of the meta-analysis. Indeed, several hierarchical systems for grading evidence state that the results of studies must be consistent or homogeneous to obtain the highest grading.2–4 Tests for heterogeneity are commonly used to decide on methods for combining studies and for concluding consistency or inconsistency of findings.5 6 But what does the test achieve in practice, and how should the resulting P values be interpreted? A test for heterogeneity examines the null hypothesis that all studies are evaluating the same effect. The usual test statistic …

Bias in meta-analysis detected by a simple, graphical test

Abstract: Objective: Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses. Design: Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30% of the trial); analysis of funnel plots from 37 meta-analyses identified from a hand search of four leading general medicine journals 1993-6 and 38 meta-analyses from the second 1996 issue of the Cochrane Database of Systematic Reviews . Main outcome measure: Degree of funnel plot asymmetry as measured by the intercept from regression of standard normal deviates against precision. Results: In the eight pairs of meta-analysis and large trial that were identified (five from cardiovascular medicine, one from diabetic medicine, one from geriatric medicine, one from perinatal medicine) there were four concordant and four discordant pairs. In all cases discordance was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but in none of concordant pairs. In 14 (38%) journal meta-analyses and 5 (13%) Cochrane reviews, funnel plot asymmetry indicated that there was bias. Conclusions: A simple analysis of funnel plots provides a useful test for the likely presence of bias in meta-analyses, but as the capacity to detect bias will be limited when meta-analyses are based on a limited number of small trials the results from such analyses should be treated with considerable caution. Key messages Systematic reviews of randomised trials are the best strategy for appraising evidence; however, the findings of some meta-analyses were later contradicted by large trials Funnel plots, plots of the trials9 effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials Funnel plot asymmetry was found in 38% of meta-analyses published in leading general medicine journals and in 13% of reviews from the Cochrane Database of Systematic Reviews Critical examination of systematic reviews for publication and related biases should be considered a routine procedure

The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration

Abstract: Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safety of health care interventions accurately and reliably. The clarity and transparency of these reports, however, is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users. Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement—a reporting guideline published in 1999—there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realizing these issues, an international group that included experienced authors and methodologists developed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions. The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this Explanation and Elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA Statement, this document, and the associated Web site (http://www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.

Cochrane Handbook for Systematic Reviews of Interventions

Abstract: The Cochrane Handbook for Systematic Reviews of Interventions is the official document that describes in detail the process of preparing and maintaining Cochrane systematic reviews on the effects of healthcare interventions.