Deborah J. Cook

Bio: Deborah J. Cook is an academic researcher from McMaster University. The author has contributed to research in topics: Intensive care & Intensive care unit. The author has an hindex of 173, co-authored 907 publications receiving 148928 citations. Previous affiliations of Deborah J. Cook include McMaster University Medical Centre & Queen's University.

Basic statistics for clinicians: 1. Hypothesis testing

Abstract: In the first of a series of four articles the authors explain the statistical concepts of hypothesis testing and p values. In many clinical trials investigators test a null hypothesis that there is no difference between a new treatment and a placebo or between two treatments. The result of a single experiment will almost always show some difference between the experimental and the control groups. Is the difference due to chance, or is it large enough to reject the null hypothesis and conclude that there is a true difference in treatment effects? Statistical tests yield a p value: the probability that the experiment would show a difference as great or greater than that observed if the null hypothesis were true. By convention, p values of less than 0.05 are considered statistically significant, and investigators conclude that there is a real difference. However, the smaller the sample size, the greater the chance of erroneously concluding that the experimental treatment does not differ from the control--in statistical terms, the power of the test may be inadequate. Tests of several outcomes from one set of data may lead to an erroneous conclusion that an outcome is significant if the joint probability of the outcomes is not taken into account. Hypothesis testing has limitations, which will be discussed in the next article in the series.

Payments for care at private for-profit and private not-for-profit hospitals: a systematic review and meta-analysis

Abstract: Background: It has been shown that patients cared for at private for-profit hospitals have higher risk-adjusted mortality rates than those cared for at private not-for-profit hospitals. Uncertainty remains, however, about the economic implications of these forms of health care delivery. Since some policy-makers might still consider for-profit health care if expenditure savings were sufficiently large, we undertook a systematic review and meta-analysis to compare payments for care at private forprofit and private not-for-profit hospitals. Methods: We used 6 search strategies to identify published and unpublished observational studies that directly compared the payments for care at private for-profit and private not-forprofit hospitals. We masked the study results before teams of 2 reviewers independently evaluated the eligibility of all studies. We confirmed data or obtained additional data from all but 1 author. For each study, we calculated the payments for care at private for-profit hospitals relative to private notfor-profit hospitals and pooled the results using a random effects model. Results: Eight observational studies, involving more than 350 000 patients altogether and a median of 324 hospitals each, fulfilled our eligibility criteria. In 5 of 6 studies showing higher payments for care at private for-profit hospitals, the difference was statistically significant; in 1 of 2 studies showing higher payments for care at private not-for-profit hospitals, the difference was statistically significant. The pooled estimate demonstrated that private for-profit hospitals were associated with higher payments for care (relative payments for care 1.19, 95% confidence interval 1.07–1.33, p = 0.001). Interpretation: Private for-profit hospitals result in higher payments for care than private not-for-profit hospitals. Evidence strongly supports a policy of not-for-profit health care delivery at the hospital level.

Glutamine and antioxidants in the critically ill patient: a post hoc analysis of a large-scale randomized trial.

Abstract: Background: The recent large randomized controlled trial of glutamine and antioxidant supplementation suggested that high-dose glutamine is associated with increased mortality in critically ill patients with multiorgan failure. The objectives of the present analyses were to reevaluate the effect of supplementation after controlling for baseline covariates and to identify potentially important subgroup effects. Materials and Methods: This study was a post hoc analysis of a prospective factorial 2 × 2 randomized trial conducted in 40 intensive care units in North America and Europe. In total, 1223 mechanically ventilated adult patients with multiorgan failure were randomized to receive glutamine, antioxidants, both glutamine and antioxidants, or placebo administered separate from artificial nutrition. We compared each of the 3 active treatment arms (glutamine alone, antioxidants alone, and glutamine + antioxidants) with placebo on 28-day mortality. Post hoc, treatment effects were examined within subgroups ...

Transthoracic needle aspiration biopsy for the diagnosis of localised pulmonary lesions: a meta-analysis.

Abstract: BACKGROUND Persisting controversy surrounds the use of transthoracic needle aspiration biopsy (TNAB) stemming from its uncertain diagnostic accuracy. A systematic review and meta-analysis was therefore conducted to evaluate the accuracy of TNAB for the diagnosis of solitary or multiple localised pulmonary lesions. METHODS Searches for English literature papers in Index Medicus (1963–1965) and Medline (1966–1996) were performed and the bibliographies of the retrieved articles were systematically reviewed. Articles evaluating the accuracy of TNAB in series of consecutive patients presenting with solitary or multiple pulmonary lesions were considered. Only papers in which ⩾90% of patients were given a final diagnosis according to an appropriate reference standard were included in the meta-analysis. RESULTS A total of 48 studies were included and five meta-analyses were conducted according to four diagnostic thresholds. From the pooled sensitivity and specificity corresponding to each diagnostic threshold, associated likelihood ratios (LRs) were derived for malignant disease as follows: (1) malignant versus all other categories, LR = 72; (2) malignant or suspicious versus all others, LR = 49; (3) suspicious versus all categories but malignant, LR = 15; (4) benign versus all others, LR = 0.07; and (5) specific benign diagnosis versus all others, LR = 0.005. Differences in methodological quality of the studies, needle types, or whether a cytopathologist participated in the procedure failed to explain the heterogeneity of the results found in almost every meta-analysis. Given a 50% probability of malignancy prior to the TNAB, post-test probabilities of malignancy upon receiving the results would be malignant, 99%; suspicious, 94%; non-specific benign, 7%; and benign with a specific diagnosis, 0.6%. CONCLUSIONS Given the intermediate pre-test probabilities that would probably lead to performing TNAB, findings of “malignant” or of a specific diagnosis of a benign condition provide definitive results. Findings of “suspicious” markedly increase the probability of malignancy, and “benign” markedly decreases it but may not be considered definitive.

A randomized trial of daily awakening in critically ill patients managed with a sedation protocol: A pilot trial*

Abstract: Objective:Protocolized sedation (PS) and daily sedative interruption (DI) in critically ill patients have both been shown to shorten the durations of mechanical ventilation (MV) and intensive care unit (ICU) stay. Our objective was to determine the safety and feasibility of a randomized trial to det

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

Abstract: David Moher and colleagues introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.

Abstract: Systematic reviews and meta-analyses have become increasingly important in health care. Clinicians read them to keep up to date with their field,1,2 and they are often used as a starting point for developing clinical practice guidelines. Granting agencies may require a systematic review to ensure there is justification for further research,3 and some health care journals are moving in this direction.4 As with all research, the value of a systematic review depends on what was done, what was found, and the clarity of reporting. As with other publications, the reporting quality of systematic reviews varies, limiting readers' ability to assess the strengths and weaknesses of those reviews. Several early studies evaluated the quality of review reports. In 1987, Mulrow examined 50 review articles published in 4 leading medical journals in 1985 and 1986 and found that none met all 8 explicit scientific criteria, such as a quality assessment of included studies.5 In 1987, Sacks and colleagues6 evaluated the adequacy of reporting of 83 meta-analyses on 23 characteristics in 6 domains. Reporting was generally poor; between 1 and 14 characteristics were adequately reported (mean = 7.7; standard deviation = 2.7). A 1996 update of this study found little improvement.7 In 1996, to address the suboptimal reporting of meta-analyses, an international group developed a guidance called the QUOROM Statement (QUality Of Reporting Of Meta-analyses), which focused on the reporting of meta-analyses of randomized controlled trials.8 In this article, we summarize a revision of these guidelines, renamed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses), which have been updated to address several conceptual and practical advances in the science of systematic reviews (Box 1). Box 1 Conceptual issues in the evolution from QUOROM to PRISMA

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 …