Clinically useful measures of effect in binary analyses of randomized trials

Sinclair, John C.; Bracken, Michael B.

doi:10.1016/0895-4356(94)90191-0

Cited by 247 publications

(154 citation statements)

References 12 publications

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“…This symmetry led Senn [7] to suggest that the OR should be the gold standard measure of association. Sinclair and Bracken [5] note the lack of symmetry of the RR, but discount this as a reason for choosing the OR over the RR.…”

Section: Ors and Logistic Regressionmentioning

confidence: 99%

“…Altman et al [4] cite an example where an RR of 88 was reported, based on the OR, when the actual value should have been 7. Sinclair and Bracken [5] cite a similar example where an RR, based on the OR, was 16.2, when the actual value should have been 8. 6.…”

Section: Introductionmentioning

confidence: 99%

“…McNutt et al [3] discuss this issue in their study of health outcomes in violence research. Altman et al [4] and Sinclair and Bracken [5] discuss the extent to which the OR overestimates the RR in prospective studies and clinical trials. Altman et al [4] cite an example where an RR of 88 was reported, based on the OR, when the actual value should have been 7.…”

Section: Introductionmentioning

confidence: 99%

“…Several articles have appeared in the past few years on the confusion between the OR and the RR [2][3][4][5][6][7][8][9]. Holcomb et al [2] recently reviewed the obstetrics and gynecology literature for uses of the OR.…”

Section: Introductionmentioning

confidence: 99%

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Interpreting Measures of Treatment Effect in Cancer Clinical Trials

et al. 2002

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The efficacy of a new cancer regimen is usually assessed by analyzing outcomes such as tumor response and overall survival. Many publications summarizing results of cancer clinical trials report measures such as odds ratios and hazard ratios, as these are the estimators of treatment effect obtained from regression models used to analyze the data.However, these measures are sometimes misinterpreted, as they are not necessarily familiar to many readers. The most common mistake is to interpret both measures as relative risks, an interpretation that can lead to an incorrect impression of the impact of the treatment on response and survival. The Oncologist 2002;7:181-187 The Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157, USA. Telephone: 336-716-1048; Fax: 336-716-5425; e-mail: dcase@wfubmc.edu Received January 25, 2002; accepted for publication March 1, 2002. ©AlphaMed Press 1083-7159/2002 INTRODUCTIONThe effectiveness of a new agent or combination therapy in a prospective cancer clinical trial is usually assessed by analyzing outcomes such as tumor response and overall survival. Due to the widespread use of regression techniques for analyzing these outcomes, many publications summarizing results of cancer trials report measures of treatment effect such as odds ratios (ORs) and hazard ratios (HRs). Logistic regression, used to analyze tumor response, and Cox's proportional hazards regression, used to analyze censored survival data, give rise to the OR and HR, respectively, as measures of treatment effect. These are not necessarily familiar concepts to many readers, and they are sometimes misinterpreted. The tendency is to interpret both of these measures as relative risks (RRs), a more natural measure of effect in prospective clinical trials. For example, a doubling of the odds might be interpreted as a doubling in response or a 30% decrease in the hazard might be interpreted as a 30% improvement in 1-year or 5-year survival, both of which are incorrect interpretations. This misunderstanding is due partly to lack of training and

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Section: Ors and Logistic Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interpreting Measures of Treatment Effect in Cancer Clinical Trials

et al. 2002

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“…Cook and Sackett argue that for clinical decision making the number needed to treat is more meaningful that either the relative risk reduction or the odds ratio [24], while Jaeschke et al suggest that the odds ratio and the relative risk provide limited information [25]. Finally, Sinclair and Bracken argue that clinically important questions are best addressed using relative risks, relative risk reductions, risk differences, and the number needed to treat [26]. In the face of these proposals, some medical journals require that absolute risk reductions and the associated number needed to treat (NNT) be reported for any randomized controlled trial with a dichotomous outcome (http://www.bmj.com/about-bmj/resources-authors/article-types/ research.…”

Section: Discussionmentioning

confidence: 99%

Using G-Computation to Estimate the Effect of Regionalization of Surgical Services on the Absolute Reduction in the Occurrence of Adverse Patient Outcomes

Austin

Urbach²

2013

Medical Care

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Background-Numerous studies have found that increased hospital or surgeon operative volumes, as measured by the number of procedures performed, are associated with improved patient outcomes following surgery. These findings have been used to support important health policy decisions about regionalization of surgical services, in which provision of specific surgical services is restricted to hospitals that maintain operative volumes above a specified threshold. The most common statistical approach in volume-outcome studies is to regress patient outcomes on a set of patient characteristics and a variable denoting provider volume. When outcomes are binary, such as operative mortality, logistic regression is used, resulting in the odds ratio being the reported measure of association. However, the odds ratio is a relative measure of effect and does not allow policy-makers to estimate the absolute benefit of regionalization.Objectives-To describe how G-computation can be used to estimate the expected number of lives saved due to regionalization of surgical services.Research Design-Retrospective cohort design of patients undergoing one of three different surgical procedures in Ontario, Canada.Results-Regionalization of colorectal cancer surgery, esophagectomy, or pancreaticoduodenectomy in Ontario could reduce the average annual number of peri-operative deaths by 20.2, 2.0, and 3.6, for the three procedures respectively. Conclusions-The absolute reduction in number of operative deaths due to regionalization of surgical procedures can be calculated. This can help inform health policy debate about benefits of regionalization.

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The (Relative) Risks of Using Odds Ratios

Katz¹

2006

Arch Dermatol

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Clinically useful measures of effect in binary analyses of randomized trials

Cited by 247 publications

References 12 publications

Interpreting Measures of Treatment Effect in Cancer Clinical Trials

Interpreting Measures of Treatment Effect in Cancer Clinical Trials

Using G-Computation to Estimate the Effect of Regionalization of Surgical Services on the Absolute Reduction in the Occurrence of Adverse Patient Outcomes

The (Relative) Risks of Using Odds Ratios

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