Use of odds ratio or relative risk to measure a treatment effect in clinical trials with multiple correlated binary outcomes: data from the NINDS t-PA stroke trial

2001 ◽  
Vol 20 (13) ◽  
pp. 1891-1901 ◽  
Author(s):  
Mei Lu ◽  
Barbara C. Tilley ◽  
Keyword(s):  
Clinical Trials ◽  
Relative Risk ◽  
Treatment Effect ◽  
Odds Ratio ◽  
Binary Outcomes ◽  
Correlated Binary Outcomes ◽  
Stroke Trial

scholarly journals Efficacy of parecoxib sodium on postoperative shivering: meta-analysis of clinical trials

2017 ◽  
Vol 46 (1) ◽  
pp. 3-10 ◽  
Author(s):  
Yu Zhu ◽  
Chengmao Zhou ◽  
Yuting Yang ◽  
Yijian Chen
Keyword(s):  
Clinical Trials ◽  
Relative Risk ◽  
Confidence Interval ◽  
Odds Ratio ◽  
Meta Analysis ◽  
Control Group ◽  
Effective Strategy

Objective To evaluate the effect of parecoxib on preventing postoperative shivering. Methods Main outcomes were the relative risk (odds ratio, OR) and 95% confidence interval (CI) relative to the incidence of shivering. Results Fourteen trials with 1,175 patients were analyzed. The pooled evidence suggested that parecoxib sodium, given before anesthesia or postoperatively (only 4 cases), had the potential to prevent postoperative shivering (OR = 0.21, 95% CI, 0.16, 0.29). Compared with the placebo, parecoxib sodium significantly lowered the incidence of postoperative shivering as follows: mild shivering [OR = 0.51, 95% CI (0.35, 0.74)]; moderate shivering [OR = 0.28, 95% CI (0.18, 0.45)]; severe shivering [OR = 0.18, 95% CI (0.10, 0.33)]. Compared with placebo, there was no significant association of parecoxib sodium with restlessness [OR = 0.95, 95% CI (0.59, 1.52)] or nausea/vomiting [OR = 0.24, 95% CI (0.09, 0.66)]. In addition, pethidine rescue was used significantly more often in the control group than in the parecoxib sodium group [OR = 0.22, 95% CI (0.09, 0.53)]. Conclusions Parecoxib sodium may be an effective strategy for preventing postoperative shivering.

scholarly journals Decision-making with multiple correlated binary outcomes in clinical trials

2020 ◽  
Vol 29 (11) ◽  
pp. 3265-3277
Author(s):  
Xynthia Kavelaars ◽  
Joris Mulder ◽  
Maurits Kaptein
Keyword(s):  
Decision Making ◽  
Clinical Trials ◽  
Decision Criterion ◽  
Error Rates ◽  
Binary Outcomes ◽  
Compensatory Mechanism ◽  
Type I ◽  
Positive Effects ◽  
Multiple Outcome ◽  
Correlated Binary Outcomes

Clinical trials often evaluate multiple outcome variables to form a comprehensive picture of the effects of a new treatment. The resulting multidimensional insight contributes to clinically relevant and efficient decision-making about treatment superiority. Common statistical procedures to make these superiority decisions with multiple outcomes have two important shortcomings, however: (1) Outcome variables are often modeled individually, and consequently fail to consider the relation between outcomes; and (2) superiority is often defined as a relevant difference on a single, on any, or on all outcome(s); and lacks a compensatory mechanism that allows large positive effects on one or multiple outcome(s) to outweigh small negative effects on other outcomes. To address these shortcomings, this paper proposes (1) a Bayesian model for the analysis of correlated binary outcomes based on the multivariate Bernoulli distribution; and (2) a flexible decision criterion with a compensatory mechanism that captures the relative importance of the outcomes. A simulation study demonstrates that efficient and unbiased decisions can be made while Type I error rates are properly controlled. The performance of the framework is illustrated for (1) fixed, group sequential, and adaptive designs; and (2) non-informative and informative prior distributions.

scholarly journals The choice and definition of summary measure for meta-analysis of clinical studies with binary outcomes: effect on clinical interpretation

2020 ◽  
Vol 93 (1108) ◽  
pp. 20190976
Author(s):  
Andrew A Plumb ◽  
Steve Halligan ◽  
Susan Mallett
Keyword(s):  
Relative Risk ◽  
Cancer Screening ◽  
Odds Ratio ◽  
Meta Analysis ◽  
Binary Outcomes ◽  
Healthcare Intervention ◽  
Binary Outcome ◽  
Summary Measure ◽  
Definition Of ◽  
Meta Analyses

Many systematic reviews and meta-analyses concern the effect of a healthcare intervention on a binary outcome i.e. occurrence (or not) of a particular event. Usually, the overall effect, pooled across all studies included in the meta-analysis, is summarised using the odds ratio (OR) or the relative risk (RR). Under most circumstances, it is obvious how to identify what should be considered as the event of interest—for example, death or a clinically important side-effect. However, on occasion it may not be clear in which “direction” the event should be specified—such as attendance (vs non-attendance) at cancer screening. Usually, this choice is not critical to the overall conclusion of the meta-analysis, but occasionally it can lead to differences in how the included studies are pooled, ultimately affecting the overall meta-analytic result, particularly when using RRs rather than ORs. In this commentary, we will explain this phenomenon in more detail using examples from the literature, and explore how analysts and readers can avoid some potential pitfalls.

scholarly journals Estimating and reporting treatment effects in clinical trials for weight management: using estimands to interpret effects of intercurrent events and missing data

2021 ◽  
Author(s):  
Sean Wharton ◽  
Arne Astrup ◽  
Lars Endahl ◽  
Michael E. J. Lean ◽  
Altynai Satylganova ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Weight Management ◽  
Treatment Effect ◽  
Repeated Measures ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Weight Losses ◽  
The Difference ◽  
Treatment Of Obesity

AbstractIn the approval process for new weight management therapies, regulators typically require estimates of effect size. Usually, as with other drug evaluations, the placebo-adjusted treatment effect (i.e., the difference between weight losses with pharmacotherapy and placebo, when given as an adjunct to lifestyle intervention) is provided from data in randomized clinical trials (RCTs). At first glance, this may seem appropriate and straightforward. However, weight loss is not a simple direct drug effect, but is also mediated by other factors such as changes in diet and physical activity. Interpreting observed differences between treatment arms in weight management RCTs can be challenging; intercurrent events that occur after treatment initiation may affect the interpretation of results at the end of treatment. Utilizing estimands helps to address these uncertainties and improve transparency in clinical trial reporting by better matching the treatment-effect estimates to the scientific and/or clinical questions of interest. Estimands aim to provide an indication of trial outcomes that might be expected in the same patients under different conditions. This article reviews how intercurrent events during weight management trials can influence placebo-adjusted treatment effects, depending on how they are accounted for and how missing data are handled. The most appropriate method for statistical analysis is also discussed, including assessment of the last observation carried forward approach, and more recent methods, such as multiple imputation and mixed models for repeated measures. The use of each of these approaches, and that of estimands, is discussed in the context of the SCALE phase 3a and 3b RCTs evaluating the effect of liraglutide 3.0 mg for the treatment of obesity.

scholarly journals Competing risk and heterogeneity of treatment effect in clinical trials

Trials ◽  
2008 ◽  
Vol 9 (1) ◽  
Author(s):  
David M Kent ◽  
Alawi Alsheikh-Ali ◽  
Rodney A Hayward
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Competing Risk ◽  
Heterogeneity Of Treatment Effect
Sign in / Sign up

Export Citation Format

Share Document