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The Maimed Martian, credible intervals and bias against benefit
1College of Science and Engineering, James Cook University, Townsville, Queensland, Australia.
Abstract:
A sad little story about a maimed Martian astronaut is used to illustrate a method of improving confidence interval (CI) calculations. CIs in medical statistics are currently calculated from the data available in a clinical trial or meta-analysis considered in isolation from all other information available on earth. Likewise, the Martian in the story uses only information available to it, in isolation from further information from earth. However, there is further objective knowledge available to people on earth to improve the Martian's estimate. In the same way, we have objective prior knowledge available to us outside of the current clinical trial results which we can use to improve CI calculations. This prior knowledge is incorporated into the CI calculations using Bayesian methods. The objective prior knowledge that is available is the fact that there were researchers who felt it worthwhile to conduct the trial and journal editors who felt it worthwhile publishing the results. It is shown here that the use of this information contracts the width of the log CI by a factor of about three quarters on average. Unlike standard CIs, these new intervals also have the advantage of being directly interpretable in terms of probabilities. These probabilities also enable calculation of improved point estimates. These calculations are applied to 100 randomly selected Cochrane systematic reviews and show serious problems in assessing medical treatments. For treatments not involving new drugs or devices, it is shown that there is evidence of a bias towards a negative assessment. The calculations here make a quantitative adjustment for publication bias. They show that the proportion of negative assessments do not reflect an appropriate adjustment for publication bias.
Insights
This study introduces Bayesian methods to enhance confidence interval (CI) calculations in medical statistics. Incorporating prior knowledge significantly narrows CIs and improves probability interpretations, addressing issues in systematic reviews.
Area of Science:
- Medical Statistics
- Bayesian Inference
- Publication Bias
Background:
- Current confidence intervals (CIs) in medical statistics are calculated in isolation from available objective knowledge.
- This approach overlooks valuable prior information that could refine statistical estimates.
- A novel method is proposed to integrate external knowledge into CI calculations.
Purpose of the Study:
- To develop and demonstrate a method for improving confidence interval (CI) calculations using Bayesian approaches.
- To address limitations in standard CI calculations by incorporating objective prior knowledge.
- To re-evaluate medical treatment assessments in Cochrane systematic reviews using the enhanced methodology.
Main Methods:
- Bayesian methods are employed to incorporate objective prior knowledge into confidence interval (CI) calculations.
- The study utilizes the existence of research trials and journal publications as sources of prior knowledge.
- The enhanced CI calculations are applied to 100 randomly selected Cochrane systematic reviews.
Main Results:
- The proposed method contracts the width of log confidence intervals by approximately 25% on average.
- New confidence intervals provide direct probabilistic interpretation, enabling improved point estimates.
- Application to Cochrane reviews reveals significant issues in assessing medical treatments, including a bias towards negative assessments for non-drug/device interventions.
Conclusions:
- Standard confidence intervals in medical statistics are suboptimal due to their isolated calculation.
- Bayesian methods offer a superior approach to confidence interval calculation by integrating prior knowledge.
- The findings highlight potential biases in systematic reviews and the need for quantitative adjustments for publication bias.
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