10x Coffee Talk - Assessing Measurement Agreement with Bland Altamn Plots

Описание: The discussion revolves around the Bland-Altman Plot, a statistical method used to compare two measurement techniques by assessing their agreement rather than just correlation. The presentation covers how the plot is constructed, its interpretation, and its applications in medical device validation, particularly in FDA submissions. The discussion also includes limitations such as sample size dependency, normality assumptions, and dealing with outliers. Participants also explore alternative statistical methods and real-world applications, emphasizing the importance of defining acceptable error thresholds before conducting studies.

Participants

1. Ethan Ulrich – Software Engineer, AI/ML
2. Joshua Tzucker – Senior Software Engineer
3. Kris Huang – Senior Software Engineer
4. Matt Hancock – Software Engineer
5. Reece Stevens – Director of Engineering
6. Meri Martinez - Regulatory Affairs Project Manager
7. Quinten Sodia - Software Engineer
8. Bimba Shrestha - Software Engineer
9. Juan Pablo Centeno - Software Engineer, AI/ML
10. Mary Vater - Director of Regulatory Affairs


5 Key Takeaways

1. While correlation measures the strength of a relationship between two methods, the Bland-Altman Plot highlights biases, variability, and agreement levels, making it more informative for evaluating whether two measurement techniques can be used interchangeably.
2. The bias line represents the average difference between methods, and the limits of agreement (mean ± 2 standard deviations) indicate acceptable variation. If the data shows systematic bias or wide variability, the methods may not be interchangeable.
3. The method assumes that the differences between measurements are normally distributed. If this assumption is violated, alternative statistical approaches such as non-parametric methods, bootstrapping, or robust statistics (e.g., using percentiles) may be necessary.
4. Regulatory bodies like the FDA often require Bland-Altman Plots to assess measurement device accuracy. It’s crucial to predefine acceptable error limits, as justifying them post-analysis can be challenging, especially without clinical backing.
5. Outliers can distort standard deviation-based limits, affecting interpretation. The plot can also be used in device calibration, helping manufacturers adjust measurement biases for better accuracy in real-world conditions.

Timestamps

00:22 - What is a Bland-Altman Plot?
00:46 - Why Use a Bland-Altman Plot?
01:52 - Understanding Bias and Variability
02:24 - Insights from Bland-Altman Plots
02:53 - How to Build a Bland-Altman Plot
02:53 - How to Build a Bland-Altman Plot
05:31 - Case Study: Glucose Meter Comparison
06:34 - Interactive Example: Interpretation of Plots
09:07 - Limitations of Bland-Altman Plots
10:03 - How to Check for Normal Distribution?
12:55 - Alternative Methods for Comparison
14:20 - Practical Applications in Medical Devices
21:50 - Using Robust Statistics for Better Analysis
27:42 - Planning for Acceptable Error in Submissions
30:50 - Clinical Context and Acceptable Bias
33:00 - Using Bland-Altman Plots for Calibration
34:52 - Examples of Variability Patterns in Plots
35:56 - Closing Remarks

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#DataAnalysis
#StatisticalMethods
#MedicalDevices
#FDACompliance
#MeasurementAgreement
#BiasDetection
#CorrelationVsAgreement
#MedicalResearch
#OutliersHandling
#DataVisualization
#StatisticalValidation
#ClinicalStudies
#ErrorThreshold
#DeviceCalibration
#LimitsOfAgreement
#NormalityAssumption
#RobustStatistics
#Bootstrapping
#MedicalData