Accendo Reliability

Your Reliability Engineering Professional Development Site

  • Home
  • About
    • Contributors
    • About Us
    • Colophon
    • Survey
  • Reliability.fm
    • Speaking Of Reliability
    • Rooted in Reliability: The Plant Performance Podcast
    • Quality during Design
    • CMMSradio
    • Way of the Quality Warrior
    • Critical Talks
    • Asset Performance
    • Dare to Know
    • Maintenance Disrupted
    • Metal Conversations
    • The Leadership Connection
    • Practical Reliability Podcast
    • Reliability Matters
    • Reliability it Matters
    • Maintenance Mavericks Podcast
    • Women in Maintenance
    • Accendo Reliability Webinar Series
  • Articles
    • CRE Preparation Notes
    • NoMTBF
    • on Leadership & Career
      • Advanced Engineering Culture
      • ASQR&R
      • Engineering Leadership
      • Managing in the 2000s
      • Product Development and Process Improvement
    • on Maintenance Reliability
      • Aasan Asset Management
      • AI & Predictive Maintenance
      • Asset Management in the Mining Industry
      • CMMS and Maintenance Management
      • CMMS and Reliability
      • Conscious Asset
      • EAM & CMMS
      • Everyday RCM
      • History of Maintenance Management
      • Life Cycle Asset Management
      • Maintenance and Reliability
      • Maintenance Management
      • Plant Maintenance
      • Process Plant Reliability Engineering
      • RCM Blitz®
      • ReliabilityXperience
      • Rob’s Reliability Project
      • The Intelligent Transformer Blog
      • The People Side of Maintenance
      • The Reliability Mindset
    • on Product Reliability
      • Accelerated Reliability
      • Achieving the Benefits of Reliability
      • Apex Ridge
      • Field Reliability Data Analysis
      • Metals Engineering and Product Reliability
      • Musings on Reliability and Maintenance Topics
      • Product Validation
      • Reliability by Design
      • Reliability Competence
      • Reliability Engineering Insights
      • Reliability in Emerging Technology
      • Reliability Knowledge
    • on Risk & Safety
      • CERM® Risk Insights
      • Equipment Risk and Reliability in Downhole Applications
      • Operational Risk Process Safety
    • on Systems Thinking
      • Communicating with FINESSE
      • The RCA
    • on Tools & Techniques
      • Big Data & Analytics
      • Experimental Design for NPD
      • Innovative Thinking in Reliability and Durability
      • Inside and Beyond HALT
      • Inside FMEA
      • Institute of Quality & Reliability
      • Integral Concepts
      • Learning from Failures
      • Progress in Field Reliability?
      • R for Engineering
      • Reliability Engineering Using Python
      • Reliability Reflections
      • Statistical Methods for Failure-Time Data
      • Testing 1 2 3
      • The Manufacturing Academy
  • eBooks
  • Resources
    • Accendo Authors
    • FMEA Resources
    • Glossary
    • Feed Forward Publications
    • Openings
    • Books
    • Webinar Sources
    • Podcasts
  • Courses
    • Your Courses
    • Live Courses
      • Introduction to Reliability Engineering & Accelerated Testings Course Landing Page
      • Advanced Accelerated Testing Course Landing Page
    • Integral Concepts Courses
      • Reliability Analysis Methods Course Landing Page
      • Applied Reliability Analysis Course Landing Page
      • Statistics, Hypothesis Testing, & Regression Modeling Course Landing Page
      • Measurement System Assessment Course Landing Page
      • SPC & Process Capability Course Landing Page
      • Design of Experiments Course Landing Page
    • The Manufacturing Academy Courses
      • An Introduction to Reliability Engineering
      • Reliability Engineering Statistics
      • An Introduction to Quality Engineering
      • Quality Engineering Statistics
      • FMEA in Practice
      • Process Capability Analysis course
      • Root Cause Analysis and the 8D Corrective Action Process course
      • Return on Investment online course
    • Industrial Metallurgist Courses
    • FMEA courses Powered by The Luminous Group
      • FMEA Introduction
      • AIAG & VDA FMEA Methodology
    • Barringer Process Reliability Introduction
      • Barringer Process Reliability Introduction Course Landing Page
    • Fault Tree Analysis (FTA)
    • Foundations of RCM online course
    • Reliability Engineering for Heavy Industry
    • How to be an Online Student
    • Quondam Courses
  • Calendar
    • Call for Papers Listing
    • Upcoming Webinars
    • Webinar Calendar
  • Login
    • Member Home
  • Upcoming Live Events
  • Accendo Reliability Webinar Series
You are here: Home / Articles / Reliability of Breast Implants

by Larry George Leave a Comment

Reliability of Breast Implants

Reliability of Breast Implants

Dear Larry

Thank you for your data request for breast implant data and apologies for the delay in responding. The data available is:

  • The number of women receiving implants, by year, by major manufacturer
  • Number of Explants: All Manufacturers (inc. Others and Unknown Brands)

My colleagues have been copied into this email to show your request has been actioned. I hope this is helpful.

Kind regards

Amina.., Department of Health, Quarry House, UK

before and after images of a breast implant - the after is shriveled and yellowed, not smooth and white as in the before image.

Amina’s email went on to say…”On data quality you might like to note that: two patients apparently received PIP implants after their recall. This was most likely a data entry error. Since errors of this kind in a very small number of data entries would have no material impact on the primary purpose of the analysis-i.e. to compare the performance of PIP and other implants-we did not investigate further. Analysing the explant data by month would involve disproportionate cost and would not add materially to the accuracy of the overall analysis.”

Background

UK National Health Service (NHS) did breast implants, some elective and some reconstructive, starting in 2001. In the mid-2000s, leakage and rupture was cause for some explants. Patients sued to get UK NHS to pay for those explants. Manufacturer “Poly Implant Prosthèse” (PIP) (France) was implicated. PIP claimed quality control problem and had swapped industrial for medical grade silicone. A UK “Expert Group” investigated, concurred, and paid [Keough]. I asked for data, and Amina…, Department of Health, sent annual implant and explant counts for nonparametric survival analyses (tables 1 and 2).

Table 1. Implant data by manufacturer (M1, M2, M3, and PIP) 

YearM1M2M3PIPTotal
20017023526625402256
200214656726346193390
200316508938758174235
200421921225110753309854
2005398613161165400910476
2006734211571386246912354
2007910614881891258015065
20081014815702204513719059
20091168915641961395119165
2010139921588215252818260
20111343212982088216820
Total75704131231612525982130934

Table 2. Explant data by manufacturer

YearM1M2M3PIPAll
200198693437238
200294645821237
2003141807233326
20041289782259566
200520812790219644
200624811592150605
200733212289176719
2008365109115280869
200935766102120645
201037749669501
201115927372225
Total250792583713065575

Survival Analyses

The FDA and others require serial numbers on implantable medical and arthroplasty devices [FDA, Ranstam et al.] devices, so that times to failures or censoring times can be collected. It is unnecessary to track implant patients by name or their implants by serial number to provide early warning, management by exception, estimate survival functions, and forecast explants. Implant and explant counts are statistically sufficient to make populationnonparametric estimates of what biostatisticians call survival functions and what engineers call reliability functions: P[Time from implant to explant>t], where t = 1, 2,…., are calendar time periods. These estimates show differences between manufacturers and changes within manufacturers’ products over time, without tracking implants by patient name and implant serial number. 

Figure 1. Survival function estimates (reliability) of breast implants. (“npmle” is nonparametric maximum likelihood estimator and “nplse” is nonparametric least squares estimator.)
Figure 1. Survival function estimates (reliability) of breast implants. (“npmle” is nonparametric maximum likelihood estimator and “nplse” is nonparametric least squares estimator.) 

Figure 1 shows the nonparametric reliability function estimates by manufacturer. Most explants occur within the first year after implant (~5% varying by manufacturer). Manufacturer M2 probably had problems (lowest reliability, orange line, “M2 nplse”). US implants in early 2000s, except for Allergan (AbbVie), were made in Brazil: Mentor (J&J Santa Barbara and Irving, TX), Sientra-Silimed (Garland, TX), and a few others. PIP shows an additional ~0.3-0.4% occurring four or five years after implant. Manufacturer M2 shows an additional almost 0.1% occurring between six and nine years after implant. The BMI-PIP rupture reliability estimate shows ~2% ruptures beginning in the fourth year after implant. “PIP BMI Rupture” is based on life data from one source BMI (highest reliability, red line). BMI is a British HealthCare company, not “Body-Mass-Index”.

Table 3. Probability of explant within first year after implant

 M1M2M3PIPTotal
1st year explant4.3%7.0%4.6%4.7%4.3%

Figure 2 includes BMI-PIP rupture survival function estimate obtained from the expert group report. It is the highest reliability estimate, because rupture is a subset of PIP explant modes. 

Figure 2.Survival function broom chart for PIP Implants. Shorter lines represent earlier cohorts; longer lines include more cohorts, starting with 2001 cohort.
Figure 2.Survival function broom chart for PIP Implants. Shorter lines represent earlier cohorts; longer lines include more cohorts, starting with 2001 cohort. 

Figure 2 shows BMI-PIP rupture explant broom chart. Survival function estimates for broom charts were computed using successively smaller, earlier subsets of cohort data. The broom chart shows survival function estimates from subsets from: 2001-2002, 2001-2003, 2001-2004,…,all. Early 2001-2006 PIP implants were the cohorts with rupture problems. This is an example of reliability deterioration, then growth. Despite the early problems, the broom chart shows that implant survival function eventually improved; longer lines include longer implant-explant lives as more years are included in the estimates. The first-year PIP explant probability estimate 0. 46% did not change. 

 

Methods

The methods were nonparametric maximum likelihood (npmle) and least squares (nplse) survival function estimation [George]. The PIP npmle and nplse survival function estimates agreed closely. The broom chart survival function estimates give the standard deviations of the estimates, at each age t =1,2,…,5. 

Table 4 shows the  lower confidence limit (LCL) on average survival function estimates from all manufacturers’ data (“All LCL”), and from PIP data (“PIP LCL”}. PIP LCLs are slightly greater than all LCLs indicating that PIP survival function could be better than average despite the BMI-PIP rupture data. That conclusions is not statistically significant, because of the slight variations between manufacturers, Table 4 is not a “confidence band” for all ages shown [Hall and Wellner]. Table 4 gives indications only. 

Table 4. Lower confidence limits on survival function estimates, average survival function minus one standard deviation. 

Age, YearsAll LCLPIP LCL
10.938560.946265
20.9431760.954401
30.9449380.954588
40.9473580.954313
50.948670.970014

Proportions of implants for reconstruction varied between manufacturers. PIP market share for reconstruction was smallest and dwindled to zero over time since 2001. Other factors such as hospital, patient, and experience may affect results. Please let me know if you have questions, would like the Excel workbook, or would like more information or additional computations, such as population nonparametric reliability function estimates by failure mode, without life data.

References

Docket FDA-2012-N-0359, “Strengthening our National System for Medical Device Postmarket Surveillance”  Sept. 2012

L. L. George, “Estimate Reliability Functions Without Life Data,” ASQ Reliability Review, Vol. 13, pp. 21-25, 1993

…, “Actuarial Forecasts, Least Squares Reliability, and Martingales,” https://fred-schenkelberg-project.prev01.rmkr.net/actuarial-forecasts-least-squares-reliability-martingales/#more-421021, 2022

W. J. Hall and Jon A. Wellner , “Confidence Bands for a Survival Curve from Censored Data,” Biometrika, Vol. 67, No. 1, pp. 133-143, April 1980

Jonas Ranstam, Johan Kärrholm, Pekka Pulkkinen, Keijo Mäkelä, Birgitte Espehaug, Alma Becic Pedersen, Frank Mehnert, and Ove Furnes, “Statistical Analysis of Arthroplasty Data,” Acta Orthopaedica, 82 (3), pp. 253-257, 2011

Sir Bruce Keough, “Poly Implant Prosthèse, (PIP) breast implants: Final report of the Expert Group,”https://www.gov.uk/government/publications/poly-implant-prothese-pip-breast-implants-final-report-of-the-expert-group, June 2012

… Volume 2, Appendices, June 2012

Filed Under: Articles, on Tools & Techniques, Progress in Field Reliability?

About Larry George

UCLA engineer and MBA, UC Berkeley Ph.D. in Industrial Engineering and Operations Research with minor in statistics. I taught for 11+ years, worked for Lawrence Livermore Lab for 11 years, and have worked in the real world solving problems ever since for anyone who asks. Employed by or contracted to Apple Computer, Applied Materials, Abbott Diagnostics, EPRI, Triad Systems (now http://www.epicor.com), and many others. Now working on actuarial forecasting, survival analysis, transient Markov, epidemiology, and their applications: epidemics, randomized clinical trials, availability, risk-based inspection, Statistical Reliability Control, and DoE for risk equity.

« Safety Rap
Screw Conveyor Auger Shaft Drive End Break »

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Articles by Larry George
in the Progress in Field Reliability? article series

Join Accendo

Receive information and updates about articles and many other resources offered by Accendo Reliability by becoming a member.

It’s free and only takes a minute.

Join Today

Recent Articles

  • Leadership Values in Maintenance and Operations
  • Today’s Gremlin – It’ll never work here
  • How a Mission Statement Drives Behavioral Change in Organizations
  • Gremlins today
  • The Power of Vision in Leadership and Organizational Success

© 2025 FMS Reliability · Privacy Policy · Terms of Service · Cookies Policy