Medical statistician: an oxymoron? Balancing teaching and research: reflections over 30 years
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Teachers of Medical Statistics- Manchester 2012
Medical statistician: an oxymoron?
Balancing teaching and research:reflections over 30 years
Mike Campbell
University of Sheffield
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Review of talk
• Experience over >30 years
• How to balance research/consulting/teaching
• How the balance has changed
• Speaker not a good role model
Teachers of Medical Statistics- Manchester 2012
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Oxymoron
‘Putting together words which seem to contradict each other’:
Examples: Bitter-sweet, Airline food,
American Beer,
Microsoft Works,
Random Order,
unbiased opinion ,
Military IntelligenceTeachers of Medical Statistics-
Manchester 20123
A bit of history….
‘The past is a different country – they do things differently there.’
L.P. Hartley – The Go Between
The past helps you understand why you are where you are, now (MJC)
Teachers of Medical Statistics- Manchester 2012
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Teachers of Medical Statistics- Manchester 2012
Computing in the ‘70s and ‘80s
DIY: Programs Fortran, Algol
Packages: ‘Batch mode’ SPSS BMDP – SPSS-PC 1989
Language: Genstat
Interactive: Glim, Minitab
Commodore Pet/Apple (1978), Basic
IBM PC 1981, BBC Computer 1981
Specially written stats packages for BBC Computer
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Teachers of Medical Statistics- Manchester 2012
Developments in computing
Now
All packages run on a PC
‘R’
Manuals for packages run to 15 volumes
Technology for teaching
eg OHP=> Powerpoint
Web2.0 - information available - MOLE
Much easier to analyse data (calculators on phones)
Remote voting etc
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Teachers of Medical Statistics- Manchester 2012
Burwalls 1985
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Teachers of Medical Statistics- Manchester 2012
Early important books and papers
Armitage P (1971) Statistical methods in medical research
Nelder JA and Wedderburn RWM (1972) Generalized linear models JRSSA
Cox DR (1972) Regression models and life tables JRSSB
Breslow NE (1984) Extra-Poisson variation in log-linear models. Appl Stat
Bland M and Altman (1986) Method of agreement Lancet
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Teachers of Medical Statistics- Manchester 2012
New techniques that have had a big effect on the practice of medical
statisticsBootstrap (Efron 1979 JASA) (extension of
Fisher’s permutation methods)
Random effects (Laird and Ware, 1982 Biometrics)
Ordinal models (McCullagh, 1980 JRSSB)
Multi-level models (Goldstein , 1987)
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Teachers of Medical Statistics- Manchester 2012
Techniques no longer used
ANOVA
Probit analysis
Discriminant analysis
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Teachers of Medical Statistics- Manchester 2012
Other developments
New journals
Statistics in Medicine (1981)
Biostatistics (2001)
Pharmaceutical Statistics (2002)
New Societies
Statisticians in the Pharmaceutical Industry (1977)
International Society of Clinical Biostatistics (1980)
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Teachers of Medical Statistics- Manchester 2012
Changes in emphasis in medical statistics
• Estimation not P-values
Modelling not testing• ‘ EBM’ In particular more on binary data
Eg Relative risk, number needed to treat• Bayesian methods (now feasible)• Latent variables (propensity scores, SEM etc)• Many papers in the literature now use methods which are difficult to
understand (and validate) – because software available– Makes finding current examples from the literature for teaching more difficult
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Teachers of Medical Statistics- Manchester 2012
What has stayed the same
• Demand for teaching medical statistics
(In fact much more demand than in the past)• Content of courses (see next slide)• Communicating statistics to doctors
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Standard courses
Basic course:
Summary stats
Sampling: populations
Estimation: Confidence intervals
Testing: P-values
Binary data – Relative risk etc
Correlation/regression
More advanced course:
Multiple regression
Logistic regression
Survival analysis
Sample size
Random effects
Meta analysis
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Communication with doctors
• Important skill
• Doctors get less statistics in their courses than earlier (at least in Sheffield)
• Statisticians no longer the ‘high-priests’ of technology- doctors can do it themselves
• Often simple statistics will still suffice (eg summary measures, Matthews et al 1990)
Teachers of Medical Statistics- Manchester 2012
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Teachers of Medical Statistics- Manchester 2012
Evolution of the job
1982Possible to ‘know’ most of medical stats
2012Specialization
Journals becoming more mathematical
An applied statistician can learn more from newsletters of software packages than from journals
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Balancing act
IMHO being a medical statistician in a medical school is both very rewarding and very demanding
Rewarding‘Get to play in everyone’s sandpit’ (Tukey)
In general given respect as a professional
DemandingSeen as a technician – promotion and being given positions of responsibility can be difficult (despite being on large numbers of papers and numerous grants)
Not seen as an independent researcher – glass wall between medics and others
Not seen as ‘serious’ by Statistics departments (jack of all trades etc)
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Solutions
• Know your own worth
• Don’t spread yourself too thin
• Have one small area of statistical expertise to talk about at stats conferences, to give some professional standing
• Do what you enjoy!
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