medical statistician: an oxymoron? balancing teaching and research: reflections over 30 years

Teachers of Medical Statistics- Manchester 2012

Medical statistician: an oxymoron?

Balancing teaching and research:reflections over 30 years

Mike Campbell

University of Sheffield

1

Review of talk

• Experience over >30 years

• How to balance research/consulting/teaching

• How the balance has changed

• Speaker not a good role model


2

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)


4


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

5


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

6


Burwalls 1985

7


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

8


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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Techniques no longer used

ANOVA

Probit analysis

Discriminant analysis

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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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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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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


14

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)


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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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medical statistician: an oxymoron? balancing teaching and research: reflections over 30 years

Documents

medical statistician

medical statisticsestimation

pharmaceutical statistics

medical researchnelder

regression models

loglinear models

bbc computer

new techniques