FREE UK WORKSHOP Missing data in health research 5th December 2001
organised by the Department of Psychiatry, University of Cambridge with
support from the Stanley Foundation
Target audience includes generalists in health research, not just
statisticians.
Open to all. Free of charge to those employed by Universities or NHS.
Pre-registration by email is required (to [log in to unmask])
since the venue restricts attendance to 100 persons.
Time and Place: 10.15am-4.15pm on Weds 5th December 2001
Alice Fisher Lecture Theatre, Addenbrooke's Hospital site, Cambridge
http://www.addenbrookes.org.uk/directcontact/maps/locationmap1.html
Detailed joining instructions will be sent in response to email
registration.
Any further enquiries please contact:
Tim Croudace 01223 336599 [log in to unmask])
Please forward this message to interested parties
thank you
Tim Croudace
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Programme
10.15 Coffee on arrival
10.30 Welcome
Prof Peter Jones, Head of Dept of Psychiatry
University of Cambridge
10.35 Introduction
Simon Thompson
Director MRC Biostatistics Unit, Cambridge
10.45 Health research warning: ignoring missing data
can seriously bias your estimates
Margaret Ely,
Anglia Polytechnic University, Cambridge
11.45 Missing information in practice:
a model based approach applied to asthma trials
James Carpenter, Medical Statistics Unit
London School of Hygiene and Tropical Medicine
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12.45-2.00 Buffet LUNCH
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Chair: Prof Glenys Parry, University of Sheffield
2.00 The modelling approach to missing data:
a re-analysis of the UK700 trial
Ian White
MRC Biostatistics Unit, Cambridge
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3.00 KEYNOTE SPEAKER: Mike Kenward, SB Professor of Biostatistics,
London School of Hygiene and Tropical Medicine
***** Perspectives on missing data: an overview ***
____________________________________________________________
4.00 Summing up / Close
Tim Croudace, Dept of Psychiatry, Univ of Cambridge
4.15 TEA and departure
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Abstracts of talks
*Missing Values: an Introduction and Overview *
MIKE KENWARD
SB Professor of Biostatistics, London School of Hygiene and Tropical
Medicine
The problem of missing values will be considered from a
statistical perspective. Some standard definitions and concepts
in the topic will be introduced and related to practical problems.
The relationships between aims and methods of analysis will be
stressed, and contrasts drawn between simplicity of analysis
and simplicity of assumptions. The ideas behind some some
common methods such as simple imputation and multiple
imputation and model based analyses will be will be sketched.
______________________________________________________________
*Health Research Warning: ignoring missing data can seriously bias your
estimates*
MARGARET ELY
Centre for Research in Health and Social Care, Anglia Polytechnic
University, Cambridge
Objectives -To demonstrate the serious bias resulting from
ignoring missing data values or using naïve imputation methods,
and the advantages of using multiple imputation (MI) in an
epidemiological study of alcohol consumption.
Design - birth cohort study Subjects - Men and women in
the MRC National Survey of Health and Development,
a national cohort study of 5362 births in 1946, of whom
3262 were interviewed in 1989 at the age of 43.
Outcome measures - Alcohol consumption is derived from
a seven day diet diary. Measures of excessive consumption
are drinking in excess of 3U(f) or 4U(m),
and of double this recommended limit, 6U(f) or 8U(m),
on any day of the week.
Methods - Only 2002 (61%) of the 3262 study members
interviewed in 1989completed the diet diary. Using this
complete data MCAR, MAR and MNAR mechanisms of
missingness were simulated and listwise deletion (LD),
group mean imputation (GM) and MI were applied. Multiple
imputed data sets were generated using SOLAS software, based
on regression models using covariates gender, reported
weekly consumption, smoking, CAGE score, systolic blood
pressure, day of the week and consumption on recorded days.
The model was applied to the 3262 study members.
Results - LD and GM produced progressively greater biased
estimates with departure from MCAR, whereas those using MI
were unbiased even when the missingness of the data was
related to the amount people drank (MNAR). Average estimates
of the proportion of men drinking >8U, known to be 38.0%,
was 27.7% for LD, 38.8% for MI. Further, MI is more efficient
than LD (RMSE 1.2, 10.4). Applying MI to the full data
gives 42.5% (95% CI 39.4 to 45.6).
Conclusion - Missing data poses a problem for
epidemiological studies in which the reason for missing data
is not known and in which it is unlikely to be missing at random.
Multiple Imputation provides insurance against bias in even
when the data is not missing at random.
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*Missing information in practice:
a model based approach applied to asthma trials*
JAMES CARPENTER
Medical Statistics Unit, London School of Hygiene and Tropical Medicine
In most clinical trials, some patients do not complete their
intended follow-up according to protocol, for a variety of
reasons, and are often described as having 'dropped out'
before the conclusion of the trial. Their subsequent measurements
are missing, and this makes the analysis of the trial's repeated
measures data more difficult.
We briefly review the reasons for patient dropout, and their
implications for some commonly used methods of analysis.
We then propose a selection models for modelling both the
response to treatment and the dropout process. Such
models are readily fitted in a Bayesian framework using
non-informative priors with the software BUGS. The results
from such models are then compared with the results of
standard methods for dealing with missing data in clinical trials,
such as last observation carried forward. We further propose
the use of a time transformation to linearise an asymptotic
pattern of repeated measures over time and therefore simplify
the modelling.
All these ideas are illustrated using data from a 5-arm asthma clinical
trial.
______________________________________________________________
*The modelling approach to missing data: a re-analysis of the UK700
trial*
IAN WHITE
MRC Biostatistics Unit, Cambridge
The UK700 trial was a large randomised controlled trial in community
psychiatry.The proportion of patients who were interviewed at follow-up
differed between the two arms, and there was therefore concern that any
analysis which excluded those not followed up would be biased.
I will describe a sequence of analyses which included successively
more information in the model: first baseline values of the outcome
variables, then values of the outcome variables measured mid-way
through follow-up, and finally other outcome variables which had less
missing data. This sequence of analyses aims (1) to reduce bias by
making the underlying assumptions about the missing data more
plausible, and (2) to increase precision by including more information.
The simpler models were fitted in standard software but the more
complex models were fitted in MLWin software.In the UK700 trial
the estimated treatment effect differed little between analyses
and we conclude that bias due to missing data is probably small.
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Sponsor:
This workshop is sponsored by the Stanley Foundation.
http://www.stanleyresearch.org/about/
The Stanley Foundation is a nonprofit organization that supports
research on the causes and treatment of schizophrenia
and bipolar disorder (manic-depressive illness). The Dept of
Psychiatry, University of Cambridge is a Stanley Foundation
European Research Centre.This funding supports population-based
epidemiological studies of schizophrenia and bipolar disorder,
including MRI and neuropsychological components,
and collaborative work on treatment trials.
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