Hansen, CH

Murray, G

Sharpe, M

Trials

PubMed

Christian Hansen

Gordon Murray

Michael Sharpe

Crossref

Using evidence from observational healthcare data to inform a trial design

LSHTM Research Online

Hansen, CH; Murray, G; Sharpe, M (2013) Using evidence from ob-servational healthcare data to inform a trial design. In: UNSPECI-FIED. DOI: https://doi.org/10.1186/1745-6215-14-s1-o1Downloaded from: http://researchonline.lshtm.ac.uk/2535721/DOI: 10.1186/1745-6215-14-s1-o1Usage GuidelinesPlease refer to usage guidelines at http://researchonline.lshtm.ac.uk/policies.html or alterna-tively contact researchonline@lshtm.ac.uk.Available under license: http://creativecommons.org/licenses/by/2.5/ORAL PRESENTATION Open AccessUsing evidence from observational healthcaredata to inform a trial designChristian Holm Hansen1*, Gordon Murray1, Michael Sharpe2From 2nd Clinical Trials Methodology Conference: Methodology MattersEdinburgh, UK. 18-19 November 2013Clinical trials are expensive. Understanding where a trialis needed and what questions to address is thereforeimportant. We ask if pilot work using observationalhealthcare data can be used to inform a trial.Symptoms of significant psychological distress arecommon in cancer patients but are often left untreated.It is unknown to what extent such distress symptomspersist over time and require treatment. Should a clini-cal trial be devised to determine if patients would bene-fit from treatment? How many and what patients arelikely to suffer from persistent distress and to requiretreatment? On what criteria should patients be includedin a trial, and when should trial outcomes be evaluated?We address these questions using repeated, psycholo-gical symptom data that were routinely collected fromover 20,000 outpatients who attended selected oncologyclinics in Scotland. However, the limitations to routinelycollected observational healthcare data are many. Weapproach the analysis within a missing data context anddevelop a Bayesian model in WinBUGS to estimate theposterior predictive distribution for the incomplete long-itudinal responses and covariates under both MAR andMNAR mechanisms and use this model to generatemultiply imputed datasets for analysis in SAS. The find-ings are compared with those from a purpose-designed,prospective, clinical study in the same population.Meaningful analysis of observational healthcare datarequires subject-matter knowledge, good quality data andappropriate statistical methods. With flexible modellingtechniques now more practicable, we believe pilot workwith such data could help inform future trial designs.Authors’ details1University of Edinburgh, Edinburgh, UK. 2University of Oxford, Oxford, UK.Published: 29 November 2013doi:10.1186/1745-6215-14-S1-O1Cite this article as: Hansen et al.: Using evidence from observationalhealthcare data to inform a trial design. Trials 2013 14(Suppl 1):O1.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submit1University of Edinburgh, Edinburgh, UKFull list of author information is available at the end of the articleHansen et al. Trials 2013, 14(Suppl 1):O1http://www.trialsjournal.com/content/14/S1/O1 TRIALS© 2013 Hansen et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.

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ORAL PRESENTATION Open Access
Using evidence from observational healthcare
data to inform a trial design
Christian Holm Hansen1*, Gordon Murray1, Michael Sharpe2
From 2nd Clinical Trials Methodology Conference: Methodology Matters
Edinburgh, UK. 18-19 November 2013
Clinical trials are expensive. Understanding where a trial
is needed and what questions to address is therefore
important. We ask if pilot work using observational
healthcare data can be used to inform a trial.
Symptoms of significant psychological distress are
common in cancer patients but are often left untreated.
It is unknown to what extent such distress symptoms
persist over time and require treatment. Should a clini-
cal trial be devised to determine if patients would bene-
fit from treatment? How many and what patients are
likely to suffer from persistent distress and to require
treatment? On what criteria should patients be included
in a trial, and when should trial outcomes be evaluated?
We address these questions using repeated, psycholo-
gical symptom data that were routinely collected from
over 20,000 outpatients who attended selected oncology
clinics in Scotland. However, the limitations to routinely
collected observational healthcare data are many. We
approach the analysis within a missing data context and
develop a Bayesian model in WinBUGS to estimate the
posterior predictive distribution for the incomplete long-
itudinal responses and covariates under both MAR and
MNAR mechanisms and use this model to generate
multiply imputed datasets for analysis in SAS. The find-
ings are compared with those from a purpose-designed,
prospective, clinical study in the same population.
Meaningful analysis of observational healthcare data
requires subject-matter knowledge, good quality data and
appropriate statistical methods. With flexible modelling
techniques now more practicable, we believe pilot work
with such data could help inform future trial designs.
Authors’ details
1University of Edinburgh, Edinburgh, UK. 2University of Oxford, Oxford, UK.
Published: 29 November 2013
doi:10.1186/1745-6215-14-S1-O1
Cite this article as: Hansen et al.: Using evidence from observational
healthcare data to inform a trial design. Trials 2013 14(Suppl 1):O1.
Submit your next manuscript to BioMed Central
and take full advantage of: 
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at 
www.biomedcentral.com/submit1University of Edinburgh, Edinburgh, UK
Full list of author information is available at the end of the article
Hansen et al. Trials 2013, 14(Suppl 1):O1
http://www.trialsjournal.com/content/14/S1/O1 TRIALS
© 2013 Hansen et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.


http://creativecommons.org/licenses/by/2.0

Using evidence from observational healthcare data to inform a trial design

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