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© Nuffield Trust22 June 2015
Matched Control Studies:Methods and case studies
Cono Ariti
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Predictive risk modelling
Resource allocation
Descriptive studies Evaluations
Integrated care pilots
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Nuffield Trust Research team – data linkage projects
Risk sharing for CCGs
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Combined predictive model
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Person based resource allocation
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Social care at end of life
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Cancer and social care
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Predicting social care costs
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Virtual Wards
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WSD
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British Red Cross
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Need for evaluation
Need to know what works• In a practical setting – “real world evaluation”• Clarify the debate• Likely impacts – unbiased results• Link to qualitative work
Refine programs• Obtain feedback and learnings – the pain of
implementation• Explore sub-groups – where did it work? Where
could it work?
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Issues with evaluations
Randomised control trials • “Gold standard”• May not be feasible or ethical• Inclusion and exclusion rules can limit generalisation• Are still subject to poor implementation – can induce
bias• Potentially expensive!
Observational studies• Typically no “natural” experiment exists• Often no comparable control group to provide a fair
assessment
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Matched Control Studies - Methods
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Matched Control Studies
The basic idea
• Match controls to those treated based on measured characteristics in existing datasets
• The control group and treated group should look similar “on balance”
• Mimics the idea of an RCT
• Based on propensity score theory (Rubin & Rosenbaum, 1983) and earlier work on matching (Cochran, 1965)
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Matched Control Studies
Matching• Prognostic risk score• Demographics – age, gender, deprivation, ethnicity• Prior acute care service use – admissions, OP and
A&E attendances• Prior diagnoses, targeted chronic conditions
Balance• In this case all matching variables• Additional variables such as length of stay, additional
diagnoses and longer service use history• Assures comparability between the groups
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Matching Algorithm
Algorithm• Exact match not possible• Computer intensive “genetic algorithm”• Uses a weighted Mahalanobis “distance” to
determine closest match• Automatically assesses balance and moves to an
improved solution
Assessing Balance• On overall group similarity• Compares means and distribution of variables in the
two groups
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Analysis of matched control studies
• Standard statistical methods to estimate the difference in the two groups
• Regression models, difference in difference analysis
• By including matching variables in the statistical adjustment remaining imbalances can be reduced – “doubly robust”
• Methods exist for sensitivity analysis – impact of unobserved variables
• Some controversy around accommodating the matching in analysis
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Case Study 1: Telehealth Programme
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Case Study 1: Telehealth program
Intervention:
• Remote monitoring for patients with long term conditions
Nuffield commissioned to evaluate impact:• Primary: Reduction in emergency hospital admissions?• Secondary: Reductions in Emergency attendances, outpatient
attendances, mortality
Methods:• Retrospective matched control study – use of already existing
administrative data
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Description: Telehealth program
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Matched control studies – broad aim
>30,000 individuals – resident in local area June 2010 to March 2013, did not receive telehealth and were eligible for matching
(local controls)
Aim to find 716 individuals who match almost exactly on a broad range of characteristics
Use this group as study control group
716 individuals – enrolled June 2010 to March 2013 & received Telehealth intervention & eligible for matching
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Datasets available
Telehealth Nuffield trust
N = 716
• person details• dates of
service • type of service
Identifiers:Names, DOB,Addresses, etc
• dates & place of death for all people in England,
• associated hospital (HES) records
Identifiers:Nuffield Trust specific HESID
Administrative data ONS deaths Hospital inpatient, outpatient, AE
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Telehealth Data Linkage Service Nuffield TrustNew Identifier New Identifier New Identifier
(NHS no) (NHS no)
Names Names
Address Address
DOB DOB
HESID HESID
Telehealth person identifiers (File A)
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Final datasets available for analysis
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Identifiers:
HESID on all
ONS deaths Hospital inpatient, outpatient, AETelehealth data - desensitised
Use all this info to carry out matched control analysis
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Control group – how well matched?
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Control group – how well matched?
Telehealth Controls
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Control group – how well matched?
Telehealth Controls
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Key Result 1: Risk of admissions or death
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Key Result 2: Changes in admissions or attendances (six months pre and post intervention)
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Results
• Telehealth patients tended to be admitted for an emergency admission earlier than control patients
• There was no difference in mortality between the telehealth and control groups
• There were no statistically significant reductions in hospital admissions when comparing the period six months before and after the telehealth intervention
• In summary the Telehealth program did not have a significant impact on acute care outcomes
• Sensitivity analysis showed little evidence of an important unobserved variable
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Matched Control Studies: Summary
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Matched Controls: Summary
Benefits• Makes full use existing data, with relative ease• Techniques applicable to many different types of
services and datasets• Decisions on what seems to work (and what may
not) based on more robust analyses leading to better informed decisions
Caveats• If important unobserved variables exist results may
be biased• The routine data sources must contain the relevant
data
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Implementing locally – key enablers
Do you have … Can you …
• Access to data that contains the outcomes relevant to your evaluation?
• Access to data containing relevant matching characteristics?
• Do you have consent to access/link the data?
• Analysis tools to apply statistical methods to the data?
• Skilled analysts to analyse the data?
• Link multiple sources of data?• Handle large amounts of data
(millions of observations)?• Identify recipients of the
intervention?• Transform and augment that data
with bespoke variables?• Apply sophisticated matching
algorithms routinely to this data?• Analyse the data with a variety of
statistical methods and interpret the results appropriately?
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