Applying meta analysis to trauma registry
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Applying meta-analysis to trauma registry. Ammarin Thakkinstian, Ph.D. Clinical Epidemiology Unit Faculty of Medicine, Ramathibodi Hospital Tel: 2011269,2011762 Fax: 02-2011284 e-mail: [email protected] Meta-analysis.

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Applying meta-analysis to trauma registry

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Applying meta analysis to trauma registry

Applying meta-analysis to trauma registry

Ammarin Thakkinstian, Ph.D.

Clinical Epidemiology Unit

Faculty of Medicine,

Ramathibodi Hospital

Tel: 2011269,2011762

Fax: 02-2011284

e-mail: [email protected]


Meta analysis

Meta-analysis

  • A tool for pooling results/data of the same topics from different sources/centres in order to

    • estimates treatment/intervention effects

    • leading to reduces probability of false negative results

    • potentially to a more timely introduction of effective treatments/intervention/program

    • Objective evidence & quantitative conclusion


Type of meta analysis

Type of meta-analysis

  • Summary data

    • Unit of analysis is study

    • Mean (SD)

    • Count/frequency data by intervention & outcome

    • Person-time data


Summary data

Summary-data

  • Continuous data

StudyiNMeanSD

Rx/Exp+N1Mean1SD1

Cont/Exp-N2Mean2SD2


Summary data1

Summary-data

  • Categorical data


Type of meta data

Type of meta-data

  • Individual patient data (IPD)

    • Raw databases

    • Unit of analysis is patient

    • Analogous to multi-centre trials

    • More retrospective than prospective

    • Data registry


Applying meta analysis to trauma registry

IPD

  • Carry out data checking (data validation)

  • Better standardization of information

    • Categorization of eligible participants

    • Definition of Outcomes

    • Variables’ Classification

      • ICD-10

      • Type of trauma

      • AIS


Applying meta analysis to trauma registry

IPD

  • Flexible to apply statistic modeling

  • Better adjust for confounders & adjust for the same confounders simultaneously

  • More flexible to assess interaction effects

  • More flexible and capable in assessing cause of heterogeneity

  • Allow to assess which subgroup of patients (centre) that intervention/program may/may not work

  • Establishment of international networks of collaborating investigators


Applying meta analysis to trauma registry

IPD

  • Disadvantage

    • Data quality

      • Missing data

      • Data validation

    • More cost & time consuming

    • Substantial effort and infrastructure require to

      • Develop & administer a standardized protocol

      • Collect, manage, & data management

      • Communicate with collaborators


Applying meta analysis to trauma registry

Hospitals

Data collection & management

  • Data Registry

Databases

Data coding

Data manager

QC

Data entry

Cleaning Checking

Validate data

Validated Data


Applying meta analysis to trauma registry

Retrieve databases

Combine data

Statistician

Re-check data

Analyse data

Report results

Writing report (manuscript)

Publish (annual, twice/year)


Data analysis

Data analysis

  • Heterogeneity test

    • Different source data are homogeneous?

  • Homogeneity


Analysis

Analysis

  • Heterogeneity


Outcomes

Outcomes

  • Death/alive

  • Disability/Non-disability

  • Complications

    • Infection

    • Fracture

  • Hospitalization

  • Hospital days

  • QoL

  • Cost


Count discrete outcome

Count (discrete) outcome

  • Poisson regression

    • Number of death

    • Number of infection

    • Number of disability

    • Number of fracture


Hospital standardised mortality ratio

Hospital standardised mortality ratio


Applying meta analysis to trauma registry

HSMR

  • Definition

    • The ratio of actual number of deaths to expected number of deaths in the hospital


Expected number of deaths

Expected number of deaths


Applying meta analysis to trauma registry

  • Original HSMR

  • X

    • Age in year

    • Sex

    • Admission category

      • Emergency versus elective

    • Length of stay

    • Diagnosis group

      • Account for 80% of death

    • Co-morbidity

      • Chalson’s index

      • Might be able to use AIS scores

    • Transfer

      • Patient was transferred from acute care


Step of analysis

Step of analysis

  • Fit logistic regression with death as the outcome

  • Estimate probability of death from the logit model

  • E = sum(p)


Modified hsmr

Modified HSMR

  • age in year

  • sex

  • Length of stay

  • Admission category

    • Emergency vs elective

  • Transfers

    • Acute care

  • Diagnosis group

    • Account for 80% of death

  • Co-morbidity

    • Chalson’s index

  • age in year

  • sex

  • Length of stay

  • Patient transferring

    • Ambulance

    • Non-ambulance

      AIS scores

      Add

  • Risk behavior

    • Alcohol

    • Transquilizer/sedation

  • Type of trauma


  • Problem

    Problem

    • Missing

      • Diagnosis

      • Co-morbid

      • Length o stay

    • Data validation??


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