Retrospective Study Design by Dr Ashok S Gavaskar, Assistant Editor, Indian Journal of Orthopaedics

1. Ashok S Gavaskar
Asst. Editor - Indian Journal of Orthopaedics
Retrospective
study design
How to set it up?
Research workshop - IOACON’ 16

2. Retrospective study - design
• most common form of analysis
(Data originally collected for other reasons)
• quick
• not expensive
• rare outcomes
• long latent period
• generates hypothesis
• Bias
• Cannot provide valid solutions

3. `
Outcome
-measurable parameter of clinical interest
“Has already occurred”
Retrospective design: Key points

4. Retrospective design: Key points
Exposure: ‘Factor of interest’
Interventional (can only be prospective)
you control the factor of interest
Observational (prospective/retrospective)
“you just observe”

5. Retrospective design: Key points
Cross-sectional
Cohort
Case control
Observational
(retrospective)

6. Cross sectional design
No direction
One time
(eg: Survey)
1
3
2
Different groups
compared at
ONE time
• Descriptive purposes
(states the problem)
• Poor inference

7. Case control design
Unexposed
Exposed
Exposed
Unexposed
DISEASE
(cases)
DISEASE
(controls)
Review
records
Review
records
• Rare outcome
(only one outcome)
• Multiple exposures
• Inference - moderate

8. Cohort design
Unexposed
Exposed
outcomes
(study begins)
records
review
Disease
NoDisease
Disease
NoDisease
• common outcomes
(multiple outcomes)
• Multiple exposures
• Strongest - Observational

9. Doing a good retrospective study
Research Question
• Description
• Relationship
• Comparison
what is going on? (incidence/prevalence research)
proportion/percentage/ central tendency/ variability
how phenomena are related?
correlation co-efficients
variable of interest (difference among groups)
central tendency

10. Literature review
• An essential pre-requisite
• Systematic review
(study’s area of focus, demographics, criteria)
• Multiple databases
• Background - key concepts & variables

11. Study proposal
• abstract
• introduction
• research question
• literature review
• methodology
• significance
• limitations
• budget
• references
Sample
Design
Variables
Instruments

12. Key elements: Sampling issues
• Sample size
• Sampling strategy
• Key element in any research proposal

13. Sample size
• Power analysis
(probability of rejecting the null hypothesis)
related to sample size
(10 cases per variable)
Tools:
• textbooks
• journal articles
• downloadable software programs (G*Power 3.0)

14. Sampling strategy
• Convenience sampling
what is available at disposal (e.g:cases with in a particular time frame)
• rare cases, outcomes
• small sample size

15. Sampling strategy
Gold standard, (has equal chance)
• suitable for multi-centre trials
• common disorders
• Random sampling

16. Sampling strategy
every nth case is selected (not truly random)
access to large number of records
• Systematic sampling

17. Study proposal
• abstract
• introduction
• research question
• literature review
• methodology
• significance
• limitations
• budget
• references
• Future prospective studies
• Variables
Define
Operationalise (literature review)
translating a construct to its
manifestation

18. Study proposal
• Design
• flow of information
• go through a few charts
• on site clinicians (multi-centre)
• abstract
• introduction
• research question
• literature review
• methodology
• significance
• limitations
• budget
• references

19. Methodology
• Instruments (paper/digital)
Paper
cost effective
pre-printed form
(avoids coder’s interpretation of data)
not so good…..
• Handwriting
• storage
• maintenance

20. Methodology
• Instruments
• Digital
• large RCRs
• centralisation of data storage
• entry and transcription errors
• can be generated from software packages

21. Data abstraction
Inclusion/ Exclusion criteria
• lack of sufficient variables recorded
• presence of excessive/confounding co-
morbidities
• confounding factors that can degrade the
validity of data
Constant review to assess excluded data

22. Data abstraction
• Coding/procedure manual
to ensure accuracy, reliability & consistency of data
• clear definitions
• protocols
• steps for data extraction

23. Data abstraction
• Data abstractors:
• selection & training
• blinding
reviewer bias
• Intra and inter -rater reliability

24. Data abstraction
• Intra & Inter rater reliability
• (statistical estimate to report
consistency in coding)
Inter:
Cohen kappa
(extent of agreement
-1 to +1, for RCR: 0.6)
Intra:
calculating ICC (intra class correlation)

25. Data management
• Data management
Software package
(Microsoft access, Medquest)
• data input
• statistics
• reporting

26. Pilot study
• Very useful
helps to assess
study design
feasibility
evaluate methodology
• 10% of the target population

27. Summary
• Well defined research questions
• Sampling: size & strategy
• Operationalise variables
• Data abstraction process: most important
• Inclusion and exclusion criteria
• Observer reliability
• Pilot test
For a good RCR…