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Retrospective Study Design
This document discusses key considerations for designing a retrospective study. It explains that retrospective studies analyze existing data collected for other purposes. The document outlines different retrospective study designs including cross-sectional, case-control, and cohort studies. It emphasizes important elements like defining a research question, conducting a literature review, determining sample size and sampling strategy, developing data collection instruments, establishing inclusion/exclusion criteria, ensuring reliability of data abstraction, and piloting the study methodology. Overall, the document provides guidance on setting up a rigorous retrospective study design.
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Retrospective Study Design
- 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 ofclinical interest “Has already occurred” Retrospective design: Key points
- 4. Retrospective design: Keypoints Exposure: ‘Factor of interest’ Interventional (can only be prospective) you control the factor of interest Observational (prospective/retrospective) “you just observe”
- 5. Retrospective design: Keypoints Cross-sectional Cohort Case control Observational (retrospective)
- 6. Cross sectional design Nodirection 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 goodretrospective 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 • Anessential 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: Samplingissues • Sample size • Sampling strategy • Key element in any research proposal
- 13. Sample size • Poweranalysis (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 • Conveniencesampling 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 nthcase 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 costeffective 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/ Exclusioncriteria • 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/proceduremanual to ensure accuracy, reliability & consistency of data • clear definitions • protocols • steps for data extraction
- 23. Data abstraction • Dataabstractors: • 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 • Datamanagement Software package (Microsoft access, Medquest) • data input • statistics • reporting
- 26. Pilot study • Veryuseful helps to assess study design feasibility evaluate methodology • 10% of the target population
- 27. Summary • Well definedresearch questions • Sampling: size & strategy • Operationalise variables • Data abstraction process: most important • Inclusion and exclusion criteria • Observer reliability • Pilot test For a good RCR…