Retrospective Study Design

151 views

Published on

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

Published in: Health & Medicine
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
151
On SlideShare
0
From Embeds
0
Number of Embeds
121
Actions
Shares
0
Downloads
2
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Retrospective Study Design

  1. 1. Ashok S Gavaskar Asst. Editor - Indian Journal of Orthopaedics Retrospective study design How to set it up? Research workshop - IOACON’ 16
  2. 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. 3. ` Outcome -measurable parameter of clinical interest “Has already occurred” Retrospective design: Key points
  4. 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. 5. Retrospective design: Key points Cross-sectional Cohort Case control Observational (retrospective)
  6. 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. 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. 8. Cohort design Unexposed Exposed outcomes (study begins) records review Disease NoDisease Disease NoDisease • common outcomes (multiple outcomes) • Multiple exposures • Strongest - Observational
  9. 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. 10. Literature review • An essential pre-requisite • Systematic review (study’s area of focus, demographics, criteria) • Multiple databases • Background - key concepts & variables
  11. 11. Study proposal • abstract • introduction • research question • literature review • methodology • significance • limitations • budget • references Sample Design Variables Instruments
  12. 12. Key elements: Sampling issues • Sample size • Sampling strategy • Key element in any research proposal
  13. 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. 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. 15. Sampling strategy Gold standard, (has equal chance) • suitable for multi-centre trials • common disorders • Random sampling
  16. 16. Sampling strategy every nth case is selected (not truly random) access to large number of records • Systematic sampling
  17. 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. 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. 19. Methodology • Instruments (paper/digital) Paper cost effective pre-printed form (avoids coder’s interpretation of data) not so good….. • Handwriting • storage • maintenance
  20. 20. Methodology • Instruments • Digital • large RCRs • centralisation of data storage • entry and transcription errors • can be generated from software packages
  21. 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. 22. Data abstraction • Coding/procedure manual to ensure accuracy, reliability & consistency of data • clear definitions • protocols • steps for data extraction
  23. 23. Data abstraction • Data abstractors: • selection & training • blinding reviewer bias • Intra and inter -rater reliability
  24. 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. 25. Data management • Data management Software package (Microsoft access, Medquest) • data input • statistics • reporting
  26. 26. Pilot study • Very useful helps to assess study design feasibility evaluate methodology • 10% of the target population
  27. 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…

×