Ashok S Gavaskar Asst. Editor - Indian Journal of Orthopaedics

Retrospective study design How to set it up?

Research workshop - IOACON’ 16

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

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Outcome -measurable parameter of clinical interest

“Has already occurred”

Retrospective design: Key points

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”

Retrospective design: Key points

Cross-sectional

Cohort

Case controlObservational (retrospective)

Cross sectional design

No directionOne time

(eg: Survey)

1

3

2Different groups

compared at ONE time

• Descriptive purposes(states the problem)

• Poor inference

Case control design

Unexposed

Exposed

Exposed

Unexposed

DISEASE(cases)

DISEASE(controls)

Reviewrecords

Reviewrecords

• Rare outcome(only one outcome)

• Multiple exposures• Inference - moderate

Cohort design

Unexposed

Exposed

outcomes(study begins)

recordsreview

Disease

NoDisease

Disease

NoDisease

• common outcomes(multiple outcomes)

• Multiple exposures• Strongest - Observational

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

Literature review

• An essential pre-requisite

• Systematic review (study’s area of focus, demographics, criteria)

• Multiple databases

• Background - key concepts & variables

Study proposal

• abstract• introduction• research question• literature review• methodology• significance• limitations• budget• references

Sample

Design

Variables

Instruments

Key elements: Sampling issues

• Sample size

• Sampling strategy

• Key element in any research proposal

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)

Sampling strategy

• Convenience sampling

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

Sampling strategy

Gold standard, (has equal chance) • suitable for multi-centre trials • common disorders

• Random sampling

Sampling strategy

every nth case is selected (not truly random) access to large number of records

• Systematic sampling

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

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

Methodology

• Instruments (paper/digital)

Paper

cost effective pre-printed form

(avoids coder’s interpretation of data)

not so good…..

• Handwriting• storage• maintenance

Methodology

• Instruments

• Digital

• large RCRs • centralisation of data storage • entry and transcription errors • can be generated from software packages

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

Data abstraction

• Coding/procedure manual

to ensure accuracy, reliability & consistency of data

• clear definitions• protocols• steps for data extraction

Data abstraction

• Data abstractors:

• selection & training• blinding reviewer bias• Intra and inter -rater reliability

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)

Data management

• Data management Software package

(Microsoft access, Medquest)

• data input• statistics• reporting

Pilot study

• Very useful helps to assess study design feasibility evaluate methodology

• 10% of the target population

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…