- 1The aims and objectives or the study to test specific hypotheses should be specified in advance. Develop and refine your hypothesis with clinical colleagues.
- 2Conduct a literature review, preferably a systematic review, to assess what is already known on the topic.
- 3Write a formal research proposal, with introduction, aims, methods, and sample size estimation. The selection (inclusion and exclusion criteria), size of target population and the choice of which variables to measure (outcomes, exposures, and potential confounders) should be specified and appropriate case record forms (CRFs), paper or electronic, designed, with codes specified for missing data. Check that the relevant variables usually are documented in the medical records. Sample size should be based on the anticipated outcomes (using information coming from the review conducted in 2 above), rather than from the number of cases exposed to the treatment/environment/gene of interest. This research proposal should be discussed with and reviewed by colleagues.
- 4The reviewed research proposal should be discussed with your Institutional Ethics or Review panel for their formal approval.
- 5Selection and recruitment of patients needs careful consideration, to avoid bias. In a case–cohort study, cases who have been exposed to the risk factor of interest may be more likely to enrol in the study (e.g. if they have a vested interest in the results of the study), which can bias results, whereas the controls recruited need to be from the same population as the cases (often done by matching controls to cases using key demographics such as age and sex and possibly other important information such as workplace restrictions if assessing an occupational exposure).
- 6Data should be abstracted only on to the CRFs. At least a sample of the data should be extracted by two people to ensure consensus. For this you may need to write a coding manual, e.g. for aspirin use code 1 for yes, 2 for no, 3 for discontinued, 4 for unknown or missing. Try a pilot study, with subsequent revision of the CRF and coding manual. Check for interobserver reliability/consensus for data extraction: using data extractors who are blind to the hypothesis being investigated is a helpful tool for reducing bias. It may be possible to undertake these checks, using anonymized records, while waiting for ethical review.
- 7In analysis, investigators need to measure and control for potential confounders using appropriate statistical methodology before reliably interpreting results. Appropriate statistical techniques should be used for matched case–control studies (e.g. conditional logistic regression). It also may be necessary to account for missing data using appropriate methodology (e.g. multiple imputation). Careful consideration before beginning the study must be taken to identify and subsequently measure important prognostic variables that may differ between the exposure groups: differences in these known characteristics need to be adjusted for in the final analysis. Stratification (subgrouping) and adjustment with multivariate regression models are the two most common statistical techniques employed, but neither of these techniques can eliminate bias related to unmeasured or unknown confounders.
- 8Reporting of retrospective cohort and case–control studies should follow STROBE guidelines. For instance, length of follow up should always be reported. Reading these guidelines before starting a study may help improve the study design.
- The retrospective chart review: important methodological considerations.J Educ Eval Health Prof. 2013; 10: 12
Strengthening the reporting of observational studies. STROBE statement. Retrieved July 13 2015 from: http://www.strobe-statement.org.
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