National Information Center on Health Services Research and Health Care Technology (NICHSR)
Introduction to Health Services Research : A Self-Study Course Module 5: Quality Filtering and Evidence-Based Medicine and Health (page 12 of 15)
Introduction | Sampling | Assignment | Assessment | Analysis | Interpretation | Extrapolation
Analyzing the Literature for Quality
When preparing to analyze the literature, use these questions as a guide to evaluating each paper as is shown in the research cases. (Questions are also organized in a
table for you to use in that format).
What is/are your study question or questions?
What kind of study design was used?
Was the study design appropriate for this research question?
Who was studied?
Is there an appropriate number of people in the sample?
What was the sample size?
Samples and sampling
Were the people in the study assigned to groups?
What groups were compared?
Examine the table that describes the people in each group. Check to see that they are similar in age, socioeconomic status, health characteristics, and others (excluding the factors being studied).
Were confounders indicated? How were they dealt with?
How will these patient outcomes be defined and measured?
Are measurements accurate and complete?
Is there any evidence of recall bias?
Is there any indication of "measurement errors"?
Is the data complete and suitable for measuring the question?
If the researchers compared outcomes of specific procedures across hospitals were the patients similar? Case-mix might be different in different hospitals.
Analysis will answer three main questions
How strong was the association between variables?
What is the likelihood of getting the results from the sample if there was no relationship between variables in the larger population from which the sample came?
Were the groups in the study different in any way that could affect the results?
Was a causal relationship supported by the data?
What was the relative risk?
Was the association consistent across different groups?
Have the authors of the studies you found generalized too much from their data?
Have the authors extend the data farther than the data supports?