 Critically Reviewing an
 Epidemiologic Study
 Learning Objectives:
 Describe the approach to reviewing a manuscript
 Identify the research hypothesis of a manuscript
 Identify the quality of the research and the validity of the findings of a manuscript
 Describe the factors which may raise concern about the truth of a research finding
Reviewing a Paper is about Asking Questions  Is the study adequately powered?
 What level of measurement error exists?
 What is the study hypothesis?
 What population do the study subjects originate from?
 Are there unstated confounding factors?
 Were appropriate statistical
 procedures used?
 What is my overall
 impression?
 Critique of data collection
 Critique of data analysis
 Critique of data interpretation
Example Manuscript  Fitzpatrick AL, Kuller LH, Lopez OL, Diehr P, O’Meara ES, Longstreth Jr. WT, Luchsinger JA. Midlife and LateLife Obesity and the Risk of Dementia. Archives of Neurology 66(3):336342, 2009.
What to Examine When Evaluating Data Collection  Study Context
 Study Objectives
 Exposure and Outcome Variables
 Study Design
 Study Population
 Potential for
 Selection Bias
 Information Bias
 Confounding
Study Context  Several Issues to consider
 What is the public health significance of this study?
 Does this study generate new hypotheses or confirm previous results with improved methods?
 Is the study hypothesis biologically plausible?
Study Context – Fitzpatrick, et. al.   What is the public health significance of this study?
 Rising obesity levels in US
 Dementia increasing in US
 Aging population
 Does this study generate new hypotheses or confirm previous results with improved methods?
 Seeks to clarify conflicting results in the literature by examining a large study sample longitudinally
 Is the study hypothesis biologically plausible?
Study Objectives  What do the investigators want to achieve in this research?
 What is the hypothesis of this study?
 There may be more then one
 Is the hypothesis specific or too general to refute?
Exposure and Outcome Variables  Primary exposure
 How was variable defined?
 How was information on exposure collected?
 Best method?
 Sensitivity/specificity of this method?
 Potential for misclassification?
 Primary outcome
 Conceptual vs. operational outcome?
 e.g. breast cancer vs. malignant neoplasm of the breast tissue
 How was information on outcome collected?
 Best method?
 Sensitivity/specificity of this method?
 Potential for misclassification?
Type of Study  What study design was employed?
 Is it an appropriate design?
 Exposure or outcome rare?
 New hypothesis?
 What are the limitations and strengths of this design?
Type of Study  Fitzpatrick  What study design was employed?
 Cohort study (retrospective)
 Is it an appropriate design?
 Exposure or outcome rare? Neither
 New hypothesis? No, but conflicting study results
 What are the limitations and strengths of this design?
 Strengths: longitudinal assessment, incidence of dementia, uses previously collected data
 Limitations: short period of assessment
The Study Population  What was the source of study population?
 How does the study population compare to the general population?
 How were subjects selected?
 Could this method introduce selection bias?
 What was the sample size?
The Study Population  Fitzpatrick  What was the source of study population?
 Community dwellers who were Medicare eligible over age 65 years  4 sites
 How were subjects selected? Not stated
 Could this method introduce selection bias?
 What was the sample size? 2798 out of original cohort of 5888 adults
 Is the statistical power of the study identified? Yes
 Out of the projected study sample, how many persons participated? Unknown from original study.
Potential for Bias  Could there have been bias in the selection of subjects?
 What type of bias would this be?
 In which direction would this bias affect the measure of association?
 Could there have been bias in the collection of information?
 What type of bias would this be?
 In which direction would this bias affect the measure of association?
Potential for Confounding  What factors were potentially confounding the study relationship?
 What methods did the authors use to minimize the influence of confounding when planning the study?
 E.g. restriction, matching, randomization, etc.
 Is there still residual confounding?
 What factors were potentially confounding the study relationship?
 What methods did the authors use to minimize the influence of confounding when planning the study?
 E.g. restriction, matching, randomization, etc.
 Is there still residual confounding? likely
What to Examine When Evaluating Data Analysis  Confounding
 Measures of Association
 Measures of Statistical Stability
Data Analysis  Confounding  What methods were used to control confounding?
 Standardization: Indirect and Direct. Usually used to control for differences in age distribution among populations.
 Stratification: Allows you to examine data more closely. However, it is difficult to control for more than 1 confounder.
 Matching: Done in CaseControl Studies.
 Multivariate Analysis: Linear Regression, Logistic Regression, Poisson Regression, Cox Proportional Hazards model. Allows you to control for multiple confounders simultaneously.
Data Analysis – Measures of Association  What Measures of Association were reported in the study? Was the correct measure used?
 Cohort Study: Relative Risk (RR), Odds Ratio (OR), Hazard Ratio (HR), Incidence Rate Ratio (IRR.
 CaseControl: Exposure or Disease OR (if nested). Can not use RR. However, the OR is a good estimate of the RR when the prevalence of the disease in the study population is very low.
 Crosssectional Study: Prevalence Ratio.
 Ecologic Study: Correlation coefficient.

Data Analysis – Statistical Stability  How was the potential for random error accounted for in the study?
 Hypothesis Testing: Can use pvalues or confidence Intervals (CI) to test the null hypothesis.
 Pvalue: The probability of observing the study results given that the null hypothesis is true. P<0.05 is a standard value that investigators use to reject the null hypothesis of no association and declare that there is a significant relationship between 2 variables.
Data Analysis – Statistical Stability  95% CI: This measure can be used for hypothesis testing and interval estimation. Can be defined as, if one will repeat the study 100 times the true association will lie inside the interval 95% of the time.
 We fail to reject the null hypothesis when a confidence interval contains the null value of 1 between its lower and upper limits for relative measures.
Data Analysis – Statistical Stability  Large confidence intervals indicate that the standard error is high. A high standard error is often related to a small sample size. Underpowered studies normally have wider confidence intervals and thus difficulty in rejecting the null hypothesis.
 The problem, therein, lies that it is difficult to know if the nonassociation is real or false.
What to Examine When Interpreting the Results of the Study  Major findings of the research
 Influence (on the results) of:
 Bias and confounding
 misclassification
Major Findings  The first paragraph of the discussion section in a manuscript should summarize the main findings of the study.
 Example: Sedentary individuals in this study have 3 (95% CI:1.54.9) times the risk of developing a Myocardial (MI) compared to active individuals after controlling for potential confounders.
 Reader should be able to recognize information bias, selection bias, or confounding in the study and assess their magnitude and direction in the study.
 Bias or confounding that is large in magnitude signals that the findings in this sample may not approximate what you would expect to see in the population.
Misclassification  Misclassification of the exposure or the outcome (or both) can influence study results
 NonDifferential: Misclassification is similar in the exposure or outcome groups. This would bias the results to the null making it unlikely for investigators to reject the null hypothesis.
 Differential: Misclassification occurs at a different rate in exposure or outcome groups.
 Example of differential misclassification, a larger number of individuals are classified as high stress instead of medium stress than individuals classified as medium stress instead of high stress. This type of misclassification can bias results away or towards the null hypothesis.
Formulating an Overall Impression of the Manuscript  What are the strengths and limitations of the report?
 How do these balance?
 Can the results be generalized to the whole population?
Strengths and Limitations  Examine the overall issues related to data collection, data analysis, and data interpretation.
 What conclusions do you draw from the results based upon your interpretation of the strengths and limitations of the study?
 Do the strengths outweigh the limitations?
 They are often mentioned in the discussion section of a manuscript.
Generalizability  Goal is to have a study where the results can be used to infer what is going on in the population
 Major problems with the internal validity of the study make it difficult to for the results to be generalized to any population.
 Example, the study population excluded a certain groups, minorities, women, blacks, or low income individuals. The results would not be generalizable to these groups.
Conclusions and Justification  The conclusions are a brief summary of the findings.
 Authors tend to include recommendations for future studies or policy.
 It is essential that the recommendations do not stray far from the study findings. Recommendations should be made in the context of the findings or the readers may be deceived and make incorrect conclusions about the actual results of the study.
The Big Picture of Research Findings  Publication bias
 John P.A. Ioannidis
 Why Most Published Research Findings are False. PLoS Medicine 2(8):e124, 2005.
Publication bias  Definition:
 “Publication bias refers to the greater likelihood that studies with positive results will be published”
 JAMA 2002;287:28252828
Publication bias may ….  Distort the scientific record
 Hide the “truth” of association/no association
 Influence doctors’ decision making
 Mislead policy makers
 Etc.
Ioannidis Corollaries  The smaller the studies conducted, the less likely the research findings are to be true
 The smaller the effect size, the less likely the research findings are to be true
 The greater the financial interest and prejudice, the less likely the research findings are to be true
 The hotter a topic interest, the less likely the research findings are to be true
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