Fitzpatrick AL, Kuller LH, Lopez OL, Diehr P, O’Meara ES, Longstreth Jr. WT, Luchsinger JA. Midlife and Late-Life Obesity and the Risk of Dementia. Archives of Neurology 66(3):336-342, 2009.

-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 Case-Control 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.

Case-Control: 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.

Cross-sectional 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 p-values or confidence Intervals (CI) to test the null hypothesis.

P-value: 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 non-association 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:1.5-4.9) 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

Non-Differential: 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.

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.