Using the results of a meta-analysis

Most meta-analyses will initially give you a table of included studies.  In our study, that table is published on the Web only.  We see that most studies used doxycycline, sulfa/trimethoprim, or erythromycin.  This is a possible limitation - maybe the pharmaceutical reps are right, and Biaxin or Zithromax are really better, although I doubt it!

Let's look at the results now.  The three key figures are reproduced below.  In Figure 2, the first column has the study name;  the second column the proportion receiving antibiotic with productive cough at follow-up;   the third column the proportion receiving placebo with productive cough at follow-up.  Let's stop and think here.  If antibiotics work, patients receiving them should be less likely to have cough at follow-up.  That would make the relative risk of cough less than 1.0.  In the light blue "relative risk diagram" below, the vertical line indicates a relative risk of 1.0.  The horizontal dots and bars represent the relative risk and 95% confidence interval for each study.  The weight is proportional to the inverse of the variance of the study - high variance, associated with a small study, means less weight given to that study, and vice versa.   The relative risk is shown numberically in the final column of each table.   Finally, the summary estimate of relative risk with its confidence interval is shown in the last row, along with an estimate of homogeneity (the chi-square). 

Figure 2.  Productive cough at 7 to 11 days follow-up.

Looking at this table, you see that the summary estimate of relative risk just barely includes one, suggesting that the association is NOT statistically significant.   Look at the next figure below:

Figure 3.  Failure to clinically improve at 7 to 11 days follow-up.

In this case, the confidence interval of the summary relative risk also includes 1.0 (it is 0.36 - 1.09).

Figure 4.  Adverse drug effects.

Finally, while there appear to be more adverse drug effects with antibiotic use, this association is also not statistically significant.

So, have we learned nothing?  No.  Even meta-analyses can suffer from Type II error, i.e. too small a sample size.  The total number of patients studied was only about 600 in the 6 studies, and it is interesting to note that while the results were not significant, they were consistent.  That is, 5 out of 6 studies fell either to the left or right of the vertical bar for each outcome.