## INTRODUCTION

## HOW TO PERFORM A META-ANALYSIS

## Identification of relevant studies

## Heterogeneity

*I*

^{2}value, have been developed to detect and quantify heterogeneity in meta-analysis. Specifically, Cochran’s Q test is used to determine whether there are differences between primary studies or if the variation seen is due to chance [12]. Cochran’s Q-value is calculated by summing the squared deviations of the estimate of each study from the overall estimate and subsequently comparing it with the chi-square distribution with κ–1 degrees of freedom (df), where κ is the number of studies [12]. However, the Q test may be unreliable when the meta-analysis involves a small number of studies. Therefore, a heterogeneity

*p*< 0.10 (not 0.05) indicates the presence of heterogeneity, since Cochran’s Q test has low statistical strength and is insensitive [13]. Another commonly used method for testing heterogeneity is the

*I*

^{2}value, which quantifies the effect of heterogeneity, and does not depend on the number of studies or the type of outcome data.

*I*

^{2}values range between 0% and 100%, and represent the proportion of inter-study variability that can be attributed to heterogeneity rather than chance [

*I*

^{2}= 100% × (Q – df)/Q] [14].

*I*

^{2}values of 25%, 50%, and 75% are considered low, moderate, and high estimates, respectively [14,15].

## Fixed versus random effects models

*p*≥ 0.10), a fixed effect model is used. However, when the Q-value is significant (

*p*< 0.10), indicating the existence of heterogeneity in the studies, a random effect model should be used for the meta-analysis [19]. When the study groups are homogeneous, both models offer similar results; nonetheless, in the case of heterogeneity, the random effect model typically provides wider confidence intervals (CIs) than the fixed effect one [20].

## Evaluation of the causes of heterogeneity

## Publication bias

## Meta-analysis publication

## STRENGTHS AND WEAKNESSES OF META-ANALYSIS

## EXAMPLE OF A META-ANALYTIC STUDY

*p*= 0.011) (Table 2, Fig. 1). Similarly, the ACR20 response rate was significantly higher in the tofacitinib 10 mg group than in the controls (RR, 2.597; 95% CI, 1.514 to 4.455;

*p*= 0.001) (Table 2, Fig. 1). Significant improvements were observed in the tofacitinib 5 and 10 mg groups compared with the controls for all efficacy outcomes, such as the number of tender and swollen joints, pain, patient and physician global assessments of disease activity, the Health Assessment Questionnaire, and C-reactive protein levels (Table 2). The safety outcomes did not differ between the tofacitinib 5 and 10 mg groups and placebo groups, except for infection in the tofacitinib 10 mg group (RR, 2.133; 95% CI, 1.268 to 3.590;

*p*= 0.004) (Table 2, Fig. 2). The two phase 3 trials confirmed the findings of the meta-analysis of the phase 2 studies. The meta-analysis found that tofacitinib, at dosages 5 or 10 mg twice daily, was effective in active RA and had a manageable safety profile.