Warning: Undefined variable $zfal in C:\Inetpub\vhosts\kidney.de\httpdocs\mlpefetch.php on line 525
Deprecated: str_replace(): Passing null to parameter #3 ($subject) of type array|string is deprecated in C:\Inetpub\vhosts\kidney.de\httpdocs\mlpefetch.php on line 525
Warning: Undefined variable $sterm in C:\Inetpub\vhosts\kidney.de\httpdocs\mlpefetch.php on line 530
 free
Warning: Undefined variable $sterm in C:\Inetpub\vhosts\kidney.de\httpdocs\mlpefetch.php on line 531
 free
 free
 
English Wikipedia
Nephropedia Template TP (
Twit Text
DeepDyve
Pubget Overpricing |  
lüll
Quantifying, displaying and accounting for heterogeneity in the meta-analysis of RCTs using standard and generalised Q statistics Bowden J; Tierney JF; Copas AJ; Burdett SBMC Med Res Methodol 2011[Apr]; 11 (ä): 41BACKGROUND: Clinical researchers have often preferred to use a fixed effects model for the primary interpretation of a meta-analysis. Heterogeneity is usually assessed via the well known Q and I2 statistics, along with the random effects estimate they imply. In recent years, alternative methods for quantifying heterogeneity have been proposed, that are based on a 'generalised' Q statistic. METHODS: We review 18 IPD meta-analyses of RCTs into treatments for cancer, in order to quantify the amount of heterogeneity present and also to discuss practical methods for explaining heterogeneity. RESULTS: Differing results were obtained when the standard Q and I2 statistics were used to test for the presence of heterogeneity. The two meta-analyses with the largest amount of heterogeneity were investigated further, and on inspection the straightforward application of a random effects model was not deemed appropriate. Compared to the standard Q statistic, the generalised Q statistic provided a more accurate platform for estimating the amount of heterogeneity in the 18 meta-analyses. CONCLUSIONS: Explaining heterogeneity via the pre-specification of trial subgroups, graphical diagnostic tools and sensitivity analyses produced a more desirable outcome than an automatic application of the random effects model. Generalised Q statistic methods for quantifying and adjusting for heterogeneity should be incorporated as standard into statistical software. Software is provided to help achieve this aim.|*Data Interpretation, Statistical[MESH]|*Meta-Analysis as Topic[MESH]|*Randomized Controlled Trials as Topic[MESH]|Analysis of Variance[MESH]|Clinical Trials as Topic[MESH]|Humans[MESH]|Neoplasms/therapy[MESH]|Statistics as Topic[MESH] |
