Methodological quality of test accuracy studies included in systematic reviews in obstetrics and gynaecology: sources of bias.

BACKGROUNDObstetrics and gynaecology have seen rapid growth in the development of new tests with research on these tests presented as diagnostic accuracy studies. To avoid errors in judgement it is important that the methodology of these studies is such that bias is minimised. Our objective was to determine the methodological quality of test accuracy studies in obstetrics and gynaecology using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) checklist and to assess sources of bias.METHODSA prospective protocol was developed to assess the impact of QUADAS on ten systematic reviews performed over the period 2004-2007.We investigated whether there was an improvement in study quality since the introduction of QUADAS, whether a correlation existed between study sample size, country of origin of study and its quality. We also investigated whether there was a correlation between reporting and methodological quality and by the use of meta-regression analyses explored for items of quality that were associated with bias.RESULTSA total of 300 studies were included. The overall quality of included studies was poor (> 50% compliance with 57.1% of quality items). However, the mean compliance with QUADAS showed an improvement post-publication of QUADAS (54.9% versus 61.4% p = 0.002). There was no correlation with study sample size. Gynaecology studies published from the United States of America showed higher quality (USA versus Western Europe p = 0.002; USA versus Asia p = 0.004). Meta-regression analysis showed that no individual quality item had a significant impact on accuracy. There was an association between reporting and methodological quality (r = 0.51 p <0.0001 for obstetrics and r = 0.56 p <0.0001 for gynaecology).CONCLUSIONSA combination of poor methodological quality and poor reporting affects the inferences that can be drawn from test accuracy studies. Further compliance with quality checklists is required to ensure that bias is minimised.