{Reference Type}: Journal Article
{Title}: Methodological survey of missing outcome data in an alteplase for ischemic stroke meta-analysis.
{Author}: Garg R;Garg R;
{Journal}: Acta Neurol Scand
{Volume}: 146
{Issue}: 3
{Year}: Sep 2022
{Factor}: 3.915
{DOI}: 10.1111/ane.13656
{Abstract}: <B>OBJECTIVE: </B>Recent national guidelines recommend alteplase treatment for ischemic stroke within 4.5 h of symptom-onset based on meta-analyses of randomized controlled clinical trials (RCT). A detailed description of missing outcome data (MOD) due to participant loss to follow-up has never been published. The objective of this study was to perform a methodlogical survey on missing outcome data in an alteplase for ischemic stroke meta-analysis.<BR><B>METHODS: </B>A methodological survey was performed on a chosen meta-analysis of alteplase for ischemic stroke RCTs that most closely aligns with recent national guideline recommendations. Data were collected to assess the number of participants lost to follow-up; differential lost to follow-up between allocation groups; baseline characteristics of those lost to follow-up; and the imputation methods used by individual trials and the chosen meta-analysis. The number of participants lost to follow-up was compared with the fragility index; and repeated for individually positive RCTs in the meta-analysis.<BR><B>RESULTS: </B>The methodological survey revealed a substantial degree of missing information regarding MOD in the chosen meta-analysis and in individual RCTs. Single imputation was exclusively used in all RCTs and in the meta-analysis. The number of participants lost to follow-up was greater than the fragility index in the chosen meta-analysis and individually positive component RCTs suggesting that MOD may impact the direction of the reported effect or effect size.<BR><B>CONCLUSIONS: </B>This methodological survey of an alteplase for ischemic stroke meta-analysis revealed MOD may be an important source of unrecognized bias. This survey highlights the need for sensitivity analyses using more robust methods of imputation.