Handling Missing Outcome Data in Infertility Clinical Trials

Abstract

Introduction: Missing outcome data is a common issue in clinical trials, particularly in the context of infertility research. Inaccurate handling of missing data can lead to biased results, complicating the interpretation of treatment effects. This study examines current practices for handling missing live birth outcomes in infertility trials and assesses the impact of various methods on trial outcomes. Methods: A two-part approach was employed. First, a review of 16 infertility trials was conducted to analyse the strategies used for managing missing live birth outcomes. Second, a secondary analysis of an RCT dataset was performed, applying multiple methods for handling missing data, including conservative, optimistic, complete-case analysis, and multiple imputation approaches. Main outcome measure: The primary outcome measured was the live birth rate, analysed under different missing data handling scenarios to assess the impact on treatment effect estimates. Results: The review revealed a median missing outcome rate of 0.55% across the trials, with most studies adopting a conservative approach by treating missing data as failures. The secondary analysis demonstrated that the method of handling missing data could significantly influence the reported treatment effects, particularly in extreme cases. While the best-case scenario indicated a significant treatment effect, other methods, including multiple imputation, showed no significant difference between treatment and control groups. Conclusion: Effective management of missing data is vital for ensuring the validity of infertility trial outcomes. While the need for careful handling increases with the proportion of missing data, even small amounts should not be overlooked due to their potential to introduce bias. Standardized guidelines and sensitivity analyses are recommended to improve the reliability and generalizability of findings in infertility research.