The impact of missing data on the results of a schizophrenia study

Missing data pose a serious challenge to the integrity of randomized clinical trials, especially of treatments for prolonged illnesses such as schizophrenia, in which long-term impact assessment is of great importance, but the follow-up rates are often no more than 50%. Sensitivity analysis using Bayesian modeling for missing data offers a systematic approach to assessing the sensitivity of the inferences made on the basis of observed data. This paper uses data from an 18-month study of veterans with schizophrenia to demonstrate this approach. Data were obtained from a randomized clinical trial involving 369 patients diagnosed with schizophrenia that compared long-acting injectable risperidone with a psychiatrist's choice of oral treatment. Bayesian analysis utilizing a pattern-mixture modeling approach was used to validate the reported results by detecting bias due to non-random patterns of missing data. The analysis was applied to several outcomes including standard measures of schizophrenia symptoms, quality of life, alcohol use, and global mental status. The original study results for several measures were confirmed against a wide range of patterns of non-random missingness. Robustness of the conclusions was assessed using sensitivity parameters. The missing data in the trial did not likely threaten the validity of previously reported results.