MMRM versus MI in dealing with missing data--a comparison based on 25 NDA data sets.

Both multiple imputation (MI) and mixed-effects model repeated measures (MMRM) approaches appear to be better choices than the traditional last-observation-carried-forward (LOCF) approach in analyzing incomplete clinical trial data sets in drug development research. However, relative performances of these two approaches are unknown in controlling type I error rate and statistical power in the hypothesis testing of determining the efficacy of an investigational drug. Little research has been done in comparing robustness of the two approaches in analyzing ignorable missing data of clinical trials. In this research, a comparison between the MI and MMRM approaches is made in analyzing the simulated incomplete data sets and 25 New Drug Application (NDA) data sets of neuropsychiatric drug products. The MMRM approach appears to be a better choice in maintaining statistical properties of a test as compared to the MI approach in dealing with ignorable missing data of clinical trials.

Method of balanced adjustment in testing co-primary endpoints.

In a clinical trial, if there are three or more co-primary endpoints, the type II error could increase depending on the correlation among the endpoints and their treatment effect sizes. To keep the type II error under control one may have to consider larger sample sizes. However, in cases where treatment effect size of at least one of the endpoints is likely to be small, the required sample size estimates can exceed reasonable bounds. Patel (1991) proposed an approach that adjusts the significance level for testing each primary endpoint based on the idea of restricting the null space. In Chuang-Stein et al. (2007), the upward adjustment to the significance levels is based on controlling an average type I error rate. In the scenario that statistical significance of each individual hypothesis is not required, we introduce a compromise testing approach in which the significance level for a co-primary endpoint is adjusted upward only if the treatment shows high significance in one (or more than one) of the remaining co-primary endpoints. The adjustment depends on the correlation among the endpoints: larger adjustment is needed for cases of smaller correlation. The method is applicable for the scenario where the null space is restricted. Our testing approach controls maximum joint false positive rate over the restricted null space.

Statistical methods to analyze adverse events data of randomized clinical trials.

The adverse events data of randomized clinical trials are often analyzed based on either crude incidence rates or exposure-adjusted incidence rates. These rates do not adequately account for an individual patient's profile of adverse events over the study period when an individual may remain in the trial after experiencing one or more events (i.e., occurrence of multiple events of the same kind or different kinds). Moreover, the required statistical assumptions (e.g., constant hazard rate over time) for valid estimates of incidence rates are not likely to be met in practice by adverse events data of clinical trials. A nonparametric approach called the mean cumulative function (MCF) provides a valid statistical inference on recurrent adverse event profiles of drugs in randomized clinical trials. The estimate involves no assumptions about the form of MCF. To demonstrate the applicability and utility of the MCF approach in clinical trial datasets, an adverse event dataset obtained from a clinical trial is analyzed in this article. As compared to the crude or exposure-adjusted incidence rates of adverse events, the MCF estimates facilitate more understanding of safety profiles of a drug in a randomized clinical trial.

MMRM vs. LOCF: a comprehensive comparison based on simulation study and 25 NDA datasets.

In recent years, the use of the last observation carried forward (LOCF) approach in imputing missing data in clinical trials has been greatly criticized, and several likelihood-based modeling approaches are proposed to analyze such incomplete data. One of the proposed likelihood-based methods is the Mixed-Effect Model Repeated Measure (MMRM) model. To compare the performance of LOCF and MMRM approaches in analyzing incomplete data, two extensive simulation studies are conducted, and the empirical bias and Type I error rates associated with estimators and tests of treatment effects under three missing data paradigms are evaluated. The simulation studies demonstrate that LOCF analysis can lead to substantial biases in estimators of treatment effects and can greatly inflate Type I error rates of the statistical tests, whereas MMRM analysis on the available data leads to estimators with comparatively small bias, and controls Type I error rates at a nominal level in the presence of missing completely at random (MCAR) or missing at random (MAR) and some possibility of missing not at random (MNAR) data. In a sensitivity analysis of 48 clinical trial datasets obtained from 25 New Drug Applications (NDA) submissions of neurological and psychiatric drug products, MMRM analysis appears to be a superior approach in controlling Type I error rates and minimizing biases, as compared to LOCF ANCOVA analysis. In the exploratory analyses of the datasets, no clear evidence of the presence of MNAR missingness is found.

Endpoints and analyses to discern disease-modifying drug effects in early Parkinson's disease.

Parkinson's disease is an age-related degenerative disorder of the central nervous system that often impairs the sufferer's motor skills and speech, as well as other functions. Symptoms can include tremor, stiffness, slowness of movement, and impaired balance. An estimated four million people worldwide suffer from the disease, which usually affects people over the age of 60. Presently, there is no precedent for approving any drug as having a modifying effect (i.e., slowing or delaying) for disease progression of Parkinson's disease. Clinical trial designs such as delayed start and withdrawal are being proposed to discern symptomatic and protective effects. The current work focused on understanding the features of delayed start design using prior knowledge from published and data submitted to US Food and Drug Administration (US FDA) as part of drug approval or protocol evaluation. Clinical trial simulations were conducted to evaluate the false-positive rate, power under a new statistical analysis methodology, and various scenarios leading to patient discontinuations from clinical trials. The outcome of this work is part of the ongoing discussion between the US FDA and the pharmaceutical industry on the standards required for demonstrating disease-modifying effect using delayed start design.