A multiple imputation method for sensitivity analyses of time-to-event data with possibly informative censoring.

This article presents a multiple imputation method for sensitivity analyses of time-to-event data with possibly informative censoring. The imputed time for censored values is drawn from the failure time distribution conditional on the time of follow-up discontinuation. A variety of specifications regarding the post-discontinuation tendency of having events can be incorporated in the imputation through a hazard ratio parameter for discontinuation versus continuation of follow-up. Multiple-imputed data sets are analyzed with the primary analysis method, and the results are then combined using the methods of Rubin. An illustrative example is provided.

Analysis of longitudinal binary data with missing data due to dropouts.

Longitudinal binary data from clinical trials with missing observations are frequently analyzed by using the Last Observation Carry Forward (LOCF) method for imputing missing values at a visit (e.g., the prospectively defined primary visit time point for analysis at the end of treatment period). Usually, to understand time trend in treatment response, analyses are also performed separately on data at intermediate time points. The objective of such analyses is to estimate the proportion of "response" at a time point and then to compare two treatment groups (e.g., drug vs. placebo) by testing for the difference in the two proportions of response. The commonly used methods are Fisher's exact test, chi-squared test, Cochran-Mantel-Haenszel test, and logistic regression. Analyses based on the Observed Cases (OC) data are usually also performed and compared with those obtained by LOCF. Another approach that is gaining popularity (after the introduction of PROC GENMOD by the SAS Institute) is to use the method of Generalized Estimating Equations (GEE) with a view to include all repeated observations in the analysis in a more comprehensive manner. It is now well recognized, however, that results obtained by these methods are susceptible to bias, depending on the "missing data mechanism." Of particular concern is the bias introduced by NMAR dropouts. Because there is no one method to satisfactorily handle dropouts in data analysis, consensus is gathering toward doing analyses by several methods (including methods to handle NMAR dropouts) to evaluate sensitivity of results to model assumptions. In this article, we demonstrate application of the following methods for handling dropouts in longitudinal binary data: Generalized Linear Mixture Models (GLMM) (for handling NMAR dropouts), Weighted GEE (for handling MAR dropouts), and GEE (MCAR dropouts). The results are also compared with those obtained by logistic regression (univariate) on both LOCF and OC data.

Switching patients to aripiprazole from other antipsychotic agents: a multicenter randomized study.

RATIONALE: Switching patients from one antipsychotic to another can lead to tolerability problems or transient symptom exacerbations. It is important to compare switching strategies to determine which methods produce the best possible patient outcomes. OBJECTIVE: To investigate the efficacy, safety and tolerability of three dosing strategies for switching chronic, stable patients with schizophrenia from current oral antipsychotic monotherapy to once-daily oral aripiprazole monotherapy. METHOD: Patients in this 8-week, open-label, outpatient study were randomized to: 1). immediate initiation of 30 mg/day aripiprazole with simultaneous immediate discontinuation of current antipsychotic; 2). immediate initiation of 30 mg/day aripiprazole while tapering off current antipsychotic over 2 weeks; or 3). up-titrating aripiprazole to 30 mg/day over 2 weeks, while simultaneously tapering off current antipsychotic. Efficacy assessments included PANSS, CGI-S, and CGI-I scores. Safety assessments included: adverse events (AEs) recording, evaluation of extrapyramidal symptoms (EPS), vital signs, ECG, and clinical laboratory tests. RESULTS: Efficacy with aripiprazole was maintained during the study with numerical improvements compared with baseline in all three groups. The overall incidence of AEs was broadly comparable across all groups, and AEs were generally mild to moderate in severity and time-limited. Discontinuations due to AEs were comparable across the groups. No deterioration in EPS occurred in any group. The reduction in body weight and plasma prolactin levels following switch to aripiprazole were comparable across the three groups. CONCLUSION: Any of the three strategies evaluated can be used safely for switching patients to aripiprazole from antipsychotic monotherapy. Furthermore, patients' symptoms may continue to improve after switching to aripiprazole.

Efficacy and safety of aripiprazole and haloperidol versus placebo in patients with schizophrenia and schizoaffective disorder.

BACKGROUND: Aripiprazole is an investigational agent for treating schizophrenia that has a novel pharmacologic profile. The present study investigated the efficacy, safety, and tolerability of aripiprazole and haloperidol compared with placebo. METHOD: A 4-week, double-blind, randomized study, conducted at 36 U.S. centers between July 1997 and June 1998, compared aripiprazole (15 mg/day, 30 mg/day) to placebo, with haloperidol (10 mg/day) as an active control. Fixed doses of each agent were administered from day 1 throughout the study. A total of 414 patients with a primary DSM-IV diagnosis of schizophrenia or schizoaffective disorder were randomized. Efficacy measures included the Positive and Negative Syndrome Scale (PANSS) total, PANSS positive, PANSS negative, PANSS-derived Brief Psychiatric Rating Scale (BPRS) core, Clinical Global Impressions (CGI)-Severity of Illness, and mean CGI-Improvement scores. Safety and tolerability evaluations included extrapyramidal symptoms (EPS), weight gain, serum prolactin level, and QTc interval. RESULTS: Both doses of aripiprazole and haloperidol, 10 mg, produced statistically significant (p < or = .05) improvements from baseline in PANSS total, PANSS positive, PANSS-derived BPRS core, and CGI-Severity scores and significantly lower CGI-Improvement scores at endpoint, compared with placebo. Aripiprazole, 15 mg, and haloperidol, 10 mg, significantly improved PANSS negative score compared with placebo. Both aripiprazole doses and haloperidol separated from placebo for PANSS total scores at week 2. Unlike haloperidol, aripiprazole was not associated with significant EPS or prolactin elevation at endpoint compared with placebo. There were no statistically significant differences in mean changes in body weight across the treatment groups versus placebo, and no patients receiving aripiprazole experienced clinically significant increases in QTc interval. CONCLUSION: Aripiprazole, effective against positive and negative symptoms, is a safe and well-tolerated potential treatment for schizophrenia and schizoaffective disorder.