BACKGROUND: The increasing prevalence of Alzheimer disease (AD) and lack of effective agents to attenuate progression have accelerated research and development of disease modifying (DM) therapies. The traditional parallel group design and single time point analysis used in the support of past AD drug approvals address symptomatic benefit over relatively short treatment durations. More recent trials investigating disease modification are by necessity longer in duration and require larger sample sizes. Nevertheless, trial design and analysis remain mostly unchanged and may not be adequate to meet the objective of demonstrating disease modification. Randomized start design (RSD) has been proposed as an option to study DM effects, but its application in AD trials may have been hampered by certain methodological challenges.
PURPOSE: To address the methodological issues that have impeded more extensive use of RSD in AD trial and to encourage other researchers to develop novel design and analysis methodologies to better ascertain DM effects for the next generation of AD therapies, we propose a stepwise testing procedure to evaluate potential DM effects of novel AD therapies.
METHODS: Alzheimer Disease Assessment Scale-Cognitive Subscale (ADAS-cog) is used for illustration. We propose to test three hypotheses in a stepwise sequence. The three tests pertain to treatment difference at two separate time points and a difference in the rate of change. Estimation is facilitated by the Mixed-effects Model for Repeated Measures approach. The required sample size is estimated using Monte Carlo simulations and by modeling ADAS-cog data from prior longitudinal AD studies.
RESULTS: The greatest advantage of the RSD proposed in this article is its ability to critically address the question on a DM effect. The AD trial using the new approach would be longer (12-month placebo period plus 12-month delay-start period; total 24-month duration) and require more subjects (about 1000 subjects per arm for the non-inferiority margin chosen in the illustration). It would also require additional evaluations to estimate the rate of ADAS-cog change toward the end of the trial.
LIMITATIONS: A regulatory claim of disease modification for any compound will likely require additional verification of a drug's effect on a validated biomarker of Alzheimer's pathology.
CONCLUSIONS: Incorporation of the RSD in AD trials is feasible. With proper trial setup and statistical procedures, this design could support the detection of a disease-modifying effect. In our opinion, a two-phase RSD with a stepwise hypothesis testing procedure could be a reasonable option for future studies.

In central nervous system therapeutic areas, there are general concerns with establishing efficacy thought to be sources of high attrition rate in drug development. For instance, efficacy endpoints are often subjective and highly variable. There is a lack of robust or operational biomarkers to substitute for soft endpoints. In addition, animal models are generally poor, unreliable or unpredictive. To increase the probability of success in central nervous system drug development program, adaptive design has been considered as an alternative designs that provides flexibility to the conventional fixed designs and has been viewed to have the potential to improve the efficiency in drug development processes. In addition, successful implementation of an adaptive design trial relies on establishment of a trustworthy logistics model that ensures integrity of the trial conduct.

In accordance with the spirit of the U.S. Food and Drug Administration adaptive design draft guidance document recently released, this paper enlists the critical considerations from both methodological aspects and regulatory aspects in reviewing an adaptive design proposal and discusses two general types of adaptations, sample size planning and re-estimation, and two-stage adaptive design. Literature examples of adaptive designs in central nervous system are used to highlight the principles laid out in the U.S. FDA draft guidance. Four logistics models seen in regulatory adaptive design applications are introduced. In general, complex adaptive designs require simulation studies to access the design performance. For an adequate and well-controlled clinical trial, if a Learn-and-Confirm adaptive selection approach is considered, the study-wise type I error rate should be adhered to. However, it is controversial to use the simulated type I error rate to address a strong control of the study-wise type I error rate.

Adaptive clinical trial designs offer significant opportunities to optimize the conduct of clinical trials for the benefit of the subjects in the trial, the subjects that may be treated after the trial and the trial sponsor. However currently, the use of adaptive designs is limited, due to statistical, regulatory and logistical concerns. In this article we share our experience of overcoming the last of these over a range of phase 2, response adaptive, dose finding studies. Based on our experience we feel quite strongly that the logistics of executing adaptive trials should not be a barrier to their use.

A number of pharmacological agents for treating negative symptoms in schizophrenia are currently in development. Unresolved questions regarding the design of clinical trials in this area were discussed at an international meeting in Florence, Italy in April 2012. Participants included representatives from academia, the pharmaceutical industry, and the European Medicines Agency (EMA). Prior to the meeting, participants submitted key questions for debate and discussion. Responses to the questions guided the discussion during the meeting. The group reached agreement on a number of issues: (1) study subjects should be under the age of 65; (2) subjects should be excluded for symptoms of depression that do not overlap with negative symptoms; (3) functional measures should not be required as a co-primary in negative symptom trials; (4) information from informants should be included for ratings when available; (5) Phase 2 negative symptom trials should be 12 weeks and 26 weeks is preferred for Phase 3 trials; (6) prior to entry into a negative symptom study, subjects should demonstrate clinical stability for a period of 4 to 6 months by collection of retrospective information; and (7) prior to entry, the stability of negative and positive symptoms should be confirmed prospectively for four weeks or longer. The participants could not reach agreement on whether predominant or prominent negative symptoms should be required for study subjects.

Attrition is a ubiquitous problem in randomized controlled clinical trials (RCT) of psychotropic agents that can cause biased estimates of the treatment effect, reduce statistical power, and restrict the generalizability of results. The extent of the problem of attrition in central nervous system (CNS) trials is considered here and its consequences are examined. The taxonomy of missingness mechanisms is then briefly reviewed in order to introduce issues underlying the choice of data analytic strategies appropriate for RCTs with various forms of incomplete data. The convention of using last observation carried forward to accommodate attrition is discouraged because its assumptions are typically inappropriate for CNS RCTs, whereas multiple imputation strategies are more appropriate. Mixed-effects models often provide a useful data analytic strategy for attrition as do the pattern-mixture and propensity adjustments. Finally, investigators are encouraged to consider asking participants, at each assessment session, the likelihood of attendance at the subsequent assessment session. This information can be used to eliminate some of the very obstacles that lead to attrition, and can be incorporated in data analyses to reduce bias, but it will not eliminate all attrition bias.

BACKGROUND: Attrition, which is virtually ubiquitous in randomized controlled clinical trials, introduces problems of increased bias and reduced statistical power. Although likelihood-based statistical models such as mixed-effects models can accommodate incomplete data, the assumption of ignorable attrition is usually required for valid inferences.
PURPOSE: In an effort to make the ignorability assumption more plausible, we consider the value of one readily obtained covariate that has been recommended by others, asking participants to rate their Intent to Attend the next assessment session.
METHODS: Here we present a simulation study that compares the bias and coverage in mixed-effects outcome analyses that do and do not include Intent to Attend as a covariate.
RESULTS: For the simulation specifications that we examined, the results are promising in the sense of reduced bias and greater precision. Specifically, if the time-varying Intent to Attend variable is associated with attrition, outcome and treatment group, bias is substantially reduced by including it in the outcome analyses.
LIMITATIONS: Analyses that are adjusted in this way will only yield unbiased estimates of efficacy if attrition is ignorable based on the self-rated intentions.
CONCLUSIONS: Accounting for participants' Intent to Attend the next assessment session will reduce attrition bias under conditions examined here. The item adds little burden and can be used both for data analyses and to identify participants at risk of attrition.