Clinical Trial Size: Fixed versus Group-Sequential Designs

1986 ◽  
Vol 25 (04) ◽  
pp. 233-236
Author(s):  
M. A. A. Moussa

SummaryThis paper deals with the estimation of the size of clinical trials for comparing two binomial proportions in both fixed and group-sequential designs. In the fixed size approach, it focuses on 1.) equal sample size design; 2.) unequal allocation designs that either maximize the test power subject to fixed total cost or minimize the total expenditure subject to prespecified power using the simplex procedure for function minimization. Emphasis is also placed on group-sequential designs, based upon closed stopping rules, multiple testing and range of clinical equivalence. The efficiency of different allocation designs is assessed by computing the power of the exact conditional Fisher-Irwin test. Designs were applied in planning a clinical trial in which the drug Pancuronium Bromide was compared with a standard treatment to reduce intraventricular haemorrhage in preterm infants.

2020 ◽  
Author(s):  
Pauline Manchon ◽  
Drifa Belhadi ◽  
France Mentré ◽  
Cédric Laouénan

Abstract Background Viral haemorrhagic fevers are characterized by irregular outbreaks with high mortality rate. Difficulties arise when implementing therapeutic trials in this context. The outbreak duration is hard to predict and can be short compared to delays of trial launch and number of subject needed (NSN) recruitment. Our objectives were to compare, using clinical trial simulation, different trial designs for experimental treatment evaluation in various outbreak scenarios. Methods Four type of designs were compared: fixed or group-sequential, each being single- or two-arm. The primary outcome was 14-day survival rate. For single-arm designs, results were compared to a pre-trial historical survival rate pH. Treatments efficacy was evaluated by one-sided tests of proportion (fixed designs) and Whitehead triangular tests (group-sequential designs) with type-I-error = 0.025. Both survival rates in the control arm pC and survival rate differences Δ (including 0) varied. Three specific cases were considered: “standard” (fixed pC, reaching NSN for fixed designs and maximum sample size NMax for group-sequential designs); “changing with time” (increased pC\(\text{ }\)over time); “stopping of recruitment” (epidemic ends). We calculated the proportion of simulated trials showing treatment efficacy, with K = 93,639 simulated trials to get a type-I-error PI95% of [0.024;0.026]. Results Under H0 (Δ = 0), for the “standard” case, the type-I-error was maintained regardless of trial designs. For “changing with time” case, when pC>pH, type-I-error was inflated, and when pC<pH it decreased. Wrong conclusions were more often observed for single-arm designs due to an increase of Δ over time. Under H1 (Δ=+0.2), for the “standard” case, the power was similar between single- and two-arm designs when pC=pH. For “stopping of recruitment” case, single-arm performed better than two-arm designs, and fixed designs reported higher power than group-sequential designs. A web R-Shiny application was developed. Conclusions At an outbreak beginning, group-sequential two-arm trials should be preferred, as the infected cases number increases allowing to conduct a strong randomized control trial. Group-sequential designs allow early termination of trials in cases of harmful experimental treatment. After the epidemic peak, fixed single-arm design should be preferred, as the cases number decreases but this assumes a high level of confidence on the pre-trial historical survival rate.


2013 ◽  
Vol 32 (23) ◽  
pp. 3991-4005 ◽  
Author(s):  
Ping Gao ◽  
Lingyun Liu ◽  
Cyrus Mehta

2018 ◽  
Vol 28 (8) ◽  
pp. 2385-2403 ◽  
Author(s):  
Tobias Mütze ◽  
Ekkehard Glimm ◽  
Heinz Schmidli ◽  
Tim Friede

Robust semiparametric models for recurrent events have received increasing attention in the analysis of clinical trials in a variety of diseases including chronic heart failure. In comparison to parametric recurrent event models, robust semiparametric models are more flexible in that neither the baseline event rate nor the process inducing between-patient heterogeneity needs to be specified in terms of a specific parametric statistical model. However, implementing group sequential designs in the robust semiparametric model is complicated by the fact that the sequence of Wald statistics does not follow asymptotically the canonical joint distribution. In this manuscript, we propose two types of group sequential procedures for a robust semiparametric analysis of recurrent events. The first group sequential procedure is based on the asymptotic covariance of the sequence of Wald statistics and it guarantees asymptotic control of the type I error rate. The second procedure is based on the canonical joint distribution and does not guarantee asymptotic type I error rate control but is easy to implement and corresponds to the well-known standard approach for group sequential designs. Moreover, we describe how to determine the maximum information when planning a clinical trial with a group sequential design and a robust semiparametric analysis of recurrent events. We contrast the operating characteristics of the proposed group sequential procedures in a simulation study motivated by the ongoing phase 3 PARAGON-HF trial (ClinicalTrials.gov identifier: NCT01920711) in more than 4600 patients with chronic heart failure and a preserved ejection fraction. We found that both group sequential procedures have similar operating characteristics and that for some practically relevant scenarios, the group sequential procedure based on the canonical joint distribution has advantages with respect to the control of the type I error rate. The proposed method for calculating the maximum information results in appropriately powered trials for both procedures.


Sign in / Sign up

Export Citation Format

Share Document