Blinded Sample Size Re-estimation Research Articles

Blinded sample size re-estimation in 2 × 2 crossover trial

Blinded sample size re-estimation is often considered to have realistic estimate of the required sample size, during the mid-course of a trial, without compromising the trial's integrity, to save a trial from being under or over-powered. We consider blinded sample size re-estimation, in the context of 2 × 2 crossover trial, following permuted block randomization. The underlying likelihood function suffers from identifiability problems. Blinded re-estimation procedures are prescribed under this setting and it is shown that the associated risk of unblinding may not always be negligible.

A randomized, double-blind, placebo-controlled phase 3 study to assess efficacy and safety of ropeginterferon alfa-2b in patients with early/lower-risk primary myelofibrosis

Primary myelofibrosis (PMF) is the most aggressive of the myeloproliferative neoplasms and patients require greater attention and likely require earlier therapeutic intervention. Currently approved treatment options are limited in their selective suppression of clonal proliferation resulting from driver- and coexisting gene mutations. Janus kinase inhibitors are approved for symptomatic patients with higher-risk PMF. Additionally, most ongoing clinical studies focus on patients with higher-risk disease and/or high rates of transfusion dependency. Optimal treatment of early/lower-risk PMF remains to be identified and needs randomized clinical trial evaluations. Pegylated interferon alfa is recommended for symptomatic lower-risk PMF patients based on phase 2 non-randomized studies and expert opinion. Ropeginterferon alfa-2b (ropeg) is a new-generation pegylated interferon-based therapy with favorable pharmacokinetics and safety profiles, requiring less frequent injections than prior formulations. This randomized, double-blind, placebo-controlled phase 3 trial will assess its efficacy and safety in patients with “early/lower-risk PMF”, defined as pre-fibrotic PMF or PMF at low or intermediate-1 risk according to Dynamic International Prognostic Scoring System-plus. Co-primary endpoints include clinically relevant complete hematologic response and symptom endpoint. Secondary endpoints include progression- or event-free survival, molecular response in driver or relevant coexisting gene mutations, bone marrow response, and safety. Disease progression and events are defined based on the International Working Group criteria and well-published reports. 150 eligible patients will be randomized in a 2:1 ratio to receive either ropeg or placebo. Blinded sample size re-estimation is designed. Ropeg will be administered subcutaneously with a tolerable, higher starting-dose regimen. The study will provide important data for the treatment of early/lower-risk PMF for which an anti-clonal, disease-modifying agent is highly needed.

Denosumab biosimilar (LY01011) in the treatment of bone metastases in patients with solid tumors: A multicenter, randomized, double-blind, active comparator-controlled phase 3 study.

12126 Background: The study was to evaluate the equivalence of LY01011 to the reference product denosumab in terms of reduction of bone metabolism markers. Methods: Eligible patients were randomized at 1:1 ratio to receive 120 mg LY01011 injection or 120 mg denosumab every four weeks subcutaneously. Following the completion of three doses of 120 mg LY01011 or denosumab in a 12-week double-blind treatment period (DBTP), all enrolled patients would continue to receive LY01011 administration until the week 53 of follow-up period. The primary end point was the natural logarithm change at week 13 from baseline in urinary cross-linked N terminal telopeptide of type I collagen, corrected for urine creatinine (uNTx/uCr). The equivalence between LY01011 and denosumab would be concluded if the 90% CI was completely inside the pre-defined equivalence boundary between -0.135 and 0.135. A formal blinded sample-size re-estimation (BSSR) was planned to take place once 60% of the planned participants had completed the Week 13 assessments. After BSSR, the sample size increased from 560 to 850 according to prespecified procedure based on pooled standard deviationfrom the blinded data, which was set as a cap to ensure a feasible sample size for the study and no need to do any multiplicity adjustment. Results: All eligible 850 patients, included in the full analysis set, were randomized into the LY01011 group (n = 424) and denosumab group (n = 426). The samples from the 12-week DBTP were collected for analysis in both groups. The least square mean (LSM) (SE) of the natural logarithm change of uNTX/uCr at week 13 from baseline was -1.740 (0.0420) in the LY01011 group and -1.745 (0.0421) in denosumab group. The LSM difference was 0.005 between LY01011 and denosumab groups with 90% CI of [-0.088, 0.097] (p > 0.05). Skeletal related events occurring in the LY01011 and denosumab groups were 3.5% and 2.8%, respectively. The mean (SD) percentage changes of serum bone-specific alkaline phosphatase (s-BALP) from baseline to week 13 were -31.154% (39.6790) and -33.021% (37.3826) in the LY01011 and denosumab groups, respectively. The median was -36.866% and -37.457% (p > 0.05). The incidence of treatment-emergent adverse events (TEAEs) of any grade was 91.7% and 90.8% in LY01011 and denosumab groups(p > 0.05). Both groups had 38.4% of Grade ≥3 TEAEs, of which 5.4% (LY01011) and 4.5% (denosumab) were judged to be related to the study drug. Immunogenicity analysis showed that no participant (0/399) had a positive ADA test in denosumab group and only one participant (0.2%, 1/401) was tested ADA positive in LY01011 group. Conclusions: The study demonstrated the equivalent efficacy of LY01011 in the reduction of bone metabolism biomarker NTX to reference product, denosumab, which met the primary endpoint. It had a good safety profile with no unexpected adverse reactions in the study. Clinical trial information: NCT04859569 .

Multicenter Randomized Controlled Trial of Surveillance Versus Endoscopic Therapy for Barrett’s Esophagus With Low-grade Dysplasia: The SURVENT Trial: Study Rationale, Methodology, Innovation, and Implications

Multicenter Randomized Controlled Trial of Surveillance Versus Endoscopic Therapy for Barrett’s Esophagus With Low-grade Dysplasia: The SURVENT Trial: Study Rationale, Methodology, Innovation, and Implications

Blinded sample size re-estimation in a comparative diagnostic accuracy study

BackgroundThe sample size calculation in a confirmatory diagnostic accuracy study is performed for co-primary endpoints because sensitivity and specificity are considered simultaneously. The initial sample size calculation in an unpaired and paired diagnostic study is based on assumptions about, among others, the prevalence of the disease and, in the paired design, the proportion of discordant test results between the experimental and the comparator test. The choice of the power for the individual endpoints impacts the sample size and overall power. Uncertain assumptions about the nuisance parameters can additionally affect the sample size.MethodsWe develop an optimal sample size calculation considering co-primary endpoints to avoid an overpowered study in the unpaired and paired design. To adjust assumptions about the nuisance parameters during the study period, we introduce a blinded adaptive design for sample size re-estimation for the unpaired and the paired study design. A simulation study compares the adaptive design to the fixed design. For the paired design, the new approach is compared to an existing approach using an example study.ResultsDue to blinding, the adaptive design does not inflate type I error rates. The adaptive design reaches the target power and re-estimates nuisance parameters without any relevant bias. Compared to the existing approach, the proposed methods lead to a smaller sample size.ConclusionsWe recommend the application of the optimal sample size calculation and a blinded adaptive design in a confirmatory diagnostic accuracy study. They compensate inefficiencies of the sample size calculation and support to reach the study aim.

Type I Error Inflation of Blinded Sample Size Re-Estimation in Equivalence Testing

Biosimilar products are a relative newcomer in the pharmaceutical industry. It was only in 2006 that the first biosimilar product, Omnitrope[textregistered] (somatropin), a human growth hormone produced by Sandoz was approved by the European Medicine Agency, followed later the same year by the US Food & Drug Administration, and in 2009 by Pharmaceuticals and Medical Devices Agency in Japan. The clinical component of a biosimilar development borrows extensively from traditional drug development. However, when it comes to clinical study designs, this may not always be applicable. This article investigates Type I error violations that occur when blinded sample size reviews are applied in equivalence testing as used in biosimilar drug development. We give a derivation which explains why such violations are more pronounced in equivalence testing than in the case of superiority testing. In addition, the amount of Type I error inflation is quantified by simulation as well as by some theoretical considerations. Nonnegligible Type I error violations arise when blinded interim reassessments of sample sizes are performed particularly if sample sizes are small, but within the range of what is practically relevant.

Blinded Sample Size Re-Estimation in Comparative Clinical Trials With Overdispersed Count Data: Incorporation of Misspecification of the Variance Function

In randomized clinical trials to compare overdispersed count data between treatment groups, blinded sample size re-estimation (BSSR) is an effective approach to ensure power control even under possible misspecifications of the variance function on overdispersion specified at the design stage. Based on interim clinical trial data, the existing BSSR methods try to find a more appropriate value of the dispersion parameter for a “fixed” variance function specified at the design stage. In this article, we develop a new BSSR method that allows for modification of the variance function itself. Simulation studies demonstrated a stable power control of the proposed method. An application to a phase III clinical trial in chronic obstructive pulmonary disease also demonstrated effectiveness of the proposed method.

Blinded continuous information monitoring of recurrent event endpoints with time trends in clinical trials.

Blinded sample size re-estimation and information monitoring based on blinded data has been suggested to mitigate risks due to planning uncertainties regarding nuisance parameters. Motivated by a randomized controlled trial in pediatric multiple sclerosis (MS), a continuous monitoring procedure for overdispersed count data was proposed recently. However, this procedure assumed constant event rates, an assumption often not met in practice. Here we extend the procedure to accommodate time trends in the event rates considering two blinded approaches: (a) the mixture approach modeling the number of events by a mixture of two negative binomial distributions and (b) the lumping approach approximating the marginal distribution of the event counts by a negative binomial distribution. Through simulations the operating characteristics of the proposed procedures are investigated under decreasing event rates. We find that the type I error rate is not inflated relevantly by either of the monitoring procedures, with the exception of strong time dependencies where the procedure assuming constant rates exhibits some inflation. Furthermore, the procedure accommodating time trends has generally favorable power properties compared with the procedure based on constant rates which stops often too late. The proposed method is illustrated by the clinical trial in pediatric MS.

Non-specific effects of rabies vaccine on the incidence of common infectious disease episodes: study protocol for a randomized controlled trial

BackgroundVaccines may cause non-specific effects (NSEs) on morbidity and mortality through immune-mediated mechanisms that are not explained by the prevention of the targeted disease. Much of the evidence for NSEs comes from observational studies with a high risk of bias, and there is a clear need for new data from randomized controlled trials. Recently, it was proposed that rabies vaccine has protective NSEs in people and in animals. The aim of the proposed study is to determine whether rabies vaccine reduces the incidence rate of episodes of common infectious disease syndromes in a population of veterinary students on the island of St. Kitts.MethodsThe trial design is a single-site, two-arm, parallel-group, participant-blinded, randomized, placebo-controlled, two-sided comparative study, with an internal pilot study for blinded sample size re-estimation. Allocation to study arm is by block randomization stratified by sex within cohort with a 1:1 allocation ratio. The primary study outcome is the number of new weekly episodes of common infectious diseases including respiratory, diarrheal and febrile illnesses. A vaccine immunogenicity ancillary study is planned.DiscussionDemonstration of a non-specific protective effect of rabies vaccine against unrelated respiratory, gastrointestinal and febrile illnesses would provide supportive evidence for the design of similar studies in children in populations with a high burden of these illnesses.Trial registrationClinicalTrials.gov, ID: NCT03656198. Registered on 24 August 2018.

Blinded sample size reestimation for negative binomial regression with baseline adjustment.

In randomized clinical trials, it is standard to include baseline variables in the primary analysis as covariates, as it is recommended by international guidelines. For the study design to be consistent with the analysis, these variables should also be taken into account when calculating the sample size to appropriately power the trial. Because assumptions made in the sample size calculation are always subject to some degree of uncertainty, a blinded sample size reestimation (BSSR) is recommended to adjust the sample size when necessary. In this article, we introduce a BSSR approach for count data outcomes with baseline covariates. Count outcomes are common in clinical trials and examples include the number of exacerbations in asthma and chronic obstructive pulmonary disease, relapses, and scan lesions in multiple sclerosis and seizures in epilepsy. The introduced methods are based on Wald and likelihood ratio test statistics. The approaches are illustrated by a clinical trial in epilepsy. The BSSR procedures proposed are compared in a Monte Carlo simulation study and shown to yield power values close to the target while not inflating the type I error rate.

Distribution theory following blinded and unblinded sample size re-estimation under parametric models

Asymptotic distribution theory for maximum likelihood estimators under fixed alternative hypotheses is reported in the literature even though the power of any realistic test converges to one under fixed alternatives. Under fixed alternatives, authors have established that nuisance parameter estimates are inconsistent when sample size re-estimation (SSR) follows blinded randomization. These results have helped to inhibit the use of SSR. In this paper, we argue for local alternatives to be used instead of fixed alternatives. We treat unavailable treatment assignments in blinded experiments as missing data and rely on single imputation from marginal distributions to fill in for missing data. With local alternatives, it is sufficient to proceed only with the first step of the EM algorithm mimicking imputation under the null hypothesis. Then, we show that blinded and unblinded estimates of the nuisance parameter are consistent, and re-estimated sample sizes converge to their locally asymptotically optimal values. This theoretical finding is confirmed through Monte-Carlo simulation studies. Practical utility is illustrated through a multiple logistic regression example. We conclude that, for hypothesis testing with a predetermined minimally clinically relevant local effect size, both blinded and unblinded SSR procedures lead to similar sample sizes and power.

Follow up after sample size re-estimation in a breast cancer randomized trial for disease-free survival

BackgroundWhile the clinical trials and statistical methodology literature on sample size re-estimation (SSRE) is robust, evaluation of SSRE procedures following the completion of a clinical trial has been sparsely reported. In blinded sample size re-estimation, only nuisance parameters are re-estimated, and the blinding of the current trial treatment effect is preserved. Blinded re-estimation procedures are well-accepted by regulatory agencies and funders. We review our experience of sample size re-estimation in a large international, National Institutes of Health funded clinical trial for adjuvant breast cancer treatment, and evaluate our blinded sample size re-estimation procedure for this time-to-event trial. We evaluated the SSRE procedure by examining assumptions made during the re-estimation process, estimates resulting from re-estimation, and the impact on final trial results with and without the addition of participants, following sample size re-estimation.MethodsWe compared the control group failure probabilities estimated at the time of SSRE to estimates used in the original planning, to the final un-blinded control group failure probability estimates for those included in the SSRE procedure (SSRE cohort), and to the final total control group failure probability estimates. The impact of re-estimation on the final comparison between randomized treatment groups is evaluated for those in the originally planned cohort (n = 340) and for the combination of those recruited in the originally planned cohort and those added after re-estimation (n = 509).ResultsVery little difference is observed between the originally planned cohort and all randomized patients in the control group failure probabilities over time or in the overall hazard ratio estimating treatment effect (originally planned cohort HR 1.25 (0.86, 1.79); all randomized cohort HR 1.24 95% CI (0.91, 1.68)). At the time of blinded SSRE, the estimated control group failure probabilities at 3 years (0.24) and 5 years (0.40) were similar to those for the SSRE cohort once un-blinded (3 years, 0.22 (0.16, 0.30); 5 years, 0.33 (0.26, 0.41)).ConclusionsWe found that our re-estimation procedure performed reasonably well in estimating the control group failure probabilities at the time of re-estimation. Particularly for time-to-event outcomes, pre-planned blinded SSRE procedures may be the best option to aid in maintaining power.Trial registrationClinicalTrials.gov, NCT00201851. Registered on 9 September 2005. Retrospectively registered.

Blinded sample size reestimation in event-driven clinical trials: Methods and an application in multiple sclerosis.

Motivated by a recently completed trial in secondary progressive multiple sclerosis, we developed blinded sample size reestimation procedures for clinical trials with time‐to‐event endpoint and assessed their properties in simulation studies. Assuming independent right‐censoring and proportional hazards for the two treatment groups, we considered event‐driven designs with fixed number of events, which guarantees the power to be at a desired level under a certain alternative. We develop reestimation procedures based on parametric models and show that these maintain the expected duration of the trial at a target length in flexible follow‐up designs across a range of nuisance parameter values by adjusting the number of patients recruited into the trial based on blinded nuisance parameter estimates. Furthermore, we provide convincing evidence from a simulation study that such procedures proposed do not inflate the type I error rate in any practically relevant way, thereby satisfying the standards set by relevant international guidelines. Inspired by practical application of these procedures, we outline a number of extensions including methods for extrapolating the observed survival curve beyond the interim time point, application of reestimation procedures to interval censored data, and situations in which a confirmation of event is required leading to a certain lag time.

Sample size re-estimation for clinical trials with longitudinal negative binomial counts including time trends.

In some diseases, such as multiple sclerosis, lesion counts obtained from magnetic resonance imaging (MRI) are used as markers of disease progression. This leads to longitudinal, and typically overdispersed, count data outcomes in clinical trials. Models for such data invariably include a number of nuisance parameters, which can be difficult to specify at the planning stage, leading to considerable uncertainty in sample size specification. Consequently, blinded sample size re-estimation procedures are used, allowing for an adjustment of the sample size within an ongoing trial by estimating relevant nuisance parameters at an interim point, without compromising trial integrity. To date, the methods available for re-estimation have required an assumption that the mean count is time-constant within patients. We propose a new modeling approach that maintains the advantages of established procedures but allows for general underlying and treatment-specific time trends in the mean response. A simulation study is conducted to assess the effectiveness of blinded sample size re-estimation methods over fixed designs. Sample sizes attained through blinded sample size re-estimation procedures are shown to maintain the desired study power without inflating the Type I error rate and the procedure is demonstrated on MRI data from a recent study in multiple sclerosis.

Blinded continuous monitoring in clinical trials with recurrent event endpoints.

In studies with recurrent event endpoints, misspecified assumptions of event rates or dispersion can lead to underpowered trials or overexposure of patients. Specification of overdispersion is often a particular problem as it is usually not reported in clinical trial publications. Changing event rates over the years have been described for some diseases, adding to the uncertainty in planning. To mitigate the risks of inadequate sample sizes, internal pilot study designs have been proposed with a preference for blinded sample size reestimation procedures, as they generally do not affect the type I error rate and maintain trial integrity. Blinded sample size reestimation procedures are available for trials with recurrent events as endpoints. However, the variance in the reestimated sample size can be considerable in particular with early sample size reviews. Motivated by a randomized controlled trial in paediatric multiple sclerosis, a rare neurological condition in children, we apply the concept of blinded continuous monitoring of information, which is known to reduce the variance in the resulting sample size. Assuming negative binomial distributions for the counts of recurrent relapses, we derive information criteria and propose blinded continuous monitoring procedures. The operating characteristics of these are assessed in Monte Carlo trial simulations demonstrating favourable properties with regard to type I error rate, power, and stopping time, ie, sample size.

Blinded and unblinded sample size reestimation procedures for stepped-wedge cluster randomized trials.

The ability to accurately estimate the sample size required by a stepped‐wedge (SW) cluster randomized trial (CRT) routinely depends upon the specification of several nuisance parameters. If these parameters are misspecified, the trial could be overpowered, leading to increased cost, or underpowered, enhancing the likelihood of a false negative. We address this issue here for cross‐sectional SW‐CRTs, analyzed with a particular linear‐mixed model, by proposing methods for blinded and unblinded sample size reestimation (SSRE). First, blinded estimators for the variance parameters of a SW‐CRT analyzed using the Hussey and Hughes model are derived. Following this, procedures for blinded and unblinded SSRE after any time period in a SW‐CRT are detailed. The performance of these procedures is then examined and contrasted using two example trial design scenarios. We find that if the two key variance parameters were underspecified by 50%, the SSRE procedures were able to increase power over the conventional SW‐CRT design by up to 41%, resulting in an empirical power above the desired level. Thus, though there are practical issues to consider, the performance of the procedures means researchers should consider incorporating SSRE in to future SW‐CRTs.

Blinded and unblinded sample size reestimation in crossover trials balanced for period.

The determination of the sample size required by a crossover trial typically depends on the specification of one or more variance components. Uncertainty about the value of these parameters at the design stage means that there is often a risk a trial may be under‐ or overpowered. For many study designs, this problem has been addressed by considering adaptive design methodology that allows for the re‐estimation of the required sample size during a trial. Here, we propose and compare several approaches for this in multitreatment crossover trials. Specifically, regulators favor reestimation procedures to maintain the blinding of the treatment allocations. We therefore develop blinded estimators for the within and between person variances, following simple or block randomization. We demonstrate that, provided an equal number of patients are allocated to sequences that are balanced for period, the proposed estimators following block randomization are unbiased. We further provide a formula for the bias of the estimators following simple randomization. The performance of these procedures, along with that of an unblinded approach, is then examined utilizing three motivating examples, including one based on a recently completed four‐treatment four‐period crossover trial. Simulation results show that the performance of the proposed blinded procedures is in many cases similar to that of the unblinded approach, and thus they are an attractive alternative.

Bayesian blinded sample size re-estimation

ABSTRACTInformation before unblinding regarding the success of confirmatory clinical trials is highly uncertain. Current techniques using point estimates of auxiliary parameters for estimating expected blinded sample size: (i) fail to describe the range of likely sample sizes obtained after the anticipated data are observed, and (ii) fail to adjust to the changing patient population. Sequential MCMC-based algorithms are implemented for purposes of sample size adjustments. The uncertainty arising from clinical trials is characterized by filtering later auxiliary parameters through their earlier counterparts and employing posterior distributions to estimate sample size and power. The use of approximate expected power estimates to determine the required additional sample size are closely related to techniques employing Simple Adjustments or the EM algorithm. By contrast with these, our proposed methodology provides intervals for the expected sample size using the posterior distribution of auxiliary parameters. Future decisions about additional subjects are better informed due to our ability to account for subject response heterogeneity over time. We apply the proposed methodologies to a depression trial. Our proposed blinded procedures should be considered for most studies due to ease of implementation.

Modelling and sample size reestimation for longitudinal count data with incomplete follow up.

We consider modelling and inference as well as sample size estimation and reestimation for clinical trials with longitudinal count data as outcomes. Our approach is general but is rooted in design and analysis of multiple sclerosis trials where lesion counts obtained by magnetic resonance imaging are important endpoints. We adopt a binomial thinning model that allows for correlated counts with marginal Poisson or negative binomial distributions. Methods for sample size planning and blinded sample size reestimation for randomised controlled clinical trials with such outcomes are developed. The models and approaches are applicable to data with incomplete observations. A simulation study is conducted to assess the effectiveness of sample size estimation and blinded sample size reestimation methods. Sample sizes attained through these procedures are shown to maintain the desired study power without inflating the type I error. Data from a recent trial in patients with secondary progressive multiple sclerosis illustrate the modelling approach.

Blinded sample size re-estimation in three-arm trials with 'gold standard' design.

In this article, we study blinded sample size re-estimation in the 'gold standard' design with internal pilot study for normally distributed outcomes. The 'gold standard' design is a three-arm clinical trial design that includes an active and a placebo control in addition to an experimental treatment. We focus on the absolute margin approach to hypothesis testing in three-arm trials at which the non-inferiority of the experimental treatment and the assay sensitivity are assessed by pairwise comparisons. We compare several blinded sample size re-estimation procedures in a simulation study assessing operating characteristics including power and type I error. We find that sample size re-estimation based on the popular one-sample variance estimator results in overpowered trials. Moreover, sample size re-estimation based on unbiased variance estimators such as the Xing-Ganju variance estimator results in underpowered trials, as it is expected because an overestimation of the variance and thus the sample size is in general required for the re-estimation procedure to eventually meet the target power. To overcome this problem, we propose an inflation factor for the sample size re-estimation with the Xing-Ganju variance estimator and show that this approach results in adequately powered trials. Because of favorable features of the Xing-Ganju variance estimator such as unbiasedness and a distribution independent of the group means, the inflation factor does not depend on the nuisance parameter and, therefore, can be calculated prior to a trial. Moreover, we prove that the sample size re-estimation based on the Xing-Ganju variance estimator does not bias the effect estimate. Copyright © 2017 John Wiley & Sons, Ltd.