ERDO - a framework to select an appropriate randomization procedure for clinical trials

Choosing and evaluating randomisation methods in clinical trials: a qualitative study

BACKGROUND

There exist many different methods of allocating participants to treatment groups during a randomised controlled trial. Although there is research that explores trial characteristics that are associated with the choice of method, there is still a lot of variety in practice not explained. This study used qualitative methods to explore more deeply the motivations behind researchers' choice of randomisation, and which features of the method they use to evaluate the performance of these methods.

METHODS

Data was collected from online focus groups with various stakeholders involved in the randomisation process. Focus groups were recorded and then transcribed verbatim. A thematic analysis was used to analyse the transcripts.

RESULTS

Twenty-five participants from twenty clinical trials units across the UK were recruited to take part in one of four focus groups. Four main themes were identified: how randomisation methods are selected; researchers' opinions of the different methods; which features of the method are desirable and ways to measure method features. Most researchers agree that the randomisation method should be selected based on key trial characteristics; however, for many, a unit standard is in place. Opinions of methods were varied with some participants favouring stratified blocks and others favouring minimisation. This was generally due to researchers' perception of the effect these methods had on balance and predictability. Generally, predictability was considered more important than balance as adjustments cannot be made for it; however, most researchers felt that the importance of these two methods was dependent on the design of the study. Balance is usually evaluated by tabulating variables by treatment arm and looking for perceived imbalances, predictability was generally considered much harder to measure, partly due to differing definitions.

CONCLUSION

There is a wide variety in practice on how randomisation methods are selected and researcher's opinions on methods. The difference in practice observed when looking at randomisation method selection can be explained by a difference in unit practice, and also by a difference in researchers prioritisation of balance and predictability. The findings of this study show a need for more guidance on randomisation method selection.

Istore: a project on innovative statistical methodologies to improve rare diseases clinical trials in limited populations

BACKGROUND

The conduct of rare disease clinical trials is still hampered by methodological problems. The number of patients suffering from a rare condition is variable, but may be very small and unfortunately statistical problems for small and finite populations have received less consideration. This paper describes the outline of the iSTORE project, its ambitions, and its methodological approaches.

METHODS

In very small populations, methodological challenges exacerbate. iSTORE's ambition is to develop a comprehensive perspective on natural history course modelling through multiple endpoint methodologies, subgroup similarity identification, and improving level of evidence.

RESULTS

The methodological approaches cover methods for sound scientific modeling of natural history course data, showing similarity between subgroups, defining, and analyzing multiple endpoints and quantifying the level of evidence in multiple endpoint trials that are often hampered by bias.

CONCLUSION

Through its expected results, iSTORE will contribute to the rare diseases research field by providing an approach to better inform about and thus being able to plan a clinical trial. The methodological derivations can be synchronized and transferability will be outlined.

Selecting a randomization method for a multi-center clinical trial with stochastic recruitment considerations

BACKGROUND

The design of a multi-center randomized controlled trial (RCT) involves multiple considerations, such as the choice of the sample size, the number of centers and their geographic location, the strategy for recruitment of study participants, amongst others. There are plenty of methods to sequentially randomize patients in a multi-center RCT, with or without considering stratification factors. The goal of this paper is to perform a systematic assessment of such randomization methods for a multi-center 1:1 RCT assuming a competitive policy for the patient recruitment process.

METHODS

We considered a Poisson-gamma model for the patient recruitment process with a uniform distribution of center activation times. We investigated 16 randomization methods (4 unstratified, 4 region-stratified, 4 center-stratified, 3 dynamic balancing randomization (DBR), and a complete randomization design) to sequentially randomize n = 500 patients. Statistical properties of the recruitment process and the randomization procedures were assessed using Monte Carlo simulations. The operating characteristics included time to complete recruitment, number of centers that recruited a given number of patients, several measures of treatment imbalance and estimation efficiency under a linear model for the response, the expected proportions of correct guesses under two different guessing strategies, and the expected proportion of deterministic assignments in the allocation sequence.

RESULTS

Maximum tolerated imbalance (MTI) randomization methods such as big stick design, Ehrenfest urn design, and block urn design result in a better balance-randomness tradeoff than the conventional permuted block design (PBD) with or without stratification. Unstratified randomization, region-stratified randomization, and center-stratified randomization provide control of imbalance at a chosen level (trial, region, or center) but may fail to achieve balance at the other two levels. By contrast, DBR does a very good job controlling imbalance at all 3 levels while maintaining the randomized nature of treatment allocation. Adding more centers into the study helps accelerate the recruitment process but at the expense of increasing the number of centers that recruit very few (or no) patients-which may increase center-level imbalances for center-stratified and DBR procedures. Increasing the block size or the MTI threshold(s) may help obtain designs with improved randomness-balance tradeoff.

CONCLUSIONS

The choice of a randomization method is an important component of planning a multi-center RCT. Dynamic balancing randomization with carefully chosen MTI thresholds could be a very good strategy for trials with the competitive policy for patient recruitment.

Influence of study characteristics, methodological rigour and publication bias on efficacy of pharmacotherapy in obsessive-compulsive disorder: a systematic review and meta-analysis of randomised, placebo-controlled trials

QUESTION

We examined the effect of study characteristics, risk of bias and publication bias on the efficacy of pharmacotherapy in randomised controlled trials (RCTs) for obsessive-compulsive disorder (OCD).

STUDY SELECTION AND ANALYSIS

We conducted a systematic search of double-blinded, placebo-controlled, short-term RCTs with selective serotonergic reuptake inhibitors (SSRIs) or clomipramine. We performed a random-effect meta-analysis using change in the Yale-Brown Obsessive-Compulsive Scale (YBOCS) as the primary outcome. We performed meta-regression for risk of bias, intervention, sponsor status, number of trial arms, use of placebo run-in, dosing, publication year, age, severity, illness duration and gender distribution. Furthermore, we analysed publication bias using a Bayesian selection model.

FINDINGS

We screened 3729 articles and included 21 studies, with 4102 participants. Meta-analysis showed an effect size of -0.59 (Hedges' G, 95% CI -0.73 to -0.46), equalling a 4.2-point reduction in the YBOCS compared with placebo. The most recent trial was performed in 2007 and most trials were at risk of bias. We found an indication for publication bias, and subsequent correction for this bias resulted in a depleted effect size. In our meta-regression, we found that high risk of bias was associated with a larger effect size. Clomipramine was more effective than SSRIs, even after correcting for risk of bias. After correction for multiple testing, other selected predictors were non-significant.

CONCLUSIONS

Our findings reveal superiority of clomipramine over SSRIs, even after adjusting for risk of bias. Effect sizes may be attenuated when considering publication bias and methodological rigour, emphasising the importance of robust studies to guide clinical utility of OCD pharmacotherapy.

PROSPERO REGISTRATION NUMBER

CRD42023394924.

Composite endpoints, including patient reported outcomes, in rare diseases

BACKGROUND

When assessing the efficacy of a treatment in any clinical trial, it is recommended by the International Conference on Harmonisation to select a single meaningful endpoint. However, a single endpoint is often not sufficient to reflect the full clinical benefit of a treatment in multifaceted diseases, which is often the case in rare diseases. Therefore, the use of a combination of several clinically meaningful outcomes is preferred. Many methodologies that allow for combining outcomes in a so-called composite endpoint are however limited in a number of ways, not in the least in the number and type of outcomes that can be combined and in the poor small-sample properties. Moreover, patient reported outcomes, such as quality of life, often cannot be integrated in a composite analysis, in spite of their intrinsic value.

RESULTS

Recently, a class of non-parametric generalized pairwise comparisons tests have been proposed, which members do allow for any number and type of outcomes, including patient reported outcomes. The class enjoys good small-sample properties. Moreover, this very flexible class of methods allows for prioritizing the outcomes by clinical severity, allows for matched designs and for adding a threshold of clinical relevance. Our aim is to introduce the generalized pairwise comparison ideas and concepts for rare disease clinical trial analysis, and demonstrate their benefit in a post-hoc analysis of a small-sample trial in epidermolysis bullosa. More precisely, we will include a patient relevant outcome (Quality of life), in a composite endpoint. This publication is part of the European Joint Programme on Rare Diseases (EJP RD) series on innovative methodologies for rare diseases clinical trials, which is based on the webinars presented within the educational activity of EJP RD. This publication covers the webinar topic on composite endpoints in rare diseases and includes participants' response to a questionnaire on this topic.

CONCLUSIONS

Generalized pairwise comparisons is a promising statistical methodology for evaluating any type of composite endpoints in rare disease trials and may allow a better evaluation of therapy efficacy including patients reported outcomes in addition to outcomes related to the diseases signs and symptoms.

Regression analysis for covariate-adaptive randomization: A robust and efficient inference perspective

Linear regression is arguably the most fundamental statistical model; however, the validity of its use in randomized clinical trials, despite being common practice, has never been crystal clear, particularly when stratified or covariate-adaptive randomization is used. In this article, we investigate several of the most intuitive and commonly used regression models for estimating and inferring the treatment effect in randomized clinical trials. By allowing the regression model to be arbitrarily misspecified, we demonstrate that all these regression-based estimators robustly estimate the treatment effect, albeit with possibly different efficiency. We also propose consistent non-parametric variance estimators and compare their performances to those of the model-based variance estimators that are readily available in standard statistical software. Based on the results and taking into account both theoretical efficiency and practical feasibility, we make recommendations for the effective use of regression under various scenarios. For equal allocation, it suffices to use the regression adjustment for the stratum covariates and additional baseline covariates, if available, with the usual ordinary-least-squares variance estimator. For unequal allocation, regression with treatment-by-covariate interactions should be used, together with our proposed variance estimators. These recommendations apply to simple and stratified randomization, and minimization, among others. We hope this work helps to clarify and promote the usage of regression in randomized clinical trials.

A roadmap to using randomization in clinical trials

BACKGROUND

Randomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial.

METHODS

We survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice.

RESULTS

Restricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size.

CONCLUSIONS

The choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators.

Assessing the quality of randomization methods in randomized control trials

IMPORTANCE

The randomization process is considered among the most important components of a randomized control trial (RCTs) and a core advantage of RCTs. Proper randomization should eliminate most population biases, in which some populations, or members of a population are more likely to be selected or not selected than others, such that similar comparison groups are produced to evaluate treatments.^4,5 OBJECTIVE: To assess the methodologic quality of the descriptions of randomization methods used to allocate participants to comparison groups in randomized controlled trials.

EVIDENCE REVIEW

A cross-sectional review of phase 3 clinical trials reported in Clinicaltrials.gov. Beginning at all records available (n = 345,278) we included studies only listed for stage 3 RCTs in the U.S. National Library of Medicine database. A total of 1528 protocols were identified as of June 1, 2020. Exclusion criteria involved no protocol listed or non-randomized studies, of which 517 were excluded. There were 693 text articles excluded due to unclear methods of randomization. Inclusion criteria involved randomization methods based on "A review of randomization methods in clinical trials" by Berger and Antsygina.1 Each study protocol was extracted to identify the randomization methods described by three independent reviewers. Classification of randomization methods described in the study protocols for randomized clinical trials.

FINDINGS

Only 20.8 % of the study protocols described a method for randomly assigning participants to groups. Of this subset that defined protocols, the Permuted-Block Design was used most often (85.9 %). More than three quarters of all study protocols (77.7 %) provided incomplete descriptions about the type of randomization method (i.e. no protocol, n/a, unclear).

CONCLUSIONS

and Relevance:Proper randomization is required to generate unbiased comparison groups in controlled trials, yet the majority of study protocols for RCTs currently in Clinicaltrials.gov provide inadequate or unacceptable information regarding their randomization methods.

Statistical consideration when adding new arms to ongoing clinical trials: the potentials and the caveats

BACKGROUND

Platform trials improve the efficiency of the drug development process through flexible features such as adding and dropping arms as evidence emerges. The benefits and practical challenges of implementing novel trial designs have been discussed widely in the literature, yet less consideration has been given to the statistical implications of adding arms. MAIN: We explain different statistical considerations that arise from allowing new research interventions to be added in for ongoing studies. We present recent methodology development on addressing these issues and illustrate design and analysis approaches that might be enhanced to provide robust inference from platform trials. We also discuss the implication of changing the control arm, how patient eligibility for different arms may complicate the trial design and analysis, and how operational bias may arise when revealing some results of the trials. Lastly, we comment on the appropriateness and the application of platform trials in phase II and phase III settings, as well as publicly versus industry-funded trials.

CONCLUSION

Platform trials provide great opportunities for improving the efficiency of evaluating interventions. Although several statistical issues are present, there are a range of methods available that allow robust and efficient design and analysis of these trials.

Including non-concurrent control patients in the analysis of platform trials: is it worth it?

Background

Platform trials allow adding new experimental treatments to an on-going trial. This feature is attractive to practitioners due to improved efficiency. Nevertheless, the operating characteristics of a trial that adds arms have not been well-studied. One controversy is whether just the concurrent control data (i.e. of patients who are recruited after a new arm is added) should be used in the analysis of the newly added treatment(s), or all control data (i.e. non-concurrent and concurrent).

Methods

We investigate the benefits and drawbacks of using non-concurrent control data within a two-stage setting. We perform simulation studies to explore the impact of a linear and a step trend on the inference of the trial. We compare several analysis approaches when one includes all the control data or only concurrent control data in the analysis of the newly added treatment.

Results

When there is a positive trend and all the control data are used, the marginal power of rejecting the corresponding hypothesis and the type one error rate can be higher than the nominal value. A model-based approach adjusting for a stage effect is equivalent to using concurrent control data; an adjustment with a linear term may not guarantee valid inference when there is a non-linear trend.

Conclusions

If strict error rate control is required then non-concurrent control data should not be used; otherwise it may be beneficial if the trend is sufficiently small. On the other hand, the root mean squared error of the estimated treatment effect can be improved through using non-concurrent control data.

Randomization-based interval estimation in randomized clinical trials

Randomization-based interval estimation takes into account the particular randomization procedure in the analysis and preserves the confidence level even in the presence of heterogeneity. It is distinguished from population-based confidence intervals with respect to three aspects: definition, computation, and interpretation. The article contributes to the discussion of how to construct a confidence interval for a treatment difference from randomization tests when analyzing data from randomized clinical trials. The discussion covers (i) the definition of a confidence interval for a treatment difference in randomization-based inference, (ii) computational algorithms for efficiently approximating the endpoints of an interval, and (iii) evaluation of statistical properties (ie, coverage probability and interval length) of randomization-based and population-based confidence intervals under a selected set of randomization procedures when assuming heterogeneity in patient outcomes. The method is illustrated with a case study.

Design and analysis of stratified clinical trials in the presence of bias

BACKGROUND

Among various design aspects, the choice of randomization procedure have to be agreed on, when planning a clinical trial stratified by center. The aim of the paper is to present a methodological approach to evaluate whether a randomization procedure mitigates the impact of bias on the test decision in clinical trial stratified by center.

METHODS

We use the weighted t test to analyze the data from a clinical trial stratified by center with a two-arm parallel group design, an intended 1:1 allocation ratio, aiming to prove a superiority hypothesis with a continuous normal endpoint without interim analysis and no adaptation in the randomization process. The derivation is based on the weighted t test under misclassification, i.e. ignoring bias. An additive bias model combing selection bias and time-trend bias is linked to different stratified randomization procedures.

RESULTS

Various aspects to formulate stratified versions of randomization procedures are discussed. A formula for sample size calculation of the weighted t test is derived and used to specify the tolerated imbalance allowed by some randomization procedures. The distribution of the weighted t test under misclassification is deduced, taking the sequence of patient allocation to treatment, i.e. the randomization sequence into account. An additive bias model combining selection bias and time-trend bias at strata level linked to the applied randomization sequence is proposed. With these before mentioned components, the potential impact of bias on the type one error probability depending on the selected randomization sequence and thus the randomization procedure is formally derived and exemplarily calculated within a numerical evaluation study.

CONCLUSION

The proposed biasing policy and test distribution are necessary to conduct an evaluation of the comparative performance of (stratified) randomization procedure in multi-center clinical trials with a two-arm parallel group design. It enables the choice of the best practice procedure. The evaluation stimulates the discussion about the level of evidence resulting in those kind of clinical trials.

Small samples and increased variability - discussing the need for restricted types of randomization in exercise interventions in old age

Background

Randomization provides an equal chance for participants to be allocated to intervention groups, in order to create an equal distribution of all variables at baseline in all groups. However, this is not guaranteed, particularly if the groups are too small, or if the researched groups consist of older adults. The aims of this commentary are to discuss the increased variability in old age which intensifies the risk of baseline inequalities, to elaborate on the need to estimate potential baseline group differences in small samples of older participants in exercise intervention, to discuss alternative procedures for creating equal groups at baseline and to provide specific guidelines for selecting the design of small studies.

Main body

Small groups with increased inter-individual differences may lead to reduced power, thus differences that truly exist may not be detected, or false group differences may appear in the outcome following the treatment. Studies that focused exclusively on older adults have found increased variability in advanced age. Therefore, baseline group differences are more common in older adults as compared to younger persons, and may lead to misinterpretation of the intervention′s results. Imbalances can be reduced by covariate-adaptive randomization procedures, such as stratified permuted-block randomization or minimization. Specific guidelines are provided for selecting a randomization procedure by assessing the probability of unequal groups at baseline in typical, widely used functional tests in old age. A calculation of the required number of participants for creating equal groups for these functional tests is provided, and can be used when increasing the number of participants is possible. R-scripts specifically created for assessing the probability of unequal groups, or for determining the sample size assuring equal groups, are recommended.

Conclusions

In exercise interventions assessing older adults, it is recommended to have a sample large enough for creating equal groups. If this is not possible, as is the case quite often in intervention studies in old age, it is recommended to assess the probability of inequality in the study groups and to apply an alternative randomization.

Protocol of a randomized, double-blind, placebo-controlled, parallel-group, multicentre study of the efficacy and safety of nicotinamide in patients with Friedreich ataxia (NICOFA)

Introduction

Currently, no treatment that delays with the progression of Friedreich ataxia is available. In the majority of patients Friedreich ataxia is caused by homozygous pathological expansion of GAA repeats in the first intron of the FXN gene. Nicotinamide acts as a histone deacetylase inhibitor. Dose escalation studies have shown, that short term treatment with dosages of up to 4 g/day increase the expression of FXN mRNA and frataxin protein up to the levels of asymptomatic heterozygous gene carriers. The long-term effects and the effects on clinical endpoints, activities of daily living and quality of life are unknown.

Methods

The aim of the NICOFA study is to investigate the efficacy and safety of nicotinamide for the treatment of Friedreich ataxia over 24 months. An open-label dose adjustment wash-in period with nicotinamide (phase A: weeks 1–4) to the individually highest tolerated dose of 2–4 g nicotinamide/day will be followed by a 2 (nicotinamide group): 1 (placebo group) randomization (phase B: weeks 5–104). In the nicotinamide group, patients will continue with their individually highest tolerated dose between 2 and 4 g/d per os once daily and the placebo group patients will be receiving matching placebo. Safety assessments will consist of monitoring and recording of all adverse events and serious adverse events, regular monitoring of haematology, blood chemistry and urine values, regular measurement of vital signs and the performance of physical examinations including cardiological signs. The primary outcome is the change in the Scale for the Assessment and Rating of Ataxia (SARA) over time as compared with placebo in patients with Friedreich ataxia based on the linear mixed effect model (LMEM) model. Secondary endpoints are measures of quality of life, functional motor and cognitive measures, clinician’s and patient’s global impression-change scales as well as the up-regulation of the frataxin protein level, safety and survival/death.

Perspective

The NICOFA study represents one of the first attempts to assess the clinical efficacy of an epigenetic therapeutic intervention for this disease and will provide evidence of possible disease modifying effects of nicotinamide treatment in patients with Friedreich ataxia.

Trial registration

EudraCT-No.: 2017-002163-17, ClinicalTrials.govNCT03761511.

Dealing with Possible Baseline Inequalities Between Experimental Groups - The Case of Motor Learning

One important concept of experimental design is the random assignment of participants to experimental groups. This randomization process is used to prevent selection bias, as well as to provide a strong basis for a cause-and-effect relationship between the independent variable/s and the dependent variable/s. In small sample sizes, simple randomization may not provide equal groups at baseline for one or more of the variables, and therefore more restricted types of randomization, such as the stratified permuted-block randomization, can be used. A code was written to calculate the probability that simple randomization will not lead to equality between groups at baseline, and then an example of stratified permuted-block randomization was examined. The findings suggest that for certain variables that are commonly measured in experiments in motor learning, there is a relatively high probability that groups will not be equal at baseline after simple randomization. This observation reflects the small sample sizes usually found in the literature on motor learning. However, stratified permuted-block randomization does lead to greater equality among groups. Implications for researchers are discussed, and a flowchart is proposed that will allow researchers to decide whether to use simple or stratified randomization.

Randomization in survival studies: An evaluation method that takes into account selection and chronological bias

The random allocation of patients to treatments is a crucial step in the design and conduct of a randomized controlled trial. For this purpose, a variety of randomization procedures is available. In the case of imperfect blinding, the extent to which a randomization procedure forces balanced group sizes throughout the allocation process affects the predictability of allocations. As a result, some randomization procedures perform superior with respect to selection bias, whereas others are less susceptible to chronological bias. The choice of a suitable randomization procedure therefore depends on the expected risk for selection and chronological bias within the particular study in question. To enable a sound comparison of different randomization procedures, we introduce a model for the combined effect of selection and chronological bias in randomized studies with a survival outcome. We present an evaluation method to quantify the influence of bias on the test decision of the log-rank test in a randomized parallel group trial with a survival outcome. The effect of selection and chronological bias and the dependence on the study setting are illustrated in a sensitivity analysis. We conclude with a case study to showcase the application of our model for comparing different randomization procedures in consideration of the expected type I error probability.

Improve hip fracture outcome in the elderly patient (iHOPE): a study protocol for a pragmatic, multicentre randomised controlled trial to test the efficacy of spinal versus general anaesthesia

INTRODUCTION

Hip fracture surgery is associated with high in-hospital and 30-day mortality rates and serious adverse patient outcomes. Evidence from randomised controlled trials regarding effectiveness of spinal versus general anaesthesia on patient-centred outcomes after hip fracture surgery is sparse.

METHODS AND ANALYSIS

The iHOPE study is a pragmatic national, multicentre, randomised controlled, open-label clinical trial with a two-arm parallel group design. In total, 1032 patients with hip fracture (>65 years) will be randomised in an intended 1:1 allocation ratio to receive spinal anaesthesia (n=516) or general anaesthesia (n=516). Outcome assessment will occur in a blinded manner after hospital discharge and inhospital. The primary endpoint will be assessed by telephone interview and comprises the time to the first occurring event of the binary composite outcome of all-cause mortality or new-onset serious cardiac and pulmonary complications within 30 postoperative days. In-hospital secondary endpoints, assessed via in-person interviews and medical record review, include mortality, perioperative adverse events, delirium, satisfaction, walking independently, length of hospital stay and discharge destination. Telephone interviews will be performed for long-term endpoints (all-cause mortality, independence in walking, chronic pain, ability to return home cognitive function and overall health and disability) at postoperative day 30±3, 180±45 and 365±60. ETHICS AND DISSEMINATION: iHOPE has been approved by the leading Ethics Committee of the Medical Faculty of the RWTH Aachen University on 14 March 2018 (EK 022/18). Approval from all other involved local Ethical Committees was subsequently requested and obtained. Study started in April 2018 with a total recruitment period of 24 months. iHOPE will be disseminated via presentations at national and international scientific meetings or conferences and publication in peer-reviewed international scientific journals.

TRIAL REGISTRATION NUMBER

DRKS00013644; Pre-results.

Implementing Optimal Designs for Dose-Response Studies Through Adaptive Randomization for a Small Population Group

In dose-response studies with censored time-to-event outcomes, D-optimal designs depend on the true model and the amount of censored data. In practice, such designs can be implemented adaptively, by performing dose assignments according to updated knowledge of the dose-response curve at interim analysis. It is also essential that treatment allocation involves randomization-to mitigate various experimental biases and enable valid statistical inference at the end of the trial. In this work, we perform a comparison of several adaptive randomization procedures that can be used for implementing D-optimal designs for dose-response studies with time-to-event outcomes with small to moderate sample sizes. We consider single-stage, two-stage, and multi-stage adaptive designs. We also explore robustness of the designs to experimental (chronological and selection) biases. Simulation studies provide evidence that both the choice of an allocation design and a randomization procedure to implement the target allocation impact the quality of dose-response estimation, especially for small samples. For best performance, a multi-stage adaptive design with small cohort sizes should be implemented using a randomization procedure that closely attains the targeted D-optimal design at each stage. The results of the current work should help clinical investigators select an appropriate randomization procedure for their dose-response study.

Lessons learned from IDeAl - 33 recommendations from the IDeAl-net about design and analysis of small population clinical trials

BACKGROUND

IDeAl (Integrated designs and analysis of small population clinical trials) is an EU funded project developing new statistical design and analysis methodologies for clinical trials in small population groups. Here we provide an overview of IDeAl findings and give recommendations to applied researchers.

METHOD

The description of the findings is broken down by the nine scientific IDeAl work packages and summarizes results from the project's more than 60 publications to date in peer reviewed journals. In addition, we applied text mining to evaluate the publications and the IDeAl work packages' output in relation to the design and analysis terms derived from in the IRDiRC task force report on small population clinical trials.

RESULTS

The results are summarized, describing the developments from an applied viewpoint. The main result presented here are 33 practical recommendations drawn from the work, giving researchers a comprehensive guidance to the improved methodology. In particular, the findings will help design and analyse efficient clinical trials in rare diseases with limited number of patients available. We developed a network representation relating the hot topics developed by the IRDiRC task force on small population clinical trials to IDeAl's work as well as relating important methodologies by IDeAl's definition necessary to consider in design and analysis of small-population clinical trials. These network representation establish a new perspective on design and analysis of small-population clinical trials.

CONCLUSION

IDeAl has provided a huge number of options to refine the statistical methodology for small-population clinical trials from various perspectives. A total of 33 recommendations developed and related to the work packages help the researcher to design small population clinical trial. The route to improvements is displayed in IDeAl-network representing important statistical methodological skills necessary to design and analysis of small-population clinical trials. The methods are ready for use.

The impact of selection bias in randomized multi-arm parallel group clinical trials

The impact of selection bias on the results of clinical trials has been analyzed extensively for trials of two treatments, yet its impact in multi-arm trials is still unknown. In this paper, we investigate selection bias in multi-arm trials by its impact on the type I error probability. We propose two models for selection bias, so-called biasing policies, that both extend the classic guessing strategy by Blackwell and Hodges. We derive the distribution of the F-test statistic under the misspecified outcome model and provide a formula for the type I error probability under selection bias. We apply the presented approach to quantify the influence of selection bias in multi-arm trials with increasing number of treatment groups using a permuted block design for different assumptions and different biasing strategies. Our results confirm previous findings that smaller block sizes lead to a higher proportion of sequences with inflated type I error probability. Astonishingly, our results also show that the proportion of sequences with inflated type I error probability remains constant when the number of treatment groups is increased. Realizing that the impact of selection bias cannot be completely eliminated, we propose a bias adjusted statistical model and show that the power of the statistical test is only slightly deflated for larger block sizes.