Futility stopping in clinical trials, optimality and practical considerations

Efficient testing of the biomarker positive and negative subgroups in a biomarker-stratified trial

In many randomized placebo-controlled trials with a biomarker defined subgroup, it is believed that this subgroup has the same or higher treatment effect compared with its complement. These subgroups are often referred to as the biomarker positive and negative subgroups. Most biomarker-stratified pivotal trials are aimed at demonstrating a significant treatment effect either in the biomarker positive subgroup or in the overall population. A major shortcoming of this approach is that the treatment can be declared effective in the overall population even though it has no effect in the biomarker negative subgroup. We use the isotonic assumption about the treatment effects in the two subgroups to construct an efficient way to test for a treatment effect in both the biomarker positive and negative subgroups. A substantial reduction in the required sample size for such a trial compared with existing methods makes evaluating the treatment effect in both the biomarker positive and negative subgroups feasible in pivotal trials especially when the prevalence of the biomarker positive subgroup is less than 0.5.

Evaluation of 'implications for research' statements in systematic reviews of interventions in advanced cancer patients - a meta-research study

BACKGROUND

Implications for research (IfR) sections are an important part of systematic reviews (SRs) to inform health care researchers and policy makers. PRISMA 2020 recommends reporting IfR, while Cochrane Reviews require a separate chapter on IfR. However, it is unclear to what extent SRs discuss IfR. We aimed i) to assess whether SRs include an IfR statement and ii) to evaluate which elements informed IfR statements.

METHODS

We conducted a meta-research study based on SRs of interventions in advanced cancer patients from a previous project (CRD42019134904). As suggested in the Cochrane Handbook, we assessed if the following predefined variables were referred to in IfR statements: patient, intervention, control, outcome (PICO) and study design; concepts underlying Grading of Recommendations, Assessment, Development and Evaluation (GRADE) domains: risk of bias, inconsistency, indirectness, imprecision, publication bias. Data were independently extracted by three reviewers after piloting the data extraction form. Discrepancies were resolved in weekly in-depth discussions.

RESULTS

We included 261 SRs. The majority evaluated a pharmacological intervention (n = 244, 93.5%); twenty-nine were Cochrane Reviews (11.1%). Four out of five SRs included an IfR statement (n = 210, 80.5%). IfR statements commonly addressed 'intervention' (n = 121, 57.6%), 'patient ' (n = 113, 53.8%), and 'study design' (n = 107, 51.0%). The most frequent PICO and study design combinations were 'patient and intervention ' (n = 71, 33.8%) and 'patient, intervention and study design ' (n = 34, 16.2%). Concepts underlying GRADE domains were rarely used for informing IfR recommendations: 'risk of bias ' (n = 2, 1.0%), and 'imprecision ' (n = 1, 0.5%), 'inconsistency ' (n = 1, 0.5%). Additional elements informing IfR were considerations on cost effectiveness (n = 9, 4.3%), reporting standards (n = 4, 1.9%), and individual patient data meta-analysis (n = 4, 1.9%).

CONCLUSION

Although about 80% of SRs included an IfR statement, the reporting of PICO elements varied across SRs. Concepts underlying GRADE domains were rarely used to derive IfR. Further work needs to assess the generalizability beyond SRs in advanced cancer patients. We suggest that more specific guidance on which and how IfR elements to report in SRs of interventions needs to be developed. Utilizing PICO elements and concepts underlying GRADE according to the Cochrane Handbook to state IfR seems to be a reasonable approach in the interim.

REGISTRATION

CRD42019134904.

Drawing causal inferences from randomized controlled trials in psychotherapy research

OBJECTIVE

Randomized control trials (RCTs) have played a critical role in psychotherapy research. This article discusses RCTs in the context of the criteria for drawing causal inferences in psychotherapy and intervention research more generally. The article also highlights underused variations of RCTs and how they not only establish causal relations but also address critical questions that can improve our intervention portfolio and patient care.

CONCLUSION

Random assignment is discussed in terms of what it can and cannot do in relation to drawing conclusions about the effects of interventions. Finally, RCTs are examined in the context of multiple questions that can guide therapy research, improve patient care, and develop treatments that reach people in need of psychological services.

Expanding the scope, reach, and impact of evidence-based psychological treatments

The development and evaluation of evidence-based treatments (EBTs) for mental disorders represent an enormous advance with continued progress designed to understand the techniques and increase their use in clinical practice. This article suggests ways of expanding research along several fronts including the extension of the types of randomized controlled trials that are conducted, the use of more diverse samples to encompass different cultures and countries, the expansion of assessments to better reflect client functioning in everyday life, consideration of the impact of treatments for mental disorders on physical health, the careful evaluation of exceptional responders, the use of mixed-methods research, and the development of versions of EBTs that can be scaled. EBTs have been studied in well-controlled settings and extended to clinical settings, albeit less often. The least attention has been accorded their evaluation on a large scale to reach a greater portion of people in need of services but who do not receive any treatment.

Feasibility of Randomized Controlled Trials for Cancer Drugs Approved by the Food and Drug Administration Based on Single-Arm Studies

Background

The US Food and Drug Administration (FDA) introduced an Accelerated Approval (AA) pathway to expedite patient access to new drugs. AA accepts less rigorous trial designs, including single-arm studies (SAS), owing to perceived lack of feasibility of timely randomized controlled trials (RCTs).

Methods

We designed hypothetical RCTs with endpoints of overall response rate (ORR), progression-free survival (PFS), and overall survival (OS) for FDA approvals based on SAS for solid tumors during 2010-2019. Existing standards of care served as controls. RCTs were designed to detect a difference with power of 0.80, α-error of 5% (2-sided), and 1:1 randomization. Accrual duration was estimated based on participation by less than 5% of eligible patients derived from cancer-specific incidence and mortality rates in the United States.

Results

Of 172 (18.0%) approvals during the study period, 31 (18.0%) were based on SAS. Median sample size was 104 (range = 23-411), and 77.4% were AA. All studies reported ORR, 55% reported duration of response, 19.4% reported PFS, and 22.5% reported OS. Median sample sizes needed to conduct RCTs with endpoints of ORR, PFS, and OS were 206, 130, and 396, respectively. It would have been theoretically possible to conduct RCTs within duration comparable with that required by SAS for 84.6%, 94.1%, and 80.0% of approvals with endpoints of ORR, PFS, and OS, respectively.

Conclusion

An overwhelming majority of FDA approvals based on SAS should be feasible as RCTs within a reasonable time frame. Given the collateral harms to patients and to scientific rigor, drug approval based on SAS should only be permitted under exceptional circumstances.

The precision interventions for severe and/or exacerbation-prone asthma (PrecISE) adaptive platform trial: statistical considerations

The Precision Interventions for Severe and/or Exacerbation-prone Asthma (PrecISE) study is an adaptive platform trial designed to investigate novel interventions to severe asthma. The study is conducted under a master protocol and utilizes a crossover design with each participant receiving up to five interventions and at least one placebo. Treatment assignments are based on the patients' biomarker profiles and precision health methods are incorporated into the interim and final analyses. We describe key elements of the PrecISE study including the multistage adaptive enrichment strategy, early stopping of an intervention for futility, power calculations, and the primary analysis strategy.

Multi-stage adaptive enrichment trial design with subgroup estimation

We consider the problem of estimating the best subgroup and testing for treatment effect in a clinical trial. We define the best subgroup as the subgroup that maximizes a utility function that reflects the trade-off between the subgroup size and the treatment effect. For moderate effect sizes and sample sizes, simpler methods for subgroup estimation worked better than more complex tree-based regression approaches. We propose a three-stage design with a weighted inverse normal combination test to test the hypothesis of no treatment effect across the three stages.