Minimization in randomized clinical trials.

In randomized trials, comparability of the treatment groups is ensured through allocation of treatments using a mechanism that involves some random element, thus controlling for confounding of the treatment effect. Completely random allocation ensures comparability between the treatment groups for all known and unknown prognostic factors. For a specific trial, however, imbalances in prognostic factors among the treatment groups may occur. Although accidental bias can be avoided in the presence of such imbalances by stratifying the analysis, most trialists, regulatory agencies, and other stakeholders prefer a balanced distribution of prognostic factors across the treatment groups. Some procedures attempt to achieve balance in baseline covariates, by stratifying the allocation for these covariates, or by dynamically adapting the allocation using covariate information during the trial (covariate-adaptive procedures). In this Tutorial, the performance of minimization, a popular covariate-adaptive procedure, is compared with two other commonly used procedures, completely random allocation and stratified blocked designs. Using individual patient data of 2 clinical trials (in advanced ovarian cancer and age-related macular degeneration), the procedures are compared in terms of operating characteristics (using asymptotic and randomization tests), predictability of treatment allocation, and achieved balance. Fifty actual trials of various sizes that applied minimization for treatment allocation are used to investigate the achieved balance. Implementation issues of minimization are described. Minimization procedures are useful in all trials but especially when (1) many major prognostic factors are known, (2) many centers of different sizes accrue patients, or (3) the trial sample size is moderate.

Trial Design for Cancer Immunotherapy: A Methodological Toolkit.

Immunotherapy with checkpoint inhibitors (CPIs) and cell-based products has revolutionized the treatment of various solid tumors and hematologic malignancies. These agents have shown unprecedented response rates and long-term benefits in various settings. These clinical advances have also pointed to the need for new or adapted approaches to trial design and assessment of efficacy and safety, both in the early and late phases of drug development. Some of the conventional statistical methods and endpoints used in other areas of oncology appear to be less appropriate in immuno-oncology. Conversely, other methods and endpoints have emerged as alternatives. In this article, we discuss issues related to trial design in the early and late phases of drug development in immuno-oncology, with a focus on CPIs. For early trials, we review the most salient issues related to dose escalation, use and limitations of tumor response and progression criteria for immunotherapy, the role of duration of response as an endpoint in and of itself, and the need to conduct randomized trials as early as possible in the development of new therapies. For late phases, we discuss the choice of primary endpoints for randomized trials, review the current status of surrogate endpoints, and discuss specific statistical issues related to immunotherapy, including non-proportional hazards in the assessment of time-to-event endpoints, alternatives to the Cox model in these settings, and the method of generalized pairwise comparisons, which can provide a patient-centric assessment of clinical benefit and be used to design randomized trials.

DECRESCENDO: de-escalating chemotherapy in HER2-positive, estrogen receptor-negative, node-negative early breast cancer.

The human epidermal growth factor receptor 2 (HER2)-enriched intrinsic subtype represents up to 75% of all HER2-positive hormone receptor (HR)-negative breast cancer (BC). Optimizing HER2-targeting therapy in this population might allow the omission of anthracycline-based chemotherapy, which is associated with potentially severe toxicities. DECRESCENDO (NCT04675827) is a large, multicenter, single-arm phase II trial in patients with HR-negative, HER2-positive, node-negative early BC evaluating a neoadjuvant pertuzumab and trastuzumab fixed-dose combination administered subcutaneously plus taxane-based chemotherapy followed by adjuvant treatment, adapted according to response to neoadjuvant therapy. The primary end point is the 3-year recurrence-free survival rate in patients with 'HER2-enriched' tumors and a pathological complete response. This flexible care substudy offers adjuvant treatment administration outside the hospital to some patients.

Breast cancer is the most frequent cancer type among women worldwide. Different types of breast cancer exist, defined by the type of proteins on the tumor cell surface: HER2-positive: overproduction of human epidermal growth factor receptor 2 (HER2); Hormone receptor-positive: overproduction of the estrogen and/or progesterone hormone receptors. In the past 30 years, effective anti-HER2 drugs have been developed. However, they are often combined with chemotherapy, which can cause serious side effects (also called toxicities). HER2-positive tumors, which are also hormone receptor-negative, respond better to HER2-targeting drugs with less toxicity than chemotherapy. The DECRESCENDO trial aims to test treating HER2-positive, hormone receptor-negative patients (with a maximum breast cancer tumor size of 5 cm, without swollen lymph nodes) with pertuzumab + trastuzumab. The combination therapy would be given presurgery to reduce the tumor size as much as possible first (known as neoadjuvant therapy). The intensity of the patient's chemotherapy would be reduced with only one chemotherapy drug instead of standard three to four drugs. Patients that respond well will require less intense treatment after their surgery. Tissue from the tumors will be tested to see if any of the HER2-positive tumors belong to a subtype known as 'HER2-enriched' ­ this subtype is predicted to be more responsive to the trastuzumab and pertuzumab combination therapy. In a separate study of the DECRESCENDO trial, patients with good responses to neoadjuvant therapy and no safety concerns may continue their postsurgery treatment outside the hospital, such as at home.

The case against censoring of progression-free survival in cancer clinical trials - A pandemic shutdown as an illustration.

BACKGROUND: Missing data may lead to loss of statistical power and introduce bias in clinical trials. The Covid-19 pandemic has had a profound impact on patient health care and on the conduct of cancer clinical trials. Although several endpoints may be affected, progression-free survival (PFS) is of major concern, given its frequent use as primary endpoint in advanced cancer and the fact that missed radiographic assessments are to be expected. The recent introduction of the estimand framework creates an opportunity to define more precisely the target of estimation and ensure alignment between the scientific question and the statistical analysis. METHODS: We used simulations to investigate the impact of two basic approaches for handling missing tumor scans due to the pandemic: a "treatment policy" strategy, which consisted in ascribing events to the time they are observed, and a "hypothetical" approach of censoring patients with events during the shutdown period at the last assessment prior to that period. We computed the power of the logrank test, estimated hazard ratios (HR) using Cox models, and estimated median PFS times without and with a hypothetical 6-month shutdown period with no patient enrollment or tumor scans being performed, varying the shutdown starting times. RESULTS: Compared with the results in the absence of shutdown, the "treatment policy" strategy slightly overestimated median PFS proportionally to the timing of the shutdown period, but power was not affected. Except for one specific scenario, there was no impact on the estimated HR. In general, the pandemic had a greater impact on the analyses using the "hypothetical" strategy, which led to decreased power and overestimated median PFS times to a greater extent than the "treatment policy" strategy. CONCLUSION: As a rule, we suggest that the treatment policy approach, which conforms with the intent-to-treat principle, should be the primary analysis to avoid unnecessary loss of power and minimize bias in median PFS estimates.

Clinical Trial Endpoints in Metastatic Cancer: Using Individual Participant Data to Inform Future Trials Methodology.

Meta-analysis based on individual participant data (IPD) is a powerful methodology for synthesizing evidence by combining information drawn from multiple trials. Hitherto, its principal application has been in questions of clinical management, but an increasingly important use is in clarifying trials methodology, for instance in the selection of endpoints, as discussed in this review. In oncology, the Aide et Recherche en Cancérologie Digestive (ARCAD) Metastatic Colorectal Cancer Database is a leader in the use of IPD-based meta-analysis in methodological research. The ARCAD database contains IPD from more than 38 000 patients enrolled in 46 studies and continues to collect phase III trial data. Here, we review the principal findings of the ARCAD project in respect of endpoint selection and examine their implications for cancer trials. Analysis of the database has confirmed that progression-free survival (PFS) is no longer a valid surrogate endpoint predictive of overall survival in the first-line treatment of colorectal cancer. Nonetheless, PFS remains an endpoint of choice for most first-line trials in metastatic colorectal cancer and other solid tumors. Only substantial PFS effects are likely to translate into clinically meaningful benefits, and accordingly, we advocate an oncology research model designed to identify highly effective treatments in carefully defined patient groups. We also review the use of the ARCAD database in assessing clinical response including novel response metrics and prognostic markers. These studies demonstrate the value of IPD as a tool for methodological studies and provide a reference point for the expansion of this approach within clinical cancer research.

Evaluating the potential of relapse-free survival as a surrogate for overall survival in the adjuvant therapy of melanoma with checkpoint inhibitors.

INTRODUCTION: Recent changes in the adjuvant treatment of melanoma have raised interest in confirming relapse-free survival (RFS) as a surrogate for overall survival (OS). METHODS: We explore this issue with the meta-analytic framework, using individual patient data from the European Organisation for Research and Treatment of Cancer (EORTC) 18071 trial of ipilimumab and published results from other adjuvant trials. RESULTS: The individual patient data analysis results at a median follow-up of 5.3 years showed a strong association between RFS and OS at the patient level (ρ = 0.84; 95% confidence interval [CI]: 0.82-0.87) and a moderate association at the trial level (R2 = 0.59; 95% CI: 0.08-1.00). CONCLUSIONS: The trial-level association previously observed in interferon-based trials appeared to be maintained when the EORTC 18071 results were added to a regression analysis using published results from other trials. More data from adjuvant trials are required to confirm the strength of association between RFS and OS in this setting.

Evaluation of Continuous Tumor-Size-Based End Points as Surrogates for Overall Survival in Randomized Clinical Trials in Metastatic Colorectal Cancer.

Importance: Tumor measurements can be used to estimate time to nadir and depth of nadir as potential surrogates for overall survival (OS). Objective: To assess time to nadir and depth of nadir as surrogates for OS in metastatic colorectal cancer. Design, Setting, and Participants: Pooled analysis of 20 randomized clinical trials within the Aide et Recherche en Cancerologie Digestive database, which contains academic and industry-sponsored trials, was conducted. Three sets of comparisons were performed: chemotherapy alone, antiangiogenic agents, and anti-epidermal growth factor receptor agents in first-line treatment for patients with metastatic colorectal cancer. Main Outcomes and Measures: Surrogacy of time to nadir and depth of nadir was assessed at the trial level based on joint modeling of relative tumor-size change vs baseline and OS. Treatment effects on time to nadir and on depth of nadir were defined in terms of between-arm differences in time to nadir and in depth of nadir, and both were assessed in linear regressions for their correlation with treatment effects (hazard ratios) on OS within each set. The strengths of association were quantified using sample-size-weighted coefficients of determination (R2), with values closer to 1.00 indicating stronger association. At the patient level, the correlation was assessed between modeled relative tumor-size change and OS. Results: For 14 chemotherapy comparisons in 4289 patients, the R2 value was 0.63 (95% CI, 0.30-0.96) for the association between treatment effects on time to nadir and OS and 0.08 (95% CI, 0-0.37) for depth of nadir and OS. For 11 antiangiogenic agent comparisons (4854 patients), corresponding values of R2 were 0.25 (95% CI, 0-0.72) and 0.06 (95% CI, 0-0.35). For 8 anti-epidermal growth factor receptor comparisons (2684 patients), corresponding values of R2 were 0.24 (95% CI, 0-0.83) and 0.21 (95% CI, 0-0.78). Conclusions and Relevance: In contrast with early reports favoring depth of response as a surrogate, these results suggest that neither time to nadir nor depth of nadir is an acceptable surrogate for OS in the first-line treatment of metastatic colorectal cancer.

Understanding and Communicating Measures of Treatment Effect on Survival: Can We Do Better?

Time-to-event end points are the most frequent primary end points in phase III oncology trials, both in the adjuvant and advanced settings. The evaluation of these end points is important to inform clinical practice. However, although different measures can be used to describe the effect of treatment on these end points, we believe that any treatment benefit in a given trial is best reported using various absolute and relative measures. Our goal is to help clinicians understand the strengths and limitations of the traditional and novel measures used to denote the effect of treatment in randomized trials. Although none of these measures can reliably predict the outcome of individual patients, some measures could be added to the commonly used hazard ratio to provide a more patient-oriented assessment of treatment benefit. In particular, the difference of mean survival times quantifies the average survival benefit for a patient receiving a new treatment compared with a patient treated with standard of care, whereas the net benefit quantifies the probability of a patient receiving the new treatment to live longer by at least m months (for any number of months m of interest) than a patient receiving the standard treatment. We encourage statisticians and clinical scientists to include various measures of treatment benefit in the reports of phase III trials, acknowledging that different clinical situations may call for different measures of treatment effect. By using the various available measures, we may better inform ourselves and communicate results to our patients.

The impact of data errors on the outcome of randomized clinical trials.

Background/aims Considerable human and financial resources are typically spent to ensure that data collected for clinical trials are free from errors. We investigated the impact of random and systematic errors on the outcome of randomized clinical trials. Methods We used individual patient data relating to response endpoints of interest in two published randomized clinical trials, one in ophthalmology and one in oncology. These randomized clinical trials enrolled 1186 patients with age-related macular degeneration and 736 patients with metastatic colorectal cancer. The ophthalmology trial tested the benefit of pegaptanib for the treatment of age-related macular degeneration and identified a statistically significant treatment benefit, whereas the oncology trial assessed the benefit of adding cetuximab to a regimen of capecitabine, oxaliplatin, and bevacizumab for the treatment of metastatic colorectal cancer and failed to identify a statistically significant treatment difference. We simulated trial results by adding errors that were independent of the treatment group (random errors) and errors that favored one of the treatment groups (systematic errors). We added such errors to the data for the response endpoint of interest for increasing proportions of randomly selected patients. Results Random errors added to up to 50% of the cases produced only slightly inflated variance in the estimated treatment effect of both trials, with no qualitative change in the p-value. In contrast, systematic errors produced bias even for very small proportions of patients with added errors. Conclusion A substantial amount of random errors is required before appreciable effects on the outcome of randomized clinical trials are noted. In contrast, even a small amount of systematic errors can severely bias the estimated treatment effects. Therefore, resources devoted to randomized clinical trials should be spent primarily on minimizing sources of systematic errors which can bias the analyses, rather than on random errors which result only in a small loss in power.

Protein Kinase Activity Decreases with Higher Braak Stages of Alzheimer's Disease Pathology.

Alzheimer's disease (AD) is characterized by a long pre-clinical phase (20-30 years), during which significant brain pathology manifests itself. Disease mechanisms associated with pathological hallmarks remain elusive. Most processes associated with AD pathogenesis, such as inflammation, synaptic dysfunction, and hyper-phosphorylation of tau are dependent on protein kinase activity. The objective of this study was to determine the involvement of protein kinases in AD pathogenesis. Protein kinase activity was determined in postmortem hippocampal brain tissue of 60 patients at various stages of AD and 40 non-demented controls (Braak stages 0-VI) using a peptide-based microarray platform. We observed an overall decrease of protein kinase activity that correlated with disease progression. The phosphorylation of 96.7% of the serine/threonine peptides and 37.5% of the tyrosine peptides on the microarray decreased significantly with increased Braak stage (p-value <0.01). Decreased activity was evident at pre-clinical stages of AD pathology (Braak I-II). Increased phosphorylation was not observed for any peptide. STRING analysis in combination with pathway analysis and identification of kinases responsible for peptide phosphorylation showed the interactions between well-known proteins in AD pathology, including the Ephrin-receptor A1 (EphA1), a risk gene for AD, and sarcoma tyrosine kinase (Src), which is involved in memory formation. Additionally, kinases that have not previously been associated with AD were identified, e.g., protein tyrosine kinase 6 (PTK6/BRK), feline sarcoma oncogene kinase (FES), and fyn-associated tyrosine kinase (FRK). The identified protein kinases are new biomarkers and potential drug targets for early (pre-clinical) intervention.