Publications by authors named "Peter Thall"

Modern advances in systemic and localized therapies for patients with renal cell carcinoma (RCC) have significantly improved patients' outcomes. If disease progression occurs after initial treatment, clinicians often have multiple options for a first salvage therapy. Because salvage and initial treatments both may affect overall survival time, and they may interact in unanticipated ways, there is a growing need to determine sequences of initial therapy and first salvage therapy that maximize overall survival while maintaining quality of life.

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For phase II clinical trials that determine the acceptability of an experimental treatment based on ordinal toxicity and ordinal response, most monitoring methods require each ordinal outcome to be dichotomized using a selected cut-point. This allows two early stopping rules to be constructed that compare marginal probabilities of toxicity and response to respective upper and lower limits. Important problems with this approach are loss of information due to dichotomization, dependence of treatment acceptability decisions on precisely how each ordinal variable is dichotomized, and ignoring association between the two outcomes.

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Article Synopsis
  • Optimizing drug doses across multiple indications is complicated because different conditions may react differently to doses and toxicity levels, leading to varying optimal doses for each condition.
  • The proposed solution is a Randomized two-stage basket trial design (ROMI) that first evaluates a high dose for safety and effectiveness, then compares it with a lower dose for indications that pass the initial evaluation.
  • ROMI utilizes a Bayesian statistical model to share data across indications while acknowledging their differences, ultimately selecting the best dose based on a calculated trade-off between response and toxicity.
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Refractory aggressive lymphomas can be treated with allo-SCT, pursuing a graft-vs-lymphoma effect. While reduced intensity conditioning is safe, tumors often progress rapidly, indicating the need for more active conditioning regimens. The preclinical synergy we saw between gemcitabine (Gem), clofarabine (Clo) and busulfan (Bu) against lymphoma cell lines led us to study Gem/Clo/Bu clinically.

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Background: Breast cancer is the most frequent type of cancer and the second leading cause of cancer-related mortality in women. Mastectomies remain a key component of the treatment of non-metastatic breast cancer, and strategies to treat acute postoperative pain, a complication affecting nearly all patients undergoing surgery, continues to be an important clinical challenge. This study aimed to determine the impact of intraoperative methadone administration compared to conventional short-acting opioids on pain-related perioperative outcomes in women undergoing a mastectomy.

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A dynamic treatment regime (DTR) is a mathematical representation of a multistage decision process. When applied to sequential treatment selection in medical settings, DTRs are useful for identifying optimal therapies for chronic diseases such as AIDs, mental illnesses, substance abuse, and many cancers. Sequential multiple assignment randomized trials (SMARTs) provide a useful framework for constructing DTRs and providing unbiased between-DTR comparisons.

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Background And Methods: Although signet ring cell (SRC) histology is associated with resistance to neoadjuvant chemoradiotherapy and worse overall survival (OS) in esophageal adenocarcinoma (EAC), its prognostic relationship among patients who survive the early period following resection is unknown. EAC patients who underwent trimodality therapy at a single institution (2006-2018) were identified. Bayesian multivariable regression (BMR) analyses of OS and additional OS from a 3-year landmark were performed.

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Article Synopsis
  • Advances in targeted agents and immunotherapies have revealed that traditional methods for determining drug doses are often ineffective, especially in clinical research.
  • The FDA's Oncology Center of Excellence launched Project Optimus to improve the way doses are optimized and selected in cancer drug development.
  • The articles in this special issue discuss innovative designs for dose selection and highlight regulatory changes aimed at enhancing clinical trial strategies.
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A generalized phase 1-2-3 design, Gen 1-2-3, that includes all phases of clinical treatment evaluation is proposed. The design extends and modifies the design of Chapple and Thall (2019), denoted by CT. Both designs begin with a phase 1-2 trial including dose acceptability and optimality criteria, and both select an optimal dose for phase 3.

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There is a pressing need for allogeneic chimeric antigen receptor (CAR)-immune cell therapies that are safe, effective and affordable. We conducted a phase 1/2 trial of cord blood-derived natural killer (NK) cells expressing anti-CD19 chimeric antigen receptor and interleukin-15 (CAR19/IL-15) in 37 patients with CD19 B cell malignancies. The primary objectives were safety and efficacy, defined as day 30 overall response (OR).

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Background: Identifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain regulatory approval. Identifying appropriate doses for new agents is a complex process that involves balancing the risks and benefits of outcomes such as biological efficacy, toxicity, and patient quality of life.

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Relapse is the major cause of failure of high-dose chemotherapy (HDC) with autologous stem cell transplantation (ASCT) for B cell non-Hodgkin lymphomas (B-NHL). Improvement strategies include use in combination with effective immunotherapies. We hypothesized that the combination of rituximab/HDC/ASCT with expanded cord blood (CB)-derived natural killer (NK) cells is safe and active in B-NHL.

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Background: Stereotactic body radiotherapy (SBRT) has the potential to ablate localised pancreatic ductal adenocarcinoma. Selective dismutase mimetics sensitise tumours while reducing normal tissue toxicity. This trial was designed to establish the efficacy and toxicity afforded by the selective dismutase mimetic avasopasem manganese when combined with ablative SBRT for localised pancreatic ductal adenocarcinoma.

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This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial's sample is unlikely to represent a definable patient population.

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Conventional designs for choosing a dose for a new therapy may select doses that are unsafe or ineffective and fail to optimize progression-free survival time, overall survival time, or response/remission duration. We explain and illustrate limitations of conventional dose-finding designs and make four recommendations to address these problems. When feasible, a dose-finding design should account for long-term outcomes, include screening rules that drop unsafe or ineffective doses, enroll an adequate sample size, and randomize patients among doses.

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A Bayesian feature allocation model (FAM) is presented for identifying cell subpopulations based on multiple samples of cell surface or intracellular marker expression level data obtained by cytometry by time of flight (CyTOF). Cell subpopulations are characterized by differences in marker expression patterns, and cells are clustered into subpopulations based on their observed expression levels. A model-based method is used to construct cell clusters within each sample by modeling subpopulations as latent features, using a finite Indian buffet process.

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Background: Randomized controlled trials are considered the gold standard for evaluating experimental treatments but often require large sample sizes. Single-arm trials require smaller sample sizes but are subject to bias when using historical control data for comparative inferences. This article presents a Bayesian adaptive synthetic-control design that exploits historical control data to create a hybrid of a single-arm trial and a randomized controlled trial.

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Designs for early phase dose finding clinical trials typically are either phase I based on toxicity, or phase I-II based on toxicity and efficacy. These designs rely on the implicit assumption that the dose of an experimental agent chosen using these short-term outcomes will maximize the agent's long-term therapeutic success rate. In many clinical settings, this assumption is not true.

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A Bayesian method is proposed for personalized treatment selection in settings where data are available from a randomized clinical trial with two or more outcomes. The motivating application is a randomized trial that compared letrozole plus bevacizimab to letrozole alone as first-line therapy for hormone receptor positive advanced breast cancer. The combination treatment arm had larger median progression-free survival time, but also a higher rate of severe toxicities.

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Purpose: Dose constraints for reirradiation of recurrent primary brain tumors are not well-established. This study was conducted to prospectively evaluate composite dose constraints for conventionally fractionated brain reirradiation.

Methods And Materials: A single-institution, prospective study of adults with previously irradiated, recurrent brain tumors was performed.

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We propose a curve-free random-effects meta-analysis approach to combining data from multiple phase I clinical trials to identify an optimal dose. Our method accounts for between-study heterogeneity that may stem from different study designs, patient populations, or tumor types. We also develop a meta-analytic-predictive (MAP) method based on a power prior that incorporates data from multiple historical studies into the design and conduct of a new phase I trial.

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This paper proposes randomized controlled clinical trial design to evaluate external cooling as a means to control fever and thereby reduce mortality in patients with septic shock. The trial will include concurrent external cooling and control arms while adaptively incorporating historical control arm data. Bayesian group sequential monitoring will be done using a posterior comparative test based on the 60-day survival distribution in each concurrent arm.

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We discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to estimate treatment effects and settings where a different approach is needed. This may be because a new patient does not meet the RCT's entry criteria, or a treatment's effect is modified by biomarkers or other variables that act as mediators between treatment and outcome.

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A Bayesian design is proposed for randomized phase II clinical trials that screen multiple experimental treatments compared to an active control based on ordinal categorical toxicity and response. The underlying model and design account for patient heterogeneity characterized by ordered prognostic subgroups. All decision criteria are subgroup specific, including interim rules for dropping unsafe or ineffective treatments, and criteria for selecting optimal treatments at the end of the trial.

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