Reinventing the Wheel in Clinical Trial Design

Dr. Leon will present the biostatistical considerations that contribute to a clinical trial design and the strategies to enhance signal detection. These include minimizing bias in the estimate of treatment effect while maintaining a nominal level of type I error (i.e., false positive results) and maintaining sufficient statistical power (i.e. reducing the likelihood of false negative results). Particular attention will be paid to reducing the problems of attrition and the hazards of multiplicity. Methods to examine moderators of the treatment effect will also be explored. Examples from psychopharmacologic and psychotherapy trials for the treatment of depression and panic disorder will be provided to illustrate these issues. Following the didactic session, the participants will be encouraged to bring forth their own questions regarding clinical trial design for a 45-minute interactive discussion with the presenters. The objectives of the workshop are to improve the participants’ understanding of the goals of clinical trial design and methods to achieve those goals in order to improve their own research techniques, grantsmanship, and abilities to more accurately judge the results of studies presented in the literature.

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Methodological Approaches and Evidence for Effectiveness in Clinical Trials

European Psychiatry ◽

10.1016/s0924-9338(09)70559-0 ◽

2009 ◽

Vol 24 (S1) ◽

pp. 1-1

Author(s):

L. Davis

Keyword(s):

Clinical Trial ◽

Trial Design ◽

Clinical Trial Design ◽

Essential Elements ◽

Institute Of Medicine ◽

Trial Methodology ◽

Clinical Trial Methodology ◽

Theoretical Reasoning ◽

Design And Methods ◽

Controlled Clinical Study

Recent publications by the Institute of Medicine have unearthed several fundamental flaws in clinical trial methodology that, if corrected by the next generation of clinical investigators, can transform the field of mental health intervention research. Using a clinician-friendly approach, Dr. Davis will succinctly review the essential elements of optimal design and implementation of a randomized controlled clinical study. These elements include the need for a hypothesis that has clinical relevance based on sound theoretical reasoning, reasonable and generalizable inclusion and exclusion criteria, feasibility of an enrollment goal and study time-line, and appropriate choice of a comparator intervention and primary outcome. Techniques to reduce the study's costs, risks, and participant burden will be described. Examples from psychopharmacologic, psychotherapy, and vocational rehabilitation trials for the treatment of posttraumatic stress disorder will be discussed. Following the didactic session by the two presenters, the participants will be encouraged to bring forth their own questions regarding clinical trial design for a 45-minute interactive discussion with the presenters. The objectives of the workshop are to improve the participants’ understanding of the goals of clinical trial design and methods to achieve those goals in order to improve their own research techniques, grantsmanship, and abilities to more accurately judge the results of studies presented in the literature.

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Phase I/II clinical trial design for a novel therapy for mucopolysaccharidosis type I with an intravenously administered blood-brain barrier-crossing enzyme (JR-171)

Molecular Genetics and Metabolism ◽

10.1016/j.ymgme.2020.12.105 ◽

2021 ◽

Vol 132 (2) ◽

pp. S49

Author(s):

Ryo Higurashi ◽

Mika Okazaki ◽

Satoshi Kawashima ◽

Toshiaki Ikeda ◽

Sairei So ◽

...

Keyword(s):

Clinical Trial ◽

Phase I ◽

Blood Brain Barrier ◽

Trial Design ◽

Clinical Trial Design ◽

Brain Barrier ◽

Type I ◽

Mucopolysaccharidosis Type ◽

Mucopolysaccharidosis Type I ◽

Novel Therapy

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Clinical Trial Design and Statistics

10.2310/psych.2189 ◽

2018 ◽

Author(s):

Julie Ann Sosa

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Data Analysis ◽

Trial Design ◽

New Technologies ◽

Drug Application ◽

Clinical Trial Design ◽

Double Blind Study ◽

Type I ◽

End Points

A clinical trial is a planned experiment designed to prospectively measure the efficacy or effectiveness of an intervention by comparing outcomes in a group of subjects treated with the test intervention with those observed in one or more comparable group(s) of subjects receiving another intervention. Historically, the gold standard for a clinical trial has been a prospective, randomized, double-blind study, but it is sometimes impractical or unethical to conduct such in clinical medicine and surgery. Conventional outcomes have traditionally been clinical end points; with the rise of new technologies, however, they are increasingly being supplemented and/or replaced by surrogate end points, such as serum biomarkers. Because patients are involved, safety considerations and ethical principles must be incorporated into all phases of clinical trial design, conduct, data analysis, and presentation. This review covers the history of clinical trials, clinical trial phases, ethical issues, implementing the study, basic biostatistics for data analysis, and other resources. Figures show drug development and clinical trial process, and type I and II error. Tables list Food and Drug Administration new drug application types, and types of missing data in clinical trials. This review contains 2 highly rendered figures, 2 tables, and 38 references

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Psilocybin for depression: Considerations for clinical trial design

Journal of Psychedelic Studies ◽

10.1556/2054.2019.026 ◽

2019 ◽

Vol 3 (3) ◽

pp. 269-279

Author(s):

Kelley C. O’Donnell ◽

Sarah E. Mennenga ◽

Michael P. Bogenschutz

Keyword(s):

Clinical Trial ◽

Trial Design ◽

Depressive Symptomatology ◽

Specific Effect ◽

Clinical Trial Design ◽

Depressive Illness ◽

Trial Methodology ◽

Trial Duration ◽

Clinical Trial Methodology ◽

Psychedelic Drugs

Background and aims Given the enormous global burden of depressive illness, there is an urgent need to develop novel and more effective treatments for major depressive disorder (MDD). Recent findings have suggested that psychedelic drugs may have a role in the treatment of depressive symptoms, and a number of groups are in the process of developing protocols to study this question systematically. Given the subjective quality of both the psychedelic experience and depressive symptomatology, great care must be taken when designing a protocol to study the clinical efficacy of psychedelic drugs. This study will discuss many factors to consider when designing a clinical trial of psilocybin for MDD. Methods We provide a thorough review of pertinent research into antidepressant clinical trial methodology and review practical considerations that are relevant to the study of psychedelic-assisted treatment for depression. Results We discuss participant selection (including diagnostic accuracy, exclusion criteria, characteristics of the depressive episode, and the use of concurrent medications), study interventions (including dosing regimens, placebo selection, non-pharmacological components of treatment, and the importance of blinding), trial duration, outcome measures, and safety considerations. Conclusions Careful and transparent study design and data analysis will maximize the likelihood of generating meaningful, reproducible results, and identifying a treatment-specific effect. Meeting the highest standards for contemporary trial design may also broaden the acceptance of psychedelic research in the scientific community at large.

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Clinical Trial Design and Statistics

10.2310/7ccsp.2189 ◽

2016 ◽

Author(s):

Julie Ann Sosa ◽

Samantha M. Thomas ◽

April K.S. Salama

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Data Analysis ◽

Trial Design ◽

New Technologies ◽

Drug Application ◽

Clinical Trial Design ◽

Double Blind Study ◽

Type I ◽

End Points

A clinical trial is a planned experiment designed to prospectively measure the efficacy or effectiveness of an intervention by comparing outcomes in a group of subjects treated with the test intervention with those observed in one or more comparable group(s) of subjects receiving another intervention. Historically, the gold standard for a clinical trial has been a prospective, randomized, double-blind study, but it is sometimes impractical or unethical to conduct such in clinical medicine and surgery. Conventional outcomes have traditionally been clinical end points; with the rise of new technologies, however, they are increasingly being supplemented and/or replaced by surrogate end points, such as serum biomarkers. Because patients are involved, safety considerations and ethical principles must be incorporated into all phases of clinical trial design, conduct, data analysis, and presentation. This review covers the history of clinical trials, clinical trial phases, ethical issues, implementing the study, basic biostatistics for data analysis, and other resources. Figures show drug development and clinical trial process, and type I and II error. Tables list Food and Drug Administration new drug application types, and types of missing data in clinical trials. This review contains 2 highly rendered figures, 2 tables, and 38 references

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Clinical Trial Design and Statistics

10.2310/tywc.2189 ◽

2018 ◽

Author(s):

Julie Ann Sosa ◽

Samantha M. Thomas ◽

April K.S. Salama

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Data Analysis ◽

Trial Design ◽

New Technologies ◽

Drug Application ◽

Clinical Trial Design ◽

Double Blind Study ◽

Type I ◽

End Points

A clinical trial is a planned experiment designed to prospectively measure the efficacy or effectiveness of an intervention by comparing outcomes in a group of subjects treated with the test intervention with those observed in one or more comparable group(s) of subjects receiving another intervention. Historically, the gold standard for a clinical trial has been a prospective, randomized, double-blind study, but it is sometimes impractical or unethical to conduct such in clinical medicine and surgery. Conventional outcomes have traditionally been clinical end points; with the rise of new technologies, however, they are increasingly being supplemented and/or replaced by surrogate end points, such as serum biomarkers. Because patients are involved, safety considerations and ethical principles must be incorporated into all phases of clinical trial design, conduct, data analysis, and presentation. This review covers the history of clinical trials, clinical trial phases, ethical issues, implementing the study, basic biostatistics for data analysis, and other resources. Figures show drug development and clinical trial process, and type I and II error. Tables list Food and Drug Administration new drug application types, and types of missing data in clinical trials. This review contains 2 highly rendered figures, 2 tables, and 38 references

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Utilizing machine learning to improve clinical trial design for acute respiratory distress syndrome

npj Digital Medicine ◽

10.1038/s41746-021-00505-5 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

E. Schwager ◽

K. Jansson ◽

A. Rahman ◽

S. Schiffer ◽

Y. Chang ◽

...

Keyword(s):

Machine Learning ◽

Clinical Trial ◽

Acute Respiratory Distress Syndrome ◽

Respiratory Distress Syndrome ◽

Respiratory Distress ◽

Trial Design ◽

Statistical Power ◽

Distress Syndrome ◽

Univariate Analysis ◽

Clinical Trial Design

AbstractHeterogeneous patient populations, complex pharmacology and low recruitment rates in the Intensive Care Unit (ICU) have led to the failure of many clinical trials. Recently, machine learning (ML) emerged as a new technology to process and identify big data relationships, enabling a new era in clinical trial design. In this study, we designed a ML model for predictively stratifying acute respiratory distress syndrome (ARDS) patients, ultimately reducing the required number of patients by increasing statistical power through cohort homogeneity. From the Philips eICU Research Institute (eRI) database, no less than 51,555 ARDS patients were extracted. We defined three subpopulations by outcome: (1) rapid death, (2) spontaneous recovery, and (3) long-stay patients. A retrospective univariate analysis identified highly predictive variables for each outcome. All 220 variables were used to determine the most accurate and generalizable model to predict long-stay patients. Multiclass gradient boosting was identified as the best-performing ML model. Whereas alterations in pH, bicarbonate or lactate proved to be strong predictors for rapid death in the univariate analysis, only the multivariate ML model was able to reliably differentiate the disease course of the long-stay outcome population (AUC of 0.77). We demonstrate the feasibility of prospective patient stratification using ML algorithms in the by far largest ARDS cohort reported to date. Our algorithm can identify patients with sufficiently long ARDS episodes to allow time for patients to respond to therapy, increasing statistical power. Further, early enrollment alerts may increase recruitment rate.

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Clinical Trial Design and Statistics

10.2310/fm.2189 ◽

2018 ◽

Author(s):

Julie Ann Sosa ◽

Samantha M. Thomas ◽

April K.S. Salama

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Data Analysis ◽

Trial Design ◽

New Technologies ◽

Drug Application ◽

Clinical Trial Design ◽

Double Blind Study ◽

Type I ◽

End Points

A clinical trial is a planned experiment designed to prospectively measure the efficacy or effectiveness of an intervention by comparing outcomes in a group of subjects treated with the test intervention with those observed in one or more comparable group(s) of subjects receiving another intervention. Historically, the gold standard for a clinical trial has been a prospective, randomized, double-blind study, but it is sometimes impractical or unethical to conduct such in clinical medicine and surgery. Conventional outcomes have traditionally been clinical end points; with the rise of new technologies, however, they are increasingly being supplemented and/or replaced by surrogate end points, such as serum biomarkers. Because patients are involved, safety considerations and ethical principles must be incorporated into all phases of clinical trial design, conduct, data analysis, and presentation. This review covers the history of clinical trials, clinical trial phases, ethical issues, implementing the study, basic biostatistics for data analysis, and other resources. Figures show drug development and clinical trial process, and type I and II error. Tables list Food and Drug Administration new drug application types, and types of missing data in clinical trials. This review contains 2 highly rendered figures, 2 tables, and 38 references

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From statistical power to statistical assurance: It's time for a paradigm change in clinical trial design

Communications in Statistics - Simulation and Computation ◽

10.1080/03610918.2016.1259476 ◽

2017 ◽

Vol 46 (10) ◽

pp. 7957-7971 ◽

Cited By ~ 3

Author(s):

Ding-Geng (Din) Chen ◽

Shuyen Ho

Keyword(s):

Clinical Trial ◽

Trial Design ◽

Statistical Power ◽

Clinical Trial Design ◽

Paradigm Change

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