Enhancing Signal Detection in Clinical Trials

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, this workshop will succinctly review the essential elements of optimal design and implementation of a randomized controlled clinical study 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, psychotherapy, and vocational rehabilitation trials for the treatment of posttraumatic stress disorder, depression, and panic disorder will be provided to illustrate these issues. Techniques to reduce the study's costs, risks, and participant burden will be described. Following the didactic session, the participants are 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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Evaluation of the Impact of Calibration of Patient-Reported Outcomes Measures on Clinical Trial Results: A Simulation Study based on Rasch Measurement Theory

10.21203/rs.3.rs-1182025/v1 ◽

2021 ◽

Author(s):

Angély Loubert ◽

Antoine Regnault ◽

Véronique Sébille ◽

Jean-Benoit Hardouin

Keyword(s):

Clinical Trial ◽

Treatment Effect ◽

Type I Error ◽

Measurement Theory ◽

Item Parameter ◽

Parameter Estimates ◽

Type I ◽

Item Parameters ◽

Patient Reported ◽

The Impact

Abstract BackgroundIn the analysis of clinical trial endpoints, calibration of patient-reported outcomes (PRO) instruments ensures that resulting “scores” represent the same quantity of the measured concept between applications. Rasch measurement theory (RMT) is a psychometric approach that guarantees algebraic separation of person and item parameter estimates, allowing formal calibration of PRO instruments. In the RMT framework, calibration is performed using the item parameter estimates obtained from a previous “calibration” study. But if calibration is based on poorly estimated item parameters (e.g., because the sample size of the calibration sample was low), this may hamper the ability to detect a treatment effect, and direct estimation of item parameters from the trial data (non-calibration) may then be preferred. The objective of this simulation study was to assess the impact of calibration on the comparison of PRO results between treatment groups, using different analysis methods.MethodsPRO results were simulated following a polytomous Rasch model, for a calibration and a trial sample. Scenarios included varying sample sizes, with instrument of varying number of items and modalities, and varying item parameters distributions. Different treatment effect sizes and distributions of the two patient samples were also explored. Comparison of treatment groups was performed using different methods based on a random effect Rasch model. Calibrated and non-calibrated approaches were compared based on type-I error, power, bias, and variance of the estimates for the difference between groups.Results There was no impact of the calibration approach on type-I error, power, bias, and dispersion of the estimates. Among other findings, mistargeting between the PRO instrument and patients from the trial sample (regarding the level of measured concept) resulted in a lower power and higher position bias than appropriate targeting. ConclusionsCalibration of PROs in clinical trials does not compromise the ability to accurately assess a treatment effect and is essential to properly interpret PRO results. Given its important added value, calibration should thus always be performed when a PRO instrument is used as an endpoint in a clinical trial, in the RMT framework.

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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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Clinical Trial Design and Statistics

10.2310/im.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

Download Full-text