Considerations for Interim Analyses in Adaptive Trials, and Perspectives on the Use of DMCs

2014 ◽  
pp. 259-272 ◽  
Author(s):  
Paul Gallo ◽  
David DeMets ◽  
Lisa LaVange
Keyword(s):  
Interim Analyses ◽  
Adaptive Trials

Exploring changes in treatment effects across design stages in adaptive trials

2009 ◽  
Vol 8 (1) ◽  
pp. 62-72 ◽  
Author(s):  
Tim Friede ◽  
Robin Henderson
Keyword(s):  
Treatment Effects ◽  
Adaptive Trials

scholarly journals Monitoring of Vedolizumab Infusion Therapy (MOVE-IT) Response With Fecal Inflammation Markers, Ultrasound, and Trough Serum Level in Patients With Ulcerative Colitis: Protocol for a Multicentric, Prospective, Noninterventional Study

10.2196/14335 ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. e14335
Author(s):  
Jimmi Cording ◽  
Margit Blömacher ◽  
Berit Inga Wiebe ◽  
Jost Langhorst ◽  
Torsten Kucharzik ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Clinical Response ◽  
Clinical Remission ◽  
Infusion Therapy ◽  
Inflammation Markers ◽  
Interim Analyses ◽  
Trough Serum ◽  
Therapeutic Algorithm ◽  
Ulcerative Colitis Treatment ◽  
Noninterventional Study

Background Vedolizumab has been shown to induce clinical remission in patients with active ulcerative colitis. Treatment with anti-integrin vedolizumab leads to clinical remission in 16.9% and clinical response in 47.1% of cases after 6 weeks. However, in clinical practice, no decision to discontinue or continue vedolizumab therapy is made until 14 weeks at the earliest. Objective The aim of this study is to develop an algorithm for optimizing vedolizumab administration in patients with moderate-to-severe ulcerative colitis by calculating the probability of clinical response at week 14, on the basis of the data from week 6. Methods This is a prospective, single-arm, multicentric, noninterventional, observational study with no interim analyses and a sample size of 35 evaluable patients. Results The enrollment started in August 2018 and was still open at the date of submission. The study is expected to complete in September 2020. Conclusions The early identification of patients who are responding to an integrin antibody is therapeutically beneficial. At the same time, patients who are not responding can be identified earlier. The development of a therapeutic algorithm for identifying patients as responders or nonresponders can thus help prescribing physicians avoid ineffective treatments and stop these very early.

scholarly journals Occurrence and Severity of Physiological Disorders of Oil Palm (Elaeis guineensis Jacq. L.) in Uganda

2020 ◽  
Vol 12 (10) ◽  
pp. 86
Author(s):  
Fred Bwayo Masika ◽  
Isaac Danso ◽  
Rossette Nangonzi ◽  
Otuba Moses Amugoli ◽  
Alex Asiimwe ◽  
...  
Keyword(s):  
Oil Palm ◽  
Elaeis Guineensis ◽  
Industrial Sector ◽  
Smallholder Farmer ◽  
Palm Fruit ◽  
Ecological Zones ◽  
Physiological Disorders ◽  
Adaptive Trials ◽  
Bunch Rot ◽  
Corporate Investors

In Africa, oil palm is grown in 25 countries supported by corporate investors. In Uganda, commercial oil palm cultivation began in 2005 in Bugala Islands. Seedlings were imported from countries with established breeding programs. These seedlings were grown in areas with different environmental conditions which have resulted in a number of physiological disorders. The aim of this research was to determine the major physiological disorders in oil palm fruit bunches in Uganda. The study was carried out in the adaptive trials in Kagadi, Bugiri, Buvuma and Masaka Districts and in the different smallholder farmer blocks in Kalangala District. Data was collected on bunch rot, bunch failure and uneven ripening. Sampling was carried out in oil palm plantations above five years of age. Three fields were selected from each unit and three units from each block by the help of the Agricultural Extension Officers (AEOs). Palms were randomly sampled and assessed for presence of bunch rot, bunch failure and uneven ripening symptoms. The incidence was expressed as a percentage of the total number of palms sampled while the severity of bunch rot disease was scored on a scale of 0-4. From the results, the differences in bunch rot and bunch failure in adaptive trials were statistically significant as well as across seasons (P < 0.05). Uneven ripening was not statistically significant and severity of bunch rot in the different farmer blocks in Kalangala was statistically significant (P = 0.03). Uneven ripening was high across smallholder farmer blocks in Kalangala and was statistically significant (P = 0.05) even across seasons (P < 0.05). These results are important for sensitization of farmers on management of oil palm disorders and essential for guiding policy makers and investors as the oil palm industrial sector is being developed in Uganda. This study calls for determination of water deficit at the various ecological zones and its relationship to physiological disorders as a guide for further oil palm estate development.

A Simplified Software for Uncertainty Estimation Using Monte Carlo Method

2020 ◽  
Vol 12 (8) ◽  
pp. 1050-1053
Author(s):  
Jasveer Singh ◽  
L. A. Kumaraswamidhas ◽  
Neha Bura ◽  
Kapil Kaushik ◽  
Nita Dilawar Sharma
Keyword(s):  
Monte Carlo ◽  
Standard Deviation ◽  
Monte Carlo Method ◽  
Measurement Uncertainty ◽  
Standard Uncertainty ◽  
Uncertainty Estimation ◽  
End Points ◽  
The Monte Carlo Method ◽  
Adaptive Trials ◽  
Coverage Interval

The current paper discusses about the application of Monte Carlo method for the evaluation of measurement uncertainty using in-house developed program on C++ platform. The Monte Carlo method can be carried out by fixed trials as well as adaptive trials using this program. The program provides the four parameters viz. estimate of measurand, standard uncertainty in the form of standard deviation and end points of coverage interval as an output.

Adaptive Learning of Drug Quality and Optimization of Patient Recruitment for Clinical Trials with Dropouts

2021 ◽  
Author(s):  
Zhili Tian ◽  
Weidong Han ◽  
Warren B. Powell
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Dynamic Programming ◽  
Value Function ◽  
Standard Treatment ◽  
Treatment Effectiveness ◽  
Interim Analyses ◽  
Clinical Trial Program ◽  
Patient Enrollment ◽  
The Value Function

Problem definition: Clinical trials are crucial to new drug development. This study investigates optimal patient enrollment in clinical trials with interim analyses, which are analyses of treatment responses from patients at intermediate points. Our model considers uncertainties in patient enrollment and drug treatment effectiveness. We consider the benefits of completing a trial early and the cost of accelerating a trial by maximizing the net present value of drug cumulative profit. Academic/practical relevance: Clinical trials frequently account for the largest cost in drug development, and patient enrollment is an important problem in trial management. Our study develops a dynamic program, accurately capturing the dynamics of the problem, to optimize patient enrollment while learning the treatment effectiveness of an investigated drug. Methodology: The model explicitly captures both the physical state (enrolled patients) and belief states about the effectiveness of the investigated drug and a standard treatment drug. Using Bayesian updates and dynamic programming, we establish monotonicity of the value function in state variables and characterize an optimal enrollment policy. We also introduce, for the first time, the use of backward approximate dynamic programming (ADP) for this problem class. We illustrate the findings using a clinical trial program from a leading firm. Our study performs sensitivity analyses of the input parameters on the optimal enrollment policy. Results: The value function is monotonic in cumulative patient enrollment and the average responses of treatment for the investigated drug and standard treatment drug. The optimal enrollment policy is nondecreasing in the average response from patients using the investigated drug and is nonincreasing in cumulative patient enrollment in periods between two successive interim analyses. The forward ADP algorithm (or backward ADP algorithm) exploiting the monotonicity of the value function reduced the run time from 1.5 months using the exact method to a day (or 20 minutes) within 4% of the exact method. Through an application to a leading firm’s clinical trial program, the study demonstrates that the firm can have a sizable gain of drug profit following the optimal policy that our model provides. Managerial implications: We developed a new model for improving the management of clinical trials. Our study provides insights of an optimal policy and insights into the sensitivity of value function to the dropout rate and prior probability distribution. A firm can have a sizable gain in the drug’s profit by managing its trials using the optimal policies and the properties of value function. We illustrated that firms can use the ADP algorithms to develop their patient enrollment strategies.

Chemotherapy of drug-resistant ovarian cancer: a Southwest Oncology Group Study.

1986 ◽  
Vol 4 (9) ◽  
pp. 1374-1379 ◽  
Author(s):  
L R Laufman ◽  
J B Green ◽  
D S Alberts ◽  
R O'Toole ◽  
R D Hilgers ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Response Rate ◽  
Final Analysis ◽  
Drug Resistant ◽  
Eligibility Criteria ◽  
Interim Analyses ◽  
Southwest Oncology Group Study ◽  
Pathology Review ◽  
Insight Into ◽  
Better Than

We present a final analysis, including pathology review, of a cooperative group study of drug-resistant ovarian cancer. Of 200 patients registered, 112 were eligible and evaluable, with a response rate of 26% and median survival of 7 months. Because these results are poorer than those reported in the preliminary and interim analyses of this study, we scrutinized the 88 excluded patients, most of whom failed to meet our strict pathologic criteria for a diagnosis of ovarian cancer of epithelial type, and who, as a heterogeneous group, fared better than patients who did meet the eligibility criteria. We believe this analysis provides insight into the spectrum of diseases that are frequently called ovarian cancer, but might be more properly labeled abdominal carcinomatosis.

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