scholarly journals Design and analysis of clinical trials in the presence of delayed treatment effect

2016 ◽  
Vol 35 (11) ◽  
pp. 1774-1779 ◽  
Author(s):  
Tony Sit ◽  
Mengling Liu ◽  
Michael Shnaidman ◽  
Zhiliang Ying
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Delayed Treatment

Group sequential monitoring based on the maximum of weighted log-rank statistics with the Fleming–Harrington class of weights in oncology clinical trials

2020 ◽  
Vol 29 (12) ◽  
pp. 3525-3532
Author(s):  
Thomas J Prior
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Proportional Hazards ◽  
Rank Test ◽  
Event Data ◽  
Time To Event ◽  
Survival Curves ◽  
Delayed Treatment ◽  
Time To Event Data ◽  
Log Rank Test

Clinical trials in oncology often involve the statistical analysis of time-to-event data such as progression-free survival or overall survival to determine the benefit of a treatment or therapy. The log-rank test is commonly used to compare time-to-event data from two groups. The log-rank test is especially powerful when the two groups have proportional hazards. However, survival curves encountered in oncology studies that differ from one another do not always differ by having proportional hazards; in such instances, the log-rank test loses power, and the survival curves are said to have “non-proportional hazards”. This non-proportional hazards situation occurs for immunotherapies in oncology; immunotherapies often have a delayed treatment effect when compared to chemotherapy or radiation therapy. To correctly identify and deliver efficacious treatments to patients, it is important in oncology studies to have available a statistical test that can detect the difference in survival curves even in a non-proportional hazards situation such as one caused by delayed treatment effect. An attempt to address this need was the “max-combo” test, which was originally described only for a single analysis timepoint; this article generalizes that test to preserve type I error when there are one or more interim analyses, enabling efficacious treatments to be identified and made available to patients more rapidly.

Optimal design and analysis of phase I/II clinical trials in multiple sclerosis with gadolinium-enhanced lesions as the endpoint

2010 ◽  
Vol 16 (7) ◽  
pp. 840-847 ◽  
Author(s):  
Brian C Healy ◽  
David Ikle ◽  
Eric A Macklin ◽  
Gary Cutter
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Trials ◽  
Phase I ◽  
Treatment Effect ◽  
Repeated Measures ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Delayed Treatment ◽  
Binomial Regression

Many phase I/II clinical trials in multiple sclerosis use gadolinium-enhanced lesions as the outcome measure. The best scanning interval and analysis for this outcome has not been determined. The objective of this study was to compare timing schemes and analysis techniques in terms of power for phase I/II clinical trials. Data were simulated under four scenarios assuming a negative binomial distribution for the number of new lesions and an exponential distribution for the duration of enhancement. The first scenario assumed an immediate treatment effect on the number of new lesions, while the second scenario assumed a delayed treatment effect. The third scenario assumed a higher proportion of patients had no new lesions, and the final scenario assumed an immediate treatment effect on the duration of enhancement. For each scenario, power for a six-month trial with 100 patients per arm was calculated using 10 analysis strategies. The scanning intervals tested were monthly scans, bimonthly scans and a single end-of-study scan. In addition, cost-effectiveness of each trial design and analysis was compared. Negative binomial regression models for the total number of new lesions were the most powerful analyses under an immediate treatment effect, and repeated measures models with a categorical time effect were the most powerful analyses under a delayed treatment effect. Although monthly scans generally provided most power, this design was also most costly. Designs with fewer scans per patient provide similar power and are more cost-effective. Negative binomial regression models are more powerful than non-parametric approaches.

scholarly journals Estimating and reporting treatment effects in clinical trials for weight management: using estimands to interpret effects of intercurrent events and missing data

2021 ◽  
Author(s):  
Sean Wharton ◽  
Arne Astrup ◽  
Lars Endahl ◽  
Michael E. J. Lean ◽  
Altynai Satylganova ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Weight Management ◽  
Treatment Effect ◽  
Repeated Measures ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Weight Losses ◽  
The Difference ◽  
Treatment Of Obesity

AbstractIn the approval process for new weight management therapies, regulators typically require estimates of effect size. Usually, as with other drug evaluations, the placebo-adjusted treatment effect (i.e., the difference between weight losses with pharmacotherapy and placebo, when given as an adjunct to lifestyle intervention) is provided from data in randomized clinical trials (RCTs). At first glance, this may seem appropriate and straightforward. However, weight loss is not a simple direct drug effect, but is also mediated by other factors such as changes in diet and physical activity. Interpreting observed differences between treatment arms in weight management RCTs can be challenging; intercurrent events that occur after treatment initiation may affect the interpretation of results at the end of treatment. Utilizing estimands helps to address these uncertainties and improve transparency in clinical trial reporting by better matching the treatment-effect estimates to the scientific and/or clinical questions of interest. Estimands aim to provide an indication of trial outcomes that might be expected in the same patients under different conditions. This article reviews how intercurrent events during weight management trials can influence placebo-adjusted treatment effects, depending on how they are accounted for and how missing data are handled. The most appropriate method for statistical analysis is also discussed, including assessment of the last observation carried forward approach, and more recent methods, such as multiple imputation and mixed models for repeated measures. The use of each of these approaches, and that of estimands, is discussed in the context of the SCALE phase 3a and 3b RCTs evaluating the effect of liraglutide 3.0 mg for the treatment of obesity.

scholarly journals Competing risk and heterogeneity of treatment effect in clinical trials

Trials ◽  
2008 ◽  
Vol 9 (1) ◽  
Author(s):  
David M Kent ◽  
Alawi Alsheikh-Ali ◽  
Rodney A Hayward
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Competing Risk ◽  
Heterogeneity Of Treatment Effect

Importance of incorporating quantitative imaging biomarker technical performance characteristics when estimating treatment effects

2021 ◽  
pp. 174077452098193
Author(s):  
Nancy A Obuchowski ◽  
Erick M Remer ◽  
Ken Sakaie ◽  
Erika Schneider ◽  
Robert J Fox ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Confidence Intervals ◽  
Treatment Effect ◽  
Quantitative Imaging ◽  
Imaging Biomarker ◽  
Imaging Biomarkers ◽  
Measurement Bias ◽  
Technical Performance ◽  
Quantitative Imaging Biomarker ◽  
Performance Validation

Background/aims Quantitative imaging biomarkers have the potential to detect change in disease early and noninvasively, providing information about the diagnosis and prognosis of a patient, aiding in monitoring disease, and informing when therapy is effective. In clinical trials testing new therapies, there has been a tendency to ignore the variability and bias in quantitative imaging biomarker measurements. Unfortunately, this can lead to underpowered studies and incorrect estimates of the treatment effect. We illustrate the problem when non-constant measurement bias is ignored and show how treatment effect estimates can be corrected. Methods Monte Carlo simulation was used to assess the coverage of 95% confidence intervals for the treatment effect when non-constant bias is ignored versus when the bias is corrected for. Three examples are presented to illustrate the methods: doubling times of lung nodules, rates of change in brain atrophy in progressive multiple sclerosis clinical trials, and changes in proton-density fat fraction in trials for patients with nonalcoholic fatty liver disease. Results Incorrectly assuming that the measurement bias is constant leads to 95% confidence intervals for the treatment effect with reduced coverage (<95%); the coverage is especially reduced when the quantitative imaging biomarker measurements have good precision and/or there is a large treatment effect. Estimates of the measurement bias from technical performance validation studies can be used to correct the confidence intervals for the treatment effect. Conclusion Technical performance validation studies of quantitative imaging biomarkers are needed to supplement clinical trial data to provide unbiased estimates of the treatment effect.

scholarly journals Duration of Treatment Effect Should Be Considered in the Design and Interpretation of Clinical Trials: Results of a Discrete Choice Experiment

2019 ◽  
Vol 39 (4) ◽  
pp. 461-473 ◽  
Author(s):  
Bethan Copsey ◽  
James Buchanan ◽  
Raymond Fitzpatrick ◽  
Sarah E. Lamb ◽  
Susan J. Dutton ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Disease Severity ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Treatment Effect ◽  
Heart Attack ◽  
Choice Experiment ◽  
Ulcer Bleeding ◽  
Duration Of Treatment ◽  
Duration Of Effect

Objective. This study examined whether duration of treatment effect should be considered in a benefit-risk assessment using a case study of osteoarthritis medications. Study Design and Setting. A discrete choice experiment was completed by 300 residents of the United Kingdom with hip and/or knee osteoarthritis. In 16 choice tasks, participants selected their preferred option from 2 medications. Medications were described in terms of effect on pain, stiffness, and function; duration of treatment effect; and risk of heart attack and stomach ulcer bleeding. The analysis used mixed-effects logistic regression. Results. Pain, disease severity, and duration of treatment effect had the greatest influence on medication preferences, whereas stiffness did not significantly affect medication choice. Participants were willing to accept an increase in the risk of heart attack of 2.6% (95% confidence interval: 2.0% to 3.2%) to increase the duration of treatment effect from 1 month to 12 months. Reducing pain from moderate to mild was valued the same as increasing duration of effect from 1 month to 3 months; both were seen as equivalent to an absolute reduction of 1.2% in the risk of heart attack in the next year. Subgroup analysis suggested disease severity influenced patient preferences. Conclusions. Along with treatment benefits and risks, the results suggest that duration of treatment effect is an important factor in the medication choices of people with osteoarthritis. This could have implications for the design and interpretation of clinical trials, for example, incorporating longer-term surveillance of trial participants and accounting for duration of treatment effect in risk-benefit assessments. Future research is needed to assess whether these findings are generalizable to other samples, disease areas, and levels of duration of effect.

A simple test for the treatment effect in clinical trials with a sequential parallel comparison design and negative binomial outcomes

2018 ◽  
Vol 18 (2) ◽  
pp. 184-197 ◽  
Author(s):  
Gosuke Homma ◽  
Takashi Daimon
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Negative Binomial ◽  
Simple Test

Estimation of delay time in survival data with delayed treatment effect

2018 ◽  
Vol 29 (2) ◽  
pp. 229-243 ◽  
Author(s):  
Wei Li ◽  
Sophie Yu-Pu Chen ◽  
Alan Rong
Keyword(s):  
Treatment Effect ◽  
Survival Data ◽  
Delay Time ◽  
Delayed Treatment
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