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

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.

scholarly journals Estimands: bringing clarity and focus to research questions in clinical trials

BMJ Open ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. e052953
Author(s):  
Timothy Peter Clark ◽  
Brennan C Kahan ◽  
Alan Phillips ◽  
Ian White ◽  
James R Carpenter
Keyword(s):  
Clinical Trial ◽  
Sensitivity Analysis ◽  
Clinical Trials ◽  
Clinical Research ◽  
Recent Work ◽  
Treatment Effect ◽  
Research Question ◽  
Probability Of Success ◽  
Research Questions

Precise specification of the research question and associated treatment effect of interest is essential in clinical research, yet recent work shows that they are often incompletely specified. The ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials introduces a framework that supports researchers in precisely and transparently specifying the treatment effect they aim to estimate in their clinical trial. In this paper, we present practical examples to demonstrate to all researchers involved in clinical trials how estimands can help them to specify the research question, lead to a better understanding of the treatment effect to be estimated and hence increase the probability of success of the trial.

scholarly journals 32 Quantifying the Prevalence of Frailty in Drug Trials and the Relationship with Serious Adverse Events

2021 ◽  
Vol 50 (Supplement_1) ◽  
pp. i7-i11
Author(s):  
P Hanlon ◽  
E Butterly ◽  
J Lewsey ◽  
S Siebert ◽  
F S Mair ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Adverse Events ◽  
Disease Severity ◽  
Negative Binomial ◽  
Frailty Index ◽  
Chronic Obstructive ◽  
Drug Trials ◽  
Phase 3 ◽  
Serious Adverse Events ◽  
Increased Risk

Abstract Introduction Frailty is common in clinical practice, but trials rarely report on participant frailty. Consequently, clinicians and guideline-developers assume frailty is largely absent from trials and have questioned the relevance of trial findings to frail people. Therefore, we examined frailty in phase 3/4 industry-sponsored clinical trials of pharmacological interventions for three exemplar conditions: type 2 diabetes mellitus (T2DM), rheumatoid arthritis (RA), and chronic obstructive pulmonary disease (COPD). Methods We constructed a 40-item frailty index (FI) in 19 clinical trials (7 T2DM, 8 RA, 4 COPD, mean age 42–65 years) using individual-level participant data. Participants with a FI &gt;0.24 were considered “frail”. Baseline disease severity was assessed using HbA1c for T2DM, Disease Activity Score-28 (DAS28) for RA, and % predicted FEV1 for COPD. Using generalised gamma regression, we modelled FI on age, sex and disease severity. In negative binomial regression we modelled serious adverse event rates on FI, and combined results for each index condition in a random-effects meta-analysis. Results All trials included frail participants: prevalence 7–21% in T2DM trials, 33–73% in RA trials, and 15–22% in COPD trials. Increased disease severity and female sex were associated with higher FI in all trials. Frailty was associated with age in T2DM and RA trials, but not in COPD. Across all trials, and after adjusting for age, sex, and disease severity, higher FI predicted increased risk of serious adverse events; the pooled incidence rate ratios (per 0.1-point increase in FI scale) were 1.46 (95% CI 1.21–1.75), 1.45 (1.13–1.87) and 1.99 (1.43–2.76) for T2DM, RA and COPD, respectively. Conclusion Frailty is identifiable and prevalent among middle aged and older participants in phase 3/4 drug trials and has clinically important safety implications. Trial data may be harnessed to better understand chronic disease management in people living with frailty.

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