Planning the Size of Clinical Trials with Allowance for Patient Noncompliance

1990 ◽  
Vol 29 (03) ◽  
pp. 243-246 ◽  
Author(s):  
M. A. A. Moussa
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Event Rate ◽  
Survival Functions ◽  
Time Intervals ◽  
Patient Noncompliance ◽  
Event Rates ◽  
Larger Sample

AbstractVarious approaches are considered for adjustment of clinical trial size for patient noncompliance. Such approaches either model the effect of noncompliance through comparison of two survival distributions or two simple proportions. Models that allow for variation of noncompliance and event rates between time intervals are also considered. The approach that models the noncompliance adjustment on the basis of survival functions is conservative and hence requires larger sample size. The model to be selected for noncompliance adjustment depends upon available estimates of noncompliance and event rate patterns.

scholarly journals Chloroquine and Hydroxychloroquine could be an Available Weapons to Treat COVID-19 Associated Pneumonia

2020 ◽  
pp. 52-60
Author(s):  
Nehad J. Ahmed
Keyword(s):  
Clinical Trials ◽  
Angiotensin Converting Enzyme ◽  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Google Scholar ◽  
Converting Enzyme ◽  
Larger Sample

Aims: This study aims to review the efficacy of chloroquine and hydroxychloroquine to treat coronavirus disease 2019 (COVID-19) associated pneumonia. Methodology: This review includes searching Google scholar for publications about the use of hydroxychloroquinein the treatment of COVID-19 using the words of (Covid-19) AND hydroxychloroquine. Results: Chloroquine and hydroxychloroquine have proven effective in treating coronavirus in China in vitro, but till now only few clinical trials are available and these trials were conducted on a small sample size of the patients. The efficacy of chloroquine and hydroxychloroquine is mainly due to its effect on angiotensin-converting enzyme II (ACE2). Conclusion: The use of chloroquine and hydroxychloroquine could be very promising but more trials are needed that include larger sample size and more data are required about the comparison between chloroquine and hydroxychloroquine with other antivirals.

scholarly journals Geographic Disparities In Access To Cancer Clinical Trials In India

2020 ◽  
Author(s):  
Santam Chakraborty ◽  
Indranil Mallick ◽  
Hung N Luu ◽  
Tapesh Bhattacharyya ◽  
Arunsingh Moses ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
The State ◽  
Cancer Site ◽  
Cancer Clinical Trials ◽  
Clinical Trial Registry ◽  
Geographic Disparities ◽  
Incident Cancer ◽  
Incident Cases

Abstract Introduction The current study was aimed at quantifying the disparity in geographic access to cancer clinical trials in India. Methods We collated data of cancer clinical trials from the clinical trial registry of India (CTRI) and data on state-wise cancer incidence from the Global Burden of Disease Study. The total sample size for each clinical trial was divided by the trial duration to get the sample size per year. This was then divided by the number of states in which accrual was planned to get the sample size per year per state (SSY). For interventional trials investigating a therapy, the SSY was divided by the number of incident cancers in the state to get the SSY per 1,000 incident cancer cases. The SSY data was then mapped to visualise the geographical disparity.Results We identified 181 ongoing studies, of whom 132 were interventional studies. There was a substantial inter-state disparity - with a median SSY of 1.55 per 1000 incident cancer cases (range 0.00 - 296.81 per 1,000 incident cases) for therapeutic interventional studies. Disparities were starker when cancer site-wise SSY was considered. Even in the state with the highest SSY, only 29.7 % of the newly diagnosed cancer cases have an available slot in a therapeutic cancer clinical trial. Disparities in access were also apparent between academic (range: 0.21 - 226.60) and industry-sponsored trials (range: 0.17 - 70.21).Conclusion There are significant geographic disparities in access to cancer clinical trials in India. Future investigations should evaluate the reasons and mitigation approaches for such disparities.

Control arm heterogeneity in trials for unselected advanced triple-negative breast cancer (aTNBC).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1082-1082
Author(s):  
Kinisha Gala ◽  
Ankit Kalucha ◽  
Samuel Martinet ◽  
Anushri Goel ◽  
Kalpana Devi Narisetty ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Single Agent ◽  
Progression Free Survival ◽  
Sample Sizes ◽  
Time To Event ◽  
Sample Size Calculations ◽  
Subgroup Analyses ◽  
Combination Regimens

1082 Background: Primary endpoints of clinical trials frequently include subgroup-analyses. Several solid cancers such as aTNBC are heterogeneous, which can lead to unpredictable control arm performance impairing accurate assumptions for sample size calculations. We explore the value of a comprehensive clinical trial results repository in assessing control arm heterogeneity with aTNBC as the pilot. Methods: We identified P2/3 trials reporting median overall survival (mOS) and/or median progression-free survival (mPFS) in unselected aTNBC through a systematic search of PubMed, clinical trials databases and conference proceedings. Trial arms with sample sizes ≤25 or evaluating drugs no longer in development were excluded. Due to inconsistency among PD-L1 assays, PD-L1 subgroup analyses were not assessed separately. The primary aim was a descriptive analysis of control arm mOS and mPFS across all randomized trials in first line (1L) aTNBC. Secondary aims were to investigate time-to-event outcomes in control arms in later lines and to assess time-trends in aTNBC experimental and control arm outcomes. Results: We included 33 trials published between June 2013-Feb 2021. The mOS of control arms in 1L was 18.7mo (range 12.6-22.8) across 5 trials with single agent (nab-) paclitaxel [(n)P], and 18.1mo (similar range) for 7 trials including combination regimens (Table). The mPFS of control arms in 1L was 4.9mo (range 3.8-5.6) across 5 trials with single-agent (n)P, and 5.6mo (range 3.8-6.1) across 8 trials including combination regimens. Control arm mOS was 13.1mo (range 9.4-17.4) for 3 trials in first and second line (1/2L) and 8.7mo (range 6.7-10.8) across 5 trials in 2L and beyond. R2 for the mOS best-fit lines across control and experimental arms over time was 0.09, 0.01 and 0.04 for 1L, 1/2L and 2L and beyond, respectively. Conclusions: Median time-to-event outcomes of control arms in 1L aTNBC show considerable heterogeneity, even among trials with comparable regimens and large sample sizes. Disregarding important prognostic factors at stratification can lead to imbalances between arms, which may jeopardize accurate sample size calculations, trial results and interpretation. Optimizing stratification and assumptions for power calculations is of utmost importance in aTNBC and beyond. A digitized trial results repository with precisely defined patient populations and treatment settings could improve accuracy of assumptions during clinical trial design.[Table: see text]

Abstract 16910: High-Frequency In-Person Visits During Clinical Trial Enrollment is Associated With Relative Reduction in Event Rates in Heart Failure Patients Followed Longitudinally

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Liana C Brooks ◽  
Rohan R Bhat ◽  
Robyn F Farrell ◽  
Mark W Schoenike ◽  
John A Sbarbaro ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
High Frequency ◽  
Trial Participation ◽  
Trial Period ◽  
Single Center ◽  
Hospitalization Rates ◽  
Visit Frequency ◽  
Clinical Trial Enrollment ◽  
Event Rates

Introduction: The COVID-19 Pandemic has mandated limiting routine visit frequency for patients with chronic cardiovascular (CV) diseases. In patients with heart failure (HF) followed longitudinally, the period of clinical trial participation provides an opportunity to evaluate the influence of high-frequency per-protocol in-person visits compared to less frequent routine visits during longitudinal clinical care. Hypothesis: Patients enrolled in clinical trials will have a lower CV and HF event rates during periods of trial enrollment than during non-trial periods. Methods: We examined clinical characteristics, CV and HF hospitalization rates, and outcomes in patients with HF receiving longitudinal HF care at a single center. We evaluated hospitalization rates during the 1-year preceding trial enrollment and hospitalization and death rates during enrollment in clinical trials and for up to 1 year following trial completion. Results: Among the 121 patients enrolled in HF clinical trials, 72% were HFrEF (age 62±11, 19% females, BMI 30.4±6.0, LVEF 25±7, NYHA 2.7±0.6, NT-proBNP 2336±2671) and 28% were HFpEF (age 69±9, BMI 32.1±5.5, 29% females, LVEF 60±10, NYHA 2.4±0.5, NT-proBNP 957±997). Average clinical trial exposure was 8±6.6 months. Per-protocol visit frequency was 16±7 per year during clinical trial enrollment. In the one-year pre-trial period, compared to the within-trial period, CV hospitalizations were 0.88/patient-year vs. 0.32/patient-year (p<0.001) and HF hospitalizations were 0.63/patient-year and 0.24/patient-year (p<0.001), with a mortality rate of 0.04/patient-year during trial participation. In the period of up-to 1 year following the end of trial enrollment CV and HF hospitalizations were intermediate at 0.51/patient-year and 0.27/patient-year with an annualized incremental mortality rate of 0.03/patient-year. Conclusion: In HF patients followed longitudinally at a single center, periods of clinical trial enrollment were associated with high visit frequency and lower CV and HF hospitalization rates. These findings highlight the potential benefits of trial enrollment and high-frequency visits for HF patients at a time when routine visit frequency is being carefully considered during the COVID-19 Pandemic.

scholarly journals Outcome assessment by central adjudicators in randomised stroke trials: Simulation of differential and non-differential misclassification

2020 ◽  
Vol 5 (2) ◽  
pp. 174-183 ◽  
Author(s):  
Peter J Godolphin ◽  
Philip M Bath ◽  
Christopher Partlett ◽  
Eivind Berge ◽  
Martin M Brown ◽  
...  
Keyword(s):  
Sample Size ◽  
Outcome Assessment ◽  
Treatment Effect ◽  
Primary Outcome ◽  
Event Rate ◽  
Considerable Proportion ◽  
Binary Outcome ◽  
Primary Trial ◽  
Differential Misclassification ◽  
Larger Sample

Introduction Adjudication of the primary outcome in randomised trials is thought to control misclassification. We investigated the amount of misclassification needed before adjudication changed the primary trial results. Patients (or materials) and methods: We included data from five randomised stroke trials. Differential misclassification was introduced for each primary outcome until the estimated treatment effect was altered. This was simulated 1000 times. We calculated the between-simulation mean proportion of participants that needed to be differentially misclassified to alter the treatment effect. In addition, we simulated hypothetical trials with a binary outcome and varying sample size (1000–10,000), overall event rate (10%–50%) and treatment effect (0.67–0.90). We introduced non-differential misclassification until the treatment effect was non-significant at 5% level. Results For the five trials, the range of unweighted kappa values were reduced from 0.89–0.97 to 0.65–0.85 before the treatment effect was altered. This corresponded to 2.1%–6% of participants misclassified differentially for trials with a binary outcome. For the hypothetical trials, those with a larger sample size, stronger treatment effect and overall event rate closer to 50% needed a higher proportion of events non-differentially misclassified before the treatment effect became non-significant. Discussion: We found that only a small amount of differential misclassification was required before adjudication altered the primary trial results, whereas a considerable proportion of participants needed to be misclassified non-differentially before adjudication changed trial conclusions. Given that differential misclassification should not occur in trials with sufficient blinding, these results suggest that central adjudication is of most use in studies with unblinded outcome assessment. Conclusion: For trials without adequate blinding, central adjudication is vital to control for differential misclassification. However, for large blinded trials, adjudication is of less importance and may not be necessary.

Development of questionnaires to measure patient preferences for intranasal corticosteroids in patients with allergic rhinitis

2005 ◽  
Vol 132 (2) ◽  
pp. 197-207 ◽  
Author(s):  
Eli O. Meltzer ◽  
James Hadley ◽  
Michael Blaiss ◽  
Michael Benninger ◽  
Miriam Kimel ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Allergic Rhinitis ◽  
Focus Groups ◽  
Questionnaire Development ◽  
Cognitive Debriefing ◽  
Intranasal Corticosteroids ◽  
Larger Sample ◽  
Practice Preference ◽  
Trial Setting

OBJECTIVE: To develop a questionnaire to evaluate preferences for attributes of intranasal corticosteroids (INSs) in clinical trials with allergic rhinitis (AR) patients. STUDY DESIGN AND SETTING: Established questionnaire development practices were used, including performance of a literature review and use of patient and physician focus groups, cognitive debriefing interviews, and pilot testing before validation. RESULTS: Findings from patient and physician focus groups suggest that sensory attributes are relevant to AR patients when choosing INSs. Physician focus groups identified the need for 2 distinct preference instruments, a clinical trial patient preference questionnaire (CTPPQ) and a clinical practice preference questionnaire (CPPPQ). A pilot study suggests that the CTPPQ is capable of discriminating between 2 INSs in the clinical trial setting. CONCLUSIONS: Initial findings suggest that items in the CTPPQ and CPPPQ are easy to understand and relevant to patients. Further validation studies with larger sample sizes are needed to assess the psychometric properties of both questionnaires. EBM rating: B-20.

scholarly journals Bayesian interpretation of p values in clinical trials

2021 ◽  
pp. bmjebm-2020-111603
Author(s):  
John Ferguson
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Confidence Intervals ◽  
Statistical Significance ◽  
Large Sample Size ◽  
P Values ◽  
Clinical Trial Results ◽  
Sound Treatment ◽  
Counterintuitive Result

Commonly accepted statistical advice dictates that large-sample size and highly powered clinical trials generate more reliable evidence than trials with smaller sample sizes. This advice is generally sound: treatment effect estimates from larger trials tend to be more accurate, as witnessed by tighter confidence intervals in addition to reduced publication biases. Consider then two clinical trials testing the same treatment which result in the same p values, the trials being identical apart from differences in sample size. Assuming statistical significance, one might at first suspect that the larger trial offers stronger evidence that the treatment in question is truly effective. Yet, often precisely the opposite will be true. Here, we illustrate and explain this somewhat counterintuitive result and suggest some ramifications regarding interpretation and analysis of clinical trial results.

scholarly journals Prospective comparison of Bayesian and frequentist adaptive clinical trials : The SHADOW - SHINE project

2021 ◽  
Author(s):  
Kristine Broglio ◽  
William Meurer ◽  
Valerie Durkalski ◽  
Qi Pauls ◽  
Jason Connor ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Acute Stroke ◽  
Sample Size ◽  
Trial Design ◽  
Interim Analyses ◽  
Patient Level Data ◽  
Patient Level ◽  
Prospective Comparison ◽  
Level Data

Importance: Bayesian adaptive trial design has the potential to create more efficient clinical trials. However, one of the barriers to the uptake of Bayesian adaptive designs for confirmatory trials is limited experience with how they may perform compared to a frequentist design. Objective: Compare the performance of a Bayesian and a frequentist adaptive clinical trial design. Design: Prospective observational study comparing two trial designs using individual patient level data from a completed stroke trial, including the timing and order of enrollments and outcome ascertainment. The implemented frequentist design had group sequential boundaries for efficacy and futility interim analyses when 90-days post-randomization was met for 500, 700, 900, and 1,100 patients. The Bayesian alternative utilized predictive probability of trial success to govern early termination for efficacy and futility with a first interim analysis at 500 randomized patients, and subsequent interims after every 100 randomizations. Setting: Multi-center, acute stroke study conducted within a National Institutes of Health neurological emergencies clinical trials network. Participants: Patient level data from 1,151 patients randomized in a clinical trial comparing intensive insulin therapy to standard in acute stroke patients with hyperglycemia. Main Outcome(s) and Measure(s): Sample size at end of study. This was defined as the sample size at which each of the studies stopped accrual of patients. Results: As conducted, the frequentist design passed the futility boundary after 936 participants were randomized. Using the same sequence and timing of randomization and outcome data, the Bayesian alternative crossed the futility boundary about 3 months earlier after 800 participants were randomized. Conclusions and Relevance: Both trial designs stopped for futility prior to reaching the planned maximum sample size. In both cases, the clinical community and patients would benefit from learning the answer to the trial's primary question earlier. The common feature across the two designs was frequent interim analyses to stop early for efficacy or for futility. Differences between how this was implemented between the two trials resulted in the differences in early stopping.

scholarly journals Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention

2020 ◽  
Author(s):  
Ravinder Claire ◽  
Christian Gluud ◽  
Ivan Berlin ◽  
Tim Coleman ◽  
Jo Leonardi-Bee
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Sequential Analysis ◽  
Randomised Clinical Trial ◽  
Randomised Clinical Trials ◽  
Trial Sequential Analysis ◽  
Analysis Software ◽  
Cessation Intervention ◽  
Pilot Trials

Abstract Background Assessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately powered randomised clinical trials. When a randomised clinical trial is conducted, results from feasibility and pilot trials may be disregarded in terms of benefits and harms.MethodsWe describe using feasibility and pilot trial data in the Trial Sequential Analysis software to estimate the required sample size for one or more trials investigating a behavioural smoking cessation intervention. We show how data from a new, planned trial can be combined with data from the earlier trials using trial sequential analysis methods to assess the intervention’s effects.ResultsWe provide a worked example to illustrate how we successfully used the Trial Sequential Analysis software to arrive at a sensible sample size for a new randomised clinical trial and use it in the argumentation for research funds for the trial. ConclusionsTrial Sequential Analysis can utilise data from feasibility and pilot trials as well as other trials, to estimate a sample size for one or more, similarly designed, future randomised clinical trials. As this method uses available data, estimated sample sizes may be smaller than they would have been using conventional sample size estimation methods.

Highly efficient clinical trial designs for reliable screening of under-performing treatments: Application to the COVID-19 Pandemic

2020 ◽  
Vol 17 (5) ◽  
pp. 483-490
Author(s):  
Steven Piantadosi
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Trial Design ◽  
Ranking And Selection ◽  
Design Elements ◽  
Highly Efficient ◽  
Clinical Trial Designs ◽  
Under Performing ◽  
The Many

Background: The COVID-19 pandemic presents challenges for clinical trials including urgency, disrupted infrastructure, numerous therapeutic candidates, and the need for highly efficient trial and development designs. This paper presents design components and rationale for constructing highly efficient trials to screen potential COVID-19 treatments. Methods: Key trial design elements useful in this circumstance include futility hypotheses, treatment pooling, reciprocal controls, ranking and selection, and platform administration. Assuming most of the many candidates for COVID-19 treatment are likely to be ineffective, these components can be combined to facilitate very efficient comparisons of treatments. Results: Simulations indicate such designs can reliably discard underperforming treatments using sample size to treatment ratios under 30. Conclusions: Methods to create very efficient clinical trial comparisons of treatments for COVID-19 are available. Such designs might be helpful in the pandemic and should be considered for similar needs in the future.

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