Proposals for Sample Size Calculation Programs

2007 ◽  
Vol 46 (06) ◽  
pp. 655-661 ◽  
Author(s):  
H. Heinzl ◽  
A. Benner ◽  
C. Ittrich ◽  
M. Mittlböck
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Sample Size Calculation ◽  
Effect Sizes ◽  
Detectable Effect ◽  
Planning Stage ◽  
Type Iii ◽  
Error Risk ◽  
Q Values

Summary Objectives : Numerous sample size calculation programs are available nowadays. They include both commercial products as well as public domain and open source applications. We propose modifications for these programs in order to even better support statistical consultation during the planning stage of a two-armed clinical trial. Methods : Directional two-sided tests are commonly used for two-armed clinical trials. This may lead to a non-negligible Type III error risk in a severely underpowered study. In the case of a reasonably sized study the question for the so-called auxiliary alternative may evolve. Results : We propose that sample size calculation programs should be able to compute i) Type III errors and the so-called (q-values, ii) minimum sample sizes required to keep the (q-values below pre-specified levels, and iii) detectable effect sizes of the so-called auxiliary alternatives. Conclusions : Proposals iand ii are intended to help prevent irresponsibly underpowered clinical trials, whereas the proposal iii is meant as additional assistance for the planning of reasonably sized clinical trials.

scholarly journals Detection of Clenbuterol-Induced Changes in Heart Rate Using At-Home Recorded Smartwatch Data: Randomized Controlled Trial

10.2196/31890 ◽  
2021 ◽  
Vol 5 (12) ◽  
pp. e31890
Author(s):  
Willem O Elzinga ◽  
Samantha Prins ◽  
Laura G J M Borghans ◽  
Pim Gal ◽  
Gabriel A Vargas ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Heart Rate ◽  
Clinical Trials ◽  
Treatment Effects ◽  
Controlled Trial ◽  
Intraclass Correlation ◽  
Effect Sizes ◽  
Detectable Effect ◽  
Sleep State ◽  
Induced Changes

Background Although electrocardiography is the gold standard for heart rate (HR) recording in clinical trials, the increasing availability of smartwatch-based HR monitors opens up possibilities for drug development studies. Smartwatches allow for inexpensive, unobtrusive, and continuous HR estimation for potential detection of treatment effects outside the clinic, during daily life. Objective The aim of this study is to evaluate the repeatability and sensitivity of smartwatch-based HR estimates collected during a randomized clinical trial. Methods The data were collected as part of a multiple-dose, investigator-blinded, randomized, placebo-controlled, parallel-group study of 12 patients with Parkinson disease. After a 6-day baseline period, 4 and 8 patients were treated for 7 days with an ascending dose of placebo and clenbuterol, respectively. Throughout the study, the smartwatch provided HR and sleep state estimates. The HR estimates were quantified as the 2.5th, 50th, and 97.5th percentiles within awake and asleep segments. Linear mixed models were used to calculate the following: (1) the intraclass correlation coefficient (ICC) of estimated sleep durations, (2) the ICC and minimum detectable effect (MDE) of the HR estimates, and (3) the effect sizes of the HR estimates. Results Sleep duration was moderately repeatable (ICC=0.64) and was not significantly affected by study day (P=.83), clenbuterol (P=.43), and study day by clenbuterol (P=.73). Clenbuterol-induced changes were detected in the asleep HR as of the first night (+3.79 beats per minute [bpm], P=.04) and in the awake HR as of the third day (+8.79 bpm, P=.001). The median HR while asleep had the highest repeatability (ICC=0.70). The MDE (N=12) was found to be smaller when patients were asleep (6.8 bpm to 11.7 bpm) than while awake (10.7 bpm to 22.1 bpm). Overall, the effect sizes for clenbuterol-induced changes were higher while asleep (0.49 to 2.75) than while awake (0.08 to 1.94). Conclusions We demonstrated the feasibility of using smartwatch-based HR estimates to detect clenbuterol-induced changes during clinical trials. The asleep HR estimates were most repeatable and sensitive to treatment effects. We conclude that smartwatch-based HR estimates obtained during daily living in a clinical trial can be used to detect and track treatment effects. Trial Registration Netherlands Trials Register NL8002; https://www.trialregister.nl/trial/8002

scholarly journals Detection of Clenbuterol-Induced Changes in Heart Rate Using At-Home Recorded Smartwatch Data: Randomized Controlled Trial (Preprint)

2021 ◽  
Author(s):  
Willem O Elzinga ◽  
Samantha Prins ◽  
Laura G J M Borghans ◽  
Pim Gal ◽  
Gabriel A Vargas ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Heart Rate ◽  
Clinical Trials ◽  
Treatment Effects ◽  
Controlled Trial ◽  
Intraclass Correlation ◽  
Effect Sizes ◽  
Detectable Effect ◽  
Sleep State ◽  
Induced Changes

BACKGROUND Although electrocardiography is the gold standard for heart rate (HR) recording in clinical trials, the increasing availability of smartwatch-based HR monitors opens up possibilities for drug development studies. Smartwatches allow for inexpensive, unobtrusive, and continuous HR estimation for potential detection of treatment effects outside the clinic, during daily life. OBJECTIVE The aim of this study is to evaluate the repeatability and sensitivity of smartwatch-based HR estimates collected during a randomized clinical trial. METHODS The data were collected as part of a multiple-dose, investigator-blinded, randomized, placebo-controlled, parallel-group study of 12 patients with Parkinson disease. After a 6-day baseline period, 4 and 8 patients were treated for 7 days with an ascending dose of placebo and clenbuterol, respectively. Throughout the study, the smartwatch provided HR and sleep state estimates. The HR estimates were quantified as the 2.5th, 50th, and 97.5th percentiles within awake and asleep segments. Linear mixed models were used to calculate the following: (1) the intraclass correlation coefficient (ICC) of estimated sleep durations, (2) the ICC and minimum detectable effect (MDE) of the HR estimates, and (3) the effect sizes of the HR estimates. RESULTS Sleep duration was moderately repeatable (ICC=0.64) and was not significantly affected by study day (<i>P</i>=.83), clenbuterol (<i>P</i>=.43), and study day by clenbuterol (<i>P</i>=.73). Clenbuterol-induced changes were detected in the asleep HR as of the first night (+3.79 beats per minute [bpm], <i>P</i>=.04) and in the awake HR as of the third day (+8.79 bpm, <i>P</i>=.001). The median HR while asleep had the highest repeatability (ICC=0.70). The MDE (N=12) was found to be smaller when patients were asleep (6.8 bpm to 11.7 bpm) than while awake (10.7 bpm to 22.1 bpm). Overall, the effect sizes for clenbuterol-induced changes were higher while asleep (0.49 to 2.75) than while awake (0.08 to 1.94). CONCLUSIONS We demonstrated the feasibility of using smartwatch-based HR estimates to detect clenbuterol-induced changes during clinical trials. The asleep HR estimates were most repeatable and sensitive to treatment effects. We conclude that smartwatch-based HR estimates obtained during daily living in a clinical trial can be used to detect and track treatment effects. CLINICALTRIAL Netherlands Trials Register NL8002; https://www.trialregister.nl/trial/8002

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.

Sample size calculation for clinical trials in which entry criteria and outcomes are counts of events

1994 ◽  
Vol 13 (8) ◽  
pp. 859-870 ◽  
Author(s):  
Robert P. McMahon ◽  
Michael Proschan ◽  
Nancy L. Geller ◽  
Peter H. Stone ◽  
George Sopko
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Sample Size Calculation

Statistical Principles for Clinical Trials

1998 ◽  
Vol 26 (2) ◽  
pp. 57-65 ◽  
Author(s):  
R Kay
Keyword(s):  
Clinical Trials ◽  
Confidence Interval ◽  
Sample Size ◽  
Sample Size Calculation ◽  
P Value ◽  
Clinical Value ◽  
Treatment Groups ◽  
Correct Calculation ◽  
Intent To Treat ◽  
Comparative Trials

If a trial is to be well designed, and the conclusions drawn from it valid, a thorough understanding of the benefits and pitfalls of basic statistical principles is required. When setting up a trial, appropriate sample-size calculation is vital. If initial calculations are inaccurate, trial results will be unreliable. The principle of intent-to-treat in comparative trials is examined. Randomization as a method of selecting patients to treatment is essential to ensure that the treatment groups are equalized in terms of avoiding biased allocation in the mix of patients within groups. Once trial results are available the correct calculation and interpretation of the P-value is important. Its limitations are examined, and the use of the confidence interval to help draw valid conclusions regarding the clinical value of treatments is explored.

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.

scholarly journals Approaches to sample size calculation for clinical trials in rare diseases

2018 ◽  
Vol 17 (3) ◽  
pp. 214-230 ◽  
Author(s):  
Frank Miller ◽  
Sarah Zohar ◽  
Nigel Stallard ◽  
Jason Madan ◽  
Martin Posch ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Rare Diseases ◽  
Sample Size Calculation

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]

scholarly journals Design and analysis of a clinical trial using previous trials as historical control

2019 ◽  
Vol 16 (5) ◽  
pp. 531-538 ◽  
Author(s):  
David Alan Schoenfeld ◽  
Dianne M Finkelstein ◽  
Eric Macklin ◽  
Neta Zach ◽  
David L Ennist ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Amyotrophic Lateral Sclerosis ◽  
Sample Size ◽  
Standard Error ◽  
Controlled Trial ◽  
Sample Size Calculation ◽  
Control Group ◽  
Controlled Trials ◽  
Number Of Patients ◽  
Lateral Sclerosis

Background/AimsFor single arm trials, a treatment is evaluated by comparing an outcome estimate to historically reported outcome estimates. Such a historically controlled trial is often analyzed as if the estimates from previous trials were known without variation and there is no trial-to-trial variation in their estimands. We develop a test of treatment efficacy and sample size calculation for historically controlled trials that considers these sources of variation.MethodsWe fit a Bayesian hierarchical model, providing a sample from the posterior predictive distribution of the outcome estimand of a new trial, which, along with the standard error of the estimate, can be used to calculate the probability that the estimate exceeds a threshold. We then calculate criteria for statistical significance as a function of the standard error of the new trial and calculate sample size as a function of difference to be detected. We apply these methods to clinical trials for amyotrophic lateral sclerosis using data from the placebo groups of 16 trials.ResultsWe find that when attempting to detect the small to moderate effect sizes usually assumed in amyotrophic lateral sclerosis clinical trials, historically controlled trials would require a greater total number of patients than concurrently controlled trials, and only when an effect size is extraordinarily large is a historically controlled trial a reasonable alternative. We also show that utilizing patient level data for the prognostic covariates can reduce the sample size required for a historically controlled trial.ConclusionThis article quantifies when historically controlled trials would not provide any sample size advantage, despite dispensing with a control group.

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