Limitations of clinical trial sample size estimate by subtraction of two measurements

2021 ◽  
Author(s):  
Kewei Chen ◽  
Xiaojuan Guo ◽  
Rong Pan ◽  
Chengjie Xiong ◽  
Danielle J. Harvey ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Sample Size ◽  
Size Estimate ◽  
Sample Size Estimate

Sample size considerations for paired experimental design with incomplete observations of continuous outcomes

2017 ◽  
Vol 28 (2) ◽  
pp. 589-598
Author(s):  
Hong Zhu ◽  
Xiaohan Xu ◽  
Chul Ahn
Keyword(s):  
Missing Data ◽  
Experimental Design ◽  
Sample Size ◽  
Estimating Equation ◽  
Generalized Estimating Equation ◽  
Size Estimate ◽  
Incomplete Observations ◽  
Continuous Outcomes ◽  
Sample Size Estimate ◽  
Generalized Estimating

Paired experimental design is widely used in clinical and health behavioral studies, where each study unit contributes a pair of observations. Investigators often encounter incomplete observations of paired outcomes in the data collected. Some study units contribute complete pairs of observations, while the others contribute either pre- or post-intervention observations. Statistical inference for paired experimental design with incomplete observations of continuous outcomes has been extensively studied in literature. However, sample size method for such study design is sparsely available. We derive a closed-form sample size formula based on the generalized estimating equation approach by treating the incomplete observations as missing data in a linear model. The proposed method properly accounts for the impact of mixed structure of observed data: a combination of paired and unpaired outcomes. The sample size formula is flexible to accommodate different missing patterns, magnitude of missingness, and correlation parameter values. We demonstrate that under complete observations, the proposed generalized estimating equation sample size estimate is the same as that based on the paired t-test. In the presence of missing data, the proposed method would lead to a more accurate sample size estimate comparing with the crude adjustment. Simulation studies are conducted to evaluate the finite-sample performance of the generalized estimating equation sample size formula. A real application example is presented for illustration.

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 Assessment of boron-containing compounds and oleoylethanolamide supplementation on the recovery trend in patients with COVID-19: A structured summary of a study protocol for a randomized controlled trial

Trials ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Helda Tutunchi ◽  
Majid Mobasseri ◽  
Samira Pourmoradian ◽  
Hamid Soleimanzadeh ◽  
Behnam Kafil ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Sample Size ◽  
Complete Blood Count ◽  
Controlled Clinical Trial ◽  
Medical Sciences ◽  
Boron Compounds ◽  
Double Blind ◽  
Link Type ◽  
Nutrition Research ◽  
Full Protocol

Abstract Objectives In this study, we investigate the effect of boron-containing compounds and oleoylethanolamide supplementation on the recovery trend in patients with COVID-19. Trial design The current study is a single-center, randomized, double-blind, placebo-controlled clinical trial with parallel groups. Participants The inclusion criteria include male and female patients≥18 years of age, with a confirmed diagnosis of SARS-CoV-2 infection via polymerase chain reaction (PCR) and/or antibody test and with written informed consent to participate in this trial. The exclusion criteria include regular use of any other supplement, severe and critical COVID-19 pneumonia, pregnancy and breastfeeding. This study is being conducted at Imam Reza Hospital, Tabriz University of Medical Sciences, Tabriz, Iran. Intervention and comparator Patients are randomly assigned to four groups. The first group (A) will take one capsule containing 5 mg of boron compounds twice a day for two weeks. The second group (B) will take one capsule containing 200 mg oleoylethanolamide twice a day for two weeks. The third group (C) will take one capsule containing 5 mg boron compounds with 200 mg oleoylethanolamide twice a day for two weeks, and the fourth group (D) does not receive any additional treatment other than routine treatments. Boron-containing compounds and oleoylethanolamide capsules will be synthesized at Nutrition Research Center of Tabriz University of Medical Sciences. Main outcomes The primary end point of this study is to investigate the recovery rate of clinical symptoms, including fever, dry cough, and fatigue, as well as preclinical features, including complete blood count (CBC), the erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) profiles within two weeks of randomization. Randomisation Patients are randomized into four equal groups in a parallel design (allocation ratio 1:1). A randomized block procedure is used to divide subjects into one of four treatment blocks (A, B, C, and D) by a computer-generated allocation schedule. Blinding (masking) The participants and investigators (enrolling, assessing, and analyzing) are blinded to the intervention assignments until the end of the study and data analysis. Numbers to be randomised (sample size) The calculated total sample size is 40 patients, with 10 patients in each group. Trial Status The protocol is Version 1.0, May 17, 2020. Recruitment began May 19, 2020, and is anticipated to be completed by October 19, 2020. Trial registration This clinical trial has been registered by the title of “Assessment of boron-containing compounds and oleoylethanolamide supplementation on the recovery trend in Patients with COVID-19: A double-blind randomized placebo-controlled clinical trial” in the Iranian Registry of Clinical Trials (IRCT). The registration number is “IRCT20090609002017N35”, https://www.irct.ir/trial/48058. The registration date is 17 May 2020. Full protocol The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol.

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]

In reply: Determining the sample size in a clinical trial

2003 ◽  
Vol 178 (7) ◽  
pp. 358-358
Author(s):  
Adrienne Kirby ◽  
Val Gebski ◽  
Anthony C Keech
Keyword(s):  
Clinical Trial ◽  
Sample Size
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