scholarly journals 2304

2017 ◽  
Vol 1 (S1) ◽  
pp. 34-34
Author(s):  
Christina Azevedo ◽  
Steven Cen ◽  
Ling Zheng ◽  
Pelletier Amirhossein Jaberzadeh
Keyword(s):  
Sample Size ◽  
Phase Ii ◽  
Primary Endpoint ◽  
Effect Size ◽  
Grey Matter ◽  
Brain Regions ◽  
Simulation Based ◽  
Sample Size Calculations ◽  
The Difference ◽  
Over Time

OBJECTIVES/SPECIFIC AIMS: To identify brain regions with the highest and least variable rate of multiple sclerosis (MS)-specific atrophy using an agnostic approach, and to perform simulation-based sample size calculations for Phase II s using these regions as primary endpoint. METHODS/STUDY POPULATION: In total, 601 subjects (2638 MRI scans) were analyzed; 520 subjects with relapsing forms of MS across the spectrum of disease severity and duration were followed in a single-center prospective cohort study at an academic MS Center between 2005 and 2010 with annual 3 T MRIs and clinical visits for 5 years, including standardized 1 mm3 3D T1-weighted images (3DT1s; 2483 MRIs). Separately, a convenience sample of 81 healthy controls (HC) was recruited from the same center and scanned longitudinally using the same MRI scanner and protocol (155 MRIs). 3DT1s were processed using FreeSurfer’s longitudinal pipeline (software version 5.3). Rates of change in all cortical and subcortical regions (n=119 brain regions) were estimated in MS patients and HC with linear mixed effects models. An effect size was calculated for each region as the difference in change over time between MS patients and HC divided by the standard error of the difference [d=β(MS×time)/SE β(MS×time)]. Regions were ranked according to absolute effect size, and the top regions were chosen for simulation-based sample size calculations to estimate the number of subjects needed to achieve 80% power to detect a slowing of MS atrophy down to normal aging, assuming significance levels of 5% and 10%. Ten percent was included because some have advocated for a more relaxed alpha in Phase II s. RESULTS/ANTICIPATED RESULTS: Four regions (putamen, subcortical grey matter, caudate, and thalamus) yielded the smallest sample sizes. At 80% power, ~50 subjects per arm would be needed with putamen or subcortical grey matter volume, or ~80–85 subjects per arm with caudate or thalamic volume as primary endpoint. For the remaining regions, >140 subjects per arm would be needed. A 20%–30% increase in sample size was observed when α=5% was used. DISCUSSION/SIGNIFICANCE OF IMPACT: Using an agnostic approach considering all brain regions and simulation-based sample size calculations specifically designed for longitudinal studies, putaminal, subcortical grey, caudate, and thalamic volumes are sensitive to change over time and yield feasible sample sizes for Phase II studies in MS. Because the effect size estimates incorporate normal aging, these regions represent the most sensitive outcomes for testing therapeutic interventions that target irreversible, MS-specific brain atrophy. The clinical relevance of these regions is our next focus to help inform which of these regions should be favored as primary endpoint.

Statistical power of negative randomized clinical trials (RCTs) presented at the American Society of Clinical Oncology (ASCO) Annual Meetings

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 6516-6516
Author(s):  
P. Bedard ◽  
M. K. Krzyzanowska ◽  
M. Pintilie ◽  
I. F. Tannock
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Annual Meeting ◽  
Primary Endpoint ◽  
Effect Size ◽  
Statistical Power ◽  
Randomized Clinical Trials ◽  
Medium Effect ◽  
Post Hoc ◽  
Adequate Sample Size

6516 Background: Underpowered randomized clinical trials (RCTs) may expose participants to risks and burdens of research without scientific merit. We investigated the prevalence of underpowered RCTs presented at ASCO annual meetings. Methods: We surveyed all two-arm parallel phase III RCTs presented at the ASCO annual meeting from 1995–2003 where differences for the primary endpoint were non-statistically significant. Post hoc calculations were performed using a power of 80% and a=0.05 (two-sided) to determine the sample size required to detect a small, medium, and large effect size between the two groups. For studies reporting a proportion or time to event as a primary endpoint, effect size was expressed as an odds ratio (OR) or hazard ratio (HR) respectively, with a small effect size defined as OR/HR=1.3, medium effect size OR/HR=1.5, and large effect OR/HR=2.0. Logistic regression was used to identify factors associated with lack of statistical power. Results: Of 423 negative RCTs for which post hoc sample size calculations could be performed, 45 (10.6%), 138 (32.6%), and 333 (78.7%) had adequate sample size to detect small, medium, and large effect sizes respectively. Only 35 negative RCTs (7.1%) reported a reason for inadequate sample size. In a multivariable model, studies presented at plenary or oral sessions (p<0.0001) and multicenter studies supported by a co-operative group were more likely to have adequate sample size (p<0.0001). Conclusion: Two-thirds of negative RCTs presented at the ASCO annual meeting do not have an adequate sample to detect a medium-sized treatment effect. Most underpowered negative RCTs do not report a sample size calculation or reasons for inadequate patient accrual. No significant financial relationships to disclose.

Evolution of the randomized controlled trial (RCT) in oncology over three decades

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 6515-6515 ◽  
Author(s):  
C. M. Booth ◽  
D. W. Cescon ◽  
L. Wang ◽  
I. F. Tannock ◽  
M. K. Krzyzanowska
Keyword(s):  
Primary Endpoint ◽  
Effect Size ◽  
Response Rate ◽  
Controlled Trial ◽  
For Profit ◽  
Survival Endpoints ◽  
Trends Over Time ◽  
And Control ◽  
Median Rate ◽  
Over Time

6515 Background: The RCT is the gold standard for establishing new therapies in oncology. Here we document changes with time in design, results, author conclusions and sponsorship. Methods: Reports of RCTs evaluating systemic therapy for breast, colorectal (CRC) and non-small cell lung cancer (NSCLC) published 1975–2004 in 6 major journals were reviewed. Two authors independently abstracted data regarding trial design, effect size and author conclusions. Author conclusions were assigned a score from 1 to 7: 4/7 for a neutral statement, 7/7 and 1/7 for strong endorsement of experimental and control arm respectively. For each study the effect size for the primary endpoint was converted to a summary measure: hazard ratio [HR] for survival endpoints and relative risk [RR] for response rate. Descriptive statistics were used to analyze trends over time. Results: 326 eligible RCTs were included (48% breast, 24% CRC, 28% NSCLC). There was a significant increase in the number and size of RCTs (see Table ). Median rate of accrual increased from 7 patients/month in 1975–84 to 14 patients/month in 1995–2004 (p<0.001). There was an increase in multicenter (55 to 95%, p<0.001), international trials (26 to 52%, p<0.001) and for-profit sponsorship over time (6 to 57%, p<0.001). There was increasing use of survival (13 to 48%,) and decreasing use of response rate (32 to 14%) as primary endpoint (p<0.001). Authors have become more likely to strongly endorse the experimental arm despite no change in effect size over time (p=0.005). Studies sponsored by for-profit organizations were more likely to strongly endorse the experimental agent than studies not sponsored by for-profit groups (median author score 6/7 vs. 4/7, p<0.001). Conclusions: RCTs in oncology have become more common, larger, and are more likely to be sponsored by industry. Authors of modern RCTs are more likely to strongly endorse novel therapies despite no increase in the relative benefit of interventions. For-profit sponsorship is associated with stronger endorsement of the experimental arm. No significant financial relationships to disclose. [Table: see text]

scholarly journals Effect Size Guidelines, Sample Size Calculations, and Statistical Power in Gerontology

2019 ◽  
Vol 3 (4) ◽  
Author(s):  
Christopher R Brydges
Keyword(s):  
Individual Differences ◽  
Sample Size ◽  
Effect Size ◽  
Effect Sizes ◽  
Group Differences ◽  
Cohen’S D ◽  
Pearson's R ◽  
Sample Size Calculations ◽  
Cohen's D ◽  
Pearson’S R

Abstract Background and Objectives Researchers typically use Cohen’s guidelines of Pearson’s r = .10, .30, and .50, and Cohen’s d = 0.20, 0.50, and 0.80 to interpret observed effect sizes as small, medium, or large, respectively. However, these guidelines were not based on quantitative estimates and are only recommended if field-specific estimates are unknown. This study investigated the distribution of effect sizes in both individual differences research and group differences research in gerontology to provide estimates of effect sizes in the field. Research Design and Methods Effect sizes (Pearson’s r, Cohen’s d, and Hedges’ g) were extracted from meta-analyses published in 10 top-ranked gerontology journals. The 25th, 50th, and 75th percentile ranks were calculated for Pearson’s r (individual differences) and Cohen’s d or Hedges’ g (group differences) values as indicators of small, medium, and large effects. A priori power analyses were conducted for sample size calculations given the observed effect size estimates. Results Effect sizes of Pearson’s r = .12, .20, and .32 for individual differences research and Hedges’ g = 0.16, 0.38, and 0.76 for group differences research were interpreted as small, medium, and large effects in gerontology. Discussion and Implications Cohen’s guidelines appear to overestimate effect sizes in gerontology. Researchers are encouraged to use Pearson’s r = .10, .20, and .30, and Cohen’s d or Hedges’ g = 0.15, 0.40, and 0.75 to interpret small, medium, and large effects in gerontology, and recruit larger samples.

A phase II trial of regorafenib (REGO) in patients (pts) with advanced Ewing sarcoma and related tumors (EWS) of soft tissue and bone: SARC024 trial results.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 11005-11005 ◽  
Author(s):  
Steven Attia ◽  
Vanessa Bolejack ◽  
Kristen N. Ganjoo ◽  
Suzanne George ◽  
Mark Agulnik ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Phase Ii ◽  
Primary Endpoint ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Phase Ii Trial ◽  
Common Grade ◽  
Duration Of Response ◽  
The Difference ◽  
Line Of Therapy

11005 Background: Pazopanib is approved for soft tissue sarcoma pts after failure of other therapy, but there are few subtype-specific data regarding kinase inhibitor activity. We report on a single arm, phase II trial of REGO in advanced EWS. Methods: EWS pts (age > 18, ECOG 0-2, good organ function) who had at least 1 line of therapy and had PD within 6 mo were eligible. Prior oral kinase inhibitors were not allowed. Initial REGO dose was 160 mg PO QD x21 q28d. Dose reductions were employed for toxicity and AEs. The primary endpoint was PFS at 8 weeks (PFS8w) employing RECIST 1.1. Sample size of 30 allowed determination of the difference between PFS8w of 50% vs 25% with alpha = 0.05 and power of 91%. Results: 30 pts (median age 32, range 19-65; M/F = 20/10; ECOG 0/1/2 = 16/13/1; bone, 12; soft tissue, 18; median prior treatments 5, range 1-10) enrolled at 14 US sites (09/2014-03/2016). Most common grade (G3) toxicities were hypophosphatemia (6), hypertension (2), high ALT (2) and 1 each: fatigue, abd pain, diarrhea, hypokalemia, oral mucositis, neutropenia and rash; no G4 toxicities were noted. 13 pts required ≥1 dose reduction, most commonly hypophosphatemia (n = 7); 2 stopped REGO for toxicity. There was 1 death in the 30 day post study period, not REGO related. Median dose at study end: 140 mg (3.5 tabs, range 80-160 mg) 3 wks on/1wk off. 18/30 pts were without PD at 8 wks. Median PFS: 3.6 mo (95%CI 2.8-3.8 mo). PFS8w by KM was 73% (95%CI 57-89%). Best responses: PR/SD/PD/not evaluable of 3/18/7/2, for RECIST RR 10%. Two pts with PR had EWSR1 translocation by FISH; a third had CIC-DUX4. Median duration of response: 5.5 mo (95%CI 2.9-8.0). Median OS is not reached. Conclusions: The substudy met its primary endpoint. REGO toxicity was similar to that seen previously. Enrollment continues in LPS and OGS cohorts, and is being expanded to further study variant EWS without EWSR1-FLI1 fusion. Study of the existing tissue may elucidate which EWS patients may benefit from REGO. Clinical trial information: NCT02048371.

scholarly journals Continuing or ceasing bevacizumab beyond progression in recurrent glioblastoma: an exploratory randomized phase II trial

2017 ◽  
Vol 4 (3) ◽  
pp. 171-181 ◽  
Author(s):  
Elizabeth J Hovey ◽  
Kathryn M Field ◽  
Mark A Rosenthal ◽  
Elizabeth H Barnes ◽  
Lawrence Cher ◽  
...  
Keyword(s):  
Sample Size ◽  
Phase Ii ◽  
Small Sample Size ◽  
Progression Free Survival ◽  
Patient Characteristics ◽  
Recurrent Glioblastoma ◽  
Small Sample ◽  
Best Response ◽  
Additional Chemotherapy ◽  
The Difference

AbstractBackgroundIn patients with recurrent glioblastoma, the benefit of bevacizumab beyond progression remains uncertain. We prospectively evaluated continuing or ceasing bevacizumab in patients who progressed while on bevacizumab.MethodsCABARET, a phase II study, initially randomized patients to bevacizumab with or without carboplatin (Part 1). At progression, eligible patients underwent a second randomization to continue or cease bevacizumab (Part 2). They could also receive additional chemotherapy regimens (carboplatin, temozolomide, or etoposide) or supportive care.ResultsOf 120 patients treated in Part 1, 48 (80% of the anticipated 60-patient sample size) continued to Part 2. Despite randomization, there were some imbalances in patient characteristics. The best response was stable disease in 7 (30%) patients who continued bevacizumab and 2 (8%) patients who stopped receiving bevacizumab. There were no radiological responses. Median progression-free survival was 1.8 vs 2.0 months (bevacizumab vs no bevacizumab; hazard ratio [HR], 1.08; 95% CI, .59–1.96; P = .81). Median overall survival was 3.4 vs 3.0 months (HR, .84; 95% CI, .47–1.50; P = .56 and HR .70; 95% CI .38–1.29; P = .25 after adjustment for baseline factors). Quality-of-life scores did not significantly differ between arms. While the maximum daily steroid dose was lower in the continuation arm, the difference was not statistically significant.ConclusionsPatients who continued bevacizumab beyond disease progression did not have clear survival improvements, although the study was not powered to detect other than very large differences. While these data provide the only randomized evidence related to continuing bevacizumab beyond progression in recurrent glioblastoma, the small sample size precludes definitive conclusions and suggests this remains an open question.

scholarly journals Power and sample-size calculations for trials that compare slopes over time: Introducing the slopepower command

2021 ◽  
Vol 21 (3) ◽  
pp. 575-601
Author(s):  
Stephen Nash ◽  
Katy E. Morgan ◽  
Chris Frost ◽  
Amy Mulick
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Sample Size Calculation ◽  
Model Fit ◽  
User Input ◽  
Sample Size Calculations ◽  
User Friendly ◽  
Over Time

Trials of interventions that aim to slow disease progression may analyze a continuous outcome by comparing its change over time—its slope—between the treated and the untreated group using a linear mixed model. To perform a sample-size calculation for such a trial, one must have estimates of the parameters that govern the between- and within-subject variability in the outcome, which are often unknown. The algebra needed for the sample-size calculation can also be complex for such trial designs. We have written a new user-friendly command, slopepower, that performs sample-size or power calculations for trials that compare slope outcomes. The package is based on linear mixed-model methodology, described for this setting by Frost, Kenward, and Fox (2008, Statistics in Medicine 27: 3717–3731). In the first stage of this approach, slopepower obtains estimates of mean slopes together with variances and covariances from a linear mixed model fit to previously collected user-supplied data. In the second stage, these estimates are combined with user input about the target effectiveness of the treatment and design of the future trial to give an estimate of either a sample size or a statistical power. In this article, we present the slopepower command, briefly explain the methodology behind it, and demonstrate how it can be used to help plan a trial and compare the sample sizes needed for different trial designs.

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