Survival outcomes for various treatment modalities in advanced-stage grade 3 follicular lymphoma (FL3): A National Cancer Database (NCDB) study.

7554 Background: The prognosis, response to therapy and curability of FL3 is controversial. 5-year Overall Survival (OS) in the literature ranges from 35-72% (Ganti 2006). The aim of this study was to compare the OS for patients with advanced-stage FL3 managed with various treatment modalities. Methods: We identified patients (pts) diagnosed with stage III & IV FL3 between 2004 – 2012 from the NCDB and categorized them into 3 groups based on therapy – pts given single agent chemotherapy with or without radiotherapy were combined due to small sample sizes (SA±RT), multi agent chemotherapy without radiotherapy (MA-RT), and multi agent chemotherapy with radiotherapy (MA+RT). We calculated OS using Kaplan-Meier method and compared the results using Log Rank test. Cox regression model was used to identify other factors which had significant impact on OS. Results: 2,808 pts were identified – 1,508 (54%) with stage III and 1,300 (46%) with stage IV disease. Median age was 60 yrs (range 21-90yrs); 1,331 (47%) males, 1,477 (53%) females; 2,559 (91%) whites, 142 (5%) blacks. 170 cases (6%) were treated with SA±RT, 2,508 (89%) with MA-RT and 130 (5%) with MA+RT. There was no significant difference in 5-year OS between MA-RT (83%) and MA+RT (82%; HR 1.07, P=0.76). There was no difference between SA±RT (73%) and MA+RT (82%; HR 0.62, P=0.069) likely due to small sample sizes, but survival for MA-RT (83%) was significantly higher than SA±RT (73%; HR 1.78, p<0.001). Cox regression indicated that age (HR 1.04, P<0.001), sex (HR 0.77 for females, P=0.008), comorbidities (HR 1.48 for Charlson Deyo Score 1, P=0.001; HR 2.59 for Score 2, P<0.001), stage (HR 1.29, P=0.007), insurance status (HR 0.65 for insured, P=0.048) and increasing year of diagnosis (HR 0.91, P<0.001) also had significant impact on OS. Use of MA chemotherapy declined (2004 96% v 2012 91%, P=0.008) but there was no significant trend in use of radiotherapy (2004 5% v 2012 3%) during the periods studied. Conclusions: MA chemotherapy in pts with advanced-stage FL3 was associated with improved survival compared to SA therapy, and radiation does not appear to influence outcomes. Outcomes were superior to what has been previously reported.

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Survival outcomes for various treatment modalities in early-stage grade 3 follicular lymphoma (FL3): a National Cancer Database (NCDB) study.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.15_suppl.7551 ◽

2017 ◽

Vol 35 (15_suppl) ◽

pp. 7551-7551

Author(s):

Upama Giri ◽

Eric Vick ◽

Sophia SeoHyeon Lee ◽

Alaa Altahan ◽

Noam Avraham VanderWalde ◽

...

Keyword(s):

Cox Regression ◽

Early Stage ◽

Single Agent ◽

Treatment Strategies ◽

Small Sample ◽

Response To Therapy ◽

Stage I ◽

Treatment Modalities ◽

Rank Test ◽

Multi Agent

7551 Background: The prognosis, response to therapy and curability of FL3 is controversial. 5-year Overall Survival (OS) in the literature ranges from 35-72% (Ganti 2006). The aim of this study was to compare the OS for patients with early-stage FL3 managed with single- and multi-agent chemotherapy (CT) with and without radiotherapy (RT). Methods: We identified patients (pts) diagnosed with stage I & II FL3 between 2004 – 2012 from the NCDB and categorized into 3 groups based on therapy – pts given single agent CT with or without RT were combined due to small sample sizes (SA±RT), multi-agent CT without RT (MA-RT), and multi-agent CT with RT (MA+RT). We calculated OS for each group using Kaplan-Meier method and compared the results using Log Rank test. Cox regression model was used to identify factors which had significant impact on OS. Results: 1,563 pts were identified – 827 (53%) with stage I and 736 (47%) with stage II FL3. Median age was 61 yrs (range 18-90yrs); 750 (48%) males, 813 (52%) females; 1423 (91%) whites, 76 (5%) blacks. 112 (7%) received SA±RT, 886 (57%) MA-RT and 565 (37%) MA+RT. 5-year OS for MA+RT (95%) was significantly more than MA-RT (87%; HR 0.33, P<0.001) or SA±RT (88%; HR 0.38, P=0.007). Cox regression indicated that age (HR 1.05, P<0.001), sex (HR 0.66 for females, P=0.02), comorbidities (HR 1.60 for Charlson Deyo Score 1, P=0.04; HR 3.07 for Score 2, P=0.001), stage (HR 1.79, P=0.001), insurance status (HR 0.22 for insured, P<0.001) and increasing year of diagnosis (HR 0.92, P=0.03) also had significant impact on OS. Median radiation dose for the MA+RT was 36Gy (interquartile range 30.6 – 36Gy), and the proportion of patients who received greater than 36Gy decreased from 55% in 2004 to 38% in 2012 and at the same time, the proportion of patients who received intensity modulated RT increased from 5% in 2004 to 15% in 2012. Use of MA CT declined (2004 95% v 2012 89%, P=0.02) but there was no significant trend in use of RT (2004 39% v 2012 34%) during the periods studied. Conclusions: For pts with early-stage FL3, there was an association of improved survival with the use of MA+RT over other treatment strategies and appear to have outcomes superior to what has been previously reported.

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Problems with small sample sizes in psychophysiological research

PsycEXTRA Dataset ◽

10.1037/e526132012-267 ◽

1996 ◽

Author(s):

Todd C. Riniolo ◽

Stephen W. Porges

Keyword(s):

Small Sample ◽

Sample Sizes ◽

Psychophysiological Research ◽

Small Sample Sizes

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Bayesian Latent Growth Mixture-Modeling With Small Sample Sizes

PsycEXTRA Dataset ◽

10.1037/e568142014-001 ◽

2014 ◽

Author(s):

Sarah Depaoli

Keyword(s):

Growth Mixture Modeling ◽

Mixture Modeling ◽

Small Sample ◽

Sample Sizes ◽

Latent Growth ◽

Growth Mixture ◽

Latent Growth Mixture Modeling ◽

Small Sample Sizes

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No Evidence that Experiencing Physical Warmth Promotes Interpersonal Warmth: Two Failures to Replicate Williams and Bargh (2008)

10.31234/osf.io/mvn9b ◽

2018 ◽

Cited By ~ 1

Author(s):

Christopher Chabris ◽

Patrick Ryan Heck ◽

Jaclyn Mandart ◽

Daniel Jacob Benjamin ◽

Daniel J. Simons

Keyword(s):

Null Hypothesis ◽

Small Sample ◽

Sample Sizes ◽

Double Blind ◽

Bayesian Analyses ◽

Physical Warmth ◽

Small Sample Sizes ◽

Interpersonal Warmth

Williams and Bargh (2008) reported that holding a hot cup of coffee caused participants to judge a person’s personality as warmer, and that holding a therapeutic heat pad caused participants to choose rewards for other people rather than for themselves. These experiments featured large effects (r = .28 and .31), small sample sizes (41 and 53 participants), and barely statistically significant results. We attempted to replicate both experiments in field settings with more than triple the sample sizes (128 and 177) and double-blind procedures, but found near-zero effects (r = –.03 and .02). In both cases, Bayesian analyses suggest there is substantially more evidence for the null hypothesis of no effect than for the original physical warmth priming hypothesis.

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G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes

Scientific Reports ◽

10.1038/s41598-021-81110-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Florent Le Borgne ◽

Arthur Chatton ◽

Maxime Léger ◽

Rémi Lenain ◽

Yohann Foucher

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Statistical Power ◽

Small Sample ◽

Causal Effects ◽

Small Samples ◽

Support Vector ◽

Sample Sizes ◽

Super Learner ◽

Small Sample Sizes

AbstractIn clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of covariates and relationships between covariates, exposure statuses, and outcomes. We have also illustrated the application of these methods, in which they were used to estimate the efficacy of barbiturates prescribed during the first 24 h of an episode of intracranial hypertension. In the context of GC, for estimating the individual outcome probabilities in two counterfactual worlds, we reported that the super learner tended to outperform the other approaches in terms of both bias and variance, especially for small sample sizes. The support vector machine performed well, but its mean bias was slightly higher than that of the super learner. In the investigated scenarios, G-computation associated with the super learner was a performant method for drawing causal inferences, even from small sample sizes.

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What can we Learn from Studies Based on Small Sample Sizes? Comment on Regan, Lakhanpal, and Anguiano (2012)

Psychological Reports ◽

10.2466/21.02.07.pr0.113x12z8 ◽

2013 ◽

Vol 113 (1) ◽

pp. 221-224 ◽

Cited By ~ 3

Author(s):

David R. Johnson ◽

Lauren K. Bachan

Keyword(s):

Sample Size ◽

Reporting rigorous qualitative results: Moving beyond small sample sizes

Australian Occupational Therapy Journal ◽

10.1111/1440-1630.12475 ◽

2018 ◽

Vol 65 (2) ◽

pp. 77-78

Author(s):

Genevieve Pepin

Keyword(s):

Small Sample ◽

Sample Sizes ◽

Small Sample Sizes

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Implications of Small Samples for Generalization: Adjustments and Rules of Thumb

Evaluation Review ◽

10.1177/0193841x16655665 ◽

2016 ◽

Vol 41 (5) ◽

pp. 472-505 ◽

Cited By ~ 16

Author(s):

Elizabeth Tipton ◽

Kelly Hallberg ◽

Larry V. Hedges ◽

Wendy Chan

Keyword(s):

Observational Studies ◽

Small Sample ◽

Average Treatment Effect ◽

Small Samples ◽

Sample Sizes ◽

Random Samples ◽

Rules Of Thumb ◽

Large Populations ◽

Small Sample Sizes ◽

Combine Information

Background: Policy makers and researchers are frequently interested in understanding how effective a particular intervention may be for a specific population. One approach is to assess the degree of similarity between the sample in an experiment and the population. Another approach is to combine information from the experiment and the population to estimate the population average treatment effect (PATE). Method: Several methods for assessing the similarity between a sample and population currently exist as well as methods estimating the PATE. In this article, we investigate properties of six of these methods and statistics in the small sample sizes common in education research (i.e., 10–70 sites), evaluating the utility of rules of thumb developed from observational studies in the generalization case. Result: In small random samples, large differences between the sample and population can arise simply by chance and many of the statistics commonly used in generalization are a function of both sample size and the number of covariates being compared. The rules of thumb developed in observational studies (which are commonly applied in generalization) are much too conservative given the small sample sizes found in generalization. Conclusion: This article implies that sharp inferences to large populations from small experiments are difficult even with probability sampling. Features of random samples should be kept in mind when evaluating the extent to which results from experiments conducted on nonrandom samples might generalize.

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