Transformasi Terhadap Min bagi Menguji Taburan Terpencong

2012 ◽  
Author(s):  
Nor Haniza Sarmin ◽  
Md Hanafiah Md Zin ◽  
Rasidah Hussin
Keyword(s):  
Key Words ◽  
Simulation Study ◽  
Bias Correction ◽  
Type I Error ◽  
Small Sample Size ◽  
Small Sample ◽  
Skewed Distribution ◽  
Type I ◽  
Skewed Distributions ◽  
Chi Square

Suatu transformasi terhadap min dilakukan menggunakan penganggar pembetulan kepincangan bagi mendapatkan statistik untuk menguji min hipotesis taburan terpencong. Penghasilan statistik ini melibatkan pengubahsuaian pemboleh ubah . Kajian simulasi yang dijalankan terhadap taburan yang terpencong iaitu taburan eksponen, kuasa dua khi dan Weibull ke atas Kebarangkalian Ralat Jenis I menunjukkan bahawa statistik t3 sesuai untuk ujian satu hujung sebelah kiri dan saiz sampel yang kecil (n=5). Kata kunci: Min; statistik; taburan terpencong; penganggar pembetulan kepincangan; kebarangkalian Ralat Jenis I A transformation of mean has been done using a bias correction estimator to produce a statistic for mean hypothesis of skewed distributions. The statistic found involves a modification of the variable . A simulation study that has been done on some skewed distributions i.e. esponential, chi-square and Weibull on the Type I Error shows that t3 is suitable for the left-tailed test and a small sample size (n=5). Key words: Mean; statistic; skewed distribution; bias correction estimator; Type I Error

scholarly journals Taking population stratification into account by local permutations in rare-variant association studies on small samples

2020 ◽  
Author(s):  
J. Mullaert ◽  
M. Bouaziz ◽  
Y. Seeleuthner ◽  
B. Bigio ◽  
J-L. Casanova ◽  
...  
Keyword(s):  
Sample Size ◽  
Rare Variant ◽  
Population Stratification ◽  
Type I Error ◽  
Small Sample Size ◽  
Association Studies ◽  
Small Sample ◽  
Small Samples ◽  
Type I ◽  
Rare Variant Association

AbstractMany methods for rare variant association studies require permutations to assess the significance of tests. Standard permutations assume that all individuals are exchangeable and do not take population stratification (PS), a known confounding factor in genetic studies, into account. We propose a novel strategy, LocPerm, in which individuals are permuted only with their closest ancestry-based neighbors. We performed a simulation study, focusing on small samples, to evaluate and compare LocPerm with standard permutations and classical adjustment on first principal components. Under the null hypothesis, LocPerm was the only method providing an acceptable type I error, regardless of sample size and level of stratification. The power of LocPerm was similar to that of standard permutation in the absence of PS, and remained stable in different PS scenarios. We conclude that LocPerm is a method of choice for taking PS and/or small sample size into account in rare variant association studies.

scholarly journals A Machine Learning Approach to Assess Differential Item Functioning in Psychometric Questionnaires Using the Elastic Net Regularized Ordinal Logistic Regression in Small Sample Size Groups

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Vahid Ebrahimi ◽  
Zahra Bagheri ◽  
Zahra Shayan ◽  
Peyman Jafari
Keyword(s):  
Sample Size ◽  
Type I Error ◽  
Small Sample Size ◽  
Error Rates ◽  
Small Sample ◽  
Elastic Net ◽  
Small Samples ◽  
Type I ◽  
Item Functioning ◽  
Size Groups

Assessing differential item functioning (DIF) using the ordinal logistic regression (OLR) model highly depends on the asymptotic sampling distribution of the maximum likelihood (ML) estimators. The ML estimation method, which is often used to estimate the parameters of the OLR model for DIF detection, may be substantially biased with small samples. This study is aimed at proposing a new application of the elastic net regularized OLR model, as a special type of machine learning method, for assessing DIF between two groups with small samples. Accordingly, a simulation study was conducted to compare the powers and type I error rates of the regularized and nonregularized OLR models in detecting DIF under various conditions including moderate and severe magnitudes of DIF ( DIF = 0.4   and   0.8 ), sample size ( N ), sample size ratio ( R ), scale length ( I ), and weighting parameter ( w ). The simulation results revealed that for I = 5 and regardless of R , the elastic net regularized OLR model with w = 0.1 , as compared with the nonregularized OLR model, increased the power of detecting moderate uniform DIF ( DIF = 0.4 ) approximately 35% and 21% for N = 100   and   150 , respectively. Moreover, for I = 10 and severe uniform DIF ( DIF = 0.8 ), the average power of the elastic net regularized OLR model with 0.03 ≤ w ≤ 0.06 , as compared with the nonregularized OLR model, increased approximately 29.3% and 11.2% for N = 100   and   150 , respectively. In these cases, the type I error rates of the regularized and nonregularized OLR models were below or close to the nominal level of 0.05. In general, this simulation study showed that the elastic net regularized OLR model outperformed the nonregularized OLR model especially in extremely small sample size groups. Furthermore, the present research provided a guideline and some recommendations for researchers who conduct DIF studies with small sample sizes.

Comparing the Performance of Multivariate Multilevel Modeling to Traditional Analyses With Complete and Incomplete Data

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 100-109 ◽  
Author(s):  
Ryoungsun Park ◽  
Keenan A. Pituch ◽  
Jiseon Kim ◽  
Hyewon Chung ◽  
Barbara G. Dodd
Keyword(s):  
Missing Data ◽  
Multilevel Modeling ◽  
Error Rate ◽  
Type I Error ◽  
Small Sample Size ◽  
Univariate Analysis ◽  
Small Sample ◽  
Type I ◽  
Type I Error Rate ◽  
Dependent Variables

Abstract. A multivariate multilevel model (MVMM) extends standard multilevel modeling by including multiple dependent variables and thus could be used in place of traditional multivariate analyses. For a two-group study with two correlated dependent variables, a simulation study was conducted to compare the performance of MVMM to traditional MANOVA and a series of univariate analyses. The results showed that MVMM provides for virtually always greater power than other analyses, even for conditions that have been previously shown to favor univariate analysis. Further, this power advantage can be substantial even when no missing data are present and is often much greater when data are missing. While the Type I error rate for the overall multivariate null hypothesis can be somewhat elevated with MVMM, especially with small sample size and a large proportion of missing data, the Type I error rate for the test of a specific dependent variable is accurate.

A multinational phase III adjuvant study of immediate (I) versus deferred (D) chemotherapy (C)/hormone therapy (HT) after radical prostatectomy (RP): TAX-3501.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 4570-4570 ◽  
Author(s):  
Mario A. Eisenberger ◽  
Michael W. Kattan ◽  
Cora N. Sternberg ◽  
Ronald De Wit ◽  
Jonathan I Epstein ◽  
...  
Keyword(s):  
Type I Error ◽  
Systemic Treatment ◽  
Small Sample Size ◽  
Small Sample ◽  
Phase Iii ◽  
Type I ◽  
Grade 5 ◽  
Predicted Probability ◽  
Phase Iii Study ◽  
Central Pathology

4570 Background: TAX 3501 was designed to test I or D C/HT after RP. No adequately designed prospectively randomized surgical systemic adjuvant studies have ever been successfully completed. Methods: TAX-3501 was a randomized phase III study. Eligibility included post-operative predicted probability of 5-year freedom from progression of <60% (Kattan et al) after central pathology review, no prior C/HT, undetectable PSA, M0, normal hematology/chemistries, signed consent. Pts were randomized within 120 days after RP to I or D (at 1st progression) Rx with HT (leuprolide acetate 22.5mg S.C. q.3 months x 6 ) +/- C (docetaxel 75mg/m2 q.3wks x 6). F/U included a physical exam, PSA, CBC/chemical profile, serum T, scans yearly or at progression (PSA≥ 0.4ng/ml, clinical or radiographic and death). Main objectives comparing PFS and 5-year progression-free rates after systemic treatment (2 × 2 factorial design, 4 arms). 1,696 subjects would provide 90% power to detect the targeted treatment effect at a 5% 2-sided type I error level. Results: From 12/2005-9/2007399 pts were registered, 228 randomized after central path review ICHT=55, IHT=55, DCHT=56, DHT=62, median age 62 (41-76), pre-op PSA (ng/ml) 9.38 (2.2 - 90.0), Gleason score- 8,T3a (19.6%),R+(65%),S.V.+(50%),N+(19.7%%), all had undetectable post op PSA. Predicted probability of no progression was 21% on 228pts. Study was terminated by sponsor (9/2007) due to poor accrual F/U continued from 2007-2010. Small sample size precludes reliable analyzes 37/118 (31.3%) on the D arms received CHT/HT (all PSA progressions); ICHT 10/55, IHT 14/55, DCHT 9/56, DHT 8/62 met progression endpoint after I/D Rx (1/31 bone). AEs were characteristic and reversible (no grade 5). Leading causes of accrual impediment were inadequate site selection, no consensus regarding patient and treatment selection for this pt population, physician and patient bias re: treatment and evolving changes in the adjuvant treatment landscape during follow-up time (adjuvant RT). Conclusions: The role of adjuvant systemic treatment after RP remains undefined. Clinical trials in this pt population are challenging. Supported by Sanofi NCT000283062.

Assessing Person Fit With the Information Matrix Test

Methodology ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Jochen Ranger ◽  
Jörg-Tobias Kuhn
Keyword(s):  
Simulation Study ◽  
Type I Error ◽  
Information Matrix ◽  
Small Samples ◽  
Type I ◽  
Person Fit ◽  
Power Of The Test ◽  
Order Expansion ◽  
Trait Stability ◽  
Information Matrix Test

In this manuscript, a new approach to the analysis of person fit is presented that is based on the information matrix test of White (1982) . This test can be interpreted as a test of trait stability during the measurement situation. The test follows approximately a χ2-distribution. In small samples, the approximation can be improved by a higher-order expansion. The performance of the test is explored in a simulation study. This simulation study suggests that the test adheres to the nominal Type-I error rate well, although it tends to be conservative in very short scales. The power of the test is compared to the power of four alternative tests of person fit. This comparison corroborates that the power of the information matrix test is similar to the power of the alternative tests. Advantages and areas of application of the information matrix test are discussed.

Correction: “Influence of Selection Bias on the Test Decision – A Simulation Study”

2014 ◽  
Vol 53 (05) ◽  
pp. 343-343
Keyword(s):  
Selection Bias ◽  
Simulation Study ◽  
Error Rate ◽  
Type I Error ◽  
Block Size ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rate ◽  
Representation Error ◽  
Numeric Representation

We have to report marginal changes in the empirical type I error rates for the cut-offs 2/3 and 4/7 of Table 4, Table 5 and Table 6 of the paper “Influence of Selection Bias on the Test Decision – A Simulation Study” by M. Tamm, E. Cramer, L. N. Kennes, N. Heussen (Methods Inf Med 2012; 51: 138 –143). In a small number of cases the kind of representation of numeric values in SAS has resulted in wrong categorization due to a numeric representation error of differences. We corrected the simulation by using the round function of SAS in the calculation process with the same seeds as before. For Table 4 the value for the cut-off 2/3 changes from 0.180323 to 0.153494. For Table 5 the value for the cut-off 4/7 changes from 0.144729 to 0.139626 and the value for the cut-off 2/3 changes from 0.114885 to 0.101773. For Table 6 the value for the cut-off 4/7 changes from 0.125528 to 0.122144 and the value for the cut-off 2/3 changes from 0.099488 to 0.090828. The sentence on p. 141 “E.g. for block size 4 and q = 2/3 the type I error rate is 18% (Table 4).” has to be replaced by “E.g. for block size 4 and q = 2/3 the type I error rate is 15.3% (Table 4).”. There were only minor changes smaller than 0.03. These changes do not affect the interpretation of the results or our recommendations.

scholarly journals Perioperative Outcomes Following Kidney-Pancreas Transplantation in Alberta, Canada: Research Letter

2021 ◽  
Vol 8 ◽  
pp. 205435812110293
Author(s):  
Danielle E. Fox ◽  
Robert R. Quinn ◽  
Paul E. Ronksley ◽  
Tyrone G. Harrison ◽  
Hude Quan ◽  
...  
Keyword(s):  
Patient Outcomes ◽  
Postoperative Care ◽  
Pancreas Transplantation ◽  
Type I Diabetes ◽  
Small Sample Size ◽  
Small Sample ◽  
Perioperative Outcomes ◽  
Type I ◽  
Patient Safety Indicators ◽  
Significant Difference

Background: Simultaneous kidney-pancreas transplantation (SPK) has benefits for patients with kidney failure and type I diabetes mellitus, but is associated with greater perioperative risk compared with kidney-alone transplantation. Postoperative care settings for SPK recipients vary across Canada and may have implications for patient outcomes and hospital resource use. Objective: To compare outcomes following SPK transplantation between patients receiving postoperative care in the intensive care unit (ICU) compared with the ward. Design: Retrospective cohort study using administrative health data. Setting: In Alberta, the 2 transplant centers (Calgary and Edmonton) have different protocols for routine postoperative care of SPK recipients. In Edmonton, SPK recipients are routinely transferred to the ICU, whereas in Calgary, SPK recipients are transferred to the ward. Patients: 129 adult SPK recipients (2002-2019). Measurements: Data from the Canadian Institute for Health Information Discharge Abstract Database (CIHI-DAD) were used to identify SPK recipients (procedure codes) and the outcomes of inpatient mortality, length of initial hospital stay (LOS), and the occurrence of 16 different patient safety indicators (PSIs). Methods: We followed SPK recipients from the admission date of their transplant hospitalization until the first of hospital discharge or death. Unadjusted quantile regression was used to determine differences in LOS, and age- and sex-adjusted marginal probabilities were used to determine differences in PSIs between centers. Results: There were no perioperative deaths and no major differences in the demographic characteristics between the centers. The majority of the SPK transplants were performed in Edmonton (n = 82, 64%). All SPK recipients in Edmonton were admitted to the ICU postoperatively, compared with only 11% in Calgary. There was no statistically significant difference in the LOS or probability of a PSI between the 2 centers (LOS for Edmonton vs Calgary:16 vs 13 days, P = .12; PSIs for Edmonton vs Calgary: 60%, 95% confidence interval [CI] = 0.50-0.71 vs 44%, 95% CI = 0.29-0.59, P = .08). Limitations: This study was conducted using administrative data and is limited by variable availability. The small sample size limited precision of estimated differences between type of postoperative care. Conclusions: Following SPK transplantation, we found no difference in inpatient outcomes for recipients who received routine postoperative ICU care compared with ward care. Further research using larger data sets and interventional study designs is needed to better understand the implications of postoperative care settings on patient outcomes and health care resource utilization.

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