Test Equality and Sample Size Calculation Based on Risk Difference in a Randomized Clinical Trial with Noncompliance and Missing Outcomes

2008 ◽  
Vol 50 (2) ◽  
pp. 224-236 ◽  
Author(s):  
Kung-Jong Lui ◽  
Kuang-Chao Chang
Keyword(s):  
Clinical Trial ◽  
Sample Size ◽  
Randomized Clinical Trial ◽  
Sample Size Calculation ◽  
Risk Difference

Citation Analysis of Articles about Hand Surgery Published in Orthopaedic and Hand Surgery Journals

2019 ◽  
Vol 24 (01) ◽  
pp. 36-44 ◽  
Author(s):  
Yuki Fujihara ◽  
Nasa Fujihara ◽  
Michiro Yamamoto ◽  
Hitoshi Hirata
Keyword(s):  
Clinical Trial ◽  
Sample Size ◽  
Randomized Clinical Trial ◽  
Study Design ◽  
Comparative Studies ◽  
Clinical Studies ◽  
Hand Surgery ◽  
The Impact ◽  
Larger Sample ◽  
Surgery Journals

Background: To date, little is known about the characteristics of highly cited studies in hand surgery compared with other orthopaedic subspecialties. We aimed to assess the position of hand surgery within the orthopedic surgery literature. Methods: We conducted a bibliographic analysis using the Web of Science database to review 1,568 articles published between January 2012 and December 2012 in 4 relevant general orthopedic and 2 hand surgery journals. We used the number of citations within 3 years of publication to measure the impact of each paper. To analyze prognostic factors using logistic regression analysis, we extracted data on orthopedic subspecialty, published journal, location of authorship, and type of study for all articles. For clinical studies, we also recorded details on study design and sample size. Results: Of eligible hand surgery articles (n = 307), the majority (62%) were case reports/series. Only 19% were comparative studies, comprising a significantly smaller proportion of comparative studies from other subspecialties in general orthopedic journals. Systematic reviews/meta-analyses generated a significantly higher number of average citations, whereas educational reviews were consistently cited less frequently than other study types (14.9 and 6.1 average citations, respectively). Being published in the Journal of Bone and Joint Surgery, American volume, having authorship in North America or Europe and Australia, focusing on subspecialties like hip & knee, sports, or shoulder, utilizing a comparative or randomized clinical trial study design, and having a larger sample size increased the odds of receiving more citations. Conclusions: Clinical studies related to hand surgery published in general orthopedic journals are most often of lower quality study design. Having a larger sample size or using a comparative study or randomized clinical trial design can improve the quality of study and may ultimately increase the impact factor of hand surgery journals.

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 Clinical Outcomes After Anterior Cruciate Ligament Reconstruction: A Meta-Analysis of Autograft Versus Allograft Tissue

2009 ◽  
Vol 2 (1) ◽  
pp. 56-72 ◽  
Author(s):  
Lisa M. Tibor ◽  
Joy L. Long ◽  
Peter L. Schilling ◽  
Ryan J. Lilly ◽  
James E. Carpenter ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Anterior Cruciate Ligament ◽  
Sample Size ◽  
Randomized Clinical Trial ◽  
Acl Reconstruction ◽  
Outcome Measures ◽  
Cruciate Ligament ◽  
Allograft Reconstruction ◽  
Kt 1000 ◽  
Anterior Cruciate

Background: Clinical outcomes of autograft and allograft anterior cruciate ligament (ACL) reconstructions are mixed, with some reports of excellent to good outcomes and other reports of early graft failure or significant donor site morbidity. Objective: To determine if there is a difference in functional outcomes, failure rates, and stability between autograft and allograft ACL reconstructions. Data Sources: Medline, Cochrane Central Register of Controlled Trials (Evidence Based Medicine Reviews Collection), Cochrane Database of Systematic Reviews, Web of Science, CINAHL, and SPORTDiscus were searched for articles on ACL reconstruction. Abstracts from annual meetings of the American Academy of Orthopaedic Surgeons, American Orthopaedic Society for Sports Medicine, and Arthroscopy Association of North America were searched for relevant studies. Study Selection: Inclusion criteria for studies were as follows: primary unilateral ACL injuries, mean patient age less than 41 years, and follow-up for at least 24 months postreconstruction. Exclusion criteria for studies included the following: skeletally immature patients, multiligament injuries, and publication dates before 1990. Data Extraction: Joint stability measures included Lachman test, pivot-shift test, KT-1000 arthrometer assessment, and frequency of graft failures. Functional outcome measures included Tegner activity scores, Cincinnati knee scores, Lysholm scores, and IKDC (International Knee Documentation Committee) total scores. Results: More than 5000 studies were identified. After full text review of 576 studies, 56 were included, of which only 1 directly compared autograft and allograft reconstruction. Allograft ACL reconstructions were more lax when assessed by the KT-1000 arthrometer. For all other outcome measures, there was no statistically significant difference between autograft and allograft ACL reconstruction. For all outcome measures, there was strong evidence of statistical heterogeneity between studies. The sample size necessary for a randomized clinical trial to detect a difference between autograft and allograft reconstruction varied, depending on the outcome. Conclusions: With the current literature, only KT-1000 arthrometer assessment demonstrated more laxity with allograft reconstruction. A randomized clinical trial directly comparing allograft to autograft ACL reconstruction is warranted, but a multicenter study would be required to obtain an adequate sample size.

scholarly journals A Menu-driven Facility for Complex Sample Size Calculation in Randomized Controlled Trials with a Survival or a Binary Outcome: Update

2005 ◽  
Vol 5 (1) ◽  
pp. 123-129 ◽  
Author(s):  
Friederike M.-S. Barthel ◽  
Patrick Royston ◽  
Abdel Babiker
Keyword(s):  
Clinical Trial ◽  
Randomized Controlled Trials ◽  
Sample Size ◽  
Sample Size Calculation ◽  
Controlled Trials ◽  
Binary Outcome ◽  
Randomized Controlled ◽  
Complex Sample ◽  
Clinical Trial Designs ◽  
Survival Studies

Royston and Babiker (2002) presented a menu-driven Stata program for the calculation of sample size or power for complex clinical trial designs under a survival time or binary outcome. In the present article, the package is updated to Stata 8 under the new name ART. Furthermore, the program has been extended to incorporate noninferiority designs and provides more detailed output. This package is the only realistic sample size tool for survival studies available in Stata.

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