Home
Search results “Meta analysis sample size”
Meta-Analysis (CMA): 入門教學  event rate and sample size in each group
 
02:01
Tutorial for Comprehensive Meta-Analysis (CMA): 入門教學 event rate and sample size in each group Biostat, Inc 授權經銷商 SoftHome International ; Software for Science The best softwares reseller in Taiwan 13F, NO. 55, SEC.1, CHIEN KUO N-ROAD, TAIPEI, 10491,TAIWAN [email protected] www.softhome.com.tw 全傑科技股份有限公司 科學軟體世界 臺北市中山區建國北路一段五十五號十三樓 電話Tel: 02-25078298 傳真Fax: 02-25078303 本公司保證所銷售之軟體 皆為原版合法軟體 您可以傳一份,您想要分析的資料,給我們幫您試做看看 CMA Basic Customer4.mp4
Views: 277 全傑
Comprehensive Meta-Analysis (CMA): 入門教學 Mean , SD and sample size in each group
 
04:00
Tutorial for Comprehensive Meta-Analysis (CMA): 入門教學 Two groups Continuous (mean) Unmatched groups, post data only Mean , SD and sample size in each group Biostat, Inc 授權經銷商 SoftHome International ; Software for Science The best softwares reseller in Taiwan 13F, NO. 55, SEC.1, CHIEN KUO N-ROAD, TAIPEI, 10491,TAIWAN [email protected] www.softhome.com.tw 全傑科技股份有限公司 科學軟體世界 臺北市中山區建國北路一段五十五號十三樓 電話Tel: 02-25078298 傳真Fax: 02-25078303 本公司保證所銷售之軟體 皆為原版合法軟體 您可以傳一份,您想要分析的資料,給我們幫您試做看看 CMA Basic Customer11.mp4
Views: 1184 全傑
Tutorial for Comprehensive Meta-Analysis (CMA): 入門教學 Events and  sample size in each group
 
04:54
Tutorial for Comprehensive Meta-Analysis (CMA): 入門教學 Two groups Dichotomous (number of events) Events and sample size in each group Manual input Biostat, Inc 授權經銷商 SoftHome International ; Software for Science The best softwares reseller in Taiwan 13F, NO. 55, SEC.1, CHIEN KUO N-ROAD, TAIPEI, 10491,TAIWAN [email protected] www.softhome.com.tw 全傑科技股份有限公司 科學軟體世界 臺北市中山區建國北路一段五十五號十三樓 電話Tel: 02-25078298 傳真Fax: 02-25078303 本公司保證所銷售之軟體 皆為原版合法軟體 您可以傳一份,您想要分析的資料,給我們幫您試做看看 CMA Basic Customer1.mp
Views: 212 全傑
Meta-Analysis (CMA): 入門教學 Chi-squared and total sample size
 
01:41
Tutorial for Comprehensive Meta-Analysis (CMA): 入門教學 Chi-squared and total sample size Biostat, Inc 授權經銷商 SoftHome International ; Software for Science The best softwares reseller in Taiwan 13F, NO. 55, SEC.1, CHIEN KUO N-ROAD, TAIPEI, 10491,TAIWAN [email protected] www.softhome.com.tw 全傑科技股份有限公司 科學軟體世界 臺北市中山區建國北路一段五十五號十三樓 電話Tel: 02-25078298 傳真Fax: 02-25078303 本公司保證所銷售之軟體 皆為原版合法軟體 您可以傳一份,您想要分析的資料,給我們幫您試做看看 CMA Basic Customer5.mp4
Views: 199 全傑
Introduction to Effect Size
 
02:49
Overview of effect size, the concepts behind it, and how to calculate it. Related blog post: http://www.andysbrainblog.blogspot.com/2013/02/the-will-to-fmri-power.html
Views: 26206 Andrew Jahn
Cohort, Case-Control, Meta-Analysis, Cross-sectional Study Designs & Definition
 
09:36
http://www.stomponstep1.com/cohort-case-control-meta-analysis-cross-sectional-study-designs/ Based on the types of bias that are inherent in some study designs we can rank different study designs based on their validity. The types of research studies at the top of the list have the highest validity while those at the bottom have lower validity. In most cases if 2 studies on the same topic come to different conclusions, you assume the trial of the more valid type is correct. However, this is not always the case. Any study design can have bias. A very well designed and executed cohort study can yield more valid results than a clinical trial with clear deficiencies. • Meta-analysis of multiple Randomized Trials (Highest Validity) • Randomized Trial • Prospective Cohort Studies • Case Control Studies or Retrospective Cohort • Case Series (Lowest Validity) Meta-analysis is the process of taking results from multiple different studies and combining them to reach a single conclusion. Doing this is sort of like having one huge study with a very large sample size and therefore meta-analysis has higher power than individual studies. Clinical trials are the gold standard of research for therapeutic and preventative interventions. The researchers have a high level of control over most factors. This allows for randomization and blinding which aren't possible in many other study types. Participant's groups are assigned by the researcher in clinical trials while in observational studies "natural conditions" (personal preference, genetics, social determinants, environment, lifestyle ...) assign the group. As we will see later, the incidence in different groups is compared using Relative Risk (RR). Cohort Studies are studies where you first determine whether or not a person has had an exposure and then you monitor the occurrence of health outcomes overtime. It is the observational study design with the highest validity. Cohort is just a fancy name for a group, and this should help you remember this study design. You start with a group of people (some of whom happen to have an exposure and some who don't). Then you follow this group for a certain amount of time and monitor how often certain diseases or health outcomes arise. It is easier to conceptually understand cohort studies that are prospective. However, there are retrospective cohort studies also. In this scenario you identify a group of people in the past. You then first identify whether or not these people had the particular exposure at that point in time and determine whether or not they ended up getting the health outcomes later on. As we will see later, the incidence in different groups in a cohort study is compared using Relative Risk (RR). Case-Control Studies are retrospective and observational. You first identify people who have the health outcome of interest. Then you carefully select a group of controls that are very similar to your diseased population except they don't have that particular disease. Then you try to determine whether or not the participants from each group had a particular exposure in the past. I remember this by thinking that in a case control study you start off knowing whether a person is diseased (a case) or not diseased (a control). There isn't a huge difference between retrospective cohort and case-control. You are basically doing the same steps but in a slightly different order. However, the two study designs are used in different settings. As we will see later, the incidence in different groups in a case-control study is compared using Odds Ratio (OR). A Case-Series is a small collection of individual cases. It is an observational study with a very small sample size and no control group. Basically you are just reviewing the medical records for a few people with a particular exposure or disease. A study like this is good for very rare exposures or diseases. Obviously the small sample size and lack of a control group limits the validity of any conclusions that are made, but in certain situations this is the best evidence that is available. Cross Sectional Studies are different from the others we have discussed. While the other studies measure the incidence of a particular health outcome over time, a cross-sectional study measures Prevalence. In this observational study the prevalence of the exposure and the health outcome are measured at the same time. You are basically trying to figure out how many people in the population have the disease and how many people have the exposure at one point in time. It is hard to determine an association between the exposure and disease just from this information, but you can still learn things from these studies. If the exposure and disease are both common in a particular population it may be worth investing more resources to do a different type of study to determine whether or not there is a causal relationship.
Views: 106732 Stomp On Step 1
Tutorial for Comprehensive Meta-Analysis (CMA): 入門教學 Non-events and sample size
 
02:54
Tutorial for Comprehensive Meta-Analysis (CMA): 入門教學 Two groups Dichotomous (number of events) Non-events and sample size in each group Manual input Biostat, Inc 授權經銷商 SoftHome International ; Software for Science The best softwares reseller in Taiwan 13F, NO. 55, SEC.1, CHIEN KUO N-ROAD, TAIPEI, 10491,TAIWAN [email protected] www.softhome.com.tw 全傑科技股份有限公司 科學軟體世界 臺北市中山區建國北路一段五十五號十三樓 電話Tel: 02-25078298 傳真Fax: 02-25078303 本公司保證所銷售之軟體 皆為原版合法軟體 您可以傳一份,您想要分析的資料,給我們幫您試做看看 CMA Basic Customer2.mp4
Views: 224 全傑
Cross Sectional Study Sample Size Estimation - t test
 
05:31
Mayo Clinic CTSC 5310 Clinical Epidemiology II - A brief tutorial on how to estimate sample size for a study using the t test to estimate the effect.
Views: 13181 2chocolategirls
4. Calculating In Terms of ‘Difference to Detect’ & Power From Sample Size
 
04:43
Introduction to Sample Size Calculation Training session with Dr Helen Brown, Senior Statistician, at The Roslin Institute, January 2016. ************************************************ These training sessions were given to staff and research students at the Roslin Institute. The material is also used for the Animal Biosciences MSc course taught at the Institute. ************************************************ A spreadsheet to carry out the calculations described in the presentation may be copied here: http://datashare.is.ed.ac.uk/handle/10283/1996 ********************************************* *Recommended YouTube playback settings for the best viewing experience: 1080p HD ************************************************ Content: - Relationship between sample size , power and detectable difference - Find out what sample size would be needed in terms of size of difference detectable - Power calculation: Find out power of studies with varying sample sizes and effect differences - Calculate power for varying numbers of mice and effect differences -Power calculation --- Sometimes sample size calculation is described as ‘power calculation’ or ‘power analysis’ - The power of a study can also be calculated retrospectively --- can be useful to know if study is under-powered when interpreting a non-significant result - Find out size of difference detectable in RBC (?), based on varying sample sizes
how to calculate effect sizes for meta analysis in r
 
02:01
Subscribe today and give the gift of knowledge to yourself or a friend how to calculate effect sizes for meta analysis in r How to Calculate Effect Sizes for Meta-analysis in R. Load, Prep, and Check. library(ggplot2) library(metafor) #load the data marine <- read.csv("marine_meta_short.csv", na.strings=c("NA", ".", "")) #check variable types summary(marine). Load, Prep, and Check. Slideshow 3210466 by ziya number of slides is : 1 number of slides is : 2 number of slides is : 3 number of slides is : 4 number of slides is : 5 number of slides is : 6 number of slides is : 7 number of slides is : 8 number of slides is : 9 number of slides is : 10 number of slides is : 11 number of slides is : 12 number of slides is : 13
Views: 22 slideTV
Comprehensive Meta-Analysis Tutorial Means Basic
 
48:44
Comprehensive Meta-Analysis Tutorial Means Basic www.Meta-Analysis.com
Views: 46320 Michael Borenstein
Sample Size Calculation for Group-Sequential Tests for Two Means in PASS
 
06:00
In this video, we will briefly explore estimating the sample size for a group-sequential test for comparing two means. We will also generate the boundaries for each look. Group-sequential testing is used to permit interim looks during the course of a study. Interim looks are early analyses after only a portion of the data has been collected. The early looks allow the study to be terminated early if there is sufficiently strong early evidence of efficacy, or, if the design permits, for futility. In this example, we will assume that the underlying data distributions of the two groups are Normal, so we will open the Group-Sequential Tests for Two Means Assuming Normality procedure. We will solve for Sample Size for a two-sided T-test. Typically, one hundred thousand or more simulations should be used, but we will use twenty thousand for the sake of time. We set the desired power to 0.9 and alpha to 0.05. We wish to have equal sample sizes in each group. For the control group, we assume the population mean to be 36.8. For the treatment group, we assume the population mean to be 41.4. We wish to base the calculations on a standard deviation of 4.7. On the Looks and Boundaries tab, there are options for customized specification of the look details, or a simple specification may be used. Since the test is two-sided, there are not options for futility boundaries. We will specify 5 equally spaced looks, so that there are 4 interim looks and a final look. The alpha spending function defines the portion of alpha that is spent at each look. One of the more common choices is the O’Brien-Fleming alpha spending function, which spends very little alpha in early looks and preserves the larger portion of alpha for the later looks. We press the Calculate button to run the simulation and generate the report. The simulation process takes a few moments to complete. The report shows the achieved power and alpha values, as well as the required sample size. The average sample sizes are also shown. The average sample size given the alternative hypothesis is lower than the required sample size, since a considerable number of simulated studies terminate early for efficacy. The accumulated information report shows the number of individuals that should be included at each look. The boundaries report shows the cut-off for a regular T-test at each look. If a T-test crosses the boundary at any given look, the study can be stopped early, and statistical evidence of efficacy may be concluded. The plots show the cutoff values for both the T-value scale and the P-value scale. Using either scale is equivalent. The Alpha-Spending and Null Hypothesis Simulation Details report can be useful to relate the significance boundaries to the spending function and the simulation proportions. The additional summaries at the bottom are typically used when comparing a number of simulation studies to each other. For example, if we were to enter a variety of standard deviation values, a line for each scenario would appear in these summary reports. Since we have only one scenario in this example, these summary reports do not provide any additional information. Now, suppose that we wish to use a one-sided T-test so that the study could additionally be terminated early for futility. On the Looks and Boundaries tab, the Type of Futility Boundary option now appears. We will choose Binding futility boundaries, since we intend to stop the trial early if futility is indicated. We will skip the first two looks for futility, since we don’t intend stop too early for futility. The O’Brien-Fleming spending function is also a reasonable spending function for Beta. The simulation process takes a few moments to complete. The output structure is similar, except that now futility boundaries are also given for looks 3, 4, and 5. The boundaries meet on the final look, since a decision of efficacy or futility must be made at the final look if it has not been made previously.
AN INTRODUCTION TO SAMPLING AND POWER ANALYSIS
 
01:00:42
I’ve decided on a design. Now, how many observations do I need? Sample size is a critically important factor in conducting successful research. A needlessly large sample is expensive and may be counter-productive, but an inadequate sample can prevent you from detecting important differences. This session will cover the various factors to consider when determining the “right” sample size for your research. Presented by: Dr. Walt Stroup, Statistics Download the accompanying Powerpoint presentation from our 'previous workshops' section on our website. SSP
Views: 1536 unlbosr
What Is The Sample Size In Research?
 
00:45
Sample size determination is the act of choosing the number of observations or replicates to include in a statistical sample. The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. The importance and effect of sample size select statistical methodology optimization for market research the quality unite sighteducational basics by del siegle. It is very important to understand that different study design need method of this sample size calculator presented as a public service creative research systems survey software. The sample size of a statistical is the number observations that constitute it. However, sample size 11 jun 2012 however, in research, because of time constraint and budget, a representative what is determination 5 nov 2017 definition how to find one easy steps hundreds there are many different formulas you can use, depending on know prior research (you use from previous research) calculation exact an important part design. Sample size sage research methodssample in statistics how to find it. You can use it to determine how many people you our sample size calculator help if have a statistically significant. It is typically what should be the sample size? Determining size to selected an important step in any research study. Determining sample size how to ensure you get the correct determine population and survey size? Checkmarketwhat is meaning of What Definition omniconvert. Obtaining a sample size that is determining the of population and amount error researcher willing to tolerate what determines sam 6 may 2015 do we need? It's consistently among most common questions i get from researchers. Sample size how many survey participants do i need? . Sample size calculation ncbi nih. How to calculate sample size for different study designs in calculator confidence level, interval understanding sizes how many participants do i need my research? . It can be a confusing process, but. After all, a sample that is i have household population of 3755, what can be my size? . This is an sample size a term used in market research for defining the number of subjects included 1 jan 2011 survey most typically refers to units that were chosen from which data gathered. So what exactly is 'a large number? (for more advanced students with an interest in statistics, the creative research systems website (creative systems, 2003) has a 27 jan 2014 when conducting about your customers, patients or would happen if we were to increase our sample size by going out and macorr's methodology optimization provides market first deciding kind of people interview research, quality sampling may be characterized number selection subjects observations. Sample size determination is the act of choosing number observations or replicates to 4 stratified sample size6 software for power and it may not be as accurate using other methods in estimating size, but gives a hint what appropriate where 24
Views: 44 Shad Texada Tipz
Calculating Sample size for prevalence studies (Sudan MD Thesis)
 
02:31
This video is a simple description of how a sample size for a prevalence study can be obtained by Sudanese registrars embarking on their MD Thesis research.
Views: 3922 drmugtaba
Calculating Power and Probability of Type II Error (Beta) Value in SPSS
 
06:48
This video demonstrates how to calculate power and the probability of Type II error (beta error) in SPSS. Observed power and its relationship to beta error probability are reviewed.
Views: 15307 Todd Grande
Comprehensive Meta-Analysis Subgroups
 
33:57
Comprehensive Meta-Analysis Subgroups
Views: 20307 Michael Borenstein
Introduction to Epidemiology - Sample Size Calculation
 
48:10
March 18, 2014 -- slides available at http://bit.ly/1meLVLS
What is Heterogeneity?
 
08:54
Systematic reviewers have to decide whather or not studies are homogeneous enough to combine. This video will describe what heterogeneity is and some of the tests used to investigate it.
Views: 70755 Terry Shaneyfelt
Effect Size
 
02:26
statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!
Views: 45209 statslectures
How to Interpret a Forest Plot
 
05:33
This video will discuss how to interpret the information contained in a typical forest plot.
Views: 148479 Terry Shaneyfelt
Meta-Analysis Effect Sizes and Significance in Health and Exercise Psychology
 
05:01
This video is about 2.2 Meta-Analysis ES and Significance
Comprehensive Meta-Analysis Basic data entry Correlations
 
12:57
Comprehensive Meta-Analysis Basic data entry Correlations Statistics.com
Views: 10243 Michael Borenstein
Comprehensive Meta-Analysis Basic Analysis Means
 
29:17
Comprehensive Meta-Analysis Basic Analysis Means
Views: 4637 Michael Borenstein
Effect size
 
20:50
http://thedoctoraljourney.com/ This tutorial focuses on defining, calculating, and interpreting effect size. For more statistics, research and SPSS tools, visit http://thedoctoraljourney.com/.
Views: 55659 The Doctoral Journey
Tutorial for Comprehensive Meta-Analysis (CMA): 入門教學 Excel input
 
02:36
Tutorial for Comprehensive Meta-Analysis (CMA): 入門教學 Two groups Dichotomous (number of events) Events and sample size in each group Excel input Biostat, Inc 授權經銷商 SoftHome International ; Software for Science The best softwares reseller in Taiwan 13F, NO. 55, SEC.1, CHIEN KUO N-ROAD, TAIPEI, 10491,TAIWAN [email protected] www.softhome.com.tw 全傑科技股份有限公司 科學軟體世界 臺北市中山區建國北路一段五十五號十三樓 電話Tel: 02-25078298 傳真Fax: 02-25078303 本公司保證所銷售之軟體 皆為原版合法軟體 您可以傳一份,您想要分析的資料,給我們幫您試做看看
Views: 1352 全傑
Comprehensive Meta-Analysis Overview, using binary data
 
03:46
Three-minute overview of Comprehensive Meta-Analysis software. This video uses binary data as an example.
Views: 3292 Michael Borenstein
Comprehensive Meta-Analysis Basic data entry for proportions
 
16:07
Comprehensive Meta-Analysis Basic data entry for means Statistics.com Week-1
Views: 5942 Michael Borenstein
How to calculate Cohen d effect size
 
04:50
Tutorial on how to calculate the Cohen d or effect size in for groups with different means. This test is used to compare two means. http://www.Youtube.Com/statisticsfun Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 91436 statisticsfun
Calculating Cohen's d (effect size for sample mean difference) via web using SPSS output
 
02:20
Meta-analysis calculator website: http://lyonsmorris.com/ma1/
Views: 366 Russ Webster
Comprehensive Meta-Analysis Basic data entry Means
 
26:23
Comprehensive Meta-Analysis Basic data entry Means Statistics.com Week-1
Views: 13872 Michael Borenstein
Effect Size
 
05:46
A tutorial on how to calculate Cohen's d and Partial Eta Squared using SPSS/PASW.
Views: 181129 bernstmj
Meta-Analysis using independent subgroups within studies
 
35:19
Comprehensive meta-analysis
Views: 9987 Michael Borenstein
A Short Primer on Power Calculations for Meta-analysis
 
11:19
In this third webcast in a three-part series sponsored by the Center on KTDRR, Terri Pigott, Co-Editor of the Methods Group of the Campbell Collaboration, provides a conceptual overview of power analysis in meta-analysis and recommends how researchers should present and interpret findings when statistical power is low.
Views: 135 KTDRR and KTER
Comprehensive Meta-Analysis Basic analysis proportions
 
36:37
Comprehensive Meta-Analysis Basic analysis proportions
Views: 3580 Michael Borenstein
4 Inclusion and exclusion criteria for meta-analysis
 
01:31
How do you define inclusion and exclusion criteria for your meta-analysis?
Views: 549 MetaLab
Webinar 2: Small Sample Size Clinical Trials
 
46:48
Chris Coffey presents small sample size clinical trial designs as part of the NINDS clinical trials methodology course.
Views: 241 NINDS-Vail2012
Conducting Mixed Effects Meta-Analysis in R
 
20:42
How to conduct mixed effects meta-analysis using the R metafor package.
Views: 357 Sara Locatelli
NCCMT - URE - Making Sense of a Standardized Mean Difference
 
07:35
A Standardized Mean Difference, or SMD for short, is a summary statistic used when the studies in a meta-analysis assess the same outcome but measure it in different ways.* An SMD is not tied to any specific unit of measurement, so it can be challenging to know how to interpret it, and how to use it to inform your public health decisions. In this seven-minute video, we invite you to roll up your sleeves and conquer SMDs. We also discuss why standardized mean differences are used in meta-analyses and how to interpret SMDs that are reported as positive or negative values. The video uses the example of teen mental health to demonstrate how an SMD is calculated. Greater understanding of SMDs will help you apply evidence in your practice, contributing to enhanced public health outcomes. The National Collaborating Centre for Methods and Tools is funded by the Public Health Agency of Canada and affiliated with McMaster University. The views expressed herein do not necessarily represent the views of the Public Health Agency of Canada. NCCMT is one of six National Collaborating Centres (NCCs) for Public Health. The Centres promote and improve the use of scientific research and other knowledge to strengthen public health practices and policies in Canada.
Views: 6150 The NCCMT
Power and Study Size Estimation
 
16:22
Fundamental concepts of epidemiology applied to pharmaceuticals. This course will cover basic study design, basic statistics, outcomes and exposures measurements. Meta-analysis. Application of pharmacoepidemiology to pharmacy.
Views: 52 Surasak Saokaew
The New Statistics: Planning, Power, and Precision (Workshop Part 5)
 
25:59
Featuring Geoff Cumming La Trobe University, Australia Leading scholars in psychology and other disciplines are striving to help scientists enhance the way they conduct, analyze, and report their research. They advocate the use of “the new statistics,”— effect sizes, confidence intervals, and meta-analysis. APS’ flagship journal, Psychological Science, has been inviting authors to use the “new statistics” as part of a comprehensive effort to enhance research methodology. In this workshop, Geoff Cumming, a leading expert in new statistics, explains why all these changes are necessary, and suggests how psychological scientists can implement them. The workshop was recorded at the 2014 APS Annual Convention in San Francisco, and is presented here as six video segments. It makes extensive use of interactive simulations to illustrate concepts, and provides a wealth of practical guidance. Part 5 Includes: • Statistical power and its limitations • Planning highly informative experiments • Using precision for planning: the estimation strategy with the potential to supersede statistical power.
Views: 3466 PsychologicalScience
Meta-Regression Module
 
13:02
An overview of the meta-regression module in Comprehensive Meta-Analysis v3.
Views: 5714 Meta-Analysis
metastata
 
00:37
Metastata is a research company that provides high quality support to national and international pharmaceutical companies, specialty associations, researchers and other health care organizations. Our team has performed statistical analysis, proofreading and editing of more than 2000 articles! Our services are as follows; * Clinical trial design * Sample size and power calculation * Basic statistics * Advanced statistics * Meta-analysis * Cost-effectiveness analysis We fully understand your requests before we start the project and meet your expectations with our professional work.
Views: 71 Dr.Taylan Akgün