lecturer: dr. daisy dai department of medical research
Post on 27-Jan-2016
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STA 101: Properly Setting up and Designing a Clinical Research Study Including Power Analysis for Proper Patient Numbers
Lecturer: Dr. Daisy DaiDepartment of Medical
Research
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Who are biostatisticians? Ashley Sherman
Phone: 816-701-1347 aksherman@cmh.edu
Daisy Dai
Phone: 816-701-5233 Email: hdai@cmh.edu
Consultation Experimental design
and sampling plan Collaboration in
presentation and publication of studies
Education Research
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Statistical Courses SPSS 201: Using SPSS to
perform statistical tests I (Sep 23rd)
SPSS 202: Using SPSS to perform statistical tests II
SPSS 204: Using SPSS to manage data
SPSS 203: Summarize data with tables and graphs
STA 101: Properly Setting up and Designing a Clinical Research Study Including Power Analysis for Proper Patient Numbers (July 16th)
STA 102: Commonly Used
Statistical Tests in Medical Research - Part I (Aug. 20th)
STA 103: Commonly Used Statistical Nonparametric Tests in Medical Research - Part II (Nov. 5th)
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Statistics on Scope Daisy’s statistics website is located
in “Research” tab under scope main page.
Link: http://www.childrensmercy.org/content/view.aspx?id=9740
The most useful categories are “SPSS”, “Useful links” and “Course”.
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Why do we need sample size/power calculation in medical research?
Grant application/IRB study protocol
Peer reviewed journal publication Journal review
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Medical Research Clinical Trials
Intervention or therapeutic Preventative
Retrospective Studies
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Statistics Descriptive Statistics
Methods to organize and summarize information
Mean, median, max, min, frequency and proportions, etc. that summarize sample demographics
Inferential Statistics Methods to draw conclusions about a
population based on information obtained from a sample of the population
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Population
Sample
Descriptive Statistics
Inferential Statistics
Sampling Plan
Conclusion
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Information Collections1. Historical Data
Pro: Convenient; Save a lot of work Con: Outdated; Different Objectives and Designs;
Unknown Detailed Information
2. Census Pro: reliable, accurate and comprehensive (e.g.
Population census) Con: Time consuming; requiring more resources; difficult
to investigate all subjects in the population
3. Sampling Pro: Efficient; Less risky; exploratory; informative Caveats: Selection bias; misinterpretation; design flaw
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Misconducts in Sampling A clinical foundation used the average weight of a sample of
professional football players to make an inference about the average weight of all adult males.
A local newspaper estimated the median income of California residents by sampling the incomes of Beverly Hills residents.
Before the presidential election in 1936, the Literary Digest magazine conducted an opinion poll and predicted that Alfred Landon, the Republican candidate, would win the election. However, Franklin Roosevelt, the democratic candidate, won by the greatest landslide in the history of presidential elections!
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Why do we need sampling plan? Warrant Research Ethics.
Too many participants could put more subjects under risk.
Improve Research Efficiency. A un-planned study with too many participants may take
longer to finish and require more resources but miss the early opportunity to publish interesting findings.
Deliver Reliable Information. A study without sufficient subjects may lose evidence to
demonstrate potential effects, which could waste resources or generate misleading information to readers.
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Protocol – Surgical resection for patients with gastric cancer “Sample size calculation were based on a
pre-study survey of 26 surgeons, which indicated that the baseline 5-year survival rate of D1 surgery was expected to be 20%, and an improvement in survival to 34% (14% chance) with D2 resection would be a realistic expectation. Thus 400 patients (200 in each arm) were to be randomized, providing 90% power to detect such a difference with p-value<0.05. ” [1]
[1] Cushieri et al. (1999) Patient survival after D1 and D2 resections for gastric cancer: long-term
results of the MRC randomized surgical trial. Surgical Co-operative Group.
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Protocol – Steroid or cyclosporine for oral lichen planus “It is anticipated that in patients taking
topical steroids, the response rate at 1 month will be approximately 60%. It is anticipated that this may be raised to as much as 80% in those receiving cyclosporine. With two-sided test size 5%, power 80%, then the corresponding number of patients required is approximately 200.” [2]
[2] Poon et al. (2006) A randomized controlled trial to compare steroid with cyclosporine for the topical
treatment of oral lichen planus
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Three Steps to Calculate Sample Size
Step 1: Establish study design and study objectives.
Step 2: Select the outcome variables.
Step 3: Collect information and determine sample size.
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Key Elements in Sample Size Calculation
The level of statistical significance.
The anticipated clinical difference between treatment groups.
The chance of detecting the anticipated clinical difference.
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Statistical Testing Procedures1. Null Hypothesis
- Ho: Mean_Treatment=Mean_Control
2. Alternative Hypothesis
- Ha: Mean_Treatment ≠ Mean_Control (Two-sided Test)- Ha: Mean_Treatment > Mean_Control (One-sided Test)- Ha: Mean_Treatment < Mean_Control (One-sided Test)
3. Calculate statistics
4. Make Inference
- If P-value > 0.05, then Ho holds- If P-value < 0.05, then Ha holds
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Two Types of Decision Errors
Type I error ( ) The probability of claiming a
significant difference between two treatments that are actually in parity.
Usually = 0.05
Type II error (1- ) The probability of failing to
differentiate two treatments. Ideally, 1- 0.2.
Action:Support Ho
Action:Support Ha
Fact:Ho is true
Type I error
Fact:Ha is true
Type II Error
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Effect Size ( ) The standardized difference
between means of two treatments:
CT
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Software Commercial software: nQuery Advisor
7.0 Product Website:
http://www.statsol.ie/index.php?pageID=2 User Guide
http://www.statsol.ie/documents/nQ70_version2_manual.pdf
Free software: PS 3.0 http://biostat.mc.vanderbilt.edu/twiki/bin/vie
w/Main/PowerSampleSize
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Compare means in two groups
Control
Test
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Case Study: Asthma Control Test An asthma control Test has been conducted to
develop a patient-based tool for identifying patients with poorly controlled asthma.
Mean of total ACT score for the poorly controlled group (Control) is 15 and mean of total ACT score for the well controlled group (Test) is 21. Assume the standard deviation of total ACT score is 4.
The effect size between Control and Test is 5.1
4
1521
CT
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nQuery Advisor
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Compare Means
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Compare Means
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0.85
0.98
9 15
4 6 8 10 12 14 16
Sample Size Per Group
0.5
0.6
0.7
0.8
0.9
1.0
Pow
er
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Compare proportions in two groups
Control
Test
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Case Study: Asthma Control Test A researcher is interested to compare allergic
asthmatic patients versus non-allergic asthmatic patients in response to an antihistamine treatment.
After treatments, patients will evaluate their asthma status as 0-very bad, 1-bad, 2-good and 3-very good.
This researcher needs to find out the sample size and power of a study that hypothesizes 80% of allergic cohort versus 60% of non-allergic cohort will be in good or very good status.
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nQuery Advisor - Proportion
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Compare Proportions
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Compare Proportions
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Agreement Test (Kappa Score)
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Case Study: Helmet Cure Children with flat head
syndrome will wear helmet to keep their head in shape. The diagnosis and severity of flat head vary by physicians.
A study is planned to compare the rating consistency among physicians.
Assume that 50% of reviewed cases will be diagnosed as flat head syndrome. The null hypothesis assumes only 0.4 (slight) degree of agreement between two physicians. The alternative hypothesis assumes 0.7 (strong) degree of agreement.
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nQuery Advisor
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Assess agreement
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Assess Agreement
37By Julius Sim and Chris Wright
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Sample Size Calculation for Non-parametric tests
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What is non-parametric test? Tests that are
distribution free. Compare medians
rather than mean.
Wilcoxon Signed Rank Test
Wilcoxon Rank Sum Test
Kruskall Wallis Test We will cover these
tests in details with more examples in STA103.
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Case Study: SeroxateneA studied was conducted to evaluate whether a new anti-depressant, Seroxatene has a benefit of pain relief. Patients (n=28) with MRI-confirmed disk herniation and symptomatic leg pain were enrolled and randomly assigned to receive Seroxatene or a placebo for 8 weeks. At the end of the study, patients were asked to provide a overall rating of their pain, relative to baseline.
Deterioration
No Change
Improvement
Marked Moderate Slight Sight Moderate Marked
-3 -2 -1 0 1 2 3
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Pain Relieving Scores------- Seroxatene Group -------
ID Score ID Score
2 0 16 -1
3 2 17 2
5 3 20 -3
6 3 21 3
8 -2 22 3
10 1 24 0
12 3 26 2
14 3 27 -1
------- Placebo Group -------
ID Score ID Score
1 3 15 0
4 -1 18 -1
7 2 19 -3
9 3 23 -2
11 -2 25 1
13 1 28 0
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Histograms of Pain Scores
-3 -2 -1 0 1 2 3
PainScore
0.0
0.5
1.0
1.5
2.0
2.5
Fre
qu
en
cy
Mean = 0.08Std. Dev. = 1.975N = 12
Group: Placebo
-3 -2 -1 0 1 2 3
PainScore
0
1
2
3
4
5
6
Fre
qu
ency
Mean = 1.12Std. Dev. = 2.029N = 16
Group: Seroxatene
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Sample Size Calculation for Nonparametric Tests
Although the non-parametric tests do not reply on distribution, the corresponding sample size calculation is based on distribution.
A general rule of thumb is to compute the sample size required for a t test and add 15%.
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Practicalities More than one primary outcome
Internal pilot studies
More than two groups
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Rules of Thumb The level of significance needs to be
determined beforehand. One can balance the testing sensitivity and
resources by appropriately choose sample size and power.
Feel free to consult statisticians if you have questions. Here we discussed some principles in sample size calculation. More sophisticated methods are available for experimenters.
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Summary Review research ethics. Avoid research misconducts. Raise awareness in statistical sampling
and design. Learn basic sample size and power
calculation for means, proportions and agreement.
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Thank You For more information, visit my
websitehttp://www.childrensmercy.org/content/view.aspx?id=9740Or go to Scope -> Research ->
Statistics
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