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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 Dai Department of Medical Research. Ashley Sherman Phone: 816-701-1347 Daisy Dai Phone: 816-701-5233 Email:

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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 Dai

Department of Medical Research

who are biostatisticians
Ashley Sherman

Phone: 816-701-1347

Daisy Dai

Phone: 816-701-5233



Experimental design and sampling plan

Collaboration in presentation and publication of studies



Who are biostatisticians?
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)

Statistical Courses
statistics on scope
Statistics on Scope
  • Daisy’s statistics website is located in “Research” tab under scope main page.
  • Link:
  • The most useful categories are “SPSS”, “Useful links” and “Course”.
why do we need sample size power calculation in medical research
Why do we need sample size/power calculation in medical research?
  • Grant application/IRB study protocol
  • Peer reviewed journal publication
  • Journal review
medical research
Medical Research
  • Clinical Trials
    • Intervention or therapeutic
    • Preventative
  • Retrospective Studies
  • 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


Inferential Statistics

Sampling Plan



Descriptive Statistics

information collections
Information Collections
  • Historical Data
    • Pro: Convenient; Save a lot of work
    • Con: Outdated; Different Objectives and Designs; Unknown Detailed Information
  • Census
    • Pro: reliable, accurate and comprehensive (e.g. Population census)
    • Con: Time consuming; requiring more resources; difficult to investigate all subjects in the population
  • Sampling
    • Pro: Efficient; Less risky; exploratory; informative
    • Caveats: Selection bias; misinterpretation; design flaw
misconducts in sampling
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!
why do we need sampling plan
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.
protocol surgical resection for patients with gastric cancer
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.

protocol steroid or cyclosporine for oral lichen planus
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

three steps to calculate sample size
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.
key elements in sample size calculation
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.
statistical testing procedures
Statistical Testing Procedures
  • Null Hypothesis
    • Ho: Mean_Treatment=Mean_Control
  • 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)
  • Calculate statistics
  • Make Inference
    • If P-value > 0.05, then Ho holds
    • If P-value < 0.05, then Ha holds
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.

Two Types of Decision Errors
effect size
Effect Size ( )
  • The standardized difference between means of two treatments:
  • Commercial software: nQuery Advisor 7.0
    • Product Website:
    • User Guide
  • Free software: PS 3.0
case study asthma control test
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
case study asthma control test1
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.
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.

Case Study: Helmet Cure
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.

What is non-parametric test?
case study seroxatene
Case Study: Seroxatene

A 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.

sample size calculation for nonparametric tests
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%.
  • More than one primary outcome
  • Internal pilot studies
  • More than two groups
rules of thumb
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.
  • 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.
thank you
Thank You
  • For more information, visit my website

Or go to Scope -> Research -> Statistics