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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 1 Who are biostatisticians? Ashley Sherman Phone: 816-701-1347 [email protected] Daisy Dai Phone: 816-701-5233 Email: [email protected] Consultation Experimental design and sampling plan Collaboration in presentation and publication of studies Education Research 2 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) 3 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”. 4 Why do we need sample size/power calculation in medical research? Grant application/IRB study protocol Peer reviewed journal publication Journal review 5 Medical Research Clinical Trials Intervention or therapeutic Preventative Retrospective Studies 6 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 7 Population Sampling Plan Conclusion Inferential Statistics Sample Descriptive Statistics 8 Information Collections 1. Historical Data 2. Census 3. Pro: Convenient; Save a lot of work Con: Outdated; Different Objectives and Designs; Unknown Detailed Information Pro: reliable, accurate and comprehensive (e.g. Population census) Con: Time consuming;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 9 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! 10 Why do we need sampling plan? Warrant Research Ethics. Improve Research Efficiency. Too many participants could put more subjects under risk. 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. 11 Protocol – Surgical resection for patients with gastric cancer “Sample size calculation were based on a prestudy 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 pvalue<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. 12 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 13 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. 14 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. 15 Statistical Testing Procedures 1. Null Hypothesis - 2. 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) 3. Calculate statistics 4. Make Inference - If P-value > 0.05, then Ho holds If P-value < 0.05, then Ha holds 16 Two Types of Decision Errors Action: Support Ho Type I error ( ) The probability of claiming a significant difference between two treatments that are actually in parity. Usually = 0.05 Action: Support Ha Fact: Ho is true Type I error Fact: Ha is true Type II Error Type II error (1- ) The probability of failing to differentiate two treatments. Ideally, 1- 0.2. 17 Effect Size ( ) The standardized difference between means of two treatments: T C 18 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/view/Ma in/PowerSampleSize 19 Compare means in two groups Control Test 20 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 T C 21 15 1.5 4 21 22 nQuery Advisor 23 Compare Means 24 Compare Means 25 1.0 0.98 0.9 0.85 Power 0.8 0.7 0.6 9 0.5 4 6 8 15 10 12 14 16 Sample Size Per Group 26 Compare proportions in two groups Control Test 27 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. 28 nQuery Advisor - Proportion 29 Compare Proportions 30 Compare Proportions 31 Agreement Test (Kappa Score) 32 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. 33 nQuery Advisor 34 Assess agreement 35 Assess Agreement 36 By Julius Sim and Chris Wright 37 Sample Size Calculation for Nonparametric tests 38 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. 39 Case Study: Seroxatene A studied was conducted to evaluate whether a new antidepressant, 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 Marked Moderate Slight No Change -3 -2 -1 0 Improvement Sight Moderate Marked 1 2 3 40 Pain Relieving Scores ------- Placebo Group ------- ------- Seroxatene Group ------- ID Score ID Score ID Score ID Score 1 3 15 0 2 0 16 -1 4 -1 18 -1 3 2 17 2 7 2 19 -3 5 3 20 -3 6 3 21 3 8 -2 22 3 10 1 24 0 12 3 26 2 14 3 27 -1 9 3 23 -2 11 -2 25 1 13 1 28 0 41 Histograms of Pain Scores Group: Placebo Group: Seroxatene 2.5 6 5 2.0 Frequency Frequency 4 1.5 3 1.0 2 0.5 1 Mean = 1.12 Std. Dev. = 2.029 N = 16 Mean = 0.08 Std. Dev. = 1.975 N = 12 0 0.0 -3 -2 -1 0 PainScore 1 2 3 -3 -2 -1 0 1 2 3 PainScore 42 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%. 43 Practicalities More than one primary outcome Internal pilot studies More than two groups 44 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. 45 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. 46 Thank You For more information, visit my website http://www.childrensmercy.org/content/vi ew.aspx?id=9740 Or go to Scope -> Research -> Statistics 47