1. Biostatistics in Practice Session 2: Quantitative and Inferential Issues II Youngju Pak Biostatistician http://research.LABioMed.org/Biostat 1

2. What we have learned in Session 1?  Basic Study Design  Parameters vs. Statistics  Inferential vs. Descriptive statistics  Categorical vs. Quantitative Data? Why important?  Summarizing the data with graphs: Contingency Tables, Box Plots, Histogram, etc.  How to run MYSTAT 2

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78. Today’s topics  Article : McCann, et al., Lancet 2007 Nov 3;370(9598):1560-7 Subject selection /Randomization Efficiency from study design What statistics were used? Experimental Units / Independence of Measurements  Normal Distributions  Confidence Intervals & P-values 78

79. McCann, et al., Lancet 2007 Nov 3;370(9598):1560-7  Food additives and hyperactive behaviour in 3-year- old and 8/9-year-old children in the community: a randomised, double-blinded, placebo-controlled trial.  Target population: 3-4, 8-9 years old children  Study design: randomized, double-blinded, controlled, crossover trial  Sample size: 153 (3 years), 144(8-9 years) in Southampton UK  Objective: test whether intake of artificial food color and additive (AFCA) affects childhood behavior

80. McCann, et al., Lancet 2007 Nov 3;370(9598):1560-7  Sampling: Stratified sampling based on SES in Southampton, UK  Baseline measure: 24h recall by the parent of the child’s pretrial diet  Group: Three groups, for 3 years old –mix A : 20 mg of food colorings + 45 mg sodium benzoate, which is a widely used food preservative –mix B : 30mg of food coloring + 45 mg sodium benzoate(current average daily consumption) –Placebo –For 8/9 years old: multiply these by 1.25  Cross-over Design  A participants receive one of 6 possible random sequences. In a separate study with N=20, no significant difference in looks and taste of drinks among three groups was found even though people ask about which diet type they got when they received placebo (65%) > mix B (52%) > mix A (40%) 80 T0 (baseline)Week 1Week 2Week 3Week 4Week 5Week 6 Randomize Typical DietWashout

81. McCann, et al., Lancet 2007 Nov 3;370(9598):1560-7  Outcomes: Global Hyper Activity(GHA) Score  Attention-Deficit Hyperactivity Disorder(ADHD) rating scale IV by teachers, scaled 1 – 5, higher number means more hyperactive  Weiss-Werry-Peters(WWP) hyperactivity scale by parents,  Classroom observation code,  Conners continuous performance test II (CPTII)  GHA to be aggregated from these four scores 81

82. Why standardized outcome measure? GHA = Global Hyperactivity Aggregate, where a higher value ↔ more hyperactive For each child at each time: Z1 = Z-Score for ADHD from Teachers Z2 = Z-Score for WWP from Parents Z3 = Z-Score for ADHD in Classroom Z4 = Z-Score for Conner on Computer, where Z-score= (Score-Score at T0)/SD to make each measure scaled similarly. GHA= Mean of Z1, Z2, Z3, Z4 82

83. Why normal distribution? Symmetric. One peak. Roughly bell-shaped. No outliers. Many statistical tests(parametric) rely on the assumption that outcome measures follow the normal distribution. 83

84. A property of the normal distribution For bell-shaped distributions of data (“normally” distributed): ~ 68% of values are within mean ±1 SD ~ 95% of values are within mean ±2 SD “(Normal) Reference Range” ~ 99.7% of values are within mean ±3 SD 84

85. What if it is not normally distributed Skewed Need to transform intensity to another scale, e.g. Log(intensity) Or Nonparametric tests Multi-Peak Need to summarize with percentiles, not mean. Nonparametric tests 85

86. Representative or Random Samples How were the children to be studied selected (second column on the first page)? The authors purposely selected "representative" social classes. Is this better than a "randomly" chosen sample that ignores social class? Often hear: Non-random = Non-scientific.

87. Case Study: Participant Selection No mention of random samples.

88. Case Study: Participant Selection It may be that only a few schools are needed to get sufficient individuals. If, among all possible schools, there are few that are lower SES, none of these schools may be chosen. So, a random sample of schools is chosen from the lower SES schools, and another random sample from the higher SES schools.

89. Non-Completing or Non-Adhering Subjects Is it really a random sample? If not, what are the problems?

90. Why Randomize? So that groups will be similar except for the intervention. So that, when enrolling, we will not unconsciously choose an “appropriate” treatment for a particular subject. Minimizes the chances of introducing bias when attempting to systematically remove it, as in plant yield example.

91. Case Study: Crossover Design Each child is studied on 3 occasions under different diets. Is this better than three separate groups of children? Why, intuitively? How could you scientifically prove your intuition?

92. Estimated mean changes and their Confidence Intervals Line or Profile Plot What information was given by these confidence intervals? 92 Confidence Interval

93. Confidence Interval (CI) How well your sample mean(m) reflects the true( or population) mean  How confident?  95%? A confidence interval (CI) is one of inferential statistics that estimate the true unknown parameter using interval scales. 93

94. Confidence Interval for Population Mean 95% Reference range or “Normal Range”, is sample mean ± 2(SD) _____________________________________ 95% Confidence interval (CI) for the (true, but unknown) mean for the entire population is sample mean ± 2(SD/√N) SD/√N is called “Std Error of the Mean” (SEM) 94

95. Confidence Interval: Case Study Confidence Interval: -0.14 ± 1.99(1.04/√73) = -0.14 ± 0.24 → -0.38 to 0.10 Table 2 Normal Range: -0.14 ± 1.99(1.04) = -0.14 ± 2.07 → -2.21 to 1.93 0.13 -0.12 -0.37 Adjusted CI close to 95

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97. P-values ! Used the evidence of contradiction to your null hypothesis (H 0 ) –e.g., H 0 : no difference in mean GHA scores among three different diet. Based on the statistical test –Eg., T test statistics = Signal / Noise – if Signal >> Noise  statistically significant Usually p < 0.05 called as “statistically significant” in favor of H a 97

98. Experimental Units _____ Independence of Measurements 98

99. Units and Independence Experiments may be designed such that each measurement does not give additional independent information. Many basic statistical methods require that measurements are “independent” for the analysis to be valid. In mathematics, two events are independent if and only if the occurrence of one event makes it neither more nor less probable that the other occurs. 99

100. Experimental Units in Case Study What is the experimental unit in this study? 1. School 2. Child 3. Parent 4. GHA score (results from three diets) Are all GHA scores(eg. 153 x 3 groups=459 GHA scores for 3-4 years old children) independent? The analysis MUST incorporate this possible correlation (clustering) if there exists.  eg., Mixed Model allowing for clustering due to schools. 100

101. What have we learned today?

102. Announcements Keys for HW1 and HW 2 will be posted on class website by Wednesday. 102