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Data Analysis

Data Analysis. Mohsen Askarishahi. Reference: Aviva Petrie . Medical Statistics at a Glance . Blackwell (2005) Sheldon M. Ross . Introductory Statistics . Elsevier Inc. (2010) Wayne W. Daniel . Biostatistics,A Foundation for Analysis in the Health . John Wiley & Sons.Inc.(1995).

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Data Analysis

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  1. Data Analysis Mohsen Askarishahi • Reference: • Aviva Petrie. Medical Statistics at a Glance. Blackwell (2005) • Sheldon M. Ross. Introductory Statistics . Elsevier Inc. (2010) • Wayne W. Daniel .Biostatistics,A Foundation for Analysis in the Health . John Wiley & Sons.Inc.(1995)

  2. Numerical data

  3. Comparing Odds Ratio and Relative Risk Outcome cancer no cancer Exposure Exposed (smoke) 70 300 Not Exposed(non smoke) 30 700 100 1000 OR = ad/bc = 5.44 RR= Ie/In =4.41

  4. Stating your results • OR = 5.44 Those with the disease are 5.44 times as likely to have had the exposure compared to those without the disease • RR = 4.41 Those with the exposure are 4.41 times as likely to develop the disease compared to those without the exposure

  5. We have a sample from a single group of individuals and one numerical or ordinal variable of interest. We are interested in whether the average of this variable takes a particular value. For example, we may have a sample of patients with a specific medical condition. We have been monitoring triglyceride levels in the blood of healthy individuals and know that they have a mean of 1.74mmol/L. We wish to know whether the average level in our patients is the same as this value.

  6. The one-sample t-test 1- Define the null and alternative hypotheses under study 2- Collect relevant data from a sample of individuals 3- Calculate the value of the test statistic specific to H0 4- Compare the value of the test statistic to values from a known probability distribution 5 -Interpret the P-value and results

  7. Interpret the P-value and results

  8. Numerical data: two related groups We have two samples that are related to each other and one numerical or ordinal variable of interest. • The variable may be measured on each individual in two circumstances. For example, each patient has two measurements on the variable, one while taking active treatment and one while taking placebo. • The individuals in each sample may be different, but are linked to each other in some way. For example, patients in one group may be individually matched to patients in the other group in a case–control Study .

  9. Numerical data: two related groups The paired t-test

  10. The paired t-test

  11. The paired t-test

  12. We have samples from two independent (unrelated) groups of individuals and one numerical or ordinal variable of interest. We are interested in whether the mean or distribution of the variable is the same in the two groups. For example, we may wish to compare the weights in two groups of children, each child being randomly allocated to receive either a dietary supplement or placebo.

  13. We have samples from a number of independent groups. We have a single numerical or ordinal variable and are interested in whether the average value of the variable varies in the different groups. For example, whether the average platelet count varies in groups of women with different ethnic backgrounds.

  14. خسته نباشید.

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