1. 1 Desipramine is an antidepressant affecting the brain chemicals that may become unbalanced and cause depression. It was tested for recovery from cocaine addiction. Treatment with desipramine was compared to a standard treatment (lithium, with strong anti-manic effects) and a placebo. Is desipramine effective in preventing relapses?

2. Hypothesis: no association We use the chi-square (   ) test to assess the null hypothesis of no relationship between the two categorical variables of a two-way table. Two-way tables sort the data according to two categorical variables. We want to test the hypothesis that there is no relationship between these two categorical variables (H 0 ).

3. Expected Cell Counts To test this hypothesis, we compare actual counts from the sample data with expected counts, given the null hypothesis of no relationship. The expected count in any cell of a two-way table when H 0 is true is: 3

4. 25*26/74 ≈ 8.78 25*0.35 16.22 25*0.65 9.14 26*0.35 16.86 25*0.65 8.08 23*0.35 14.92 25*0.65 Desipramine Lithium Placebo Expected relapse counts No Yes 35% Overall non-relapse % Observed Cocaine addiction NOTE: 26/74 = 0.35 15/25=.6 7/26=.27 4/23=.17 Desipramine is an antidepressant affecting the brain chemicals that may become unbalanced and cause depression. It was thus tested for recovery from cocaine addiction. Treatment with desipramine was compared to a standard treatment (lithium, with strong anti-manic effects) and a placebo.

5. 5 The Chi-Square Statistic To see if the data give convincing evidence against the null hypothesis, we compare the observed counts from our sample with the expected counts assuming H 0 is true. The test statistic that makes the comparison is the chi-square statistic. The chi-square statistic is a measure of how far the observed counts are from the expected counts. The formula for the statistic is: Where “observed count” represents an observed cell count, “expected count” represents the expected count for the same cell, and the sum is over all r  c cells in the table. The chi-square statistic is a measure of how far the observed counts are from the expected counts. The formula for the statistic is: Where “observed count” represents an observed cell count, “expected count” represents the expected count for the same cell, and the sum is over all r  c cells in the table.

6. 6 The Chi-Square Distributions If the expected counts are large and the observed counts are very different, a large value of  2 will result, providing evidence against the null hypothesis. The P-value for a  2 test comes from comparing the value of the  2 statistic with critical values for a chi-square distribution. The chi-square distributions are a family of distributions that take only positive values and are skewed to the right. A particular  2 distribution is specified by giving its degrees of freedom. The  2 test for a two-way table with r rows and c columns uses critical values from the  2 distribution with (r – 1)(c – 1) degrees of freedom. The P-value is the area under the density curve of this  2 distribution to the right of the value of the test statistic. The chi-square distributions are a family of distributions that take only positive values and are skewed to the right. A particular  2 distribution is specified by giving its degrees of freedom. The  2 test for a two-way table with r rows and c columns uses critical values from the  2 distribution with (r – 1)(c – 1) degrees of freedom. The P-value is the area under the density curve of this  2 distribution to the right of the value of the test statistic. The Chi-Square Distributions

7. When is it safe to use a  2 test? We can safely use the chi-square test when:  The samples are simple random samples (SRS).  All individual expected counts are 1 or more (≥1)  No more than 20% of expected counts are less than 5 (< 5)  For a 2x2 table, this implies that all four expected counts should be 5 or more.

8. Observed Cocaine addiction The p-value is <0.005 or half a percent. This is very significant. We reject the null hypothesis of no association and conclude that there is a significant relationship between treatment (desipramine, lithium, placebo) and outcome (relapse or not). Try using Software. Statistical software output for the cocaine study - look at the last lines of output: HW: Read section 9.1; do #9.1, 9.2, 9.7, 9.15, 9.16, 9.19, 9.20, 9.28, 9.36(a) Tests NDF -LogLike RSquare (U) 7425.3757190.1121 TestChiSquareProb>ChiSq Likelihood Ratio 10.7510.0046* Pearson10.7290.0047*

9. Summary: Analyzing two-way tables When analyzing relationships between two categorical variables, follow this procedure: 1. Calculate descriptive statistics that convey the important information in the table—usually column or row percents. Always take percents with respect to the explanatory variable's totals! 2. Find the expected counts and use them to compute the X 2 statistic. 3. Compare your X 2 statistic to the chi-square critical values from Table F to find the approximate P-value for your test - or use software to do all the computations, including the expected values! 4. Draw a conclusion about the association between the row and column variables - don't forget the context!

10. Table F gives upper critical values for many χ 2 distributions. Finding the p-value with Table F The χ 2 distributions are a family of distributions that can take only positive values, are skewed to the right, and are described by a specific degrees of freedom.

11. Table F df = (r−1)(c−1) If  2 = 10.73, the p-value is between 0.0025 and 0.005 From software we have P=.0047 In a 3x2 table, df=2x1=2