Comparing 2 Groups Most Research is Interested in Comparing 2 (or more) Groups (Populations, Treatments, Conditions) Longitudinal: Same subjects at different times Cross-sectional: Different groups of subjects Independent samples: No connection between the subjects in the 2 groups Dependent samples: Subjects in the 2 groups are “paired” in some manner.

Explanatory Variables/Responses Subjects (or measurements) in a study are first classified by which group they are in. The variable defining the group is the explanatory or independent variable. The measurement being made on the subject is the response or dependent variable. Research questions are typically of the form: Does the independent variable cause (or is associated with) the dependent variable? I.V.  D.V. ?????

Quantitative Responses For quantitative outcomes, we wish to compare 2 population means. Parameter: m2-m1 Estimator: Standard error: Sampling distribution : Approximately normal

Large-Sample CI for m2- m1 Independent, Large-samples: n120, n220 Must estimate the standard error, replacing the unknown population variances with sample variances: Large-sample (1-a)100% CI for m2-m1:

Significance Tests for m2- m1 Typically we wish to test whether the two means differ (null hypothesis being no difference, or effect). For independent samples: H0: m2- m1=0 (m2= m1) Ha: m2- m10 (m2  m1) Test Statistic: P-value: 2P(Z  |zobs|) For 1-sided tests (Ha: m2- m1>0) P=P(Z  zobs)

Qualitative Responses For quantitative outcomes, we wish to compare 2 population proportions. Parameter: p2-p1 Estimator: Standard error: Sampling distribution : Approximately normal

Large-Sample CI for p2-p1 Independent, Large samples (see sample size criteria from Chapter 6 for p) Estimated standard error of the difference in sample proportions: (1-a)100% CI for p2-p1:

Significance Tests for p2- p1 Typically we wish to test whether the two proportions differ (null hypothesis being no difference, or effect). For independent samples: H0: p2- p1=0 (p2= p1) Ha: p2- p10 (p2  p1) Test Statistic:

Significance Tests for p2- p1 P-value: 2P(Z  |zobs|) For 1-sided tests (Ha: p2- p1>0) P=P(Z  zobs) When comparing means and proportions, confidence intervals and significance tests with the same a levels provide the same conclusions Confidence intervals (when available) also provide a range of “believable” values for the difference in the parameter for the 2 groups

Small-Sample (1-a)100% Confidence Interval for m2-m1 - Normal Populations Assuming equal population variances, “pool” sample variances to get a better estimate of s :

Small-Sample Test for m2-m1 Normal Populations Case 1: Common Variances (s12 = s22 = s2) Null Hypothesis: Alternative Hypotheses: 1-Sided: 2-Sided: Test Statistic:

Small-Sample Test for m2-m1 Normal Populations Observed Significance Level (P-Value) Special Tables Needed, Printed by Statistical Software Packages 1-sided alternative P=P(t  tobs) (From the tn distribution) 2-sided alternative P=2P( t  |tobs| ) (From the tn distribution) If P-Value  a, then reject the null hypothesis

Small-Sample Inference for m2-m1 Normal Populations Case 2: s12  s22 Don’t pool variances: Use “adjusted” degrees of freedom (Satterthwaites’ Approximation) :

Fisher’s Exact Test Method of testing for testing whether p2=p1 when one or both of the group sample sizes is small Measures (conditional on the group sizes and number of cases with and without the characteristic) the chances we would see differences of this magnitude or larger in the sample proportions, if there were no differences in the populations

Example – Echinacea Purpurea for Colds Healthy adults randomized to receive EP (n1.=24) or placebo (n2.=22, two were dropped) Among EP subjects, 14 of 24 developed cold after exposure to RV-39 (58%) Among Placebo subjects, 18 of 22 developed cold after exposure to RV-39 (82%) Out of a total of 46 subjects, 32 developed cold Out of a total of 46 subjects, 24 received EP Source: Sperber, et al (2004)

Example – Echinacea Purpurea for Colds Conditional on 32 people developing colds and 24 receiving EP, the following table gives the outcomes that would have been as strong or stronger evidence that EP reduced risk of developing cold (1-sided test). P-value from SPSS is .079. 22 10 21 11 20 12 19 13 18 14 Plac/Cold EP/Cold

Example - SPSS Output

Dependent (Paired) Samples Same individual receives each “treatment” Same individual observed before/after exposure Individuals matched on demographic or psychological similarities Often referred to as “matched pairs”

Inference Based on Paired Samples (Crossover Designs) Setting: Each treatment is applied to each subject or pair (preferably in random order) Data: Di is the difference in scores (Trt2-Trt1) for subject (pair) i Parameter: mD - Population mean difference Sample Statistics:

Test Concerning mD Null Hypothesis: H0:mD=D0 (almost always 0) Alternative Hypotheses: 1-Sided: HA: mD > D0 2-Sided: HA: mD  D0 Test Statistic:

(1-a)100% Confidence Interval for mD Test Concerning mD P-value: (Based on t-distribution with n=n-1 df) 1-sided alternative P = P(t  tobs) 2-sided alternative P = 2P(t  |tobs|) (1-a)100% Confidence Interval for mD

Example - Evaluation of Transdermal Contraceptive Patch In Adolescents Subjects: Adolescent Females on O.C. who then received Ortho Evra Patch Response: 5-point scores on ease of use for each type of contraception (1=Strongly Agree) Data: Di = difference (O.C.-EVRA) for subject i Summary Statistics: Source: Rubinstein, et al (2004)

Example - Evaluation of Transdermal Contraceptive Patch In Adolescents 2-sided test for differences in ease of use (a=0.05) H0:mD = 0 HA:mD  0 Conclude Mean Scores are higher for O.C., girls find the Patch easier to use (low scores are better)

McNemar’s Test for Paired Samples Common subjects being observed under 2 conditions (2 treatments, before/after, 2 diagnostic tests) in a crossover setting Two possible outcomes (Presence/Absence of Characteristic) on each measurement Four possibilities for each subjects wrt outcome: Present in both conditions Absent in both conditions Present in Condition 1, Absent in Condition 2 Absent in Condition 1, Present in Condition 2

McNemar’s Test for Paired Samples

McNemar’s Test for Paired Samples Data: n12 = # of pairs where the characteristic is present in condition 1 and not 2 and n21 # where present in 2 and not 1 H0: Probability the outcome is Present is same for the 2 conditions (p2 = p1) HA: Probabilities differ for the 2 conditions (p2 = p1)

Example - Reporting of Silicone Breast Implant Leakage in Revision Surgery Subjects - 165 women having revision surgery involving silicone gel breast implants Conditions (Each being observed on all women) Self Report of Presence/Absence of Rupture/Leak Surgical Record of Presence/Absence of Rupture/Leak Source: Brown and Pennello (2002), “Replacement Surgery and Silicone Gel Breast Implant Rupture”, Journal of Women’s Health & Gender-Based Medicine, Vol. 11, pp 255-264

Example - Reporting of Silicone Breast Implant Leakage in Revision Surgery H0: Tendency to report ruptures/leaks is the same for self reports and surgical records HA: Tendencies differ