1. 1 SPSS 201: Using SPSS to Perform Commonly Used Statistical Testing in Medical Research (Workshop) Dr. Daisy Dai Department of Medical Research

2. 2 Who are biostatisticians? Ashley Sherman Ashley Sherman Phone: 816-701-1347 Phone: 816-701-1347 aksherman@cmh.edu aksherman@cmh.edu aksherman@cmh.edu Daisy Dai Daisy Dai Phone: 816-701-5233 Phone: 816-701-5233 Email: hdai@cmh.edu Email: hdai@cmh.eduhdai@cmh.edu Consultation Consultation Experimental design and sampling plan Experimental design and sampling plan Collaboration in presentation and publication of studies Collaboration in presentation and publication of studies Education Education Research Research

3. 3 Statistical Courses SPSS 201: Using SPSS to perform statistical tests I SPSS 201: Using SPSS to perform statistical tests I SPSS 202: Using SPSS to perform statistical tests II SPSS 202: Using SPSS to perform statistical tests II SPSS 204: Using SPSS to manage data SPSS 204: Using SPSS to manage data SPSS 203: Summarize data with tables and graphs 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 16 th ) STA 101: Properly Setting up and Designing a Clinical Research Study Including Power Analysis for Proper Patient Numbers (July 16 th ) STA 102: Commonly Used Statistical Tests in Medical Research - Part I STA 102: Commonly Used Statistical Tests in Medical Research - Part I STA 103: Commonly Used Statistical Tests in Medical Research - Part II STA 103: Commonly Used Statistical Tests in Medical Research - Part II

4. 4 Contents Review statistical tools (1 hour) Review statistical tools (1 hour) Introduce SPSS (30 minutes) Introduce SPSS (30 minutes) Practice (1 hour) Practice (1 hour) Questions and discussions ( 30 minutes) Questions and discussions ( 30 minutes)

5. 5 Statistical tools

6. 6 Medical Research Clinical Trials Clinical Trials Intervention or therapeutic Intervention or therapeutic Preventative Preventative Retrospective Studies Retrospective Studies

7. 7 Data Medical data Medical data Physics data Physics data Chemistry data Chemistry data Education Education Economics Economics Social studies Social studies Sensory Sensory Nutrition Nutrition Many more… Many more… Continuous variable Continuous variable Interval variable Ordinal variable Categorical variable Categorical variable Binary variable Discrete variable Ordinal variable

8. 8 Information Collections 1. Historical Data Pro: Convenient; Save a lot of work Pro: Convenient; Save a lot of work Con: Outdated; Different Objectives and Designs; Unknown Detailed Information Con: Outdated; Different Objectives and Designs; Unknown Detailed Information 2. Census Pro: reliable, accurate and comprehensive (e.g. Population census) Pro: reliable, accurate and comprehensive (e.g. Population census) Con: Time consuming; requiring more resources; difficult to investigate all subjects in the population Con: Time consuming; requiring more resources; difficult to investigate all subjects in the population 3. Sampling Pro: Efficient; Less risky; exploratory; informative Pro: Efficient; Less risky; exploratory; informative Caveats: Selection bias; misinterpretation; design flaw Caveats: Selection bias; misinterpretation; design flaw

9. 9 Statistics Descriptive Statistics Descriptive Statistics Methods to organize and summarize information Methods to organize and summarize information Mean, median, max, min, frequency and proportions, etc. that summarize sample demographics Mean, median, max, min, frequency and proportions, etc. that summarize sample demographics Inferential Statistics Inferential Statistics Methods to draw conclusions about a population based on information obtained from a sample of the population Methods to draw conclusions about a population based on information obtained from a sample of the population

10. 10 Population Sample Descriptive Statistics Inferential Statistics Sampling Plan Conclusion

11. 11 Summary Statistics Measures of Center Measures of Center Mean Mean Median: the middle value in its ordered list Median: the middle value in its ordered list Mode: the most frequently occurring value Mode: the most frequently occurring value Measures of variation Measures of variation Range: the difference between the largest and smallest value in the data set, i.e., Range=Max-Min. Standard deviation: measure variation by indicating how far, on average, the observations are from the mean. We will talk more about data summary and distribution graphs in SPSS 204 Workshop.

12. 12 Exercise: Determine for the mean, median and mode, which measure of center is most appropriate in the following case studies? A student takes four exams in a biology class. His grades are 88, 75, 95, and 100. A student takes four exams in a biology class. His grades are 88, 75, 95, and 100. The National Association of REALTORS publishes data on resale prices of U.S. homes. The National Association of REALTORS publishes data on resale prices of U.S. homes. In the 2003 Boston Marathon, there were two categories of official finishers: male and female, of which there were 10,737 and 6,309, respectively. In the 2003 Boston Marathon, there were two categories of official finishers: male and female, of which there were 10,737 and 6,309, respectively.

13. 13 Statistical Testing Procedures 1. Clarify study objectives. 2. Establish hypotheses. 3. Determine the outcome variables, treatment groups, risk factors and covariates. 4. Perform appropriate statistical testing. 5. Interpret results.

14. 14 Statistical Testing Procedures 1.Null Hypothesis - Ho: Mean_Treatment=Mean_Control 2.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

15. 15 Continuous Variables Two or multiple treatment groups

16. 16 Two samples t-test Compare the means of a normally distributed interval dependent variable for two independent groups.

17. 17 Case Study: FEV 1 Changes A new compound, ABC- 123, is being developed for long-term treatment of patients with chronic asthma. Asthma patients were enrolled in a double- blind study and randomized to receive daily oral or a placebo for 6 weeks. A new compound, ABC- 123, is being developed for long-term treatment of patients with chronic asthma. Asthma patients were enrolled in a double- blind study and randomized to receive daily oral or a placebo for 6 weeks. FEV 1 after 6-week treatment PlaceboTest asthmatic patients

18. 18 FEV 1 Data Test Group Patient ID Baseline week 6 1011.35n/a 1033.223.55 1062.783.15 1082.452.3 1091.842.37 1102.813.2 1131.92.65 11633.96 1182.252.97 1202.862.28 1211.562.67 1242.663.76 Placebo Group Patient ID Baseline week 6 1023.013.9 1042.243.01 1052.252.47 1071.651.99 111.95n/a 1123.053.26 1142.52.55 1151.62.2 117.772.56 1192.062.9 1221.71n/a 1233.542.92

19. 19 P-value What is the difference between std and std error?

20. 20 Mean and Error Bar Conclusion: As compared to placebo, the new drug did not show any effect on FEV 1.

21. 21 Paired t-test Compare the means of a normally distributed interval dependent variable for two related groups. Test Group Patient ID Baseline week 6 1011.35n/a 1033.223.55 1062.783.15 1082.452.3 1091.842.37 1102.813.2 1131.92.65 11633.96 1182.252.97 1202.862.28 1211.562.67 1242.663.76

22. 22 P-value Conclusion: For subjects on the new drug, FEV1 at week 6 is significantly higher than baseline.

23. 23 One-way ANOVA Test for differences of the means for continuous variables in multiple independent treatment groups.

24. 24 Case Study: HAM-A Scores in GAD HAM-A Score after 10-week treatment Placebo 100 mg SN-X95 Patients with GAD 25mg SN-X95 A new serotonin-update inhibiting agent, SN-X95, is being studied in subjects with general anxiety disorder (GAD). Fifty-two subjects diagnosed with GAD were enrolled and randomly assigned to one of three treatment groups: three treatment groups: 25mg SN-X95, 100mg SN-X95 or placebo. After 10 weeks of once-daily oral dosing in a double-blind fashion, a test based on the Hamilton Rating Scale for Anxiety (HAM- A) was administered. This test consists of 14 anxiety-related items (e.g. ‘anxious mood’, ‘tension’, ‘insomnia’, ‘fear’, etc.), each rated by the subject as ‘no present’, ‘mild’, ‘moderate’, ‘severe’, or ‘very severe’. HAM-A test scores were founded by summing the coded values of all 14 items using the numeric coding scheme of 0 for “not present”, 1 for …. Are there any differences in means HAM- A test score among the three groups? A new serotonin-update inhibiting agent, SN-X95, is being studied in subjects with general anxiety disorder (GAD). Fifty-two subjects diagnosed with GAD were enrolled and randomly assigned to one of three treatment groups: three treatment groups: 25mg SN-X95, 100mg SN-X95 or placebo. After 10 weeks of once-daily oral dosing in a double-blind fashion, a test based on the Hamilton Rating Scale for Anxiety (HAM- A) was administered. This test consists of 14 anxiety-related items (e.g. ‘anxious mood’, ‘tension’, ‘insomnia’, ‘fear’, etc.), each rated by the subject as ‘no present’, ‘mild’, ‘moderate’, ‘severe’, or ‘very severe’. HAM-A test scores were founded by summing the coded values of all 14 items using the numeric coding scheme of 0 for “not present”, 1 for …. Are there any differences in means HAM- A test score among the three groups?

25. 25 Data Lo-DoseHi-DosePlacebo 211622 182126 193129 992519 282399 222533 301837 272025 281828 191626 232499 222231 202127 191630 263325 352122 991736

26. 26 P-value

27. 27 Mean and Error Bar Conclusion: There is significant difference in mean HAM-A among three treatment at 95% confidence level.

28. 28 Categorical Variables Two or multiple treatment groups

29. 29 Fisher’s Exact Test A conservative non-parametric test about a relationship between two categorical variables. RespondersNon-respondersTotal Group 1 N11N11N11N11 N 12 N 1 1 +N 12 Group 2 N21N21N21N21 N 22 N 2 1 +N 22 Combined N 1 1 +N 2 1 N 12 + N 22 N

30. 30 Case Study: CHF Incidence in CABG after ARA A new adenosine-releasing agent (ARA), thought to reduce side effects in patients undergoing coronary artery bypass surgery (CABG), was studied in a pilot trial. CHF No CHF Total ARA 2 (6%) 3335 Placebo 5 (25%) 2025 Combined75360 Fisher’s exact test: p=0.0455

31. 31 Chi-square test Test a relationship between two categorical variables. The chi-square test assumes that the expected value for each cell is five or higher.

32. 32 Case Study: ADR Frequency with Antibiotic Treatment A study was conducted to monitor the incidence of GI adverse drug reactions of a new antibiotic used in lower respiratory tract infections. RespondersNon-respondersTotal Test (new antibiotic) 22 (33%) 4466 Control(erythromycin) 28 (54%) 2453 Combined 50 (42%) 68118 Chi-square test: p=0.0252; Fisher’s exact test: p=0.0385

33. 33 Other tests One-way repeated measures ANOVA One-way repeated measures ANOVA Repeated measures logistic regression Repeated measures logistic regression Factorial ANOVA Factorial ANOVA Friedman test Friedman test Factorial logistic regression Factorial logistic regression Simple Linear Regression Simple Linear Regression Multiple Regression Multiple Regression Factor analysis Factor analysis Multiple logistic regression Multiple logistic regression Discriminant analysis Discriminant analysis One-way MANOVA One-way MANOVA Multivariate multiple regression Multivariate multiple regression Canonical correlation Canonical correlation Analysis of covariance Analysis of covariance We will cover all tests including non-parametric tests in SPSS 202 Workshop.

34. 34 Questions?

35. 35 Introduction to SPSS

36. 36 What is SPSS? Statistical software. Statistical software. 16 server licenses. 16 server licenses. SPSS 18. SPSS 18.

37. 37 SPSS Data Entry SPSS data can be entered manually. SPSS data can be entered manually. The format is ready for analysis. The format is ready for analysis. SAS, Excel, txt, etc. data can be easily imported to SPSS. SAS, Excel, txt, etc. data can be easily imported to SPSS. SPSS data files are saved as “SPSS data document (.sav)”. SPSS data files are saved as “SPSS data document (.sav)”. SPSS output files are saved as “SPSS viewer document (.spv)”. SPSS output files are saved as “SPSS viewer document (.spv)”.

38. 38 SPSS Data Entry SPSS has a few unique features in data entry. SPSS has a few unique features in data entry. Categorical variables need to be coded. For instance, code male as 1 and female as 0 or vice versa. Categorical variables need to be coded. For instance, code male as 1 and female as 0 or vice versa. When you have two treatments, test and control, please use 1 for test and 0 for control. When you have two treatments, test and control, please use 1 for test and 0 for control. Categorical variables that are not coded in other sourced data files will not be imported or analyzed properly in SPSS. Categorical variables that are not coded in other sourced data files will not be imported or analyzed properly in SPSS. Continuous variables don’t need coding. Continuous variables don’t need coding. Missing values needs to be defined in “variable view” page. Missing values needs to be defined in “variable view” page.

39. 39 Example: CDC Survey Data An allergy survey was conducted in 2005 and 2006 to children more than 1 year old. An allergy survey was conducted in 2005 and 2006 to children more than 1 year old. Two data sets, allergy questionnaire and demographic information, are saved in sas export format. Two data sets, allergy questionnaire and demographic information, are saved in sas export format.

40. 40 Tasks Import these two SAS data files to SPSS and save them as SPSS data file. Import these two SAS data files to SPSS and save them as SPSS data file. Sort each data set by study ID. Sort each data set by study ID. Merge allergy variables and demographic variables. Merge allergy variables and demographic variables. Save new data set as SPSS data file. Save new data set as SPSS data file.

41. 41 Log in SPSS CMH offers server version SPSS 18. Any employee can log in SPSS from your employee account. CMH offers server version SPSS 18. Any employee can log in SPSS from your employee account. Go to Start Go to Start ->Program ->Program ->Accessories ->Accessories -> Remote Desktop Connection

42. 42 Log in SPSS In the prompted connection window, enter cmhterm. In the prompted connection window, enter cmhterm. Click Connect. Click Connect.

43. 43 Log in SPSS In the Log On Window, enter your cmh user name and password. In the Log On Window, enter your cmh user name and password. Choose log on to CMH Choose log on to CMH Click OK. Click OK.

44. 44 Task 1: Import Data We need to import two data sets to SPSS. We need to import two data sets to SPSS. Allergy qustionaire: aqq_d.xpt (xpt is sas export file) Allergy qustionaire: aqq_d.xpt (xpt is sas export file) Demographic information: demo_d.xpt Demographic information: demo_d.xpt Please note that SPSS is on server and data must be saved in shared drive such as u drive or w drive. You will not be able to find the file in SPSS if you save them on your local disk. Please note that SPSS is on server and data must be saved in shared drive such as u drive or w drive. You will not be able to find the file in SPSS if you save them on your local disk.

45. 45 Task 1: Import Data Double click spss 18 icon on the screen. Double click spss 18 icon on the screen. In the task wizard, click Open an existing source. In the task wizard, click Open an existing source. Click OK. Click OK.

46. 46 Task 1: Import Data Just in case wizard does not prompt, you can go to file Just in case wizard does not prompt, you can go to file -> Open -> Data

47. 47 Task 1: Import Data Select the folder. Select the folder. Choose agg_d file. Choose agg_d file. Select xpt format. Select xpt format. Click Open. Click Open. Note: SPSS is compatible with other commonly used statistical and data management software packages. Excel, SAS, Access files are all convertible to SPSS. Note: SPSS is compatible with other commonly used statistical and data management software packages. Excel, SAS, Access files are all convertible to SPSS.

48. 48 Task 1: Import Data Now the data is open. Now the data is open. You can see the data in “data View” tab. You can see the data in “data View” tab.

49. 49 Task 1: Import Data The data structure, variable name, label, etc. are in “Variable View” tab. The data structure, variable name, label, etc. are in “Variable View” tab.

50. 50 Task 2: Sort Data Variable to be sort: SEQN, that is, Respondent sequence number. Variable to be sort: SEQN, that is, Respondent sequence number.

51. 51 Task 2: Sort Data Go to Data and select Sort Cases. Go to Data and select Sort Cases. On Sort Cases page, select the variable, Respondent sequence number. On Sort Cases page, select the variable, Respondent sequence number. Click on right arrow. Click on right arrow. Choose Ascending or Descending. Choose Ascending or Descending. Click OK. Click OK.

52. 52 Practice Now let’s repeat this process by doing the following: Now let’s repeat this process by doing the following: Open the demographic data, demo_d.xpt. Open the demographic data, demo_d.xpt. Sort the data by variable, Respondent Sequence Number. Sort the data by variable, Respondent Sequence Number.

53. 53 Task 3: Merge Two Data Sets Two data sets need to be linked by key variables. Two data sets need to be linked by key variables. In our case, the key variable is SEQN- Respondent Sequence Number. In our case, the key variable is SEQN- Respondent Sequence Number. Make sure the key variable has the same name and variable type in two data sets. Make sure the key variable has the same name and variable type in two data sets. Both data sets needs to be sorted by the key variable. Both data sets needs to be sorted by the key variable.

54. 54 Task 3: Merge Two Data Sets Under any data set, go to Data Under any data set, go to Data -> Merge File -> Add Variables

55. 55 Task 3: Merge Two Data Sets Choose the other data to add on. Choose the other data to add on. Note, this page will look different in SPSS 18. By all means, choose the other data set. Note, this page will look different in SPSS 18. By all means, choose the other data set.

56. 56 Task 4: Save the New Data Go to File Go to File -> Save As… Select the folder. Select the folder. Create new file, MergedData. Create new file, MergedData. Choose SPSS data format. Choose SPSS data format. Click Save. Click Save.

57. 57 Task 4: Save the New Data Go to Data Go to Data -> Merge File -> Add Variables

58. 58 Questions? We will cover more data management in SPSS 203 workshop.

59. 59 Let’s play with SPSS

60. 60 Project 1: FEV 1 Changes

61. 61 Case Study: FEV 1 Changes A new compound, ABC- 123, is being developed for long-term treatment of patients with chronic asthma. Asthma patients were enrolled in a double- blind study and randomized to receive daily oral or a placebo for 6 weeks. A new compound, ABC- 123, is being developed for long-term treatment of patients with chronic asthma. Asthma patients were enrolled in a double- blind study and randomized to receive daily oral or a placebo for 6 weeks. FEV 1 after 6-week treatment PlaceboTest asthmatic patients

62. 62 FEV 1 Data Test Group Patient ID Baseline week 6 1011.35n/a 1033.223.55 1062.783.15 1082.452.3 1091.842.37 1102.813.2 1131.92.65 11633.96 1182.252.97 1202.862.28 1211.562.67 1242.663.76 Placebo Group Patient ID Baseline week 6 1023.013.9 1042.243.01 1052.252.47 1071.651.99 111.95n/a 1123.053.26 1142.52.55 1151.62.2 117.772.56 1192.062.9 1221.71n/a 1233.542.92

63. 63 Tasks 1. Log in to intranet and open SPSS. 2. Define variables and missing values in “variable view” tab. 3. Enter data in “data view” tab. 4. Perform two-sample t-tests to compare FEV1 at 6 weeks between test and control. 5. Generate mean and error bar graph for two groups. 6. Interpret the SPSS output and make conclusion.

64. 64 Tasks to be continued 7. Perform paired t-test to compare the FEV between baseline and 6 weeks for test group. 8. Interpret SPSS results and draw conclusions. 9. Save SPSS data and SPSS output respectively. 10. Open SPSS data and SPSS output by double clicking the icons. 11. Close both files.

65. 65 Project 2: HAM-A Scores in GAD

66. 66 Case Study: HAM-A Scores in GAD HAM-A Score after 10-week treatment Placebo 100 mg SN-X95 Patients with GAD 25mg SN-X95 A new serotonin-update inhibiting agent, SN-X95, is being studied in subjects with general anxiety disorder (GAD). Fifty-two subjects diagnosed with GAD were enrolled and randomly assigned to one of three treatment groups: three treatment groups: 25mg SN-X95, 100mg SN-X95 or placebo. After 10 weeks of once-daily oral dosing in a double-blind fashion, a test based on the Hamilton Rating Scale for Anxiety (HAM- A) was administered. This test consists of 14 anxiety-related items (e.g. ‘anxious mood’, ‘tension’, ‘insomnia’, ‘fear’, etc.), each rated by the subject as ‘no present’, ‘mild’, ‘moderate’, ‘severe’, or ‘very severe’. HAM-A test scores were founded by summing the coded values of all 14 items using the numeric coding scheme of 0 for “not present”, 1 for …. Are there any differenceds in means HAM-A test score among the three groups? A new serotonin-update inhibiting agent, SN-X95, is being studied in subjects with general anxiety disorder (GAD). Fifty-two subjects diagnosed with GAD were enrolled and randomly assigned to one of three treatment groups: three treatment groups: 25mg SN-X95, 100mg SN-X95 or placebo. After 10 weeks of once-daily oral dosing in a double-blind fashion, a test based on the Hamilton Rating Scale for Anxiety (HAM- A) was administered. This test consists of 14 anxiety-related items (e.g. ‘anxious mood’, ‘tension’, ‘insomnia’, ‘fear’, etc.), each rated by the subject as ‘no present’, ‘mild’, ‘moderate’, ‘severe’, or ‘very severe’. HAM-A test scores were founded by summing the coded values of all 14 items using the numeric coding scheme of 0 for “not present”, 1 for …. Are there any differenceds in means HAM-A test score among the three groups?

67. 67 Data Lo-DoseHi-DosePlacebo 211622 182126 193129 992519 282399 222533 301837 272025 281828 191626 232499 222231 202127 191630 263325 352122 991736

68. 68 Tasks 1. Open data in excel. Make sure the data structure, variables and missing values are set up properly. 2. Import Excel to SPSS. 3. Perform one-way ANOVA to compare high dose, low dose and control groups. 4. Generate mean and error bar graph for three groups. 5. If the global F-test is significant, then perform post- hoc pair-wise comparisons. 6. Interpret the SPSS output and make conclusion. 7. Save data and output. 8. Close files.

69. 69 Project 3: CHF Incidence in CABG after ARA

70. 70 Case study: CHF Incidence in CABG after ARA A new adenosine-releasing agent (ARA), thought to reduce side effects in patients undergoing coronary artery bypass surgery (CABG), was studied in a pilot trial That enrolled 35 patients who receive active medication and 20 patients who received a placebo. Follow-up observation revealed that 2 patients who received active medication and 5 patients who received the placebo had shown symptoms of congestive heart failure (CHF) within 90 days post surgery. Is this evidence of a reduced rate of CHF for patients treated with the ARA compound? A new adenosine-releasing agent (ARA), thought to reduce side effects in patients undergoing coronary artery bypass surgery (CABG), was studied in a pilot trial That enrolled 35 patients who receive active medication and 20 patients who received a placebo. Follow-up observation revealed that 2 patients who received active medication and 5 patients who received the placebo had shown symptoms of congestive heart failure (CHF) within 90 days post surgery. Is this evidence of a reduced rate of CHF for patients treated with the ARA compound?

71. 71 Tasks 1. Open SPSS data. 2. Summarize frequency, percentage in two-way contingency table. 3. Perform Fisher’s exact test. 4. Perform Chi-square test. 5. Compare Fisher’s exact test with Chi-square test. 6. Interpret the SPSS output and make conclusion. 7. Close files.

72. 72 Project 4: ADR Frequency with Antibiotic Treatment

73. 73 Case Study: ADR Frequency with Antibiotic Treatment A study was conducted to monitor the incidence of GI adverse drug reactions of a new antibiotic used in lower respiratory tract infections. Two parallel groups were included in the study. One group consisted of 66 LRTI patients randomized to receive the new treatment and a reference group of 52 patients randomized to receive erythromycin. A study was conducted to monitor the incidence of GI adverse drug reactions of a new antibiotic used in lower respiratory tract infections. Two parallel groups were included in the study. One group consisted of 66 LRTI patients randomized to receive the new treatment and a reference group of 52 patients randomized to receive erythromycin.

74. 74 Tasks 1. Open SPSS data. 2. Summarize frequency, percentage in two-way contingency table. 3. Perform Fisher’s exact test. 4. Perform Chi-square test. 5. Compare Fisher’s exact test with Chi-square test. 6. Interpret the SPSS output and make conclusion. 7. Close files.

75. 75 Questions? Let us know statistics topics you are interested.

76. 76 In summary…

77. 77 Thank You For more information, visit my website http://www.childrensmercy.org/content/vie w.aspx?id=9740 http://www.childrensmercy.org/content/vie w.aspx?id=9740 Or go to Scope ->Research -> Medical Research -> Statistics

78. 78 References Medical Statistics by Campbell et al. Medical Statistics by Campbell et al. Introductory Statistics by Neil Weiss Introductory Statistics by Neil Weiss Common Statistical Methods for Clinical Research by Walker Common Statistical Methods for Clinical Research by Walker