Source: Jolson, H. M., Bosco, L., Bufton, M. G., Gerstman, B. B., Rinsler, S. S., and Williams, E. (1992). Cerebellar toxicity associated with high dose cytarabine therapy. Journal of the National Cancer Institute, 84, 500 - 505.
A chemotherapeutic agent used to treat adults with leukemia and lymphoma is manufactured by two companies: Smith, Inc. and Jones, Inc. With the best of care, this drug is associated with a serious form of cerebellar toxicity that occurs in approximately 8% of patients. Recently, a tertiary care hospital has switched its source of the drug from the Smith company to the Jones company. Several clinicians now report a possible increase in the occurrence of cerebellar toxicity. To address this concern, you complete a chart review of patients with data collected on the following variables: ID (identification number), AGE (years), SEX (1 = M, 2 = F), MANUFacturer (S = Smith, J = Jones), DIAGnosis (1 = leukemia, 2 = lymphoma), STAGE of disease (1 = relapse, 2 = remission), TOXICity (1 = yes, 2 = no), DOSE (gms/M�), serum creatinine level (SCR, mg/dl) and WEIGHT (kilograms). Data are:
REC AGE SEX MANUF DIAG STAGE TOX DOSE SCR WEIGHT
--- --- ----- ------- ----- ----- --- ------ ------ ------
1 50 1 J 1 1 1 36.0 0.8 66
2 21 1 J 1 2 2 29.0 1.1 68
3 35 1 J 2 2 2 16.2 0.7 97
4 49 2 S 1 1 2 29.0 0.8 83
5 38 1 J 2 2 1 16.2 1.4 97
6 42 1 S 2 2 2 18.0 1.0 82
7 17 1 J 1 2 2 17.4 1.0 64
8 20 1 S 2 2 2 17.4 1.0 73
9 49 2 J 1 1 2 37.2 0.7 103
10 41 2 J 1 2 2 18.6 0.9 58
11 20 1 S 2 2 2 18.0 1.1 113
12 55 1 S 1 1 2 36.0 0.8 87
13 44 2 J 1 1 1 22.4 1.2 59
14 23 1 S 2 2 2 39.6 0.8 83
15 64 2 S 1 1 2 30.0 0.9 69
16 65 1 S 1 1 1 23.2 1.7 106
17 23 2 S 1 2 2 16.8 0.9 66
18 44 1 S 1 2 2 17.4 1.0 84
19 29 2 S 2 1 2 18.0 0.7 56
20 32 1 S 1 2 2 18.0 1.0 84
21 18 1 S 2 2 2 17.4 0.9 70
22 22 1 S 1 1 1 26.1 1.7 69
23 43 2 J 2 2 2 18.0 0.8 63
24 39 2 S 1 2 2 18.0 0.9 55
25 38 2 J 1 1 1 16.0 1.0 112
26 43 2 J 1 1 1 33.0 1.5 63
27 42 2 J 1 2 2 18.0 0.7 57
28 66 1 J 1 1 1 30.0 1.3 88
29 61 2 S 2 1 2 34.8 1.2 67
30 29 1 S 1 1 2 36.0 1.2 115
31 47 2 J 1 2 1 18.6 0.8 66
32 41 1 S 1 2 2 14.4 0.9 117
33 31 1 S 2 2 2 13.8 0.8 144
34 46 1 S 1 1 2 34.8 0.9 123
35 50 2 J 1 2 2 24.0 0.8 68
36 18 1 S 2 2 2 18.0 0.9 66
37 33 1 S 1 1 2 36.0 1.1 80
38 50 1 S 1 1 2 37.2 0.9 107
39 24 2 S 1 2 2 18.0 0.6 50
40 25 1 J 2 2 2 16.8 0.8 93
41 67 1 J 1 1 1 31.9 1.0 93
42 65 2 S 1 1 2 24.0 0.7 70
43 29 1 J 1 2 2 18.0 0.9 79
44 59 2 J 1 1 1 27.9 2.0 60
45 46 2 J 1 2 2 18.0 0.5 56
46 64 1 S 2 1 2 8.0 2.0 60
47 62 2 J 1 1 1 18.0 1.3 85
48 21 1 J 2 2 2 17.4 0.7 63
49 47 2 S 1 1 2 36.0 1.0 80
50 64 2 J 1 1 1 30.0 1.2 68
51 49 2 S 1 1 2 29.0 0.8 45
52 31 1 J 2 2 2 18.0 1.4 71
53 24 2 S 2 2 2 24.0 0.9 66
54 28 1 S 1 2 2 18.0 1.1 73
55 42 2 S 1 2 2 18.0 1.0 102
56 52 1 S 1 1 2 18.0 1.1 82
57 52 1 S 1 1 1 32.4 1.2 91
58 47 1 S 1 2 2 17.4 1.0 88
59 35 2 J 1 2 2 18.0 0.7 55
(A) Create an Epi Info file with these data. Name the primary data file TOXICITY.REC.(Suggestion: Work with a partner so that one partner can read the data from the data sheet while the other partner enters data at the keyboard.)
(B) Validate your data file using a double entry and validation technique. Name the secondary file TOXICIT2.REC Make corrections in both files so that both the primary file and secondary file are clean.
(C) Export the data to a fixed-length record file (TOXICITY.CAR). Accompany the card file with a data documentation file (TOXICITY.DD). The data documentation file should be saved to the same directory as the data file.
(D) Export the data to a dBASE II file (TOXICITY.DBF). Include this file on your data disk.
(E) In ANALYSIS, print a hard-copy listing of the data.
Source: Rosner, B. (1990). Fundamentals of Biostatistics (3rd ed.) Boston: PWS-Kent Publishing.)
Data represent a sample of data from a hospital study on antibiotic usage. Information on the following variables are available: DURation of hospitalization (days), AGE (years), SEX (1 = male, 2 = female), body TEMPerature (degrees Fahrenheit), while blood cell count (WBC: 100 / dl), in-hospital antibiotic use (AB: 1 = yes, 2 = no), whether a blood CULTure was taken (1 = yes, 2 = no), and admitting SERVice (1 = medical, 2 = surgical). Data are:
DUR AGE SEX TEMP WBC AB CULT SERV
--- --- --- ----- --- -- ---- ----
5 30 2 99.0 8 2 2 1
10 73 2 98.0 5 2 1 1
6 40 2 99.0 12 2 2 2
11 47 2 98.2 4 2 2 2
5 25 2 98.5 11 2 2 2
14 82 1 96.8 6 1 2 2
30 60 1 99.5 8 1 1 1
11 56 2 98.6 7 2 2 1
17 43 2 98.0 7 2 2 1
3 50 1 98.0 12 2 1 2
9 59 2 97.6 7 2 1 1
3 4 1 97.8 3 2 2 2
8 22 2 99.5 11 1 2 2
8 33 2 98.4 14 1 1 2
5 20 2 98.4 11 2 1 2
5 32 1 99.0 9 2 2 2
7 36 1 99.2 6 1 2 2
4 69 1 98.0 6 2 2 2
3 47 1 97.0 5 1 2 1
7 22 1 98.2 6 2 2 2
9 11 1 98.2 10 2 2 2
11 19 1 98.6 14 1 2 2
11 67 2 97.6 4 2 2 1
9 43 2 98.6 5 2 2 2
4 41 2 98.0 5 2 2 1
(A) Create an Epi Info file containing these data. Name this file HDUR.REC.
(B) Use a double entry and validation technique to validate your data file. Name the secondary file HDUR2.REC. Make corrections in each file, as necessary.
(C) Export the data to a fixed-length field file (HDUR.CAR). Accompany the card file with a data documentation file (HDUR.DD).
(D) Export the data set to a dBASE II file (HDUR.DBF). Include this file on your data disk.
(E) In ANALYSIS, print a hard-copy listing of the data.
Source: Selvin, S. (1991). Statistical Analysis of Epidemiologic Data. New York: Oxford University Press. p. 41.
Data from a study on behavior type and cholesterol levels are:
ID CHOL BEHAV
-- ----- ------
1 233 A
2 291 A
3 312 A
4 250 A
5 246 A
6 197 A
7 268 A
8 224 A
9 239 A
10 239 A
11 254 A
12 276 A
13 234 A
14 181 A
15 248 A
16 252 A
17 202 A
18 218 A
19 212 A
20 325 A
21 344 B
22 185 B
23 263 B
24 246 B
25 224 B
26 212 B
27 188 B
28 250 B
29 148 B
30 169 B
31 226 B
32 175 B
33 242 B
34 252 B
35 153 B
36 183 B
37 137 B
38 202 B
39 194 B
40 213 B
(A) Create an Epi Info record file these data and call it WCGS.REC.
(B) Validate the data set.
(C) Export the data file to a fixed-length field file and documentation this file in the form of WCGS.DD.
Source: Pagano, M, and Gauvreau, K. (1993). Principles of Biostatistics. Belmont, CA: Duxbury Press.
Data represent body weight expressed as a percentage of a hypothetical ideal. (Hypothetically ideal body weight is based on height, age, and sex.) For example, someone who weighs 150 who has a hypothetical ideal body weight of 160. would have a PERIDEAL value of 150 � 160 � 100 = 94. Data are: 107, 119, 99, 114, 120, 104, 88, 114, 124, 116, 101, 121, 152, 100, 125, 114, 95, 117. Create an Epi Info REC with these data and list the data in hard-copy form.