Study Design / Data: Case-Control, Descriptives Basic Medical Statistics Course: Module C October...
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Transcript of Study Design / Data: Case-Control, Descriptives Basic Medical Statistics Course: Module C October...
Study Design / Data: Case-Control, Descriptives
Basic Medical Statistics Course: Module COctober 2010Wilma Heemsbergen [email protected]
Case - Control: Theory
Type of studies: relative evidence - Correlation studies (at level of population) - - Individual studies (at level of subject) + - Transversal (cross-sectional) - - Longitudinal +
- Experimental (random assignment) - - Not experimental (observational) +
- Case-control (starting point is disease) - Cohort (starting point is determinant)
- historical / retrospective - - prospective + - combination of both
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Case - Control: Theory
Comparing a case series to a matched control series (matching: age, gender, … ).
Matching: on group basis or individual basis (pairwise).Matching: on potential risk factors.
Famous case-control series: comparing a series of lung cancer patients with patients without lung cancer, the lung cancer cases were more often “smokers”.
- Effective in studying rare diseases (+)- Relatively inexpensive (+)- Susceptible for bias (-)
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Case - Control Diagram & OR
3 1
1 3
obese (factor) y n
y diabetes (disease) n
ODDS RATIO (OR) =
(a*d) / (b*c)
Oddsexposed = 0.75/0.25=3
Oddsunexposed = 0.25/0.75=0.33
OR exp/unexp = 3 / 0.33 = 9
(OR unexp/exp = 0.33 / 3 = 0.11) A cases,
exposed
B cases,
unexposed
C controls, exposed
D controls, unexposed
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While logistic regression is a method to evaluate the association between a binary variable and other variables of any type, its main use in medicine is due to the fact that the OR calculated from case-control data is a valid estimate of the relative risk.
Therefore, a case-control study (population-based or nested in a cohort) is a valid alternative design to estimate the relative risk.
Case-Control & Logistic Regression
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OR as an estimate for RRJAMA 1998; 280: 1690. Zhang et al.
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Case-control: alcohol & lymfoma
Goal: to assess the relationship between Alcohol-consumption and risk of Non-Hodgkin Lymfoma (NHL).
Method:pooled data set,logistic regression
Lancet Oncology2005;6:469-76.
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Cancer Causes Control 2004; 14: 381-389.
Smoking and non-Hodgkin’s lymphoma: a case-control study in the Rhone-Alpes region of France
- Construct 2X2 Tables for: 1) living in France with surgical controls, 2) smoking status with medical controls.
- Calculate the Odds Ratio’s.
- Compare the outcomes with the reported OR’s in Table 1.
- Calculate the Odds Ratio for urban vs rural ever, with all controls.
Case-control: smoking & lymfoma
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SPSS syntax
Example: geographic origin, surgical controls
data list free / factor, disease, n. begin data. 1 1 10 0 1 170 1 0 170 0 163end data. weight by n. logistic regression disease with factor/print=ci.
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Answers
18 162
21 159
56 65
51 71
59 65
58 71
living in france smoking 1 (ex) smoking 2 (cur)
Factor1 0
Disease 1
0
OR = 0.84 OR = 1.20 OR = 1.11
(a*d)/(b*c)
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SPSS syntax (living in france)
data list free / risk, disease, n. begin data. 1 1 18 0 1 162 1 0 21 order of n = a,b,c,d0 0 159end data. weight by n. logistic regression disease with risk/print=ci.
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Clinical Trial: example
SPSS file: trial_rt.sav
Int J Radiation Oncology Biol Phys 2008; 72: 980-988.
Update of Dutch Multicenter Dose-escalation Trial of Radiotherapy for Localized Prostate Cancer.
Table 2 presents baseline characteristics of both arms.
- Describe the performed procedure(s) to obtain a balance in prognostic factors between both treatment arms.
- Which factor was not balanced very good, which were.
(please keep this paper for another topic in module C)
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Answers
Procedures: randomization and stratification by age, institution,
hormonal treatment, treatment group (combination of T stage,
PSA, Gleason).
Less balanced: PSA baseline values.
- 0-10: more in 78 Gy arm (41% vs. 46%).
- > 20: less in 78 Gy arm (21% vs. 26%).
Well balanced: the other baseline factors.
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