Study Design / Data: Case-Control, Descriptives

Basic Medical Statistics Course: Module COctober 2010Wilma Heemsbergen w.heemsbergen@nki.nl

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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