Trying to assess tumour size and response to treatment.

hypothesis is smaller tumours better response

Coding

TMR20 size less than 20 mm = "1"

TMR30 size less than 30 mm = "1"

TMR40 size less than 40 mm = "1"

RP2 , response where good = "1"

Why is it that when I run a binary logistic regression on each tumour size category, the test is not significant but when i cluster all three together, the TMR40 category becomes significant?

Variables in the Equation

B S.E. Wald df

**Sig.**Exp(B) Lower Upper

Step 1a TMR40 .611 .435 1.973 1

**.160**1.842 .785 4.320

Constant -.457 .293 2.427 1 .119 .633

a Variable(s) entered on step 1: TMR40.

Variables in the Equation

B S.E. Wald df

**Sig.**Exp(B) 95% C.I.for EXP(B) Lower Upper

Step 1a TMR30(1) .288 .517 .310 1

**.578**1.333 .484 3.673

Constant -.405 .456 .789 1 .374 .667

a Variable(s) entered on step 1: TMR30.

Variables in the Equation

B S.E. Wald df

**Sig.**Exp(B) 95% C.I.for EXP(B) Lower Upper

Step 1a TMR20 .847 .936 .819 1

**.365**2.333 .373 14.613

TMR30(1) 1.620 .848 3.648 1

**.056**5.056 .959 26.664

TMR40 1.230 .574 4.590 1

**.032**3.421 1.110 10.539

Constant -2.077 .898 5.355 1 .021 .125

a Variable(s) entered on step 1: TMR20, TMR30, TMR40.

Results are also significant when i run a binary regression using tumour size as a continuous variable.

Variables in the Equation

B S.E. Wald df Sig. Exp(B) 95% C.I.for EXP(B) Lower Upper

Step 1a TMRSZE -.022 .009 5.699 1

**.017**.978 .960 .996

Constant .919 .493 3.471 1 .062 2.507

Many thanks .

Stats noob. Victor