I am using Stata 15.1 for Mac. My dataset after 1:4 case:control matching has 1174 cases (patients with brain tumors) and 4696 controls (patients without brain tumors). I am comparing the levels of a serum biomarker (the value is unit-less as it is a ratio). Using the t-test mean comparison test, two sample using groups (ttest serum, by(tumor)), I get the following output. I conclude that the difference between the means is not statistically significant.
Two-sample t test with equal variances
Group Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
0 4,696 .282771 .000907 .062152 .2809929 .2845491
1 1,174 .2789174 .001807 .0619147 .2753721 .2824627
5,870 .2820003 .0008108 .0621185 .2804109 .2835897
diff .0038536 .0020265 -.0001191 .0078263
diff = mean(0) - mean(1) t = 1.9016
Ho: diff = 0 degrees of freedom = 5868
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.9714 Pr(T > t) = 0.0573 Pr(T > t) = 0.0286
However, when I use the immediate form t-test calculator with rounded values (ttesti 4696 0.283 0.062 1174 0.279 0.062), I get the following output, now with a "significant" p value.
Two-sample t test with equal variances
Obs Mean Std. Err. Std. Dev. [.95% Conf. Interval]
x 4,696 .283 .0009047 .062 .2812263 .2847737
y 1,174 .279 .0018095 .062 .2754498 .2825502
5,870 .2822 .0008094 .0620154 .2806132 .2837868
diff .004 .0020231 .000034 .007966
diff = mean(x) - mean(y) t = 1.9772
Ho: diff = 0 degrees of freedom = 5868
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.9760 Pr(T > t) = 0.0481 Pr(T > t) = 0.0240
I realize there is considerable controversy regarding reliance on p values to establish statistical significance.
So, how do I reconcile the above? I would certainly like to say the difference in the biomarker level between cases and controls is significant and therefore has clinical utility.
Thank you very much,
Richard
Two-sample t test with equal variances
Group Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
0 4,696 .282771 .000907 .062152 .2809929 .2845491
1 1,174 .2789174 .001807 .0619147 .2753721 .2824627
5,870 .2820003 .0008108 .0621185 .2804109 .2835897
diff .0038536 .0020265 -.0001191 .0078263
diff = mean(0) - mean(1) t = 1.9016
Ho: diff = 0 degrees of freedom = 5868
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.9714 Pr(T > t) = 0.0573 Pr(T > t) = 0.0286
However, when I use the immediate form t-test calculator with rounded values (ttesti 4696 0.283 0.062 1174 0.279 0.062), I get the following output, now with a "significant" p value.
Two-sample t test with equal variances
Obs Mean Std. Err. Std. Dev. [.95% Conf. Interval]
x 4,696 .283 .0009047 .062 .2812263 .2847737
y 1,174 .279 .0018095 .062 .2754498 .2825502
5,870 .2822 .0008094 .0620154 .2806132 .2837868
diff .004 .0020231 .000034 .007966
diff = mean(x) - mean(y) t = 1.9772
Ho: diff = 0 degrees of freedom = 5868
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.9760 Pr(T > t) = 0.0481 Pr(T > t) = 0.0240
I realize there is considerable controversy regarding reliance on p values to establish statistical significance.
So, how do I reconcile the above? I would certainly like to say the difference in the biomarker level between cases and controls is significant and therefore has clinical utility.
Thank you very much,
Richard
Comment