Hi,
I'm teaching myself survival analysis with free resources on the web. I'm quite confident that I'm correct, but I just want to clarify that I have interpreted everything correctly!
I am comparing the survival of a control and test group. I want to determine whether there is a significant difference in survival at 12 months.
I am using SAS proc lifetest. I have a very large dataset (in the 100s of thousands) so I am using the actuarial method. My syntax is as follows.
proc lifetest data=sample_data method=life width=30 plots=(s,h) graphics outs=survival;
time days_in_study*event (0);
strata group;
run;
My main question is:
How do I determine whether there is a significant difference in survival between the control and test groups at 12 months?
I can think of two options.
Option 1 (I think this is best): Plot out the survival function of the two groups and compare. The SAS output provides lower and upper 95% confidence intervals for the survival distribution functions. if the confidence interval for the cumulative survival functions of the control group and test group do not overlap at 12 months, then they are significantly different. Is this correct?
Option 2: Do some sort of t test of independent proportions where I find the effective sample size at 12 months for each group and the cumualtive survival for each group and then see if there's a significant different. This seems like it would lead to an incorrect repsonse as it is unclear which sample size to use. I.e. what effective sample size do you use, is it the effective sample size at 12 months? Or the effective sample size at time zero?
Keep in mind that I contrinually have subjects coming in and out of the study. Hence the need for survival analysis.
Thank you so much in advance for your help! Also, please let me know if you require any additional information
Note: I am aware that I can look at the tests of equality of strata ouput (which provides log-rank, wilcoxen and -2log(LR) ). However, this just shows whether the two survival curves are significantly different at any point in time, not whether they are specifically different at 12 months. It also doesn't indicate directionality of differences (is test>group or group>test). For example, the two survival curves could criss cross wildly and this would come up as significantly different I believe. But for my purpose there would be no clear conclusions I could draw about one having lower survival than the other, just that they're different. Please let me know if I am correct here as well!
I'm teaching myself survival analysis with free resources on the web. I'm quite confident that I'm correct, but I just want to clarify that I have interpreted everything correctly!
I am comparing the survival of a control and test group. I want to determine whether there is a significant difference in survival at 12 months.
I am using SAS proc lifetest. I have a very large dataset (in the 100s of thousands) so I am using the actuarial method. My syntax is as follows.
proc lifetest data=sample_data method=life width=30 plots=(s,h) graphics outs=survival;
time days_in_study*event (0);
strata group;
run;
My main question is:
How do I determine whether there is a significant difference in survival between the control and test groups at 12 months?
I can think of two options.
Option 1 (I think this is best): Plot out the survival function of the two groups and compare. The SAS output provides lower and upper 95% confidence intervals for the survival distribution functions. if the confidence interval for the cumulative survival functions of the control group and test group do not overlap at 12 months, then they are significantly different. Is this correct?
Option 2: Do some sort of t test of independent proportions where I find the effective sample size at 12 months for each group and the cumualtive survival for each group and then see if there's a significant different. This seems like it would lead to an incorrect repsonse as it is unclear which sample size to use. I.e. what effective sample size do you use, is it the effective sample size at 12 months? Or the effective sample size at time zero?
Keep in mind that I contrinually have subjects coming in and out of the study. Hence the need for survival analysis.
Thank you so much in advance for your help! Also, please let me know if you require any additional information
Note: I am aware that I can look at the tests of equality of strata ouput (which provides log-rank, wilcoxen and -2log(LR) ). However, this just shows whether the two survival curves are significantly different at any point in time, not whether they are specifically different at 12 months. It also doesn't indicate directionality of differences (is test>group or group>test). For example, the two survival curves could criss cross wildly and this would come up as significantly different I believe. But for my purpose there would be no clear conclusions I could draw about one having lower survival than the other, just that they're different. Please let me know if I am correct here as well!
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