Abstract
Purpose/Objective: To explain some of the most useful statistical calculation procedures which are relevant to radiation oncologists and to provide insights on what tests and procedures should be used in various situations such as when survival rates and their associated standard errors have to be determined. To describe some of the problems and pitfalls in clinical trial designs which have to be overcome if a trial is to have the possibility of reaching a successful conclusion. To review methods of computing criteria to quantitatively describe criteria of success (eg. quality of life, long-term survival, cure) of radiation oncology and to suggest possible future statistical improvements in this area. Chi-Squared Test: The chi-squared test is probably the most useful of the tests of statistical significance for the radiation oncologist. Applications will be described, including goodness of fit tests and 2x2 contingency tables which are the simplest of the generalized nxm contingency tables. Degrees of Freedom and P<0.05 for Significance Testing: An Introduction will be given to the meaning of P<0.05 in relation to significance testing and the use of tables of critical values of a test statistic (eg. chi-squared) which are given as a function of degrees of freedom and P-values.
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Citation Formats
Mould, Richard F.
Biostatistics with emphasis on life table survival rate calculations (including Kaplan Meier) and the logrank test.
United States: N. p.,
1995.
Web.
doi:10.1016/0360-3016(95)97628-E.
Mould, Richard F.
Biostatistics with emphasis on life table survival rate calculations (including Kaplan Meier) and the logrank test.
United States.
https://doi.org/10.1016/0360-3016(95)97628-E
Mould, Richard F.
1995.
"Biostatistics with emphasis on life table survival rate calculations (including Kaplan Meier) and the logrank test."
United States.
https://doi.org/10.1016/0360-3016(95)97628-E.
@misc{etde_20391313,
title = {Biostatistics with emphasis on life table survival rate calculations (including Kaplan Meier) and the logrank test}
author = {Mould, Richard F}
abstractNote = {Purpose/Objective: To explain some of the most useful statistical calculation procedures which are relevant to radiation oncologists and to provide insights on what tests and procedures should be used in various situations such as when survival rates and their associated standard errors have to be determined. To describe some of the problems and pitfalls in clinical trial designs which have to be overcome if a trial is to have the possibility of reaching a successful conclusion. To review methods of computing criteria to quantitatively describe criteria of success (eg. quality of life, long-term survival, cure) of radiation oncology and to suggest possible future statistical improvements in this area. Chi-Squared Test: The chi-squared test is probably the most useful of the tests of statistical significance for the radiation oncologist. Applications will be described, including goodness of fit tests and 2x2 contingency tables which are the simplest of the generalized nxm contingency tables. Degrees of Freedom and P<0.05 for Significance Testing: An Introduction will be given to the meaning of P<0.05 in relation to significance testing and the use of tables of critical values of a test statistic (eg. chi-squared) which are given as a function of degrees of freedom and P-values. Survival Rate Calculations for Grouped and Ungrouped Data: The life-table method (sometimes termed the actuarial method) will be explained for both grouped data (eg. survival times grouped in annual intervals for patients who have died and for those who are still alive or lost to follow-up) and for ungrouped data (when individual survival times are used). The method for ungrouped data is variously termed the Kaplan-Meier or Product Limit method. Logrank Test: This is the most useful test for comparison of the survival experience of two groups of patients and its use will be explained. In part the computation is similar to that for the Kaplan-Meier/Product Limit method.}
doi = {10.1016/0360-3016(95)97628-E}
journal = []
issue = {971}
volume = {32}
journal type = {AC}
place = {United States}
year = {1995}
month = {Jul}
}
title = {Biostatistics with emphasis on life table survival rate calculations (including Kaplan Meier) and the logrank test}
author = {Mould, Richard F}
abstractNote = {Purpose/Objective: To explain some of the most useful statistical calculation procedures which are relevant to radiation oncologists and to provide insights on what tests and procedures should be used in various situations such as when survival rates and their associated standard errors have to be determined. To describe some of the problems and pitfalls in clinical trial designs which have to be overcome if a trial is to have the possibility of reaching a successful conclusion. To review methods of computing criteria to quantitatively describe criteria of success (eg. quality of life, long-term survival, cure) of radiation oncology and to suggest possible future statistical improvements in this area. Chi-Squared Test: The chi-squared test is probably the most useful of the tests of statistical significance for the radiation oncologist. Applications will be described, including goodness of fit tests and 2x2 contingency tables which are the simplest of the generalized nxm contingency tables. Degrees of Freedom and P<0.05 for Significance Testing: An Introduction will be given to the meaning of P<0.05 in relation to significance testing and the use of tables of critical values of a test statistic (eg. chi-squared) which are given as a function of degrees of freedom and P-values. Survival Rate Calculations for Grouped and Ungrouped Data: The life-table method (sometimes termed the actuarial method) will be explained for both grouped data (eg. survival times grouped in annual intervals for patients who have died and for those who are still alive or lost to follow-up) and for ungrouped data (when individual survival times are used). The method for ungrouped data is variously termed the Kaplan-Meier or Product Limit method. Logrank Test: This is the most useful test for comparison of the survival experience of two groups of patients and its use will be explained. In part the computation is similar to that for the Kaplan-Meier/Product Limit method.}
doi = {10.1016/0360-3016(95)97628-E}
journal = []
issue = {971}
volume = {32}
journal type = {AC}
place = {United States}
year = {1995}
month = {Jul}
}