Saturday, June 24, 2017

Pick a Strategy for Survival with Kaplan-Meier Procedure

Oncological outcomes following radical prostatectomy for patients with pT4 prostate cancer
The Kaplan–Meier estimator,[1][2] also known as the product limit estimator, is a non-parametric statistic used to estimate the survival function from lifetime data. In medical research, it is often used to measure the fraction of patients living for a certain amount of time after treatment. The estimator is named after Edward L. Kaplan and Paul Meier, who each submitted similar manuscripts to the Journal of the American Statistical Association. The journal editor, John Tukey, convinced them to combine their work into one paper, which has been cited about 50,000 times since its publication.[4][5]
Kaplan-Meier estimate of time to progression (TTP) on androgen deprivation therapy (ADT)
according to whether or not patients received ADT as part of their local therapy.
Efficacy of androgen deprivation therapy (ADT) in patients with advanced prostate cancer: Association between Gleason score, prostate-specific antigen level, and prior ADT exposure with duration of ADT effect
The purpose of this study was to compare predictive factors for the efficacy of androgen deprivation therapy (ADT) in men with hormone-sensitive prostate cancer (HSPC) either with (M+) or without (M-) metastases. A cohort of prostate cancer patients was identified from a medical oncology practice treated with ADT for presumed nonlocalized prostate cancer, evaluated the efficacy of ADT using prostate-specific antigen (PSA) time to progression (TTP) and compared factors associated with TTP in M- and M+ patients. In this 553 patient cohort 51% were M- and 49% M+. The median TTP on ADT for the M- group was 33.2 months, versus 15.9 months in the M+ group (P<.0001). In multivariate analyses, lower biopsy Gleason score (GS), the absence of metastases, and lower serum PSA at ADT initiation all were associated with the efficacy of ADT. The association between GS and TTP was confined to M+ patients, whereas the association between PSA at ADT initiation and TTP was confined to M- patients. Use of ADT as part of local treatment was associated with a shortened TTP in both groups (hazard ratio [HR], 1.45, 95% confidence interval [CI], 1.10-1.91). In this large, retrospective study of HSPC patients in a medical oncology practice treated with ADT for nonlocalized prostate cancer, we found factors predicting efficacy of this treatment differed based on whether metastases were present at ADT initiation. The use of ADT as a part of local therapy was associated with a significantly decreased TTP, regardless of metastatic disease status
Efficacy of androgen deprivation therapy (ADT) in patients with advanced prostate cancer: Association between Gleason score, prostate-specific antigen level, and prior ADT exposure with duration of ADT effect.
Available from: https://www.researchgate.net/publication/5559661_Efficacy_of_androgen_deprivation_therapy_ADT_in_patients_with_advanced_prostate_cancer_Association_between_Gleason_score_prostate-specific_antigen_level_and_prior_ADT_exposure_with_duration_of_ADT_eff [accessed Jun 24, 2017].


Understanding survival analysis: Kaplan-Meier estimate
Kaplan-Meier estimate is one of the best options to be used to measure
the fraction of subjects living for a certain amount of time after
treatment. In clinical trials or community trials, the effect of an
intervention is assessed by measuring the number of subjects survived or
saved after that intervention over a period of time. The time starting
from a defined point to the occurrence of a given event, for example
death is called as survival time and the analysis of group data as
survival analysis. This can be affected by subjects under study that are
uncooperative and refused to be remained in the study or when some of
the subjects may not experience the event or death before the end of the
study, although they would have experienced or died if observation
continued, or we lose touch with them midway in the study. We label
these situations as censored observations. The Kaplan-Meier estimate is
the simplest way of computing the survival over time in spite of all
these difficulties associated with subjects or situations. The survival
curve can be created assuming various situations. It involves computing
of probabilities of occurrence of event at a certain point of time and
multiplying these successive probabilities by any earlier computed
probabilities to get the final estimate. This can be calculated for two
groups of subjects and also their statistical difference in the
survivals. This can be used in Ayurveda research when they are comparing
two drugs and looking for survival of subjects.

Kaplan-Meier Procedure | Real Statistics Using Excel

The goal of the Kaplan-Meier procedure is to create an estimator of the survival function based on empirical data, taking censoring into account.
Topics:

For those with a calculus background, you can also see the proofs of some of the properties described on the above webpages at
 Published on Jun 20, 2015

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Github link where you can download the pluginhttps://github.com/lukashalim/ExcelSurvival





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