ID: 24605
Meeting / Value in Health Info: ISPOR 4th Asia-Pacific Conference
Phuket, Thailand
September, 2010
Code: PMC1
Disease: Multiple Diseases/No Specific Disease
Topic: Conceptual Papers (CP)
Topic Subcategory: COS
Title: SURVIVAL ANALYSIS WITH COX REGRESSION MODELS: VALIDATING A WEB-BASED CALCULATOR
Author(s):

McGhan WF, Willey VJ, Zaveri VUniversity of the Sciences in Philadelphia, Philadelphia, PA, USA

Content:

OBJECTIVES: Survival analysis is often an important component when conducting outcomes research. The objective of this study was to create and validate an online software tool, which calculates, for two study arms with up to two covariates: 1) the regression coefficients and significance, 2) the Risk Ratios and confidence intervals of the regression variables, 3) plots the probability of survival over time for two arms, and 4) plots the cumulative hazard function over time for two groups. METHODS: We developed web-based software, which incorporates a proportional hazard model, using Cox regression algorithms to compare survival statistics of any two treatments or groups. The online software program was based on analyses described in the “Introductory Statistics with R” textbook, edited by Dalgaard, which details a Cox proportional hazard analysis of a dataset from a melanoma study published by Drzewiecki. The proportional hazard web application, described here, calculates and graphically displays the results, using JavaScript algorithms and is available as freeware at www.healthstrategy.com. New data can be pasted into the calculator for one or two treatment groups with up to two covariates, survival time, and outcome. RESULTS: Considering three variables from the published melanoma dataset, the web software versus the R-software coefficients compared as follows: sex (0.38 vs 0.36), log-tumor-thickness (0.58 vs 0.56) and tumor-ulcerated (-0.93 vs –0.94). CONCLUSIONS: With this online survival analysis program, a user can input their own study parameters, and then generate Cox regression coefficients and significance, the variable Risk Ratios, as well as plot survival over time, and graph the cumulative hazard function comparing two study groups. This web-based calculator has potential benefit as a basic educational tool for students and health professionals interested in exploring these analytical approaches.

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