Package CIFsmry. July 10, 2016. Index 6



Similar documents
Package empiricalfdr.deseq2

Package retrosheet. April 13, 2015

Package CRM. R topics documented: February 19, 2015

Package ChannelAttribution

Package ATE. R topics documented: February 19, Type Package Title Inference for Average Treatment Effects using Covariate. balancing.

Package changepoint. R topics documented: November 9, Type Package Title Methods for Changepoint Detection Version 2.

Package cpm. July 28, 2015

Package dunn.test. January 6, 2016

Package smoothhr. November 9, 2015

Package SurvCorr. February 26, 2015

Package MBA. February 19, Index 7. Canopy LIDAR data

Package schoolmath. R topics documented: February 20, Type Package

Package TSfame. February 15, 2013

Package EstCRM. July 13, 2015

Package forensic. February 19, 2015

Package plan. R topics documented: February 20, 2015

Package dsstatsclient

Package PACVB. R topics documented: July 10, Type Package

Package GSA. R topics documented: February 19, 2015

Package uptimerobot. October 22, 2015

Package hoarder. June 30, 2015

Package bigdata. R topics documented: February 19, 2015

Package tagcloud. R topics documented: July 3, 2015

Package survpresmooth

Package nortest. R topics documented: July 30, Title Tests for Normality Version Date

Package lmertest. July 16, 2015

Package EasyHTMLReport

Package hazus. February 20, 2015

Package PRISMA. February 15, 2013

Package pdfetch. R topics documented: July 19, 2015

Package SHELF. February 5, 2016

Package png. February 20, 2015

Package neuralnet. February 20, 2015

Package SCperf. February 19, 2015

Package translater. R topics documented: February 20, Type Package

Package GenBinomApps

1. What is the critical value for this 95% confidence interval? CV = z.025 = invnorm(0.025) = 1.96

Package decompr. August 17, 2016

Package support.ces. R topics documented: August 27, Type Package

Package DCG. R topics documented: June 8, Type Package

Package httprequest. R topics documented: February 20, 2015

Package RIGHT. March 30, 2015

SUMAN DUVVURU STAT 567 PROJECT REPORT

Package sendmailr. February 20, 2015

Binary Diagnostic Tests Two Independent Samples

Package MDM. February 19, 2015

Package polynom. R topics documented: June 24, Version 1.3-8

Package sjdbc. R topics documented: February 20, 2015

Package CoImp. February 19, 2015

Package missforest. February 20, 2015

Package LexisPlotR. R topics documented: April 4, Type Package

Lesson Outline Outline

Package cgdsr. August 27, 2015

Package date. R topics documented: February 19, Version Title Functions for handling dates. Description Functions for handling dates.

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

THE FIRST SET OF EXAMPLES USE SUMMARY DATA... EXAMPLE 7.2, PAGE 227 DESCRIBES A PROBLEM AND A HYPOTHESIS TEST IS PERFORMED IN EXAMPLE 7.

Package treemap. February 15, 2013

Package hive. January 10, 2011

Package benford.analysis

MAT 200, Midterm Exam Solution. a. (5 points) Compute the determinant of the matrix A =

Package AMORE. February 19, 2015

Package syuzhet. February 22, 2015

Package support.ces. R topics documented: April 27, Type Package

More details on the inputs, functionality, and output can be found below.

Package multivator. R topics documented: February 20, Type Package Title A multivariate emulator Version Depends R(>= 2.10.


Adverse Impact Ratio for Females (0/ 1) = 0 (5/ 17) = Adverse impact as defined by the 4/5ths rule was not found in the above data.

Data Analysis, Research Study Design and the IRB

Package mcmcse. March 25, 2016

Package DDIwR. R topics documented: February 19, Version Date Title DDI with R Depends R (>= 3.1.0) Imports XML, foreign

Software Solutions Appendix B. B.1 The R Software

Package RCassandra. R topics documented: February 19, Version Title R/Cassandra interface

Package copa. R topics documented: August 9, 2016

Package trimtrees. February 20, 2015

Package ggrepel. February 8, 2016

START Selected Topics in Assurance

Package ERP. December 14, 2015

BA 275 Review Problems - Week 6 (10/30/06-11/3/06) CD Lessons: 53, 54, 55, 56 Textbook: pp , ,

Package erp.easy. September 26, 2015

Package globe. August 29, 2016

Package DSsim. September 25, 2015

Effect of Risk and Prognosis Factors on Breast Cancer Survival: Study of a Large Dataset with a Long Term Follow-up

MATH 140 Lab 4: Probability and the Standard Normal Distribution

Package metafuse. November 7, 2015

Learning Objectives. Understand how to select the correct control chart for an application. Know how to fill out and maintain a control chart.

>

Package MFDA. R topics documented: July 2, Version Date Title Model Based Functional Data Analysis

Transcription:

Type Package Package CIFsmry July 10, 2016 Title Weighted summary of cumulative incidence functions Version 1.0.1.1 Date 2013-10-10 Author Jianing Li Maintainer Jianing Li <jili@mcw.edu> Depends R(>= 3.0.1) Estimate of cumulative incidence function in two samples. Provide weighted summary statistics based on various methods and weights. License GPL (>= 2) NeedsCompilation yes Repository CRAN Date/Publication 2016-07-10 15:39:40 R topics documented: CIFsm............................................ 1 print.cifsm......................................... 4 sim.dat............................................ 4 summary.cifsm....................................... 5 Index 6 CIFsm Cumulative incidence function estimate and weighted summary statistics Estimate the cumulative incidence function for cause of interest in two-sample study. Provide the weighted summary statistics based on given method and weight. 1

2 CIFsm Usage CIFsm(ds,method="dif",pp = 0,qq = 0,conf.bd=T,n.sim=500) Arguments ds method pp qq conf.bd n.sim ds is a dataset contains the time, cause of event and group. For cause, 0 means censoring, 1 is the cause of event, 2 is all other causes. Two groups need to be coded as 1 and 2. method can be chosen from "dif"=risk difference, "rr"=risk ratio and "or"=odds ratio first parameter of weight function second parameter of weight function logical; if TRUE, create confidence band cut point. Set to FALSE if the confidence band is not needed, which reduces the computational time number of simulations used in creating confidence band; will be ineffective if conf.bd is FALSE Details Value The estimates and summary statistics are described in Zhang and Fine (2008). sample used size njp tjp Total sample size from both groups Sample used in analysis. Subject with missing value in any of the three variables (time, cause or group) will be excluded from analysis Sample size for each group Total number of unique event time points in two groups Unique event time points from both groups ny1 Number of subject at risk in group 1 f1 Estimate of cumulative incidence function for group 1 f1.se Standard error of cumulative incidence function for group 1 ny2 Number of subject at risk in group 2 f2 Estimate of cumulative incidence function for group 2 f2.se Standard error of cumulative incidence function for group 2 dif Estimate of difference in cumulative incidence (risk difference) between the 2 groups dif.se dif.pv rr rr.se rr.pv Standard error of the risk difference P-value of the risk difference Risk ratio of the cumulative incidence between the 2 groups Standard error of the risk ratio P-value of the risk ratio

CIFsm 3 or or.se or.pv cbcut method weight region nbd ave avese ci95 avepval wt Odds ratio of the cumulative incidence between the 2 groups Standard error of the odds ratio P-value of the odds ratio 95% confidence band cut point of risk difference, risk ratio and odds ratio based on simulation method. Method used for the weighted summary statistics Weight used for the weighted summary statistics Time range of the data Index of beginning and end of the data. For internal use only Time integrated weighted summary statistics Standard error of the time integrated weighted summary statistics 95% confidence interval of the time integrated weighted summary statistics P-value of the time integrated weighted summary statistics Weight assigned at each unique event time point Author(s) Jianing Li References M.J. Zhang and J.P. Fine. Summarizing difference in cumulative incidence functions. Statistics in Medicine, 27:4939-4949, 2008. J. Li, M.J. Zhang and J. Le-Rademacher. Weighted Summary of two Cumulative Incidence Functions with R-CIFsmry Package. Computer Methods and Programs in Biomedicine[Submitted]. Examples library(cifsmry) data(sim.dat) out <- CIFsm(sim.dat,pp=0,qq=0) out$avepval plot(out$tjp,out$f1,type="s",ylim=c(0,1),yaxt="n",xaxt="n",xlab="time",ylab="cif", lty=1,lwd=1) points(out$tjp,out$f2,type="s",lty=1,lwd=3) axis(1,at=seq(0,6,by=1),cex=0.6) axis(2,at=seq(0,1,by=0.2),cex=0.6) legend("bottomright",c("1","2"),title="group",lty=1,lwd=c(1,3)) out10 <- CIFsm(sim.dat,pp=1,qq=0) out10$avepval

4 sim.dat print.cifsm Print CIFsm object Usage Print the estimated of cumulative incidence functions and point-wise summary statistics. ## S3 method for class 'CIFsm' print(x,...) Arguments x Details CIFsm object from function CIFsm... additional arguments to print() Risk set, cumulative incidence function estimate, standard error and p-value of each group. Pointwise summary statistics estimate, standard error and p-value. See Also CIFsm sim.dat Simulated competing risk data Format Source Simulated data with 100 subjects and 3 variates: time, cause and group. The data has 100 rows and 3 columns. time a numeric vector. Survival time. cause a numeric vector code. Survival status. 1: failure from the cause of interest; 2: failure from other causes; 0: censored. group a numeric vector code. 1: group 1; 2: group 2. Simulated data

summary.cifsm 5 Examples data(sim.dat) names(sim.dat) table(sim.dat$cause) table(sim.dat$group) tapply(sim.dat$time,sim.dat$group,summary) summary.cifsm Summary of weighted time-integrated summary statistics Usage Summary statistic estimate, standard error, 95% confidence interval, p-value depending the method and weight. ## S3 method for class 'CIFsm' summary(object,...) Arguments object See Also object of function CIFsm... additional arguments to summary() CIFsm

Index Topic cumulative incidence function CIFsm, 1 print.cifsm, 4 summary.cifsm, 5 Topic datasets sim.dat, 4 Topic summary statistics CIFsm, 1 Topic weight function CIFsm, 1 CIFsm, 1, 4, 5 print.cifsm, 4 sim.dat, 4 summary.cifsm, 5 6