Preface |
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vii | |
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1 | (8) |
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What Is Survival Analysis? |
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1 | (1) |
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2 | (2) |
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Why Use Survival Analysis? |
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4 | (1) |
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Approaches to Survival Analysis |
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5 | (1) |
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6 | (1) |
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7 | (2) |
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Chapter 2 Basic Concepts of Survival Analysis |
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9 | (20) |
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9 | (1) |
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9 | (6) |
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Describing Survival Distributions |
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15 | (3) |
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Interpretations of the Hazard Function |
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18 | (2) |
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Some Simple Hazard Models |
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20 | (3) |
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23 | (3) |
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26 | (3) |
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Chapter 3 Estimating and Comparing Survival Curves with PROCLIFETEST |
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29 | (42) |
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29 | (1) |
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30 | (8) |
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Testing for Differences in Survivor Functions |
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38 | (11) |
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49 | (6) |
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Life Tables from Grouped Data |
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55 | (4) |
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Testing for Effects of Covariates |
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59 | (5) |
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Log Survival and Smoothed Hazard Plots |
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64 | (5) |
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69 | (2) |
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Chapter 4 Estimating Parametric Regression Models with PROC LIFEREG |
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71 | (54) |
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71 | (1) |
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The Accelerated Failure Time Model |
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72 | (5) |
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Alternative Distributions |
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77 | (10) |
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Categorical Variables and the CLASS Statement |
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87 | (2) |
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Maximum Likelihood Estimation |
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89 | (6) |
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95 | (3) |
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Goodness-of-Fit Tests with the Likelihood-Ratio Statistic |
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98 | (2) |
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Graphical Methods for Evaluating Model Fit |
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100 | (3) |
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Left Censoring and Interval Censoring |
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103 | (5) |
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Generating Predictions and Hazard Functions |
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108 | (4) |
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The Piecewise Exponential Model |
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112 | (5) |
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Bayesian Estimation and Testing |
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117 | (7) |
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124 | (1) |
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Chapter 5 Estimating Cox Regression Models with PROC PHREG |
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125 | (78) |
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125 | (1) |
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The Proportional Hazards Model |
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126 | (2) |
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128 | (14) |
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142 | (11) |
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Time-Dependent Covariates |
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153 | (19) |
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Cox Models with Nonproportional Hazards |
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172 | (5) |
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Interactions with Time as Time-Dependent Covariates |
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177 | (2) |
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Nonproportionality via Stratification |
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179 | (4) |
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Left Truncation and Late Entry into the Risk Set |
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183 | (3) |
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Estimating Survivor Functions |
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186 | (6) |
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Testing Linear Hypotheses with CONTRAST or TEST Statements |
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192 | (3) |
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195 | (2) |
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Bayesian Estimation and Testing |
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197 | (3) |
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200 | (3) |
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Chapter 6 Competing Risks |
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203 | (32) |
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203 | (1) |
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204 | (3) |
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Time in Power for Leaders of Countries: Example |
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207 | (1) |
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Estimates and Tests without Covariates |
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208 | (5) |
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Covariate Effects via Cox Models |
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213 | (7) |
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Accelerated Failure Time Models |
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220 | (7) |
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Alternative Approaches to Multiple Event Types |
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227 | (5) |
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232 | (3) |
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Chapter 7 Analysis of Tied or Discrete Data with PROC LOGISTIC |
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235 | (22) |
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235 | (1) |
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The Logit Model for Discrete Time |
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236 | (4) |
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The Complementary Log-Log Model for Continuous-Time Processes |
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240 | (3) |
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Data with Time-Dependent Covariates |
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243 | (3) |
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246 | (9) |
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255 | (2) |
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Chapter 8 Heterogeneity, Repeated Events, and Other Topics |
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257 | (32) |
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257 | (1) |
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257 | (3) |
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260 | (22) |
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282 | (1) |
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Sensitivity Analysis for Informative Censoring |
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283 | (6) |
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Chapter 9 A Guide for the Perplexed |
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289 | (4) |
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289 | (3) |
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292 | (1) |
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Appendix 1 Macro Programs |
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293 | (6) |
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293 | (1) |
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293 | (3) |
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296 | (3) |
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299 | (8) |
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299 | (1) |
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The MYEL Data Set: Myelomatosis Patients |
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299 | (1) |
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The RECID Data Set: Arrest Times for Released Prisoners |
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300 | (1) |
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The STAN Data Set: Stanford Heart Transplant Patients |
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301 | (1) |
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The BREAST Data Set: Survival Data for Breast Cancer Patients |
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302 | (1) |
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The JOBDUR Data Set: Durations of fobs |
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302 | (1) |
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The ALCO Data Set: Survival of Cirrhosis Patients |
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302 | (1) |
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The LEADERS Data Set: Time in Power for Leaders of Countries |
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303 | (1) |
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The RANK Data Set: Promotions in Rank for Biochemists |
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304 | (1) |
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The JOBMULT Data Set: Repeated fob Changes |
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305 | (2) |
References |
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307 | (6) |
Index |
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313 | |