33091-33101

33091-33101 - 1 Introduction to Cox Regression Kristin...

Info iconThis preview shows pages 1–9. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: 1 Introduction to Cox Regression Kristin Sainani Ph.D. http://www.stanford.edu/~kcobb Stanford University Department of Health Research and Policy 2 History “Regression Models and Life-Tables” by D.R. Cox, published in 1972, is one of the most frequently cited journal articles in statistics and medicine Introduced “maximum partial likelihood” 3 Cox regression vs.logistic regression Distinction between rate and proportion: Incidence (hazard) rate: number of new cases of disease per population at-risk per unit time (or mortality rate, if outcome is death) Cumulative incidence: proportion of new cases that develop in a given time period 4 Cox regression vs.logistic regression Distinction between hazard/rate ratio and odds ratio/risk ratio: Hazard/rate ratio: ratio of incidence rates Odds/risk ratio: ratio of proportions By taking into account time, you are taking into account more information than just binary yes/no. Gain power/precision. Logistic regression aims to estimate the odds ratio; Cox regression aims to estimate the hazard ratio 5 Example 1: Study of publication bias By Kaplan- Meier methods From: Publication bias: evidence of delayed publication in a cohort study of clinical research projects BMJ 1997;315:640-645 (13 September) 6 From: Publication bias: evidence of delayed publication in a cohort study of clinical research projects BMJ 1997;315:640-645 (13 September) Table 4 Risk factors for time to publication using univariate Cox regression analysis Characteristic # not published # published Hazard ratio (95% CI) Null 29 23 1.00 Non-significant trend 16 4 0.39 (0.13 to 1.12) Significant 47 99 2.32 (1.47 to 3.66) Interpretation: Significant results have a 2-fold higher incidence of publication compared to null results. Univariate Cox regression 7 Example 2: Study of mortality in academy award winners for screenwriting Kaplan- Meier methods From: Longevity of screenwriters who win an academy award: longitudinal study BMJ 2001;323:1491-1496 ( 22-29 December ) Table 2. Death rates for screenwriters who have won an academy award. Values are percentages (95% confidence intervals) and are adjusted for the factor indicated Relative increase in death rate for winners Basic analysis 37 (10 to 70) Adjusted analysis Demographic: Year of birth 32 (6 to 64) Sex 36 (10 to 69) Documented education 39 (12 to 73) All three factors 33 (7 to 65) Professional: Film genre 37 (10 to 70) Total films 39 (12 to 73) Total four star films 40 (13 to 75) Total nominations 43 (14 to 79) Age at first film 36 (9 to 68) Age at first nomination 32 (6 to 64) All six factors 40 (11 to 76) All nine factors 35 (7 to 70) HR=1.37; interpretation: 37% higher incidence of death for winners compared with nominees HR=1.35; interpretation: 35% higher incidence of...
View Full Document

This note was uploaded on 02/23/2012 for the course STAT 312 taught by Professor Staff during the Fall '11 term at Rutgers.

Page1 / 62

33091-33101 - 1 Introduction to Cox Regression Kristin...

This preview shows document pages 1 - 9. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online