Estimating adjusted risk difference (RD) and number needed to treat (NNT) measures in the Cox regression model

Statistics in Medicine

Volumn 29, Issue 7-8, 2010, Pages 851-859

  Laubendera, R P ^a   Bender, R ^b,c  

^a UNIVERSITY OF MUNICH   (Germany)

^b INSTITUTE FOR QUALITY AND EFFICIENCY IN HEALTH CARE IQWIG   (Germany)

^c UNIVERSITY OF COLOGNE   (Germany)

Author keywords

Adjustment; Bootstrapping; Cox regression; Exposure impact number (EIN); Number needed to be exposed (NNE); Number needed to treat (NNT); Proportional hazards; Risk difference (RD); Survival data

Indexed keywords

ARTICLE; ATTRIBUTABLE RISK; BOOTSTRAPPING; CORRELATION COEFFICIENT; COVARIANCE; MONTE CARLO METHOD; MORTALITY; OBESITY; OBSERVATIONAL STUDY; OUTCOME ASSESSMENT; PROPORTIONAL HAZARDS MODEL; SURVIVAL TIME;

ADOLESCENT; ADULT; AGED; BIOSTATISTICS; BODY MASS INDEX; COHORT STUDIES; COMPUTER SIMULATION; CONFIDENCE INTERVALS; ENVIRONMENTAL EXPOSURE; FEMALE; HUMANS; LOGISTIC MODELS; MALE; MIDDLE AGED; MONTE CARLO METHOD; OBESITY; PROPORTIONAL HAZARDS MODELS; RISK ASSESSMENT; SMOKING; SURVIVAL ANALYSIS; YOUNG ADULT;

EID: 77949391395     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3793     Document Type: Article

References (28)

1. Bender R. Number needed to treat (NNT). In Encyclopedia of Biostatistics (2nd edn), Armitage P, Colton T (eds). Wiley: Chichester, 2005; 3752-3761.
2. Bender R, Blettner M. Calculating the 'number needed to be exposed' with adjustment for confounding variables in epidemiological studies. Journal of Clinical Epidemiology 2002; 55:525-530.
3. Bender R, Kuss O, Hildebrandt M, Gehrmann U. Estimating adjusted NNT measures in logistic regression analysis. Statistics in Medicine 2007; 26:5586-5595.
4. Altman DG, Andersen PK. Calculating the number needed to treat for trials where the outcome is time to an event. British Medical Journal 1999; 319:1492-1495.
5. Hutton JL. Number needed to treat: properties and problems. Journal of the Royal Statistical Society. Series A 2000; 163:403-419.
6. Lesaffre E, Pledger G. A note on the number needed to treat. Controlled Clinical Trials 1999; 20:439-447.
7. Thabane L. A closer look at the distribution of number needed to treat (NNT): a Bayesian approach. Biostatistics 2003; 4:365-370.
8. Jensen JLWV. Sur les fonctions convexes et les inégalités entre les valeurs moyennes. Acta Mathematica 1906; 30:175-193.
9. Walter SD. Choice of effect measures for epidemiological data. Journal of Clinical Epidemiology 2000; 53:931-939.
10. Cox DR. Regression models and life - tables (with discussion). Journal of Royal Statistical Society B 1972; 34:557-565.
11. Cox DR. Partial likelihood. Biometrika 1975; 62:269-276.
12. Klein JP, Moeschberger ML. Survival Analysis: Techniques for Censored and Truncated Data. Springer: New York, 1997.
13. Andersen PK, Borgan O, Gill RD, Keiding N. Statistical Models Based on Counting Processes. Springer: Berlin, 1993.
14. Andersen PK, Gill RD. Cox's regression model for counting processes: a large sample study. The Annals of Statistics 1982; 10:1100-1120.
15. Bishop YMM, Fienberg SE, Holland PW. Discrete Multivariate Analysis: Theory and Practice. MIT Press: Cambridge, MA, 1975.
16. Veraverbeke N. Bootstrapping in survival analysis. In Encyclopedia of Biostatistics (2nd edn), Armitage P, Colton T (eds), vol. 1. Wiley: Chichester, 2005; 541-547.
17. Burr D. A comparison of certain bootstrap confidence intervals in the Cox model. Journal of the American Statistical Association 1994; 89:1290-1302.
18. Carpenter J, Bithell J. Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Statistics in Medicine 2000; 19:1141-1164.
19. Davison AC, Hinkley DV. Bootstrap Methods and Their Application. Cambridge University Press: Cambridge, 1997.
20. Burton A, Altman DG, Royston P, Holder RL. The design of simulation studies in medical statistics. Statistics in Medicine 2006; 25:4279-4292.
21. Bender R, Augustin T, Blettner M. Generating survival times to simulate Cox proportional hazards models. Statistics in Medicine 2005; 24:1713-1723.
22. The R Foundation for Statistical Computing. R - version 2.5.0 (Statistical Software). Available from: http://www.r-project.org.
23. Kennedy P. A Guide to Econometrics (6th edn). Blackwell Publishers: Oxford, 2008.
24. Bender R, Trautner C, Spraul M, Berger M. Assessment of excess mortality in obesity. American Journal of Epidemiology 1998; 147:42-48.
25. Gail MH, Wieand S, Piantadosi S. Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates. Biometrika 1984; 71:431-444.
26. Hauck WW, Anderson S, Marcus SM. Should we adjust for covariates in nonlinear regression analyses of randomized trials? Controlled Clinical Trials 1998; 19:249-256.
27. Gail MH, Byar DP. Variance calculations for direct adjusted survival curves, with applications to testing for no treatment effect. Biometrical Journal 1986; 28:587-599.
28. Zhang X, Loberiza FR, Klein JP, Zhang MJ. A SAS macro for estimation of direct adjusted survival curves based on a stratified Cox regression model. Computer Methods and Programs in Biomedicine 2007; 88:95-101.