A "placement of death" approach for studies of treatment effects on ICU length of stay

Statistical Methods in Medical Research

Volumn 26, Issue 1, 2017, Pages 292-311

  Lin, Winston ^a   Halpern, Scott D ^b   Prasad Kerlin, Meeta ^b   Small, Dylan S ^c  

^a Och Spine at New York Presbyterian Hospitals   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

bootstrap confidence intervals; Brunner Munzel test; censoring by death; intensive care unit; length of stay; Mann Whitney U test; nonparametric Behrens Fisher problem; quantile treatment effects; rank tests; Wilcoxon rank sum test

Indexed keywords

BOOTSTRAPPING; CONFIDENCE INTERVAL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; DEATH; HUMAN; INTENSIVE CARE UNIT; LENGTH OF STAY; MORTALITY; RANDOMIZED CONTROLLED TRIAL; RANK SUM TEST; SELECTION BIAS; STATISTICAL SIGNIFICANCE; HOSPITAL MORTALITY; INTENSIVE CARE; MANPOWER; NONPARAMETRIC TEST; ORGANIZATION AND MANAGEMENT; OUTCOME ASSESSMENT; PENNSYLVANIA; PROCEDURES; RANDOMIZED CONTROLLED TRIAL (TOPIC); STATISTICS AND NUMERICAL DATA; UNIVERSITY HOSPITAL;

CONFIDENCE INTERVALS; CRITICAL CARE; HOSPITAL MORTALITY; HOSPITALS, UNIVERSITY; HUMANS; INTENSIVE CARE UNITS; LENGTH OF STAY; OUTCOME ASSESSMENT (HEALTH CARE); PENNSYLVANIA; RANDOMIZED CONTROLLED TRIALS AS TOPIC; STATISTICS, NONPARAMETRIC;

EID: 85008670912     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280214545121     Document Type: Article

References (58)

1. Harhay MO, Wagner J, Ratcliffe SJ, et al. Outcomes and statistical power in adult critical care randomized trials. Am J Respir Crit Care Med 2014; 189: 1469-1478.
2. Lilly CM, Cody S, Zhao H, et al. Hospital mortality, length of stay, and preventable complications among critically ill patients before and after tele-ICU reengineering of critical care processes. JAMA 2011; 305: 2175-2183.
3. Mehta S, Burry L, Cook D, et al. Daily sedation interruption in mechanically ventilated critically ill patients cared for with a sedation protocol: a randomized controlled trial. JAMA 2012; 308: 1985-1992.
4. Kerlin MP, Small DS, Cooney E, et al. A randomized trial of nighttime physician staffing in an intensive care unit. N Engl J Med 2013; 368: 2201-2209.
5. Freedman DA, Survival analysis: an epidemiological hazard? In: Collier D, Sekhon JS, Stark PB, (eds). Statistical models and causal inference: a dialogue with the social sciences, Cambridge: Cambridge University Press, 2010, pp. 169-192.
6. Joffe M,. Principal stratification and attribution prohibition: good ideas taken too far. Int J Biostat 2011; 7: Article 35-Article 35.
7. Rubin DB,. Causal inference through potential outcomes and principal stratification: application to studies with "censoring" due to death (with discussion). Stat Sci 2006; 21: 299-321.
8. Rosenbaum PR,. Comment: the place of death in the quality of life. Stat Sci 2006; 21: 313-316.
9. Rubin DB,. Rejoinder. Stat Sci 2006; 21: 319-321.
10. Brunner E, Munzel U,. The nonparametric Behrens-Fisher problem: asymptotic theory and a small-sample approximation. Biomet J 2000; 42: 17-25.
11. Neyman J,. On the application of probability theory to agricultural experiments (with discussion). Stat Sci 1990; 5: 463-480.
12. Rubin DB,. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol 1974; 66: 688-701.
13. Rubin DB,. Causal inference using potential outcomes: design, modeling, decisions. J Am Stat Assoc 2005; 100: 322-331.
14. Holland PW,. Statistics and causal inference (with discussion). J Am Stat Assoc 1986; 81: 945-970.
15. Lehmann EL,. Elements of large-sample theory, New York: Springer, 1999, pp. 146-157.
16. Reichardt CS, Gollob HF,. Justifying the use and increasing the power of a t test for a randomized experiment with a convenience sample. Psychol Methods 1999; 4: 117-128.
17. Freedman DA, Pisani R, Purves R,. Statistics, 4th ed. New York: Norton, 2007, pp. 508-511.
18. Lin W,. Agnostic notes on regression adjustments to experimental data: reexamining Freedman's critique. Ann Appl Stat 2013; 7: 295-318.
19. Holland PW,. Two measures of change in the gaps between the CDFs of test-score distributions. J Educ Behav Stat 2002; 27: 3-17.
20. Agresti A, Caffo B,. Simple and effective confidence intervals for proportions and differences of proportions result from adding two successes and two failures. Am Stat 2000; 54: 280-288.
21. Brown L, Li X,. Confidence intervals for two sample binomial distribution. J Stat Plan Infer 2005; 130: 359-375.
22. Hyndman RJ, Fan Y,. Sample quantiles in statistical packages. Am Stat 1996; 50: 361-365.
23. Hahn J,. Bootstrapping quantile regression estimators. Economet Theor 1995; 11: 105-121.
24. Efron B, Tibshirani RJ,. An introduction to the bootstrap, Boca Raton: CRC Press, 1993.
25. Davison AC, Hinkley DV,. Bootstrap methods and their application, Cambridge: Cambridge University Press, 1997.
26. Politis DN, Romano JP, Wolf M,. Subsampling, New York: Springer, 1999.
27. Moher D, Hopewell S, Schulz KF, et al. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ 2010; 340: c869-c869.
28. Korn EL,. Comment: causal inference in the medical area. Stat Sci 2006; 21: 310-312.
29. Rosenbaum PR,. Confidence intervals for uncommon but dramatic responses to treatment. Biometrics 2007; 63: 1164-1171.
30. Rosenbaum PR,. Design of observational studies, New York: Springer, 2010.
31. Imbens GW, Wooldridge JM,. Recent developments in the econometrics of program evaluation. J Econ Lit 2009; 47: 5-86.
32. Lehmann EL,. Parametric versus nonparametrics: two alternative methodologies (with discussion). J Nonparametr Stat 2009; 21: 397-426.
33. White IR, Thompson SG,. Choice of test for comparing two groups, with particular application to skewed outcomes. Stat Med 2003; 22: 1205-1215.
34. Romano JP,. Discussion of "Parametric versus nonparametrics: two alternative methodologies" by EL Lehmann. J Nonparametr Stat 2009; 21: 419-424.
35. Chung EY and Romano JP. Asymptotically valid and exact permutation tests based on two-sample U -statistics. Technical Report 2011-09, Department of Statistics, Stanford University, 2011.
36. Gail MH, Mark SD, Carroll RJ, et al. On design considerations and randomization-based inference for community intervention trials. Stat Med 1996; 15: 1069-1092.
37. Gibbons JD, Chakraborti S,. Nonparametric statistical inference, 5th ed. Boca Raton: CRC Press, 2011, pp. 292-293.
38. Pratt JW,. Robustness of some procedures for the two-sample location problem. J Am Stat Assoc 1964; 59: 665-680.
39. Fay MP, Proschan M,. Wilcoxon-Mann-Whitney or t -test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Stat Surv 2010; 4: 1-39.
40. McGraw KO, Wong SP,. A common language effect size statistic. Psychol Bull 1992; 111: 361-365.
41. Neubert K, Brunner E,. A studentized permutation test for the non-parametric Behrens-Fisher problem. Comput Stat Data Anal 2007; 51: 5192-5204.
42. Ho AD,. A nonparametric framework for comparing trends and gaps across tests. J Educ Behav Stat 2009; 34: 201-228.
43. Lumley T. Rock, paper, scissors, Wilcoxon test, http://notstatschat.tumblr.com/post/63237480043/rock-paper-scissors-wilcoxon-test (2013, accessed 10 July 2014).
44. Lumley T. Ordinal data and "ordinal" tests, http://cbe-test.cbenet.anu.edu.au/media/2753826/transitive-anu.pdf (2013, accessed 18 July 2014).
45. Lumley T. Loopy comparisons: when can more than two treatments be ranked?, http://faculty.washington.edu/tlumley/vanderbilt-seminar.pdf (accessed 10 July 2014).
46. Brown BM, Hettmansperger TP,. Kruskal-Wallis, multiple comparisons and Efron dice. Aust N Z J Stat 2002; 44: 427-438.
47. Thangavelu K, Brunner E,. Wilcoxon-Mann-Whitney test for stratified samples and Efron's paradox dice. J Stat Plan Infer 2007; 137: 720-737.
48. Aldous D. Review of The cult of statistical significance by S Ziliak and D McCloskey, http://www.stat.berkeley.edu/aldous/157/Books/stat.html (2008, accessed 17 July 2014).
49. Miller RG,. Beyond ANOVA: basics of applied statistics, New York: Wiley, 1986, pp. 51-51.
50. Follmann D, Fay MP, Proschan M,. Chop-lump tests for vaccine trials. Biometrics 2009; 65: 885-893.
51. Hallstrom AP,. A modified Wilcoxon test for non-negative distributions with a clump of zeros. Stat Med 2010; 29: 391-400.
52. Frangakis CE, Rubin DB,. Principal stratification in causal inference. Biometrics 2002; 58: 21-29.
53. Lachenbruch PA,. Comparison of two-part models with competitors. Stat Med 2001; 20: 1215-1234.
54. Lachenbruch PA,. Analysis of data with excess zeros. Stat Methods Med Res 2002; 11: 297-302.
55. Moulton LH, Curriero FC, Barroso PF,. Mixture models for quantitative HIV RNA data. Stat Methods Med Res 2002; 11: 317-325.
56. Zhou XH,. Inferences about population means of health care costs. Stat Methods Med Res 2002; 11: 327-339.
57. Imai K, King G, Nall C,. The essential role of pair matching in cluster-randomized experiments, with application to the Mexican universal health insurance evaluation (with discussion). Stat Sci 2009; 24: 29-72.
58. Brown SES, Ratcliffe SJ, Halpern SD,. An empirical derivation of the optimal time interval for defining ICU readmissions. Med Care 2013; 51: 706-714.