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Saddlepoint p-values and confidence intervals for a class of two sample permutation tests for current status and panel count data

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Abstract

The current status and panel count data frequently arise from cancer and tumorigenicity studies when events currently occur. A common and widely used class of two sample tests, for current status and panel count data, is the permutation class. We manipulate the double saddlepoint method to calculate the exact mid-p-values of the underlying permutation distributions of this class of tests. Permutation simulations are replaced by analytical saddlepoint computations which provide extremely accurate mid-p-values that are exact for most practical purposes and almost always more accurate than normal approximations. The method is illustrated using two real tumorigenicity panel count data. To compare the saddlepoint approximation with the normal asymptotic approximation, a simulation study is conducted. The speed and accuracy of the saddlepoint method facilitate the calculation of the confidence interval for the treatment effect. The inversion of the mid-p-values to calculate the confidence interval for the mean rate of development of the recurrent event is discussed.

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References

  • Abd-Elfattah EF (2009) Testing for independence: saddlepoint approximation to associated permutation distributions. Electron J Stat 3: 625–632

    Article  MathSciNet  Google Scholar 

  • Abd-Elfattah EF, Butler RW (2007) The weighted log-rank class of permutation tests: p-values and confidence intervals using saddlepoint methods. Biometrika 94: 543–551

    Article  MathSciNet  MATH  Google Scholar 

  • Abd-Elfattah EF, Butler RW (2009) Log-Rank permutation tests for trend: Saddlepoint p-values and survival rate confidence intervals. Can J Stat 37: 5–16

    Article  MathSciNet  MATH  Google Scholar 

  • Agresti A (1992) A survey of exact inference for contingency tables. Stat Sci 7: 131–153

    Article  MathSciNet  MATH  Google Scholar 

  • Barlow RE, Bartholomew DJ, Bremner JM, Brunk HD (1972) Statistical inference under order restrictions. John Wiley, New York

    MATH  Google Scholar 

  • Booth JG, Butler RW (1990) Randomization distributions and saddlepoint approximations in generalized linear models. Biometrika 77: 787–796

    Article  MathSciNet  MATH  Google Scholar 

  • Butler RW (2007) Saddlepoint approximations with applications. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Byar DP (1980) The veterans administration study of chemoprophylaxis for recurrent stage I bladder tumors comparison of placebo, pyridoxine and topical thiotepa. In: Pavone-Maculuso M, Smith PH, Edsmyn F (eds) Bladder tumors and other topics in urological oncology. Plenum, New York, pp 363–370

    Google Scholar 

  • Daniels HE (1954) Saddlepoint approximation in statistics. Ann Math Stat 25: 631–650

    Article  MathSciNet  MATH  Google Scholar 

  • Davis CS, Wei LJ (1988) Nonparametric methods for analyzing incomplete nondecreasing repeated measurements. Biometrics 44: 1005–1018

    Article  MathSciNet  MATH  Google Scholar 

  • Davison AC, Hinkley DV (1988) Saddlepoint approximations in resampling methods. Biometrika 75: 417–431

    Article  MathSciNet  MATH  Google Scholar 

  • Davison AC, Wang S (2002) Saddlepoint approximations as smoothers. Biometrika 89: 933–938

    Article  MathSciNet  MATH  Google Scholar 

  • Dewanji AD, Kalbfleisch JD (1986) Nonparametric methods for survival/sacrifice experiments. Biometrics 42: 325–341

    Article  MathSciNet  MATH  Google Scholar 

  • Diamond ID, McDonald JW (1992) Analysis of current status data. In: Trussell J, Hankinson R, Tilton RI (eds) Demographic applications of event history data analysis. Clarendon Press, Oxford, pp 231–252

    Google Scholar 

  • Diamond ID, McDonald JW, Shah IH (1986) Proportional hazards models for current status data: application to the study of differentials in age at weaning in Pakistan. Demography 23: 607–620

    Article  Google Scholar 

  • Dinse GE (1994) A comparison of tumor incidence analyses applicable in single-sacrifice animal experiments. Stat Med 13: 689–708

    Article  Google Scholar 

  • Dinse GE, Lagakos SW (1983) Regression analysis of tumor prevalence data. Appl Stat 32: 236–248

    Article  MATH  Google Scholar 

  • Finkelstein DM (1986) A proportion hazards model for interval censored failure time data. Biometrics 42: 845–854

    Article  MathSciNet  MATH  Google Scholar 

  • Gaver DP, O’Muircheartaigh IG (1987) Robust empirical Bayes analyses of event rates. Technometrics 29: 1–15

    Article  MathSciNet  MATH  Google Scholar 

  • Groeneboom P (1991) Nonparametric likelihood estimators for interval censoring and deconvolution. Technical Report, Faculty of Technical Mathematics and Informatics. Delft University of Technology, Delft, The Netherlands

  • Hoel DG, Walburg HE (1972) Statistical analysis of survival experiments. J Natl Cancer Inst 49: 361–372

    Google Scholar 

  • Huang J (1996) Efficient estimation for the proportional hazards model with interval censoring. Ann Stat 24: 540–568

    Article  MATH  Google Scholar 

  • Ii Y, Kikuchi R, Matsuoka K (1987) Two-dimensional (time and multiplicity) statistical analysis of multiple tumors. Math Biosci 84: 1–21

    Article  MathSciNet  MATH  Google Scholar 

  • Jawell NP, Lann M (1995) Generalization of current status data with applications. Lifetime Data Anal 1: 101–109

    Article  Google Scholar 

  • Kalbfleisch JD, Lawless JF (1985) The analysis of panel data under a Markov assumption. J Am Stat Assoc 80: 863–871

    Article  MathSciNet  MATH  Google Scholar 

  • Kiending N (1991) Age-specific incidence and prevalence: a statistical perspective (with discussion). J R Stat Soc A 154: 371–412

    Article  Google Scholar 

  • Lagakos SW, Louis TA (1988) Use of tumor lethality to interpret tumorigenicity experiments lacking cause-of-death data. Appl Stat 37: 169–179

    Article  Google Scholar 

  • Lugannani R, Rice SO (1980) Saddlepoint approximations for the distribution of the sum of independent random variables. Adv Appl Probab 12: 475–490

    Article  MathSciNet  MATH  Google Scholar 

  • Peto R, Pike MC, Day NE, Gray RG, Lee PN, Parish S, Peto J, Richards S, Wahrendorf J (1980) Guidelines for simple, sensitive significance tests for carcinogenic effects in long-term animal experiments. In: Long-term screening assays for carcinogens: a critical appraisal. International Agency for Research on Cancer, Lyon, pp 311–426

  • Robinson J (1982) Saddlepoint approximations for permutation tests and confidence intervals. J R Stat Soc B 44: 91–101

    MATH  Google Scholar 

  • Rossine AJ, Tsiatis A (1996) A semiparametric proportional odds regression model for the analysis of current status data. J Am Stat Assoc 91: 713–721

    Article  Google Scholar 

  • Routledge RD (1994) Practicing safe statistics with the mid-p. Can J Stat 22: 103–110

    Article  MathSciNet  Google Scholar 

  • Skovgaard IM (1987) Saddlepoint expansions for conditional distributions. J Appl Probab 24: 875–887

    Article  MathSciNet  MATH  Google Scholar 

  • Sun J (1999) A nonparametric test for current status data with unequal censoring. J R Stat Soc B 61: 243–250

    Article  MATH  Google Scholar 

  • Sun J (2006) The statistical analysis of interval-censored failure time data. Springer, New York

    MATH  Google Scholar 

  • Sun J, Fang h (2003) A nonparametric test for panel count data. Biometrika 90: 199–208

    Article  MathSciNet  MATH  Google Scholar 

  • Sun J, Kalbfleisch JD (1993) The analysis of current status data on point process. J Am Stat Assoc 88: 1449–1454

    Article  MathSciNet  MATH  Google Scholar 

  • Sun J, Kalbfleisch JD (1995) Estimation of the mean function of point processes based on panel count data. Stat Sin 5: 279–289

    MathSciNet  MATH  Google Scholar 

  • Sun J, Kalbfleisch JD (1996) Nonparametric tests for tumor prevalence data. Biometrics 52: 726–731

    Article  MATH  Google Scholar 

  • Sun J, Wei LJ (2000) Regression analysis of panel count data with covariate-dependent observations and censoring times. J R Stat Soc B 62: 293–302

    Article  MathSciNet  Google Scholar 

  • Thall DF, Lachin JM (1988) An analysis of recurrent events: nonparametric methods for randon-interval count data. J Am Stat Assoc 83: 339–347

    Article  Google Scholar 

  • Wei LJ, Lin DY, Weissfeld L (1989) Regression analysis of multivariate incomplete failure time data by modeling marginal distributions. J Am Stat Assoc 84: 1065–1073

    Article  MathSciNet  Google Scholar 

  • Wellner JA, Zhang Y (1998) Two estimators of the mean of a counting process with panel count data. Ann Stat 28: 779–814

    MathSciNet  Google Scholar 

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Correspondence to Ehab F. Abd-Elfattah.

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Abd-Elfattah, E.F. Saddlepoint p-values and confidence intervals for a class of two sample permutation tests for current status and panel count data. Lifetime Data Anal 17, 461–472 (2011). https://doi.org/10.1007/s10985-010-9190-9

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