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醫(yī)學統(tǒng)計學-電子教材:References

醫(yī)學統(tǒng)計學:電子教材 References:ContentReferencesReferencesGlossaryofsymbolsandabbreviationsSupportReferences.1Glossaryofsymbolsandabbreviations.19Support.21Copyright?1990-2006StatsDirectLimited,allrightsreservedDownloa

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References

Aalen OO. Non parametricinference for a family of counting processes. Annals of Statistics 1978;6:701-726.

L'Abbé KA, Detsky AS, O'Rourke K. Meta-analysis inclinical research. Annals of InternalMedicine 1987;107:224-233.

Abramowitz M, Stegun IA.Handbook of Mathematical Functions withFormulas, Graphs and Mathematical Tables (9thprinting with corrections). New York: Wiley 1972.

Ades AE, Higgins JPT. The Interpretation of Random-Effects Meta-Analysis in DecisionModels. Medical Decision Making 2005;25:646-654.

Agresti A. Anintroduction to categorical data analysis. New York: Wiley 1996.

Agresti A. Categorical data analysis (2nd edition). New York:Wiley 2002.

Agresti A. Analysis of ordinal categorical data. New York:Wiley 1984.

Ahrens JH,Dieter U. Computer methods for sampling from the exponential and normaldistributions. Communications of the ACM 1972;15:873-882.

Ahrens JH,Dieter U. Extensions of Forsythe's method for random sampling from the normaldistribution. Mathematics of Computation 1973;27:927-937.

Ahrens JH, Dieter U. Computer Generation of Poisson Deviates frommodified Normal distributions. ACM Transactionson Mathematical Software 1973;8(2):163-179.

Ahrens JH,Dieter U Computer methods for sampling from gamma, beta, Poisson and binomialdistributions. Computing 1974;12:223-246

Ahrens JH,Dieter U. Computer generation of Poisson deviates from modified normaldistributions. ACM Transactions on Mathematical Software 1982;8:163-179.

Ahrens JH,Dieter U. Generating gamma variates by a modifiedrejection technique. Communications of the ACM 1982;25(1):47-54.

Ahrens JH, Dieter U. A convenient samplingmethod with bounded computation times for Poisson distributions. American Journal of Mathematical and Management Sciences 1989:1-13.

Altman DG. Practical Statistics for Medical Research. Chapman and Hall 1991.

Altman DG.Confidence intervals for the number needed to treat. British Medical Journal1998;317:1309-12.

Altman DG, Deeks JJ. Meta-analysis, Simpson's paradox, and the numberneeded to treat. BMC Medical Research Methodology 2002;2(1):http://www.biomedcentral。com/1471-2288/2/3/abstract

Anbar D. On estimatingthe difference between two probabilities, with special reference to clinicaltrials. Biometrics 1983;39:257-262.

Andersen PK et al.. Statistical models based on counting processes. New York: Springer-Verlag 1993.

Armitage P, BerryG. Statistical Methods in Medical Research (3rd edition). Blackwell 1994.

Armitage P, BerryG, Matthews JNS. Statistical Methods inMedical Research (4th edition). Oxford:Blackwell Science 2002.

Aziz N, Buchan I, et al. Spermdeformity index: a reliable predictor of the outcome of fertilization in vitro.Fertility and Sterility 1996;66(6):1000-8.

Barnard GA. On alleged gains in power from lower P-values. Statisticsin Medicine 1989;8:1469-1477.

Bailey NTJ. Mathematics, Statistics and Systems for Health. New York: Wiley 1977.

Begg CB, MazumdarM. Operating characteristics of a rank correlation test for publication bias. Biometrics1994;50:1088-1101.

Belsley, Kuh,Welsch. Regression Diagnostics.Wiley 1980.

Berger RL.Remark AS R86 on algorithm AS 152, Cumulative hypergeometricprobability. Applied Statistics 1991;40(2):374-375.

Berkson J, Gage RP. Calculation ofsurvival rates for cancer. Proceedings of Staff Meetings of the Mayo Clinic1950;25:270-286.

Berry KJ, Mielke PW, Cran GW. R83 further to AS 64. Applied Statistics 1990;39(2).

Best DJ, Gipps PG. AS 71, Upper Tail Probabilities of Kendall's Tau. AppliedStatistics 1974;23(1).

Best DJ,Roberts DE. AS 89, Upper Tail Probabilities of Spearman's Rho. AppliedStatistics 1975;24(3).

Best DJ,Roberts DE. AS 91, The Percentage Points of the Chi²Distribution. Applied Statistics 1975;24(3).

Bland M, Altman DG. StatisticalMethods for Assessing the Difference Between TwoMethods of Measurement. Lancet 1986;i:307-310.

Bland M. An Introduction toMedical Statistics (3rd edition). Oxford MedicalPublications 2000.

Bland MJ, Altman DG. Measurement Error and correlation coefficients. BritishMedical Journal 1996;313:41-2.

Bland MJ, Altman DG. StatisticsNotes: Measurement error. British Medical Journal 1996;312:1654.

Bland MJ, Altman DG. StatisticsNotes: Transforming data. British Medical Journal 1996;312:770.

Boys R.Remark R80 on AS76, an integral useful in calculating non-central t and bivariate normal probabilities. Applied Statistics1989;38(3):580-2.

Bradburn M, Deeks J, Berlin J, LocalioA. Much ado about nothing: A comparison of theperformance of meta-analytical methods with rare events. Statistics inMedicine in press 2006.

Breslow NE. Covariance analysis of censored survival data. Biometrics 1974;30:89-99.

Breslow NE, Day NE. Statistical Methods in Cancer Research: Vol. I - The Analysis of Case-Control Studies. Lyon:International Agency for Research on Cancer 1980.

Breslow NE, Day NE. Statistical Methods in Cancer Research: Vol. II - TheDesign and Analysis of Cohort Studies. Lyon:International Agency for Research on Cancer 1987.

Brookmeyer R, CrowleyJ. A confidence interval forthe median survival time. Biometrics 1982;38:29-41.

Brower JE, Zar JH, von EndeCN. Field and LaboratoryMethods for General Ecology. Boston:McGraw-Hill 1998.

Brown M,Using Gini-style indices to evaluate the spatialpatterns of health practitioners; theoretical considerations and an applicationbased on the Albertadata. Social Science and Medicine 1994;38(9):1243-1256.

Brown LD, Cai TT, DasGuptaA. Interval Estimation for a Binomial Proportion. Statistical Science2001;16(2):101-133.

Brown MB, Forsythe AB.Robust tests for the equality of variances. Journal of the AmericanStatistical Association 1974;69:364-7.

Bryson MC,Johnson ME. The incidence of monotone likelihood in the Coxmodel. Technometrics 1981;23:381-4.

Casagrande JT, Pike MC, Smith PG. Animproved approximate formula for calculating sample sizes for comparing twobinomial distributions. Biometrics 1978;34:483-486.

Cates C.Simpson's paradox and calculation of number needed to treat from meta-analysis.BMC Medical Research Methodology 2002;2(1): http://www.biomedcentral。com/1471-2288/2/1/abstract

Chan TF. Algorithm 581. Singular valuedecomposition of a general rectangular matrix. Transactions on Mathematical Software 1982;8(1):84-88.

Chao A. Non-parametric estimation of the number of classes in apopulation. ScandanavianJournal of Statistics 1984;11:265-270.

Chatterjee S, Hadi AS, Price B. Regression Analysisby Example. New York: Wiley 2000.

Cheng RCH.Generating beta variates with nonintegralshape parameters. Communications of the ACM 1978;21:317-322.(Algorithms BB and BC)

Chiang CL. Standard error of the age-adjusted death rate. US Department of Health, Education and Welfare: Vital StatisticsSpecial Reports 1961;47:271-285.

Chiang CL. The life table and its applications. Malabar: RobertE Krieger Publishing Company 1984.

Chou YM. Remark R55 on AS76, an integral useful in calculating non-central tand bivariate normal probabilities. AppliedStatistics 1985;34(1):100-1.

Cochran W,Cox G. Experimental Designs (2nd edition). Wiley 1957.

Cody WJ, Hillstrom KE. Chebyshev Approximations for the Natural Logarithm of the Gamma Function.Mathematics of Computation 1967;21:198-203.

Collett D. Modelling Survival Data inMedical Research. Chapmanand Hall 1994.

Colton T. Statistics in Medicine. Little Brown & Co 1974.

Colwell RK, Coddington JA. Estimating terrestrialbiodiversity through extrapolation. Philosophical Transactions of theRoyal Society (Series B) 1994;345:101-118.

Conover WJ, Practical Nonparametric Statistics (3rd edition). Wiley 1999.

Cook RJ, Sackett DL. The number needed to treat: a clinically usefulmeasure of treatment effect. British Medical Journal 1995;310:452-454: http://bmj。com/cgi/content/full/310/6977/452.

Cook RD, Weisberg S. Residuals and Influence in Regression. Chapman and Hall 1982.

Copenhaver MD, HollandBS. Computation of the distribution of themaximum Studentized range statistic with application to multiple significance testing of simple effects. Journalof Statistical Computing and Simulation 1988;30:1-15.

Cowell FA. Measuring Inequality(second edition, draft third edition (May 2000) athttp://darp.lse.ac.uk/Frankweb/Frank/pdf/measuringinequality2.pdf), Hemel Hempstead: Harvester Wheatsheaf 1995.

Cox DR. Regression models and life tables. Journal of the Royal Statistical Society 1972;B34:187-220.

Cox DR,Oakes D. Analysis of survival data. London: Chapman and Hall 1984.

Cox DR,Snell EJ. The Analysis of Binary Data (2nd edition). Chapmanand Hall 1989.

Cran GW, Martin KJ. Thomas GE. R19and AS 109 further to AS 63 and AS 64. Applied Statistics 1977;26(1).

Critchlow DE, FlignerMA. On distribution-free multiple comparisons in the one-wayanalysis of variance. Communications in Statistics - Theory andMethods 1991;20:127-139.

Cronbach L, Coefficient Alpha and the Internal Structure of Tests. Psychometrika 1951;16(3):297-333.

Crow EL,Gardner RS. Confidence intervals for the expectation of aPoisson variable. Biometrika 1959;46:441-453.

Curtin LR,Klein RJ. Direct Standardization (Age-Adjusted Death Rates). Centres for Disease Control and Prevention: HealthyPeople 2000: Statistical Notes 1995;6.

Cuzick J. A Wilcoxon-Type Test for Trend. Statistics inMedicine 1985;4:87-89.

Dagpunar J. Principles of Random VariateGeneration. Oxford:Clarendon OxfordScience Publications 1988.

David HA. Order Statistics(2nd edition). New York:John Wiley & Sons 1981.

Davis CS. A computer program for non-parametric analysis of incompleterepeated measures for two samples. Computer Methods and Programs inBiomedicine 1994;42:39-52.

David CS,Hall DB. A computer program for the regression analysis ofordered categorical repeated measurements. Computer Methods andPrograms in Biomedicine 1995;51:153-169.

Deeks JJ. Issues inthe selection of a summary statistic for meta-analysis of clinical trials withbinary outcomes. Statistics in Medicine 2002.In press.

Deeks JJ, Altman DG. Effect measuresfor meta-analysis of trials with binary outcomes. in Systematicreviews in healthcare. Meta-analysis in context.Eds. Egger M, Smith GD, Altman DG. BMJ Books 2001.

DeLong, DeLong,Clarke-Pearson. Comparing the areas under two or morecorrelated receiver operating curves: A nonparametric approach.Biometrics 1988;44:837-845.

DerSimonian R, Laird N. Meta-analysis in Clinical Trials. Controlled Clinical Trials 1986;7:177-188.

Devroye L. Non-Uniform Random Variate Generation. New York:Springer-Verlag 1986.

DigitalCorporation. Digital FORTRAN, 1998.

Dinneen LC, BlakesleyBC. AS 62, A Generator for the Sampling Distributionof the Mann-Whitney U Statistic. Applied Statistics 1973;22(2).

Dixon PM,Weiner J, Mitchell-Olds T, Woodley R. Boot-strapping the Ginicoefficient of inequality. Ecology 1987;68:1548-1551.

Dixon PM.The bootstrap and the jackknife: Describing the precision of ecologicalindices. in Design and Analysis of EcologicalExperiments (Scheiner, SM, GurevitchJ eds.). New York:Chapman & Hall 1993.

Dobson AJ, Kuulasmaa K, Eberle E, Scherer J. Confidence intervals for weighted sumsof Poisson parameters. Statistics in Medicine1991;10:457-462.

Donner A, Eliasziw M. A goodness of fit approach to inference procedures for the Kappastatistic: CI construction, significance testing and sample size estimation. Statisticsin Medicine 1992;11:511-519.

Dorsey EN. The velocity of light. Transactions of the AmericanPhilosophical Society 1944;34:1-110.

Draper NR, Smith H. Applied Regression Analysis (3rdedition). New York: Wiley 1998.

Dupont WD. Power calculations for matched case-control studies. Biometrics 1988;44:1157-1168.

Dupont WD. Power and sample sizecalculations. Controlled Clinical Trials 1990;11:116-128.

Efron B, TibshiraniR. Improvements on cross-validation: The bootstrap method. Journal of theAmerican Statistical Association 1997;92:548-560.

Egger M, et al. Bias in meta-analysis detected by a simple,graphical test. British Medical Journal1997;315:629-634.

Everitt B, Dunn G. AppliedMultivariate Data Analysis. Edward Arnold 1991.

Feinstein AR. Principles of Medical Statistics. New York:Chapman & Hall/CRC 2002.

Feldt LS. Theapproximate sampling distribution of Kuder-Richardsonreliability coefficient twenty. Psychometrika1965;30:357-371.

Finney DJ. ProbitAnalysis. Cambridge UniversityPress 1971.

Finney DJ. Statistical Method in Biological Assay. Charles Griffin & Co. 1978.

Fleiss JL,Gross AJ. Meta-analysis in epidemiology, with special reference to studies ofthe association between exposure to environmental tobacco smoke and lungcancer: A critique. Journal of Clinical Epidemiology 1991;44:127-39.

Fleiss JL.Confidence intervals for the odds ratio in case-control studies: the state ofthe art. Journal of Chronic Diseases 1979;32:69-77.

Fleiss JL. StatisticalMethods for Rates and Proportions (2nd edition). New York: Wiley 1981.

Fleiss JL. The statistical basis of meta-analysis. StatisticalMethods in Medical Research 1993;2:121-145.

Fleiss JL, Cuzick J. The reliability of dichotomous judgements: unequal numbers of judges per subject. AppliedPsychological Measurement 1979;3:537-542.

Fleiss JL, Nee CM, Landis JR. Large sample variance of kappa in thecase of different sets of raters. PsychologicalBulletin 1979;86:974-977.

Fog A. Pseudo random number generators. 1999: http://www.agner。org/random.

Freeman MF, Tukey JW. Transformations related to the angular and squareroot. Annals of Statistics 1950;21:607-611.

Frome EL. Theanalysis of rates using Poisson regression methods. Biometrics1983;39:665-674.

Gail MH, Benichou J. Encyclopedia of Epidemiologic Methods. Chichester: John Wiley &Sons 2000.

Gardner MJ, Altman DG. Statistics with Confidence - Confidence Intervals andStatistical Guidelines. British Medical Journal1989.

Gart JJ, Nam J. Approximate intervalestimation for the difference in binomial parameters: correction for skewness and extension to multiple tables. Biometrics1990;46:637-643.

Gart JJ, Nam J. Approximate intervalestimation of the ratio of binomial parameters: a review and corrections for skewness. Biometrics 1988;44:323-338.

Gavaghan DJ, Moore AR, McQay HJ. An evaluation of homogeneity tests in meta-analysis in pain usingsimulations of patient data. Pain 2000;85:415-24.

Gentle JE. Random NumberGeneration and Monte Carlo Methods (Statisticsand Computing). New York:Springer-Verlag 2003.

GentlemanWM. Basic procedures for large, sparse or weighted linear least squaresproblems. Applied Statistics 1974;23:448-454.

Geodhart PW, Jansen MJW. Remark R89 on AS76, an integral useful in calculating non-central tand bivariate normal probabilities. AppliedStatistics 1992;41(2):496-7.

Gini C. "Variabilitáe mutabilita" 1912 reprinted in Memorie di metodologica statistica (Ed. Pizetti E, Salvemini, T). Rome: LibreriaEredi Virgilio Veschi 1955.

Glasser C. Variance formulas for the mean difference and coefficient ofconcentration. Journal of the AmericanStatistical Association 1962;57:648-654.

Gleason JR. Univariate summaries with boxplots. Stata Technical Bulletin 1997;36:23-25.

Gleason JR.An accurate, non-iterative approximation for Studentizedrange quantiles. Computational Statistics and DataAnalysis 1999;31(2):147-158.

Golub GH, Van Loan CF. MatrixComputations. Baltimore, Maryland:Johns Hopkins UniversityPress 1983.

Goodman D. Thetheory of diversity-stability relationships in ecology. QuarterlyReviews of Biology 1975;50:237-266.

Goodman LA, Kruskal WH. Measures of association forcross-classifications III: Approximate sampling theory. Journal of theAmerican Statistical Association 1963;58:310-364.

Goodman LA, Kruskal WH. Measures of association forcross-classifications IV: simplification of asymptotic variances. Journal ofthe American Statistical Association 1972;67:415-421.

Gong G.Cross-validation, the jackknife, and the bootstrap: Excess error estimation inforward logistic regression. Journal of the American Statistical Association1986;81:108-113.

Greenland S, Robins JM. Estimation of common effect parameter from sparse follow up data.Biometrics 1985;41:55-68.

Greenland S. RE: A simple methodto calculate the confidence interval of a standardized mortality ratio. AmericanJournal of Epidemiology 1990;133(2):212-213.

Greenland S, Salvan A. Bias in theone-step method for pooling study results. Statisticsin Medicine 1990;9:247-252.

Greenwood M. Discussion on the value of life-tables instatistical research. Journal of the RoyalStatistical Society 1922;85:537-560.

Greenwood M. The natural duration of cancer. Reportson Public Health and Medical Subjects. London:Her Majesty's Stationery Office 1926;33:1-26.

Gundmann H, Hori S, Tanner G.Determining confidence intervals when measuring genetic diversity and thediscriminatory ability of typing methods for microorganisms. Journal ofClinical Microbiology 2001;39(11):4190-4192.

Hall P. Theoretical comparison of bootstrap confidence intervals. Annals of Statistics 1988;16:927-985.

Hanley JA,McNeil BJ. The meaning and use of area under a Receiver OperatingCharacteristic (ROC) curve. Radiology 1982;143:29-36.

Harbord RM, Egger M, Sterne JAC. A modified test for small-study effects in meta-analyses ofcontrolled trials with binary endpoints. Statistics in Medicine inpress 2005.

Harding EF.An Efficient Minimal Storage Procedure for Calculating the Mann-Whitney U, Generalised U and SimilarDistributions. Applied Statistics 1983;33.

Harris EK,Albert A. Survivorship analysis for clinical studies. New York: Dekker 1991.

Harter HL. Expectedvalues of normal order statistics. Biometrika1961;48:151-165.

Haynes RB, Sackett DL. personalcommunication. McMaster University 1993.

Hedges, Olkin. Statistical methods for meta-analysis.London:Academic Press 1985.

Hedges LV,Pigott TD. The power of statistical tests inmeta-analysis, Psychological Methods 2001;6:203-217.

Higgins JPT,Thompson SG. Quantifying heterogeneity in a meta-analysis.Statistics in Medicine 2002;21:1539-1558.

Higgins JPT,Thompson SG, Deeks JJ, Altman DG. Measuringinconsistency in meta-analyses. British Medical Journal 2003;327:557-560.

Hill GW. Student's t-Quantiles (Algorithm 396). Communications of the Association for ComputingMachinery 1970;13:619-620.

Hill ID. AS66, The Normal Integral. Applied Statistics1973;22(3).

Hocking DC. TheAnalysis of Linear Models. Monterey, California: Brookes-Cole 1985.

Hogg RV, Tanis EA. Probability and Statistical Inference (4thedition). New York: MacMillan 1993.

Hollander M, Wolfe DA. Non-parametricStatistical Methods (2nd edition). New York: Wiley 1999.

Hosmer DW, LemeshowS. Applied Logistic Regression. New York: Wiley 1989.

Hosmer DW, LemeshowS. Applied Survival Analysis. New York: Wiley 1999.

Howard S.Remark on the paper by Cox, D.R. (1972): Regression methods and life tables. Journalof The Royal Statistical Society Series B 1972;34:187–220

Hsu JC. Multiple Comparisons. Chapman andHall 1996.

Hunter PR,Gaston MA. Numerical index of the discriminatory ability of typing systems: an applications of Simpson's index of diversity. Journalof Clinical Microbiology 1988;26(11):2465-2466.

Hurlbert SH. The non-concept ofspecies diversity: a critique and alternative parameters. Ecology 1971;52:577-586.

Hutton JL,Number needed to treat: properties and problems. Journal of the RoyalStatistical Society A 2000;163(3):403-419. 

IoannidisJP, et al. Early or deferred Zidovudinetherapy in HIV-infected patients without and AIDS-defining illness. Annalsof Internal Medicine 1995;122:856-66.

IoannidisJP, Lau J. The impact of high-risk patients on the results ofclinical trials. Journal of Clinical Epidemiology 1997;50:1089-1098.

Iman RL, DavenportJM. New approximations to the exact distributionof the Kruskal-Wallis test statistic. Communicationsin Statistics - Theory and Methods 1976;A5:1335-1348.

Iman RL, DavenportJM. Approximations to thecritical region of the Friedman statistic. Communications in Statistics- Theory and Methods 1980;A9:571-595.

Jenkins SP,The measurement of economic inequality in Readings on Economic Inequality(Ed. Osberg L.). New York, Armonk: Sharpe ME 1991.

Johnson NL, Kotz S. Discrete Distributions. Boston: Houghton Mifflin Company 1969.

Johnson NL, Kotz S. Continuous UnivariateDistributions (1 and 2). New York:Wiley 1970.

Johnson RA, Wichern DW. Applied Multivariate Statistical Methods(4th edition). London:Prentice-Hall 1998.

Jüni P, HolensteinF, Sterne J, Bartlett C, Egger M. Direction and impactof language bias in meta-analyses of controlled trials: empirical study. InternationalJournal of Epidemiology 2001;31:115-123.

Kachitvichyanukul V, Schmeiser BW. Binomial random variate generation (Algorithm BTPEC). Communicationsof the ACM 1988;31:216.

Kalbfleisch JD, Prentice RL. Statistical Analysis of Failure Time Data. New York: Wiley 1980.

Kalbfleisch JD, Prentice RL. Marginallikelihoods based on Cox's regression and life model. Biometrika1973;60(2):267-278.

Kaplan EL,Meier P. Nonparametric estimation from incomplete observations. Journal ofthe American Statistical Association 1958;53:457-481.

Kempton RA. Species diversity. in Encyclopediaof Environmetrics. Chichester: John Wiley & Sons 2002.

Kendall MG, Gobbons JD. Rank Correlation Methods (5th edition). London:Arnold 1990.

Keuls M. The use of'Studentized range' in connection with analysis ofvariance. Euphytica 1952;1:112-122.

Keyfitz N. Sampling variance of standardized mortality rates. Human Biology 1966;38:309-317.

Kim PJ, Jennrich RI. Tables of the exact sampling distribution of thetwo-sample Kolmogorov-Smirnov criterion. in Selected Tables in Mathematical Statistics (Vol 1). Providence:American Mathematical Society 1973.

Kleinbaum DG, et al. Applied Regression Analysis and OtherMultivariable Methods (3rd edition). DuxburyPress 1998.

Knusel L. Computation of the Chi-square and Poisson distribution. SIAMJournal on Scientific and Statistical Computing 1986;7:1022-1036.

Knuth DE. The Art ofComputer Programming : SeminumericalAlgorithms (Art of Computer Programming, Vol 2,2nd edition). Reading, Massachusetts: Addison Wesley 1997.

Knuth DE. The Art ofComputer Programming : Sorting and Searching (Artof Computer Programming, Vol 3, 2nd edition). Reading, Massachusetts:Addison Wesley 1998.

Koehler KJ, Larnz K. An empirical investigation ofgoodness-of-fit statistics for sparse multinomials.Journal of the American Statistical Associ執(zhí)業(yè)藥師ation 1980;75:336-344.

Koopman PAR. Confidence limits forthe ratio of two binomial proportions. Biometrics 1984;40:513-517.

Krailo MD, Pike MC. Algorithm AS 196.Conditional multivariate logistic analysis of strati?ed case-control studies. Applied Statistics1984;33:95–103.

Krebs CJ. Ecological methodology. New York: Harper Collins 1989.

Kristoff W. Thestatistical theory of stepped-up reliability coefficients when a test has beendivided into several equal parts. Psychometrika1963;28:221-238.

Krzanowski WJ. Principlesof Multivariate Analysis. Oxford: Oxford UniversityPress 1988.

LancasterHO. Significance tests in discrete distributions. Journal of the AmericanStatistical Association 1961;56:223-234.

Landis JR,Koch G. The measurement of observer agreement for categoricaldata. Biometrics 1977;33:159-174.

Landis JR, Heyman ER, Koch GG. Average partial association in threeway contingency tables: A review and discussion of alternative tests. InternationalStatistical Reviews 1978;46:237-254.

Landis JR, MurrayM, Cooper TK, Koch GG. Acomputer program for testing average partial association in three-waycontingency tables (PARCAT). Computer Programs in Biomedicine1979;6:196-231.

Last JM. ADictionary of Epidemiology (4th Edition). New York: Oxford University Press 2000.

Laupacis A, Sackett DL, Roberts RS. An assessment of clinically useful measures ofthe consequences of treatment. New EnglandJournal of Medicine 1988;318(26):1728-1733.

Lawless JF. StatisticalModels and Methods for Lifetime Data. New York: Wiley 1982.

Le CT. Applied survival analysis. New York:John Wiley and Sons 1997.

Lenth RV. AS 243, Cumulative distribution function of the non-central t-distribution. AppliedStatistics 1989;38(1).

Leung HM, Kupper LL. Comparison ofconfidence intervals for attributable risk. Biometrics1981;37:293-302.

Liddell FDK.Simplified exact analysis of case-referent studies; matchedpairs; dichotomous exposure. Journal of Epidemiology and Communwww.med126.comityHealth 1983;37:82-84.

Longley JW.An appraisal of least squares programs for the electronic computer from thepoint of view of the user. Journal of the American Statistical Association1967;62:819-841.

Lorenz MO. Methods for measuring the concentration ofwealth. Journal of the American StatisticalAssociation 1905;9:209-219.

Lund RE, Lund JR. AS 190, Probabilities and Upper Quantiles for the StudentizedRange. Applied Statistics 1983;34.

Macleod AJ.AS 245, A Robust and Reliable Algorithm for theLogarithm of the Gamma Function. Applied Statistics 1989;38(2).

Majumder KL, BhattcharjeeGP. AS 63, The Incomplete Beta Integral. AppliedStatistics 1973;22(3).

Majumder KL, BhattcharjeeGP. AS 64, Inverse of the Incomplete Beta Function Ratio.Applied Statistics 1973;22(3).

Makuch RW, Escobar M. AS 262, A Two Sample Test for Incomplete Multivariate Data. AppliedStatistics 1991;40(1).

Mantel N, Haenszel W. Statistical aspectsof the analysis of data from retrospective studies. Journal of the National Cancer Institute 1959;22:719-748.

Marsaglia G. Monkey tests for randomnumber generators. Computers and Mathematics with Applications 1993;26:1-10.

Marsaglia G. DIEHARD: A battery oftests of randomness. 1997: http://stat.fsu.edu/pub/diehard.

Martin DO,Austin H. An efficient program for computing conditionalmaximum likelihood estimates and exact confidence limits for a common oddsratio. Epidemiology 1991;2(5):359-62.

Martin DO,Austin H. Exact estimates for a rate ratio. Epidemiology 1996;7(1):29-33.

Martin DO,Austin H. An exact method for meta-analysis of case-controland follow-up studies. Epidemiology 2000;11(3):255-260.

Matsumoto M,Nishimura T. Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator. ACMTransactions on Modelling and Computer Simulation1998; 7(1):3--30.

Maxwell A E, Comparing the classification of subjects by twoindependent judges. British Journal ofPsychiatry 1970;116:651-5.

McClave JT, DeitrichFH. Statistics (5th edition). Macmillan 1991.

McCulloughBD, Wilson B. On the accuracy of statistical procedures inMicrosoft Excel 97. Computational Statistics and Data Analysis1999;31:27-37.

McCullagh P, NelderJA. Generalised Linear Models (2nd edition).Chapman and Hall 1989.

McDowell I,Newell C. Measuring Health: a guide to rating scales and questionnaires(2nd edition). Oxford University Press 1996.

McGraw KO,Wong SP. Forming inferences about some intraclasscorrelation coefficients. Psychological Methods 1996;1(1):30-46.

Mee RW. Confidence bounds for thedifference between two probabilities. Biometrics 1984;40:1175-1176.

Mehta CR,Patel NR. A network algorithm for performing Fisher's exacttest in r c contingency tables. Journal of the American StatisticalAssociation 1983;78:427-34.

Mehta CR,Patel NR. Algorithm 643: FEXACT: A FORTRAN subroutine for Fisher's exact teston unordered r x c contingency tables. ACM Transactions on MathematicalSoftware 1986;12:154-61.

Mehta CR,Patel NR. A hybrid algorithm for Fisher's exact test inunordered r x c contingency tables. Communications in Statistics,Series A 1986;15:387-404.

Meinert CL. Clinical Trials:Design, Conduct and Analysis. New York: Oxford UniversityPress 1986.

Menard S. Applied logistic regression analysis (2nd edition). Thousand Oaks, CA: Sage Publications, 1995.

Metz CE. Basic principles of ROC analysis. Seminars in NuclearMedicine 1978;8:283-98.

MicrosoftCorporation. Visual Studio,1999.

Miettinen OS, NurminenM. Comparative analysis of two rates. Statistics in Medicine 1985;4:213-226.

Miller JR. SurvivalAnalysis. New York:Wiley 1981.

Miller RG (jnr.). Simultaneous Statistical Inference (2ndedition). Springer-Verlag 1981.

Mills JA, Zandvakili A. Statistical inference via bootstrapping formeasures of inequality. Journal of Applied Econometrics 1997;12:133-150.

Mood AM, Graybill FA, Boes DC.Introduction to the Theory of Statistics (3rd edition). New York: McGraw-Hill1973.

Morris AH. Algorithm 708, Incomplete Beta Function. Transactions on Mathematical Software 1992;18(3):360-373.

Mortimer D. Laboratory standards in routine clinical andrology. ReproductiveMedicine Reviews 1994;3:97-111.

Mortimer D.Quality management in the IVF laboratory (52), in Towards ReproductiveCertainty: Fertility and Genetics Beyond 1999: Proceedings of the 11th WorldCongress on In Vitro Fertilization and Human Reproductive Genetics (JansenR, Mortimer D. eds.). CRC Press-Parthenon 1999.

Mouillot D, LeprêtreA. A comparison of species diversity estimators.Researches on Population Ecology 1999;41:203-215.

Mulrow CD, OxmanAD (eds). CochraneCollaboration Handbook. Oxford:Cochrane Collaboration 1996.

Nayak TK. Ondiversity measures based on entropy functions. Communications inStatistics - Theory and Methods 1985;141:203-215.

Nelson W.Theory and applications of hazard plotting for censored failure data. Technometrics 1972;14:945-966.

Neumann N. Some Procedures for Calculating the Distributions of ElementaryNon-parametric Test Statistics. Statistical Software Newsletter1988;14(3).

Newcombe R. Improved confidenceintervals for the difference between binomial proportions based on paired data.Statistics in Medicine 1998;17:2635-2650.

Newcombe R. Interval estimation for the difference between independentproportions. Statistics in Medicine 1998;17:873-890.

Newcombe R. Two sidedconfidence intervals for the single proportion: a comparative evaluation ofseven methods. Statistics in Medicine 1998;17:857-872.

Newman SC. BiostatisticalMethods in Epidemiology. New York: John Wiley & Sons 2001.

Newman D. The distribution of rangein samples from a normal population, expressed in terms of an independentestimate of standard deviation. Biometrika1939;31:20-30.

Nikiforov AM. Algorithm AS 288, ExactSmirnov two-sample tests for arbitrary distributions. Applied Statistics1994;43(1):265-284.

Odeh RE, Evans JO. AS70, Percentage Points of the Normal Distribution. Applied Statistics1974;23.

Owen DB. A special case of the bivariatenon-central t-distribution. Biometrika1965;52:437-446.

Pan American Health Organisation. Measuring Health Inequalities: Gini Coefficient and Concentration Index. EpidemiologicalBulletin of PAHO 2001;22(1):3-4. http://www.paho。org/English/SHA/EB_v22n1.pdf

Pardo L, Morales D, Salicrú M, Menedéz ML. Largesample behaviour of entropy measures when parametersare estimated. Communications in Statistics - Theory and Methods 1997;26(2):483-501.

Pearson & Hartley. Biometrika tables for statisticians (VolumesI and II, 3rd edition). Cambridge UniversityPress 1970.

Peterson AV Jr.. Expressing the Kaplan-Meier estimatoras a function of empirical subsurvival functions.Journal of the American Statistical Association 1977;72:854-858.

Peto R, Pike MC, ArmitageP, Breslow NE, Cox DR, Howard SV, Mantel N, McPhersonK, Peto J, Smith PG. Design and analysis ofrandomized clinical trials requiring prolonged observation of each patient. PartI: Introduction and design. British Journal of Cancer 1976;34:585-612.

Peto R, Pike MC, ArmitageP, Breslow NE, Cox DR, Howard SV, Mantel N, McPhersonK, Peto J, Smith PG. Design and analysis ofrandomized clinical trials requiring prolonged observation of each patient.Part II: Analysis and Examples. British Journal of Cancer 1977;35:1-39.

Peto R, PetoJ. Asymptotically efficient rank invariant procedures. Journal of the RoyalStatistical Society 1972;A135:185-207.

Petrie A. Lecture Notes on Medical Statistics. Blackwell Scientific Publications 1990.

Pike MC,Hill ID. Algorithm 291, Logarithm of the Gamma Function.Communications of the Association for Computing Machinery 1966;9:684.

Pregibon D. Logistic Regression Diagnostics. Annalsof Statistics 1981;9:705-724.

Press WH,et al. Numerical Recipes, The Art of ScientificComputing (2nd edition). Cambridge UniversityPress 1992.

Rice JA. MathematicalStatistics and Data Analysis (2nd edition). Belmont, California:Duxbury Press 1995.

Robins J, Breslow N, Greenland S. Estimators of the Mantel-Haenszel variance consistent in both sparse data and largestrata models. Biometrics 1986;42:311-323.

Ross JG. NonLinearEstimation. Springer-Verlag New York1990.

Rothman KJ,Monson RR. Survival in trigeminal neuralgia. Journalof Chronic Diseases 1973;26:303-9.

Rothman KJ, Greenland S. Modern Epidemiology (2nd edition). Philadelphia:Lippincott-Raven 1998.

Royston JP. AS 177, Expected Normal OrderStatistics (Exact and Approximate). Applied Statistics 1982;31(2):161-165.

Royston JP.AS 181, The W Test for Normality. AppliedStatistics 1982;31(2):176-180.

Royston JP. AS R94, Shapiro-Wilk normality test andP-value. Applied Statistics 1995;44(4).

Royston JP. R69 further to AS 190. Applied Statistics1987.

Rowntree D. Statistics without tears. London: Penguin 1991.

Sackett DL, et al.Interpretation of diagnostic data (5). Canadian Medical Association Journal1983;129:947-975.

Sackett DL, et al. ClinicalEpidemiology: A Basic Science for Clinical Medicine. Boston: Little, Brown & Co. 1991.

Sackett DL. On someclinically useful measures of the effects of treatment. Evidence-BasedMedicine 1996 Jan-Feb;1:37.

Sackett DL, DeeksJJ, Altman DG. Down with odds ratios! Evidence Based Medicine 1996Sept-Oct;1:164.

Sahai H, KurshidA. Statistics in epidemiology: methods techniques and applications. CRC Press 1996.

Samra B, RandlesRH. A test for correlation based on Kendall's tau. Communications in Statistics - Theory and Methods1988;17:3191-3205.

Sato T.Confidence limits for the common odds ratio based on the asymptoticdistribution of the Mantel-Haenszel estimator. Biometrics1990;46:71-80.

Schlesselman JJ. Case-ControlStudies. New York: Oxford UniversityPress 1982.

Schoenfeld DA, Richter JR. Nomograms for calculating the number of patients needed fora clinical trial with survival as endpoint. Biometrics 1982;38:163-70.

Scott WA.Reliability of Content Analysis: The Case of Nominal Scale Coding. PublicOpinion Quarterly 1955;19:321-325.

Selvin S. Statistical Analysis of Epidemiologic Data (2nd edition). Oxford: Oxford UniversityPress 1996.

Sen A. OnEconomic Inequality. Oxford:Clarendon Press 1973.

Senn S. Cross-over trials in clinical research. John Wiley 1993.

Shannon CE. A mathematical theory of communications. Bell SystemsTechnical Journal 1948;27:379-423.

Shapiro SS, Wilk MB. An analysis of variance test for normality.Biometrika 1965;52(3):591-9.

Sharp SJ,Thompson SG, Altman DG. The relation between treatmentbenefit and underlying risk in meta-analysis. British Medical Journal1996;313:735-738.

Shea BL. AS239, Chi-square and incomplete gamma integral. Applied Statistics 1988;37(3):466-73.

Shea BL.Remark AS R77 on algorithm AS 152, Cumulative hypergeometricprobability. Applied Statistics 1989;38(1):199-204.

Simpson EH. Themeasurement of diversity. Nature 1949;163:688.

Smeeth L, Haines A, Ebrahim S. Numbers needed to treat derived frommeta-analyses - sometimes informative, usually misleading. British MedicalJournal 1999;318:1548-1551.

Smith EP. Ecological statistics. in Encyclopediaof Environmetrics. Chichester: John Wiley & Sons 2002.

Smith GD, Egger M, Philips AN. Meta-analysis. Beyond the grand mean? British MedicalJournal 1997;315:1610-1614.

Smith PG, Pike MC, Hill P, Breslow NE,Day NE. Algorithm AS 162. Multivariateconditional logistic analysis of stratum-matched case-control studies. AppliedStatistics 1981;30:190–197.

Snedecor G, Cochran W, Cox D. Statistical Methods (8th edition). The Iowa State UniversityPress 1989.

Song F.Exploring heterogeneity in meta-analysis: Is the L'Abbéplot useful? Journal of Clinical Epidemiology 1999;52(8):725-730.

Stampfer MJ, et al. A prospective study of postmenopausal hormones and coronary heartdisease. New England Journalof Medicine 1985;313:1044-49.

Sterne JAC, Gavaghan D, Egger M. Publicationand related bias in meta-analysis: Power of statistical tests and prevalence inliterature. Journal of Clinical Epidemiology 2000;53:1119-1129.

Sterne JAC,Egger M, Davey-Smith G. Investigating and dealingwith publication and other biases in meta-analysis. British Medical Journal2001;323:101-105.

Sterne JAC,Egger M. Funnel plots for detecting bias in meta-analysis: Guidelines on choiceof axis. Journal of Clinical Epidemiology 2001;54:1046-1055.

Sterne JAC, Jüni P, Schulz KF, Altman DG,Bartlett C, Egger M. Statistical methods for assessing the influence of studycharacteristics on treatment effects in 'meta-epidemiological' research. Statistics in Medicine 2002;21:1513-1524.

Streiner D, Norman G. HealthMeasurement Scales: a practical guide to their development and use (2ndedition). Oxford University Press 1995.

Stuart A, Ord JK. Kendall'sAdvanced Theory of Statistics (6th edition). London: Edward Arnold 1994.

Tarone RE, Ware J. Ondistribution-free tests for equality of survival distributions. Biometrika 1977;64:165-60.

Thomas DG.AS 36, Exact Confidence Limits for the Odds Ratio in a Two by Two Table. AppliedStatistics 1971;20(1).

Thomas GE. Remark R30 on AS76, an integral useful in calculating non-central tand bivariate normal probabilities. AppliedStatistics 1979;28(1):113.

Thompson SG,Smith TC, Sharp SJ. Investigating underlying risk as a sourceof heterogeneity in meta-analysis. Statistics in Medicine 1997;16:2741-58.

Thompson SG,Higgins JPT. How should meta-regression analyses be undertaken and interpreted?Statistics in Medicine 2002;21:1559-1573.

Tukey JW. Exploratory DataAnalysis, Reading, Mass.: Addison-Wesley 1974.

Ulm K. A simple method to calculate the confidence interval of astandardized mortality ratio. American Journal of Epidemiology1990;131(2):373-375.

Verrill S, Johnson A. Tables and Large Sample Distribution Theory forCensored Data Correlation Statistics for Testing Normality. Journal of the American Statistical Association 1988;83:1192-1197.

Vives S, SalicrúM, Ocaña J. Confidence intervals for diversityindexes. Proceedings of IV St PetersbourgWorkshop on Simulation. St Petersburg:St Petersburg University Publishers. 2001:492-499.

Vollset SM. Confidence intervals for a binomial proportion. Statistics in Medicine 1993;12:809-824.

WallensteinS. Some statistical methods useful in circulation research.Circulation Research 1980;47(1).

Walter SD. Calculation of attributable risks from epidemiologicdata. International Journal of Epidemiology1978;7:175-82.

Ware JH.Linear models for the analysis of longitudinal studies. The AmericanStatistician 1985;39:95-101.

Wei LJ, Johnson WE. Combining dependent tests with incomplete repeated measurements.Biometrika 1985;72:359-364.

Wei LJ, Lachin JM. Two SampleAsymptotically Distribution Free Tests for Incomplete MultivariateObservations. Journal of the American StatisticalAssociation. 1984;79:653-661.

Weisberg HF. Central Tendency and Variability. Newbury Park, CA: Sage Publications 1992.

Wetherill GB. IntermediateStatistical Methods. Chapman Hall 1981.

Wolfson M. Health-adjusted life expectancy. HealthReports 1996;8(1):41-5.

Young JC,Minder CE. AS76, An integral useful in calculatingnon-central t and bivariate normal probabilities.Applied Statistics 1974;23(3):455-457.

Yusuf S, Peto R, Lewis J, ColinsR, Sleight P. Beta blockade during and after myocardial infarction. An overview of randomized trials. Progressin Cardiovascular Disease 1985;27:335-371.

Zhou X, Obuchowski NA, McClishDK. Statistical Methods in Diagnostic Medicine. New York:Wiley 2002.

Zwick R. Another look at inter-rateragreement. Psychological Bulletin 1988;103:374-378.

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Glossary ofsymbols and abbreviations

y^x

y to the power of x (also seen as y**x or )

^Key

Ctrl + another Key

/

divided by (see calculator for operator precedence)

*

multiplied by (see calculator for operator precedence)

abs(x), |x|

absolute value of x without regard to sign

alpha, a

significance level of a hypothesis test (also type I error rate). 1-a is the level of the confidence interval

ANOVA

analysis of variance

beta, b

type II error rate (1-power)

CI

confidence interval, see confidence intervals

df

degrees of freedom

e

base of natural logarithms (2.718281...)

!k

factorial , in simplest terms factorial of k is the product of all integers from 1 to k, with 0! defined as 1, a fuller definition relates factorial to the gamma function as gamma (k+1) which enables the calculation of fractional factorials

ln(x)

natural (base e) logarithm of x, the natural logarithm of x is the value of y such that x is equal to the e constant raised to the power of y, remember that ln(1) = 0, ln(0) = minus infinity and ln(a/b)=ln(a)-ln(b), see also transformations

MS

mean square

µ

mean of a population - see also

n

sample size (population sized is usually referred to as N)

P

probability of the data (or more extreme data) arising by chance, see P values

p

proportion of a sample with a given characteristic

q hat, the hat symbol above the q means "estimate of"

r

Pearson's product moment correlation coefficient

SD

standard deviation (of a sample, s = SQR(VAR)) - a measure of variability around the mean - Greek lower case sigma (s) is used for population standard deviation.

SE

standard error (of sample mean, ) - a measure of uncertainty of the estimate of a statistic (e.g. sample mean) and used to derive confidence intervals for the population value of the statistic

sqr(x)

square root of x, equivalent to

sum of all (1 to n) x values

product of all (1 to n) x values (x1 * x2 * x3 etc.)

VAR

variance (of the mean, ), greek s² for populations and s² for samples

vs.

versus

x

individual value from a population or sample

x bar (bar symbol above the x denotes mean) is a sample mean (arithmetic mean, ), see also m

Z, N

standardized normal deviate (from standard normal distribution)

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