Regresión logística no condicionada y tamaño de muestra: una revisión bibliográfica

La regresion logistica no condicionada es un metodo de prediccion de riesgo muy util en epidemiologia. En este articulo revisamos las diferentes soluciones que han dado diversos autores sobre la interfase entre el calculo del tamano muestral y la utilizacion de la regresion logistica. A partir del conocimiento de las primeras aportaciones, se revisan los fenomenos de regresion a la media y de la constriccion predictiva, el diseno de una exposicion ordinal con una salida binaria, el concepto de evento de interes por variable, las variables indicadoras, la formula clasica de Freeman, etc. Recogemos tambien algunas ideas escepticas sobre este tema.

[1]  Day Ne,et al.  A GENERAL MAXIMUM LIKELIHOOD DISCRIMINANT , 1967 .

[2]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[3]  C. Stein.  Inadmissibility of the Usual Estimator for the Mean of a Multivariate Normal Distribution , 1956 .

[4]  P. Peduzzi,et al.  Section 2 Design and baseline characteristics , 1985 .

[5]  Lawrence L. Kupper,et al.  How Appropriate are Popular Sample Size Formulas , 1989 .

[6]  N. Mantel.  Synthetic retrospective studies and related topics. , 1973, Biometrics.

[7]  S. le Cessie,et al.  Testing the fit of a regression model via score tests in random effects models. , 1995 .

[8]  W D Dupont,et al.  Power calculations for matched case-control studies. , 1988, Biometrics.

[9]  A. Chiarelli,et al.  Methodological issues in cohort studies. II: Power calculations. , 1988, International journal of epidemiology.

[10]  C. S. Davis,et al.  A test of the missing data mechanism for repeated categorical data. , 1993, Biometrics.

[11]  S A Hendricks,et al.  Power determination for geographically clustered data using generalized estimating equations. , 1996, Statistics in medicine.

[12]  J. Cornfield.  Joint dependence of risk of coronary heart disease on serum cholesterol and systolic blood pressure: a discriminant function analysis. , 1962, Federation proceedings.

[13]  A. Tsiatis.  A note on a goodness-of-fit test for the logistic regression model , 1980 .

[14]  S. Zeger,et al.  Longitudinal data analysis using generalized linear models , 1986 .

[15]  Stanley Lemeshow,et al.  Adequacy of Sample Size in Health Studies , 1990 .

[16]  J E White,et al.  A two stage design for the study of the relationship between a rare exposure and a rare disease. , 1982, American journal of epidemiology.

[17]  D. Bloch,et al.  A simple method of sample size calculation for linear and logistic regression. , 1998, Statistics in medicine.

[18]  J. C. van Houwelingen,et al.  A goodness-of-fit test for binary regression models, based on smoothing methods , 1991 .

[19]  F. Hsieh,et al.  Sample size tables for logistic regression. , 1989, Statistics in medicine.

[20]  David Pee,et al.  Return to a Note on Screening Regression Equations , 1989 .

[21]  D. Hosmer,et al.  A comparison of goodness-of-fit tests for the logistic regression model. , 1997, Statistics in medicine.

[22]  B. Everitt,et al.  Statistical methods for rates and proportions , 1973 .

[23]  L P Zhao,et al.  Designs and analysis of two-stage studies. , 1992, Statistics in medicine.

[24]  K Y Liang,et al.  Longitudinal data analysis for discrete and continuous outcomes. , 1986, Biometrics.

[25]  E. S. Ramírez.  Regresión logística en salud pública , 2000 .

[26]  D Schaubel,et al.  Two-stage sampling for etiologic studies. Sample size and power. , 1997, American journal of epidemiology.

[27]  F. Harrell,et al.  Regression models for prognostic prediction: advantages, problems, and suggested solutions. , 1985, Cancer treatment reports.

[28]  N. Breslow,et al.  Statistical methods in cancer research. Vol. 1. The analysis of case-control studies. , 1981 .

[29]  The logistic analysis of epidemiologic prospective studies: investigation by simulation. , 1984, Statistics in medicine.

[30]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[31]  Susan R. Wilson,et al.  Calculating Sample Sizes in the Presence of Confounding Variables , 1986 .

[32]  V. Flack,et al.  Sample size determinations using logistic regression with pilot data. , 1993, Statistics in medicine.

[33]  J. Rochon.  The application of the GSK method to the determination of minimum sample sizes. , 1989, Biometrics.

[34]  A Donner,et al.  Methods for comparing event rates in intervention studies when the unit of allocation is a cluster. , 1994, American journal of epidemiology.

[35]  R. L. Prentice,et al.  Retrospective studies and failure time models , 1978 .

[36]  Luis Carlos Silva Ayçaguer.  Excursión a la regresión logística en ciencias de la salud , 1994 .

[37]  J. Concato,et al.  Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. , 1995, Journal of clinical epidemiology.

[38]  David R. Cox.  The analysis of binary data , 1970 .

[39]  J Whitehead,et al.  Sample sizes for phase II clinical trials derived from Bayesian decision theory. , 1994, Statistics in medicine.

[40]  Steven G. Self,et al.  Power/Sample Size Calculations for Generalized Linear Models , 1988 .

[41]  A. M. Walker,et al.  Anamorphic analysis: sampling and estimation for covariate effects when both exposure and disease are known. , 1982, Biometrics.

[42]  P Peduzzi,et al.  Importance of events per independent variable in proportional hazards analysis. I. Background, goals, and general strategy. , 1995, Journal of clinical epidemiology.

[43]  N. Breslow,et al.  Statistical methods in cancer research: volume 1- The analysis of case-control studies , 1980 .

[44]  P. Simpson,et al.  Statistical methods in cancer research , 2001, Journal of surgical oncology.

[45]  Chap T. Le,et al.  Applied Categorical Data Analysis , 1998 .

[46]  J. Cornfield,et al.  A multivariate analysis of the risk of coronary heart disease in Framingham. , 1967, Journal of chronic diseases.

[47]  Luis Carlos Silva Ayçaguer.  Diseño razonado de muestras y captación de datos para la investigación sanitaria , 2000 .

[48]  S Greenland,et al.  On sample-size and power calculations for studies using confidence intervals. , 1988, American journal of epidemiology.

[49]  J. C. Barrera,et al.  Adopción de los nuevos medicamentos por los médicos prescriptores. El médico innovador , 2000 .

[50]  Strother H. Walker,et al.  Estimation of the probability of an event as a function of several independent variables. , 1967, Biometrika.

[51]  J. Copas,et al.  Using regression models for prediction: shrinkage and regression to the mean , 1997, Statistical methods in medical research.

[52]  P. Royston.  Estimating departure from normality. , 1991, Statistics in medicine.

[53]  K C Cain,et al.  Logistic regression analysis and efficient design for two-stage studies. , 1988, American journal of epidemiology.

[54]  A. Iliadis,et al.  A limited sampling strategy for the study of pirarubicin pharmacokinetics in humans , 2004, Cancer Chemotherapy and Pharmacology.

[55]  M Reilly,et al.  Optimal sampling strategies for two-stage studies. , 1996, American journal of epidemiology.

[56]  Alice S. Whittemore,et al.  Sample Size for Logistic Regression with Small Response Probability , 1981 .

[57]  S B Bull.  Sample size and power determination for a binary outcome and an ordinal exposure when logistic regression analysis is planned. , 1993, American journal of epidemiology.

[58]  J. Concato,et al.  A simulation study of the number of events per variable in logistic regression analysis. , 1996, Journal of clinical epidemiology.

[59]  D. Mauricio,et al.  Hipovitaminosis D, alta prevalencia en ancianos de Barcelona atendidos ambulatoriamente. Factores asociados , 1999 .

[60]  E W Steyerberg,et al.  Stepwise selection in small data sets: a simulation study of bias in logistic regression analysis. , 1999, Journal of clinical epidemiology.

[61]  S. Greenhouse,et al.  Validity in estimating relative risk in case-control studies. , 1973, Journal of chronic diseases.

[62]  M. C. Jones,et al.  On the Robustness of Shrinkage Predictors in Regression to Differences between Past and Future Data , 1986 .

[63]  P. Peduzzi,et al.  Veterans Administration Cooperative Study of medical versus surgical treatment for stable angina--progress report. Section 2. Design and baseline characteristics. , 1985, Progress in cardiovascular diseases.