Some interesting statistics-related articles and links (in no particular order)
(Last updated: 10/18/2016)
Marginally Significant Effects as Evidence for Hypotheses: Changing Attitudes Over Four Decades, blog post by Andrew Gelman (10/15/16)
L Breiman (2001). Statistical Modeling: The Two Cultures. Statistical Science, 16(3): 199-231.
JM Hoenig & DM Heisey (Feb 2001). The Abuse of Power: The Pervasive Fallacy of Power Calculations
for Data Analysis. The American Statistician, 55(1): 19-24.
N Schenker & JF Gentleman (2001). On Judging the Significance of Differences by Examining the Overlap Between Confidence Intervals. The American Statistician, 55(3): 182-186.
R Harper & B Reeves (1999). Reporting of precision of estimates for diagnostic accuracy: a review. British Medical Journal, 318(7194): 1322.
KJ Rothman (1990). No Adjustments Are Needed for Multiple Comparisons. Epidemiology, 1(1): 43-46.
S Nakagawa & IC Cuthill (2007). Effect size, confidence interval and statistical significance: a practical guide for biologists. Biological Reviews, 82(4): 591-605.
G Firebaugh & JP Gibbs (1985). User's guide to ratio variables. American Sociological Review, 50(5): 713-722.
RA Kronmal (1993). Spurious Correlation and the Fallacy of the Ratio Standard Revisited. Journal of the Royal Statistical Society, 156(3): 379-392.
Web appendix to An R and S-PLUS Companion to Applied Regression by John Fox (2002)
Explanation of the conditional probability fallacy at Wikipedia
Explanation of the ecological fallacy at Wikipedia and an explanation (pdf) by David Freedman
Explanation of the prosecutor's fallacy at Wikipedia
AG Barnett, JC van der Pols & AJ Dobson (2005). Regression to the mean: what it is and how to deal with it. International Journal of Epidemiology, 34(1): 215-220.
Explanation of regression to the mean at Wikipedia
JL Schafer (1999). Multiple imputation: a primer. Statistical Methods in Medical Research, 8(1): 3-15.
PD Allison (2000). Multiple Imputation for Missing Data: A Cautionary Tale. Sociological Methods & Research, 28(3): 1-309.
The Statistics QA page of Stata
For example,
- Calculating power by simulation in Stata, by Alan Feiveson (2009)
- Comparison of standard errors for robust, cluster, and standard estimators, by William Sribney, StataCorp (2009)
- A comparison of different tests for trend, by William Sribney, StataCorp (1996)
- Endogeneity versus sample selection bias, by Daniel Millimet (2001)
What to call the exponentiated coefficients from a multinomial logistic regression model, by Roberto G. Gutierrez, StataCorp (2005)
Can the standard deviation be more than half of the range?
The trouble with calculating p-values for estimates from mixed effects models
Top 10 Worst Graphs
Johns Hopkins Biostatistics Computing Club website
Let's stop attaching Word documents.
R Peng, F Dominici & SL Zeger (2006). Reproducible Epidemiologic Research. American Journal of Epidemiology, 163(9): 783-789.
Some documents by Roger Peng:
- Some debugging tools in R (pdf)
- calling C code in R (pdf)
- Some slides on classes/methods and lexical scoping in R (pdf)
- Reading large tables into R.
GJ Van Breukelen (2006). ANCOVA versus change from baseline had more power in randomized studies and more bias in nonrandomized studies. Journal of Clinical Epidemiology, 59(9): 920-5.
AJ Vickers & DG Altman (2001). Analysing controlled trials with baseline and follow up measurements. British Medical Journal, 323: 1123-4.
S Greenland (1980). The Effect of Misclassification in the presence of Covariates. American Journal of Epidemiology, 112(4): 564-569.
S Greenland & JM Robbins (1985). Confounding and Misclassification. American Journal of Epidemiology, 122(3): 495-506.
L Friedman & M Wall (2005). Graphical Views of Suppression and Multicollinearity in Multiple Linear Regression. The American Statistician, 59(2): 127-136.
E Vittinghoff & CE McCulloch (2006). Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression. American Journal of Epidemiology, 165(6): 710-718.
Phipson, B & Smyth, GK (2010). Permutation P-values Should Never Be Zero: Calculating Exact P-values When Permutations Are Randomly Drawn. Statistical Applications in Genetics and Molecular Biology, 9(1).
Problems Caused by Categorizing Continuous Variables (at Frank Harrell's wiki page).
DG Altman, B Lausen, W Sauerbrei & M Schumacher (1994). Dangers of using "optimal" cutpoints in the evaluation of prognostic factors. Journal of the National Cancer Institute, 86(11): 829-35.
P Royston, DG Altman & W Sauerbrei (2005). Dichotomizing continuous predictors in multiple regression: a bad idea. Statistics in Medicine, 25(1): 127-141.
JJ Deeks & DG Altman (1999). Sensitivity and specificity and their confidence intervals cannot exceed 100%. BMJ, 318(7177): 193.
KGM Moons, P Royston, Y Vergouwe, DE Grobbee, & DG Altman (2009). Prognosis and prognostic research: what, why, and how?. BMJ, 338:b375.
P Royston, KGM Moons, DG Altman, & Y Vergouwe (2009). Prognosis and prognostic research: Developing a prognostic model. BMJ, 338:b604.
DG Altman, Y Vergouwe, P Royston, & KGM Moons (2009). Prognosis and prognostic research: validating a prognostic model. BMJ, 338:b605.
KGM Moons, DG Altman, Y Vergouwe, & P Royston (2009). Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ, 338:b606.
P > 0.05? I can make any p-value statistically significant with adaptive FDR procedures. 8/19/15 post by Jeff Leek on the blog Simply Statistics. (Great explanation for why, among other things, a "multiple testing adjusted" p-value (or q-value) is sometimes smaller than the p-value itself)