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Medical Citations for Statistical Issues

General: ¹ ² ³ ⁴ ⁵ ⁶
Graphics: ⁷ ⁸ ⁹ ¹⁰ ¹¹ ¹² ¹³ ¹⁴ ¹⁵ ¹⁶ ¹⁷ ¹⁸ ¹⁹ ²⁰ ²¹ ²²
Confidence Intervals: ²³ ²⁴ ²⁵ ²⁶ ²⁷ ²⁸ ²⁹ ³⁰
Statistical Significance: ³¹ ²⁶ ³² ³³ ³⁰
Sample Size Determination: ³⁴
Reporting of Clinical Trials: ³⁵ ³⁶ ³⁷ ³⁸ ³⁹ ⁴⁰ ³³ ⁴¹ ⁴² ⁴³ ⁴⁴ ⁴⁵
Generalizability of Clinical Trials: ⁴³ ⁴⁴
Meta-Analysis, Publication Bias: ⁵ (Chapter 11),⁴⁶ ⁴⁷ ⁴⁸ ⁴² ⁴⁹ ⁵⁰ ⁴⁴ ⁵¹ ⁵² ⁵³ ⁵⁴ ⁵⁵
Reporting of Survival Analyses: ⁵⁶ ⁵⁷ ⁵⁸ ⁵⁹
Diagnostic Tests: ⁶⁰ ⁵⁰ ⁶¹ ⁶² ⁶³ ⁶⁴ ⁶⁵ ⁶⁶ ⁶⁷
Subgroup Analysis: ⁴² ⁶⁸ ⁶⁹ ⁴⁹ ⁷⁰
Multivariable Models: ⁷¹ ⁷² ⁷³ ⁷⁴ ⁷⁵ ⁶² ⁵⁷ ⁷⁶ ⁷⁷ ⁵⁸ ⁵⁹ ⁷⁸
Problems from Categorizing a Continuous Variable: ⁷⁹ ⁸⁰ ⁸¹ ⁸² ⁸³ ⁸⁴ ⁸⁵ ⁸⁶ ⁸⁷ ⁸⁸ ⁸⁹ ⁹⁰ ⁹¹ ⁹² ⁹³ ⁹⁴ ⁹⁵
Missing%nbsp;Data: ⁹⁶ ⁹⁷ ⁹⁸
Observational Studies, Retrospective Studies, Bias, Adjustment: ⁹⁹ ⁵⁸ ¹⁰⁰ ⁴⁵
Assessing Equivalence: ¹⁰¹
Measuring Change: ¹⁰² ¹⁰³ ¹⁰⁴ ¹⁰⁵ ¹⁰⁶ ¹⁰⁷
Analysis of Serial (Repeated) Measurements: ¹⁰⁸ ¹⁰⁹ ¹¹⁰

[1]	John C. Bailar III and Frederick editors Mosteller. Medical Uses of Statistics. NEJM Books, Boston, second edition, 1995.Key:bai95med Annotation:teaching statistics to MDs
[2]	John C. Bailar III and Frederick Mosteller. Guidelines for statistical reporting in articles in medical journals. Ann Int Med, 108:266-273, 1988.Key:bai88gui Annotation:teaching statistics to MDs;reporting statistical results
[3]	B. S. Everitt. The Cambridge Dictionary of Statistics in the Medical Sciences. Cambridge University Press, New York, 1995.Key:eve95cam Annotation: glossary;dictionary
[4]	Jane L. Garb. Understanding Medical Research: A Practitioner's Guide. Little, Brown, Boston, 1996.Key:gar96und Annotation:teaching MDs;study design;review in JASA 92:798;1997
[5]	Thomas A. Lang and Michelle Secic. How to Report Statistics in Medicine: Annotated Guidelines for Authors, Editors, and Reviewers. American College of Physicians, Philadelphia, 1997.Key:lan97how Annotation:teaching MDs;statistical review of medical articles;ISBN 0-9431 2644-4
[6]	Douglas G. Altman, Steven N. Goodman, and Sara Schroter. How statistical expertise is used in medical research. JAMA, 287:2817-2820, 2002.Key:alt02how Annotation:`Statistical input to medical research is widely recommended but inconsistently obtained. Individuals providing such expertise are often not involved until the analysis of data and many go unrecognized by either authorship or acknowledgement.`; no association between authorship by methodologist and whether methodologist was paid for her contribution; research without assistance by methodologist had a greater chance of being rejected by the editor (0.71 vs 0.57) and possibly a lower chance of being accepted for publication (0.07 vs 0.11); epidemiologists more likely to be co-authors than biostatisticians;analysis presented of what stage methodologists first got involved with the research
[7]	Peter A. Singer and Alvan R. Feinstein. Graphical display of categorical data. J Clin Epi, 46:231-236, 1993.Key:sin93gra Annotation:graphics; dot charts
[8]	William S. Cleveland. The Elements of Graphing Data. Hobart Press, Summit, NJ, 1994.Key:cle94ele Annotation: graphics
[9]	Howard Wainer. How to display data badly. Am Statistician, 38:137, 1984.Key:wai84how Annotation:graphics; examples of bad graphics
[10]	Howard Wainer. Three graphic memorials. Chance, 7:52-55, 1994.Key:wai94thr Annotation:graphics; Napolean march plot; forest fire plot
[11]	Edward R. Tufte. The Visual Display of Quantitative Information. Graphics Press, Cheshire, Connecticut, 1983.Key:tuf83vis Annotation: graphics
[12]	Edward R. Tufte. Envisioning Information. Graphics Press, Cheshire, Connecticut, 1990.Key:tuf90env Annotation: graphics
[13]	Edward R. Tufte. Visual Explanations. Graphics Press, Cheshire, CT, 1997.Key:tuf97vis Annotation: graphics
[14]	F. J. Anscombe. Graphs in statistical analysis. Am Statistician, 27:17-21, 1973.Key:ans73gra
[15]	William S. Cleveland and Robert McGill. A color-caused optical illusion on a statistical graph. Am Statistician, 37:101-105, 1983.Key:cle83col Annotation:graphics; optical illusion
[16]	William S. Cleveland. Graphs in scientific publications (c/r: 85v39 p238-239). Am Statistician, 38:261-269, 1984.Key:cle84sci
[17]	William S. Cleveland and Robert McGill. Graphical perception: Theory, experimentation, and application to the development of graphical methods. J Am Stat Assoc, 79:531-554, 1984.Key:cle84gra Annotation:graphics; graphical perception; optical illusion
[18]	Leland Wilkinson. The Grammar of Graphics. Springer, New York, second edition, 2005.Key:wil05gra Annotation: graphics
[19]	Anders Wallgren, Britt Wallgren, Rolf Persson, Ulf Jorner, and Jan-Aage Haaland. Graphing Statistics & Data. Sage Publications, Thousand Oaks, 1996.Key:wal96gra Annotation: graphics
[20]	Daniel B. Carr. Designing linked micromap plots for states with many counties. Stat Med, 20:1331-1339, 2001.Key:car01des Annotation:excellent color graphics;showing mortality and changes in mortality over counties
[21]	Andrew Gelman, Cristian Pasarica, and Rahul Dodhia. Let's practice what we preach: Turning tables into graphs. Am Statistician, 56:121-130, 2002.Key:gel02let Annotation:excellent examples of converting tables from JASA into graphs;graphical summary of simulation results;graphics;teaching;data reduction;table;visual display
[22]	Milo A. Puhan, Gerben ter Riet, Klaus Eichler, Johann Steurer, and Lucas M. Bachmann. More medical journals should inform their contributors about three key principles of graph construction. J Clin Epi, 59:1017-1022, 2006.Key:puh06mor Annotation:principles of graphical construction;graphics
[23]	Richard Simon. Confidence intervals for reporting results of clinical trials. Ann Int Med, 105:429-435, 1986.Key:sim86con Annotation:confidence intervals;teaching MDs;reporting clinical trials
[24]	L. E. Braitman. Confidence intervals extract clinically useful information from data. Ann Int Med, 108:296-298, 1988.Key:bra88con Annotation:confidence intervals;teaching MDs
[25]	S. N. Goodman and J. A. Berlin. The use of predicted confidence intervals when planning experiments and the misuse of power when interpreting results. Ann Int Med, 121:200-206, 1994.Key:goo94use Annotation:confidence intervals;teaching MDs;misuse of power
[26]	L. E. Braitman. Confidence intervals assess both clinical significance and statistical significance. Ann Int Med, 114:515-517, 1991.Key:bra91con Annotation:confidence intervals;clinical significance;statistical significance;teaching MDs
[27]	Michael Borenstein. The case for confidence intervals in controlled clinical trials. Controlled Clin Trials, 15:411-428, 1994.Key:bor94cas Annotation:confidence intervals; P values; study design; study interpretation; P-value function; confidence function
[28]	Kenneth J. Rothman. A show of confidence (editorial). NEJM, 299:1362-3, 1978.Key:rot78sho Annotation:advantages of confidence intervals over p-values
[29]	Nathaniel Schenker and Jane F. Gentleman. On judging the significance of differences by examining the overlap between confidence intervals. Am Statistician, 55:182-186, 2001.Key:sch01jud Annotation:problems of relying on overlap of confidence intervals;literature search for improper use
[30]	Stuart J. Pocock and James H. Ware. Translating statistical findings into plain English. 373:1926-1928, 2009.Key:poc09tra Annotation:teaching MDs;nice summary of how to interpret P-values and confidence limits, including an excellent summary graph
[31]	Kenneth J. Rothman. Significance questing. Ann Int Med, 105:445-447, 1986.Key:rot86sig Annotation:statistical significance;teaching MDs
[32]	D. G. Altman and J. M. Bland. Absence of evidence is not evidence of absence. BMJ, 311:485, 1995.Key:alt95abs Annotation: interpretation of negative or nonsignificant findings;teaching MDs
[33]	Lewis B. Sheiner. The intellectual health of clinical drug evaluation. Clin Pharm Ther, 50:4-9, 1991.Key:she91int Annotation:Bayesian inference;hypothesis testing;problems with traditional statistical approaches to drug evaluation;review article;problems with under-emphasis of type II error;compliance
[34]	Russell V. Lenth. Some practical guidelines for effective sample size determination. Am Statistician, 55:187-193, 2001.Key:len01som Annotation:practical guidelines for sample size determination;problems with Cohen's method
[35]	C. Begg, M. Cho, S. Eastwook, R. Horton, D. Moher, I. Olkin, and et al. Improving the quality of reporting of randomized controlled trials. The Consort statement. JAMA, 276:637-639, 1996.Key:beg96imp Annotation:reporting statistical results;reporting clinical trials;RCTs;teaching MDs
[36]	Thomas C. Chalmers, Harry Smith, Bradley Blackburn, Bernard Silverman, Biruta Schroeder, Dinah Reitman, and Alexander Ambroz. A method for assessing the quality of a randomized control trial. Controlled Clin Trials, 2:31-49, 1981.Key:cha81met Annotation:rating quality of studies; RCT
[37]	David C. Hadorn, David Baker, James S. Hodges, and Nicholas Hicks. Rating the quality of evidence for clinical practice guidelines. J Clin Epi, 49:749-754, 1996.Key:had96rat Annotation:study quality;observational studies;bias;rating studies;evidence;clinical practice guidelines
[38]	The Standards of Reporting Trials Group. A proposal for structured reporting of randomized controlled trials. JAMA, 272:1926-1931, 1994.Key:sta94pro Annotation:teaching MDs;RCTs;reporting of RCTs;reporting of statistical results
[39]	The Asilomar Working Group on Recommendations for Reporting of Clinical Trials in the Biomedical Literature. Checklist of information for inclusion in reports of clinical trials. Ann Int Med, 124:741-743, 1996.Key:asi96che Annotation:teaching MDs;RCTs;reporting of RCTs;reporting of statistical results
[40]	Cornelius J. Lynch and Peter A. Lachenbruch. Statistical issues in biologics submissions to the FDA. Drug Info J, 30:921-932, 1996.Key:lyn96sta Annotation:checklist for design and reporting of pharmaceutical studies;study design
[41]	R. M. Califf, K. F. Adams, W. J. McKenna, M. Gheorghiade, B. F. Uretsky, S. E. McNulty, H. Darius, K. Schulman, F. Zannad, E. Handberg-Thurmond, F. E. Harrell, W. Wheeler, J. Soler-Soler, and K. Swedberg. A randomized controlled trial of epoprostenol therapy for severe congestive heart failure: The Flolan International Randomized Survival Trial (FIRST). Am Heart J, pages 44-54, 1997.Key:cal97ran Annotation:reporting results (although several typos);tables with quantiles;box charts;odds ratio chart showing lack of differential treatment effect;results of interim analyses, with stopping boundaries
[42]	Carl E. Counsell, Mike J. Clarke, Jim Slattery, and Peter A. G. Sandercock. The miracle of DICE therapy for acute stroke: fact or fictional product of subgroup analysis? BMJ, 309:1677-1681, 1994.Key:cou94mir Annotation:teaching MDs;subgroup analysis;publication bias;RCT;systematic review;meta-analysis
[43]	Antonio L. Dans, Leonila F. Dans, Gordon H. Guyatt, Scott Richardson, and The Evidence-Based Medicine Working Group. How to decide on the applicability of clinical trial results to your patient. JAMA, 279:545-549, 1998.Key:dan98how Annotation:teaching MDs;applicability of RCT;generalizability
[44]	John P. A. Ioannidis and Joseph Lau. The impact of high-risk patients on the results of clinical trials. J Clin Epi, 50:1089-1098, 1997.Key:ioa97imp Annotation:high risk patients can dominate clinical trials results;high risk patients may be imbalanced even if overall study is balanced;magnesium;differential treatment effect by patient risk;GUSTO;small vs. large trials vs. meta-analysis
[45]	Victor Cohn. A perspective from the press: how to help reporters tell the truth. Stat Med, 20:1341-1346, 2001.Key:coh01per Annotation: reporters;media;nice overview of research;hierarchy of studies;examples of misleading headlines;teaching MDs
[46]	L. A. L'Abbe, A. S. Detsky, and K. O'Rourke. Meta-analysis in clinical research. Ann Int Med, 107:224-233, 1987.Key:lab87met Annotation: meta-analysis;teaching MDs
[47]	H. S. Sacks, J. Berrier, D. Reitman, V. A. Ancona-Berk, and T. C. Chalmers. Meta-analyses of randomized controlled trials. NEJM, 316:450-455, 1987.Key:sac87met Annotation:RCT; meta-analysis; teaching MDs; quality of studies
[48]	S. J. Sharp, S. G. Thompson, and D. G. Altman. The relation between treatment benefit and underlying risk in meta-analysis. BMJ, 313:735-738, 1996.Key:sha96rel Annotation: meta-analysis;regression to the mean;random effects;underlying risk
[49]	Andrew S. Levey, Christopher H. Schmid, and Joseph Lau. Antilymphocyte antibodies, renal transplantation, and meta-analysis. Ann Int Med, 128:863-865, 1998.Key:lev98ant Annotation:teaching MDs;meta-analysis;nice example of benefits of individual vs. aggregate data due to having individual event times and being able to study waning treatment effects;subgroup analysis vs. interaction test;non-PH
[50]	L. Irwig, A. Tosteson, C. Gatsonis, J. Lau, G. Colditz, T. C. Chalmers, and F. Mosteller. Guidelines for meta-analyses evaluating diagnostic tests. Ann Int Med, 120:667-676, 1994.Key:irw94gui Annotation: meta-analysis for diagnostic tests;diagnosis;testing;teaching MDs
[51]	Zachary B. Gerbarg and Ralph I. Horwitz. Resolving conflicting clinical trials: Guidelines for meta-analysis. J Clin Epi, 41:503-509, 1988.Key:ger88res Annotation:RCT; meta-analysis; quality of studies
[52]	Samuel Shapiro. Is meta-analysis a valid approach to the evaluation of small effects in observational studies? J Clin Epi, 50:223-229, 1997.Key:sha97met Annotation: meta-analysis of observational studies;bias;errors don't always cancel
[53]	S. G. Thompson. Why sources of heterogeneity in meta-analysis should be investigated. BMJ, 309:1351-1355, 1994.Key:tho94why Annotation: meta-analysis;heterogeneity;random effects
[54]	Bertram L. Kasiske, John D. A. Lakatua, Jennie Z. Ma, and Thomas A. Louis. A meta-analysis of the effects of dietary protein restriction on the rate of decline in renal function. Am J Kidney Dis, 31:954-961, 1998.Key:kas98met Annotation:excellent example of meta-analysis;covariable modeling on grouped data;multiple imputation;follow-up time as covariable;randomized vs. non-randomized study as covariable
[55]	Sharon-Lise T. Normand. Tutorial in biostatistics: Meta-analysis: Formulating, evaluating, combining, and reporting. Stat Med, 18:321-359, 1999.Key:nor99met Annotation: meta-analysis; teaching MDs; BUGS; mixed models; random effects
[56]	D. G. Altman, B. L. De Stavola, S. B. Love, and K. A. Stepniewska. Review of survival analyses published in cancer journals. Brit J Cancer, 72:511-518, 1995.Key:alt95rev Annotation:reporting survival analysis;teaching MDs;reporting statistical results
[57]	W. A. Knaus, F. E. Harrell, J. Lynn, L. Goldman, R. S. Phillips, A. F. Connors, N. V. Dawson, W. J. Fulkerson, R. M. Califf, N. Desbiens, P. Layde, R. K. Oye, P. E. Bellamy, R. B. Hakim, and D. P. Wagner. The SUPPORT prognostic model: Objective estimates of survival for seriously ill hospitalized adults. Ann Int Med, 122:191-203, 1995.Key:kna95sup Annotation:clinical prediction; prognostic model; spline examples; model validation example
[58]	Kate Bull and David Spiegelhalter. Survival analysis in observational studies. Stat Med, 16:1041-1074, 1997.Key:bul97sur Annotation: teaching;observational study;survival analysis;time origin;informative censoring;logistic models for survival;glossary;warnings in using time-dependent covariables to analysis treatment effects;tutorial
[59]	Amy C. Justice, Kenneth E. Covinsky, and Jesse A. Berlin. Assessing the generalizability of prognostic information. Ann Int Med, 130:515-524, 1999.Key:jus99ass Annotation:model validation;prognosis;generalizability;external validation;validation criteria
[60]	David L. Sackett. A primer on the precision and accuracy of the clinical examination. JAMA, 267:2638-2644, 1992.Key:sac92pri Annotation: diagnosis;testing;reporting of diagnostic tests;teaching MDs
[61]	D. B. Pryor, L. Shaw, C. B. McCants, K. L. Lee, D. B. Mark, F. E. Harrell, L. H. Muhlbaier, and R. M. Califf. Value of the history and physical examination in identifying patients at increased risk for coronary artery disease. Ann Int Med, 118:81-90, 1993.Key:pry93val Annotation:medical logistic model examples with coefficients;examples of incremental diagnostic information;diagnosis
[62]	Alan Spanos, Frank E. Harrell, and David T. Durack. Differential diagnosis of acute meningitis: An analysis of the predictive value of initial observations. JAMA, 262:2700-2707, 1989.Key:spa89
[63]	D. Bamber. The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. J Mathe Psych, 12:387-415, 1975.Key:bam75 Annotation:ROC; c-index; Wilcoxon-Mann-Whitney; discrimination; diagnosis
[64]	J. A. Hanley and B. J. McNeil. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143:29-36, 1982.Key:han82 Annotation: diagnosis;testing;ROC;c index
[65]	M. A. Hlatky, D. B. Pryor, F. E. Harrell, R. M. Califf, D. B. Mark, and R. A. Rosati. Factors affecting the sensitivity and specificity of the exercise electrocardiography. Multivariable analysis. Am J Med, 77:64-71, 1984.Key:hla84fac Annotation: diagnosis;testing;non-constancy of sensitivity and specificity
[66]	Hermann Brenner and Olaf Gefeller. Variation of sensitivity, specificity, likelihood ratios and predictive values with disease prevalence. Stat Med, 16:981-991, 1997.Key:bre97var Annotation: non-constancy of sensitivity and specificity caused by fact that most diseases represent continuous processes
[67]	Irene Guggenmoos-Holzmann and Hans C. van Houwelingen. The (in)validity of sensitivity and specificity. Stat Med, 19:1783-1792, 2000.Key:gug00inv Annotation:severe problems with sensitivity and specificity; diagnosis; testing; teaching MDs;death of sensitivity and specificity
[68]	A. D. Oxman and G. H. Guyatt. A consumer's guide to subgroup analysis. Ann Int Med, 116:78-84, 1992.Key:oxm92con Annotation:subgroup analysis;teaching MDs;multiple comparisons
[69]	S. Yusef, J. Wittes, J. Probstfield, and H. A. Tyroler. Analysis and interpretation of treatment effects in subgroups of patients in randomized clinical trials. JAMA, 266:93-98, 1991.Key:yus91ana Annotation:subgroup analysis;teaching MDs;multiple comparisons
[70]	Hans Wedel, David DeMets, and phet al. Challenges of subgroup analyses in multinational clinical trials: Experiences from the MERIT-HF trial. Am Heart J, 142:502-511, 2001.Key:wed01cha Annotation:caution against overinterpreting negative treatment effect in some subgroups
[71]	J. Concato, A. R. Feinstein, and T. R. Holford. The risk of determining risk with multivariable models. Ann Int Med, 118:201-210, 1993.Key:con93ris Annotation: multivariable modeling;teaching MDs;reporting statistical results
[72]	L. E. Braitman and F. Davidoff. Predicting clinical states in individual patients. Ann Int Med, 125:406-412, 1996.Key:bra96pre Annotation: multivariable modeling;teaching MDs;reporting statistical results
[73]	Frank E. Harrell, Kerry L. Lee, and Daniel B. Mark. Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med, 15:361-387, 1996.Key:har96mul
[74]	Alvan R. Feinstein. Multivariable Analysis. Yale University Press, New Haven, Connecticut, 1996.Key:fei96mul Annotation:teaching; interpretation of parameters
[75]	Andreas Laupacis, Nandita Sekar, and Ian G. Stiell. Clinical prediction rules: A review and suggested modifications of methodological standards. JAMA, 277:488-494, 1997.Key:lau97cli Annotation:teaching MDs;clinical prediction;multivariable modeling;ROC curves are not of interest to clinicians;debate about choosing a rule with sensitivity of 1.0;use of probabilities instead of classifications;reporting of statistical results
[76]	Scott R. Brazer, Frank S. Pancotto, Thomas T. Long III, Frank E. Harrell, Kerry L. Lee, Malcolm P. Tyor, and David B. Pryor. Using ordinal logistic regression to estimate the likelihood of colorectal neoplasia. J Clin Epi, 44:1263-1270, 1991.Key:bra91usi Annotation:ordinal logistic model; tutorial
[77]	F. E. Harrell, K. L. Lee, and B. G. Pollock. Regression models in clinical studies: Determining relationships between predictors and response. J Nat Cancer Inst, 80:1198-1202, 1988.Key:har88
[78]	Mitchell H. Katz. Multivariable analysis: A primer for readers of medical research. Ann Int Med, 138:644-650, 2003.Key:kat03mul Annotation:teaching MDs;multivariable modeling;good descriptions and background to modeling;bad advice on categorizing continuous variables and not enough warning about stepwise variable selection;slight error in discussing independent censoring
[79]	Patrick Royston, Douglas G. Altman, and Willi Sauerbrei. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med, 25:127-141, 2006.Key:roy06dic Annotation:continuous covariates;dichotomization;categorization;regression;efficiency;clinical research;residual confounding;destruction of statistical inference when cutpoints are chosen using the response variable;varying effect estimates when change cutpoints;difficult to interpret effects when dichotomize;nice plot showing effect of categorization;PBC data
[80]	D. G. Altman. Categorising continuous covariates (letter to the editor). Brit J Cancer, 64:975, 1991.Key:alt91cat Annotation: cutpoints;dichotomizing continuous variables
[81]	S. G. Hilsenbeck and G. M. Clark. Practical p-value adjustment for optimally selected cutpoints. Stat Med, 15:103-112, 1996.Key:hil96pra Annotation: cutpoints;dichotimizing continuous variables
[82]	B. Lausen and M. Schumacher. Evaluating the effect of optimized cutoff values in the assessment of prognostic factors. Comp Stat Data Analysis, 1996.Key:lau96eva Annotation: cutpoints;dichotomizing continuous variables
[83]	D. G. Altman, B. Lausen, W. Sauerbrei, and M. Schumacher. Dangers of using `optimal' cutpoints in the evaluation of prognostic factors. J Nat Cancer Inst, 86:829-835, 1994.Key:alt94dan Annotation: cutpoints;dichotomizing continuous variables
[84]	Heiko Belcher. The concept of residual confounding in regression models and some applications. Stat Med, 11:1747-1758, 1992.Key:bec92con Annotation:confounding; categorizing continuous variables; information loss
[85]	David Faraggi and Richard Simon. A simulation study of cross-validation for selecting an optimal cutpoint in univariate survival analysis. Stat Med, 15:2203-2213, 1996.Key:far96sim Annotation:bias in point estimate of effect from selecting cutpoints based on P-value; loss of information from dichotomizing continuous predictors
[86]	D. R. Ragland. Dichotomizing continuous outcome variables: Dependence of the magnitude of association and statistical power on the cutpoint. Epidemiology, 3:434-440, 1992.Key:rag92dic Annotation: categorization of continuous variables; see letters to editor May 1993 P. 274-, Vol 4 No. 3
[87]	Samy Suissa and Lucie Blais. Binary regression with continuous outcomes. Stat Med, 14:247-255, 1995.Key:sui94bin Annotation:categorizing continuous variables
[88]	Petra Buettner, Claus Garbe, and Irene Guggenmoos-Holzmann. Problems in defining cutoff points of continuous prognostic factors: Example of tumor thickness in primary cutaneous melanoma. J Clin Epi, 50:1201-1210, 1997.Key:bue97pro Annotation:choice of cut point depends on marginal distribution of predictor
[89]	S. E. Maxwell and H. D. Delaney. Bivariate median splits and spurious statistical significance. Psychological Bulletin, 113:181-190, 1993.Key:max93biv Annotation: categorization of continuous variables;dichotomization
[90]	G. Schulgen, B. Lausen, J. Olsen, and M. Schumacher. Outcome-oriented cutpoints in quantitative exposure. Am J Epi, 120:172-184, 1994.Key:sch94out Annotation: cutpoint;dichotomization;categorizing continuous variables
[91]	Douglas G. Altman. Suboptimal analysis using `optimal' cutpoints. Brit J Cancer, 78:556-557, 1998.Key:alt98sub Annotation: dichotomizing continuous variables;categorization
[92]	Norbert Hollander, Willi Sauerbrei, and Martin Schumacher. Confidence intervals for the effect of a prognostic factor after selection of an `optimal' cutpoint. Stat Med, 23:1701-1713, 2004.Key:hol04con Annotation: cutpoints;true type I error can be much greater than nominal level;one example where nominal is 0.05 and true is 0.5;minimum P-value method;CART;recursive partitioning;bootstrap method for correcting confidence interval;based on heuristic shrinkage coefficient;“It should be noted, however, that the optimal cutpoint approach has disadvantages. One of these is that in almost every study where this method is applied, another cutpoint will emerge. This makes comparisons across studies extremely difficult or even impossible. Altman et al. point out this problem for studies of the prognostic relevance of the S-phase fraction in breast cancer published in the literature. They identified 19 different cutpoints used in the literature; some of them were solely used because they emerged as the `optimal' cutpoint in a specific data set. In a meta-analysis on the relationship between cathepsin-D content and disease-free survival in node-negative breast cancer patients, 12 studies were in included with 12 different cutpoints ... Interestingly, neither cathepsin-D nor the S-phase fraction are recommended to be used as prognostic markers in breast cancer in the recent update of the American Society of Clinical Oncology.”; dichotomization; categorizing continuous variables; refs alt94dan, sch94out, alt98sub
[93]	Barry Kurt Moser and Laura P. Coombs. Odds ratios for a continuous outcome variable without dichotomizing. Stat Med, 23:1843-1860, 2004.Key:mos04odd Annotation: dichotomizing continuous response variable;large loss of efficiency and power;embeds in a logistic distribution, similar to proportional odds model;categorization;dichotomization of a continuous response in order to obtain odds ratios often results in an inflation of the needed sample size by a factor greater than 1.5
[94]	Howard Wainer. Finding what is not there through the unfortunate binning of results: The Mendel effect. Chance, 19(1):49-56, 2006.Key:wai06fin Annotation:can find bins that yield either positive or negative association;especially pertinent when effects are small;`With four parameters, I can fit an elephant; with five, I can make it wiggle its trunk.` - John von Neumann
[95]	Valerii Fedorov, Frank Mannino, and Rongmei Zhang. Consequences of dichotomization. Pharm Stat, 8:50-61, 2009.Key:fed09con Annotation: categorization of outcome variable;dichotomization;continuous variables;loss of information;loss of power;theoretical calculations;optimal cutpoint depends on unknown parameters;should only entertain dichotomization when “estimating a value of the cumulative distribution and when the assumed model is very different from the true model”;nice graphics
[96]	A. Rogier T. Donders, Geert J. M. G. van der Heijden, Theo Stijnen, and Karel G. M. Moons. Review: A gentle introduction to imputation of missing values. J Clin Epi, 59:1087-1091, 2006.Key:don06rev Annotation:missing data;imputation;simple demonstration of failure of indicator (new category) method
[97]	Karel G. M. Moons, Rogier A. R. T. Donders, Theo Stijnen, and Frank E. Harrell. Using the outcome for imputation of missing predictor values was preferred. J Clin Epi, 59:1092-1101, 2006.Key:moo06usi Annotation:missing data;use of outcome variable;response;MICE;aregImpute;imputation;excellent graphical summaries of simulations
[98]	Geert J. M. G. van der Heijden, A. Rogier T. Donders, Theo Stijnen, and Karel G. M. Moons. Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: A clinical example. J Clin Epi, 59:1102-1109, 2006.Key:hei06imp Annotation:missing data;imputation;invalidity of adding extra categories or missing value indicators;bias;precision;complete case analysis;single imputation
[99]	Ralph B. D'Agostino and Heidy Kwan. Measuring effectiveness: What to expect without a randomized control group. Med Care, 33:AS95-AS105, 1995.Key:dag95mea Annotation: retrospective study;observational study;teaching MDs;bias;adjustment;propensity score
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[103]	L. Törnqvist, P. Vartia, and Y. O. Vartia. How should relative changes be measured? Am Statistician, 39:43-46, 1985.Key:tor85how Annotation:measuring change; percent change; one-sample problem
[104]	J. S. Maritz. Models and the use of signed rank tests. Stat Med, 4:145-153, 1985.Key:mar85mod Annotation:measuring change;percent change;one-sample problem;signed rank test
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Acknowledgements: Richard Goldstein (Qualitas Inc.) and R. Localia (Center for Biostatistics and Epidemiology, Penn State U.) provided several key references.

-- FrankHarrell - 27 Jun, 14 Aug 2004, 4 Mar, 6 Oct 2006, 9 Mar, 8 Apr, 6 Jun 2009

Topic revision: r13 - 10 Jun 2009 - 14:01:49 - ColeBeck

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