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Leituras conceituais

Bibliografia comentada · 1965–2019

Os conceitos que a epidemiologia usa sem definir

Exposição, desfecho, correlação, causalidade, inferência e risco aparecem em todo artigo empírico como se fossem evidentes. Não são. Esta é a literatura que os discute diretamente — todos os itens com DOI verificado na Crossref e no PubMed, organizados pelo conceito em disputa.

Causalidade

O que a epidemiologia quer dizer quando diz que algo causa algo. Definições concorrentes, do modelo de causas componentes à crítica da teia causal.

  1. 1965
    The Environment and Disease: Association or Causation?

    Hill A. B. · Proceedings of the Royal Society of Medicine 58(5): 295–300

    Original sem DOI; o link abre a reimpressão no J R Soc Med 2015;108(1):32–37

    10.1177/0141076814562718
  2. 1976
    Causes

    Rothman K. J. · American Journal of Epidemiology 104(6): 587–592

    10.1093/oxfordjournals.aje.a112335
  3. 1980
    Concepts of Interaction

    Rothman K. J., Greenland S., Walker A. M. · American Journal of Epidemiology 112(4): 467–470

    10.1093/oxfordjournals.aje.a113015
  4. 1991
    What Is a Cause and How Do We Know One? A Grammar for Pragmatic Epidemiology

    Susser M. · American Journal of Epidemiology 133(7): 635–648

    10.1093/oxfordjournals.aje.a115939
  5. 1994
    Epidemiology and the Web of Causation: Has Anyone Seen the Spider?

    Krieger N. · Social Science & Medicine 39(7): 887–903

    10.1016/0277-9536(94)90202-X
  6. 2000
    Looking Back on “Causal Thinking in the Health Sciences”

    Kaufman J. S., Poole C. · Annual Review of Public Health 21(1): 101–119

    10.1146/annurev.publhealth.21.1.101
  7. 2001
    Causation in Epidemiology

    Parascandola M., Weed D. L. · Journal of Epidemiology and Community Health 55(12): 905–912

    10.1136/jech.55.12.905
  8. 2005
    Causation and Causal Inference in Epidemiology

    Rothman K. J., Greenland S. · American Journal of Public Health 95(S1): S144–S150

    10.2105/AJPH.2004.059204
  9. 2009
    Causation and Models of Disease in Epidemiology

    Broadbent A. · Studies in History and Philosophy of Biological and Biomedical Sciences 40(4): 302–311

    10.1016/j.shpsc.2009.09.006

O debate do IJE, 2016

Um fascículo inteiro do International Journal of Epidemiology dedicado à mesma pergunta: o artigo-alvo, cinco comentários que não se conciliam e a réplica. Leia na ordem das páginas.

  1. 2016
    Causality and Causal Inference in Epidemiology: The Need for a Pluralistic Approach

    Vandenbroucke J. P., Broadbent A., Pearce N. · International Journal of Epidemiology 45(6): 1776–1786

    10.1093/ije/dyv341
  2. 2016
    The Tale Wagged by the DAG: Broadening the Scope of Causal Inference and Explanation for Epidemiology

    Krieger N., Davey Smith G. · International Journal of Epidemiology 45(6): 1787–1808

    10.1093/ije/dyw114
  3. 2016
    Commentary: On Causes, Causal Inference, and Potential Outcomes

    VanderWeele T. J. · International Journal of Epidemiology 45(6): 1809–1816

    10.1093/ije/dyw230
  4. 2016
    Commentary: The Formal Approach to Quantitative Causal Inference in Epidemiology: Misguided or Misrepresented?

    Daniel R. M., De Stavola B. L., Vansteelandt S. · International Journal of Epidemiology 45(6): 1817–1829

    10.1093/ije/dyw227
  5. 2016
    Commentary: Counterfactual Causation and Streetlamps: What Is to Be Done?

    Robins J. M., Weissman M. B. · International Journal of Epidemiology 45(6): 1830–1835

    10.1093/ije/dyw231
  6. 2016
    Response: Formalism or Pluralism? A Reply to Commentaries on `Causality and Causal Inference in Epidemiology'

    Broadbent A., Vandenbroucke J. P., Pearce N. · International Journal of Epidemiology 45(6): 1841–1851

    10.1093/ije/dyw298
  7. 2016
    Causal Inference—So Much More Than Statistics

    Pearce N., Lawlor D. A. · International Journal of Epidemiology 45(6): 1895–1903

    10.1093/ije/dyw328

Exposição

A exigência de que a exposição seja bem definida. Se não há intervenção imaginável que a produza, o efeito estimado não tem referente.

  1. 2004
    A Definition of Causal Effect for Epidemiological Research

    Hernán M. A. · Journal of Epidemiology and Community Health 58(4): 265–271

    10.1136/jech.2002.006361
  2. 2005
    Invited Commentary: Hypothetical Interventions to Define Causal Effects—Afterthought or Prerequisite?

    Hernán M. A. · American Journal of Epidemiology 162(7): 618–620

    10.1093/aje/kwi255
  3. 2008
    Does Obesity Shorten Life? The Importance of Well-Defined Interventions to Answer Causal Questions

    Hernán M. A., Taubman S. L. · International Journal of Obesity 32(S3): S8–S14

    10.1038/ijo.2008.82
  4. 2009
    The Consistency Statement in Causal Inference: A Definition or an Assumption?

    Cole S. R., Frangakis C. E. · Epidemiology 20(1): 3–5

    10.1097/EDE.0b013e31818ef366
  5. 2011
    Compound Treatments and Transportability of Causal Inference

    Hernán M. A., VanderWeele T. J. · Epidemiology 22(3): 368–377

    10.1097/EDE.0b013e3182109296
  6. 2016
    The Consistency Assumption for Causal Inference in Social Epidemiology: When a Rose Is Not a Rose

    Rehkopf D. H., Glymour M. M., Osypuk T. L. · Current Epidemiology Reports 3(1): 63–71

    10.1007/s40471-016-0069-5
  7. 2016
    Does Water Kill? A Call for Less Casual Causal Inferences

    Hernán M. A. · Annals of Epidemiology 26(10): 674–680

    10.1016/j.annepidem.2016.08.016

Desfecho

Substitutos, compostos e o que se perde ao trocar o desfecho que importa por outro mais fácil de medir.

  1. 1996
    Surrogate End Points in Clinical Trials: Are We Being Misled?

    Fleming T. R., DeMets D. L. · Annals of Internal Medicine 125(7): 605–613

    10.7326/0003-4819-125-7-199610010-00011
  2. 2003
    Composite Outcomes in Randomized Trials: Greater Precision but with Greater Uncertainty?

    Freemantle N. et al. · JAMA 289(19): 2554–2559

    10.1001/jama.289.19.2554
  3. 2004
    A Structural Approach to Selection Bias

    Hernán M. A., Hernández-Díaz S., Robins J. M. · Epidemiology 15(5): 615–625

    10.1097/01.ede.0000135174.63482.43
  4. 2014
    Invited Commentary: Composite Outcomes as an Attempt to Escape from Selection Bias and Related Paradoxes

    Hernán M. A., Schisterman E. F., Hernández-Díaz S. · American Journal of Epidemiology 179(3): 368–370

    10.1093/aje/kwt283

Correlação e causalidade

Confundimento em versão formal e em versão didática, e os dois erros de leitura mais frequentes na literatura aplicada.

  1. 1986
    Identifiability, Exchangeability, and Epidemiological Confounding

    Greenland S., Robins J. M. · International Journal of Epidemiology 15(3): 413–419

    10.1093/ije/15.3.413
  2. 1993
    Toward a Clearer Definition of Confounding

    Weinberg C. R. · American Journal of Epidemiology 137(1): 1–8

    10.1093/oxfordjournals.aje.a116591
  3. 1999
    Confounding and Collapsibility in Causal Inference

    Greenland S., Pearl J., Robins J. M. · Statistical Science 14(1): 29–46

    10.1214/ss/1009211805
  4. 2012
    “Toward a Clearer Definition of Confounding” Revisited with Directed Acyclic Graphs

    Howards P. P. et al. · American Journal of Epidemiology 176(6): 506–511

    10.1093/aje/kws127
  5. 2013
    On the Definition of a Confounder

    VanderWeele T. J., Shpitser I. · The Annals of Statistics 41(1): 196–220

    10.1214/12-AOS1058
  6. 2013
    The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients

    Westreich D., Greenland S. · American Journal of Epidemiology 177(4): 292–298

    10.1093/aje/kws412
  7. 2018
    The C-Word: Scientific Euphemisms Do Not Improve Causal Inference from Observational Data

    Hernán M. A. · American Journal of Public Health 108(5): 616–619

    10.2105/AJPH.2018.304337

Inferência

O que testes, valores p e intervalos de confiança dizem — e a longa lista do que não dizem.

  1. 1990
    Randomization, Statistics, and Causal Inference

    Greenland S. · Epidemiology 1(6): 421–429

    10.1097/00001648-199011000-00003
  2. 1999
    The Right Answer for the Wrong Question: Consequences of Type III Error for Public Health Research

    Schwartz S., Carpenter K. M. · American Journal of Public Health 89(8): 1175–1180

    10.2105/AJPH.89.8.1175
  3. 2013
    Why Representativeness Should Be Avoided

    Rothman K. J., Gallacher J. E. J., Hatch E. E. · International Journal of Epidemiology 42(4): 1012–1014

    10.1093/ije/dys223
  4. 2016
    Statistical Tests, P Values, Confidence Intervals, and Power: A Guide to Misinterpretations

    Greenland S. et al. · European Journal of Epidemiology 31(4): 337–350

    10.1007/s10654-016-0149-3
  5. 2016
    The ASA Statement on p-Values: Context, Process, and Purpose

    Wasserstein R. L., Lazar N. A. · The American Statistician 70(2): 129–133

    10.1080/00031305.2016.1154108
  6. 2017
    For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates

    Greenland S. · European Journal of Epidemiology 32(1): 3–20

    10.1007/s10654-017-0230-6
  7. 2019
    Scientists Rise Up against Statistical Significance

    Amrhein V., Greenland S., McShane B. · Nature 567(7748): 305–307

    10.1038/d41586-019-00857-9

Risco

Escolha de medidas de efeito, a distância entre risco individual e risco populacional, e a genealogia do próprio conceito.

  1. 1987
    Interpretation and Choice of Effect Measures in Epidemiologic Analyses

    Greenland S. · American Journal of Epidemiology 125(5): 761–768

    10.1093/oxfordjournals.aje.a114593
  2. 2000
    Individual Risk Prediction and Population-Wide Disease Prevention

    Rockhill B., Kawachi I., Colditz G. A. · Epidemiologic Reviews 22(1): 176–180

    10.1093/oxfordjournals.epirev.a018017
  3. 2001
    Sick Individuals and Sick Populations

    Rose G. · International Journal of Epidemiology 30(3): 427–432

    Reimpressão do artigo de 1985, com comentários no mesmo fascículo

    10.1093/ije/30.3.427
  4. 2005
    Epidemiologic Measures and Policy Formulation: Lessons from Potential Outcomes

    Greenland S. · Emerging Themes in Epidemiology 2(1): 5

    10.1186/1742-7622-2-5
  5. 2005
    Theorizing about Causes at the Individual Level While Estimating Effects at the Population Level: Implications for Prevention

    Rockhill B. · Epidemiology 16(1): 124–129

    10.1097/01.ede.0000147111.46244.41
  6. 2010
    On the Origin of Risk Relativism

    Poole C. · Epidemiology 21(1): 3–9

    10.1097/EDE.0b013e3181c30eba
  7. 2011
    Desenvolvimento histórico-epistemológico da Epidemiologia e do conceito de risco

    Ayres J. R. C. M. · Cadernos de Saúde Pública 27(7): 1301–1311

    10.1590/S0102-311X2011000700006
  8. 2012
    The Risk Concept—Historical and Recent Development Trends

    Aven T. · Reliability Engineering & System Safety 99: 33–44

    10.1016/j.ress.2011.11.006
  9. 2013
    An Argument for a Consequentialist Epidemiology

    Galea S. · American Journal of Epidemiology 178(8): 1185–1191

    10.1093/aje/kwt172
  10. 2015
    Concepts and Pitfalls in Measuring and Interpreting Attributable Fractions, Prevented Fractions, and Causation Probabilities

    Greenland S. · Annals of Epidemiology 25(3): 155–161

    10.1016/j.annepidem.2014.11.005

Cada título abre o registro do artigo em doi.org. O acesso ao texto completo depende da assinatura de cada periódico — pela rede da universidade, boa parte deles abre direto.

A lista é gerada a partir de references/leituras.bib, que você pode baixar e importar no Zotero, no Mendeley ou no gerenciador que usar. Ela é complementar à bibliografia do curso, que reúne o que os módulos citam.