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There are many measures to report so-called treatment or causal effects: absolute difference, ratio, odds ratio, number needed to treat, and so on.
On some points relating to vital statistics, more especially statistics of occupational mortality
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A method of estimating comparative rates from clinical data; applications to cancer of the lung, breast, and cervix
Cornfield, J. et al. (1951) · 1951
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The interpretation of interaction in contingency tables
Simpson, E. H. (1951) · 1951
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Shall we count the living or the dead?
Sheps, M. C. (1958) · 1958
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Attributable risk percent in case-control studies
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Standardization of risk ratios
Miettinen, O. S. (1972) · 1972
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Collapsibility of multidimensional contingency tables
Whittemore, A. S. (1978) · 1978
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Counfounding: Essence and Detection
Miettinen, O. S. and E. F. Cook (1981, 10) · 1981
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Interpretation and choice of effect measures in epidemiologic analysies
Greenland, S. (1987, 05) · 1987
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Meta-analysis in clinical research
L’abbé, K. A., A. S. Detsky, and K. O’rourke (1987) · 1987
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An assessment of clinically useful measures of the consequences of treatment
Laupacis, A., D. L. Sackett, and R. S. Roberts (1988) · 1988
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Root- n-consistent semiparametric regression
Robinson, P. (1988) · 1988
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Blood pressure, stroke, and coronary heart disease: part 1, prolonged differences in blood pressure: prospective observational studies corrected for the regression dilution bias
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On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9
Splawa-Neyman, J., D. M. Dabrowska, and T. P. Speed (1990) · 1990
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Absolutely relative: How research results are summarized can affect treatment decisions
Forrow, L., W. C. Taylor, and R. M. Arnold (1992) · 1992
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Measured enthusiasm: does the method of reporting trial results alter perceptions of therapeutic effectiveness?
Naylor, C. D., E. Chen, and B. Strauss (1992) · 1992
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A geometric approach to assess bias due to omitted covariates in generalized linear models
Neuhaus S, J. M. and N. P. Jewell (1993, 12) · 1993
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Increased risk of venous thrombosis in oral-contraceptive users who are carriers of factor v leiden mutation
Vandenbroucke, J., T. Koster, F. Rosendaal, E. Briët, P. Reitsma, and R. Bertina (1994) · 1994
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The number needed to treat: a clinically useful measure of treatment effect
Cook, R. J. and D. L. Sackett (1995) · 1995
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Down with odds ratios!
Sackett, D. L., J. J. Deeks, and D. G. Altman (1996) · 1996
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A graphical display useful for meta-analysis
Jiménez, F. J., E. Guallar, and J. M. Martin-Moreno (1997) · 1997
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Confidence intervals for the number needed to treat
Altman, D. G. (1998) · 1998
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When can odds ratios mislead?
Davies, H. T. O., I. K. Crombie, and M. Tavakoli (1998) · 1998
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Confounding and collapsibility in causal inference
Greenland, S., J. M. Robbins, and J. Pearl (1999, 01) · 1999
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Clinical implications. Avoiding problems in clinical practice after the pill scare
Mills, A. (1999, 11) · 1999
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Causal inference without counterfactuals
Dawid, A. P. (2000) · 2000
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Coverage by the news media of the benefits and risks of medications
Moynihan, R., L. Bero, D. Ross-Degnan, D. Henry, K. Lee, J. Watkins, C. Mah, and S. B. Soumerai (2000) · 2000
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Estimating treatment effects in randomized clinical trials in the presence of non-compliance
Nagelkerke, N., V. Fidler, R. Bernsen, and M. Borgdorff (2000) · 2000
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Causality: Models, Reasoning, and Inference
Pearl, J. (2000) · 2000
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Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models
Robins, J. M., A. Rotnitzky, and D. O. Scharfstein (2000) · 2000
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Modern Epidemiology
Rothman, K. J. and S. Greenland (2000) · 2000
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Experimental and quasi-experimental designs for generalized causal inference
Cook, T. D., D. T. Campbell, and W. Shadish (2002) · 2002
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Issues in the selection of a summary statistic in meta-analysis of clinical trials with binary outcomes
Deeks, J. (2002, June) · 2002
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Estimating risk and rate levels, ratios and differences in case-control studies
King, G. and L. Zeng (2002) · 2002
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Reporting Number Needed to Treat and Absolute Risk Reduction in Randomized Controlled Trials
Nuovo, J., J. Melnikow, and D. Chang (2002, 06) · 2002
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External validity of randomised controlled trials: “to whom do the results of this trial apply?”
Rothwell, P. M. (2005) · 2005
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Variable selection for propensity score models
Brookhart, M. A., S. Schneeweiss, K. J. Rothman, R. J. Glynn, J. Avorn, and T. Stürmer (2006) · 2006
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Ratio measures in leading medical journals: structured review of accessibility of underlying absolute risks
Schwartz, L. M., S. Woloshin, E. L. Dvorin, and H. G. Welch (2006, December) · 2006
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Four types of effect modification: a classification based on directed acyclic graphs
VanderWeele, T. J. and J. M. Robins (2007, September) · 2007
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Mostly Harmless Econometrics: An Empiricist’s Companion
Angrist, J. D. and J.-S. Pischke (2008, December) · 2008
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Misunderstandings between experimentalists and observationalists about causal inference
Imai, K., G. King, and E. A. Stuart (2008) · 2008
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The Relative Merits of Risk Ratios and Odds Ratios
Cummings, P. (2009, 05) · 2009
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The hazards of hazard ratios
Hernán, M. A. (2010) · 2010
Cited alongside, same era.
Consort 2010 statement: updated guidelines for reporting parallel group randomized trials
Schulz, K. F., D. G. Altman, D. Moher, and C. Group* (2010) · 2010
Cited alongside, same era.
Common problems related to the use of number needed to treat
Stang, A., C. Poole, and R. Bender (2010) · 2010
Cited alongside, same era.
Treatment effects in the presence of unmeasured confounding: dealing with observations in the tails of the propensity score distribution—a simulation study
Stürmer, T., K. J. Rothman, J. Avorn, and R. J. Glynn (2010) · 2010
Cited alongside, same era.
Adjustments and their consequences—collapsibility analysis using graphical models
Greenland, S. and J. Pearl (2011, 12) · 2011
Cited alongside, same era.
The simpson’s paradox unraveled
Effect heterogeneity and variable selection for standardizing causal effects to a target population
Huitfeldt, A., S. Swanson, M. Stensrud, and E. Suzuki (2019, 12) · 2019
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Metalearners for estimating heterogeneous treatment effects using machine learning
Künzel, S. R., J. S. Sekhon, P. J. Bickel, and B. Yu (2019) · 2019
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Generalizing experimental results by leveraging knowledge of mechanisms
Cinelli, C. and J. Pearl (2020) · 2020
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Making apples from oranges: Comparing noncollapsible effect estimators and their standard errors after adjustment for different covariate sets
Daniel, R., J. Zhang, and D. Farewell (2020, 12) · 2020
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Questionable utility of the relative risk in clinical research: A call for change to practice
Doi, S., L. Furuya-Kanamori, C. Xu, L. Lin, T. Chivese, and L. Thalib (2020, 11) · 2020
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Hernàn, M., D. Clayton, and N. Keiding (2011, 03) · 2011
Cited alongside, same era.
Experimental design for unit and cluster randomid trials
Imbens, G. W. (2011) · 2011
Cited alongside, same era.
Transportability of causal and statistical relations: A formal approach
Pearl, J. and E. Bareinboim (2011) · 2011
Cited alongside, same era.
Epidemiology: an introduction
Rothman, K. J. (2011) · 2011
Cited alongside, same era.
The use of propensity scores to assess the generalizability of results from randomized trials
Stuart, E. A., S. R. Cole, C. P. Bradshaw, and P. J. Leaf (2011) · 2011
Cited alongside, same era.
Use of relative and absolute effect measures in reporting health inequalities: structured review
King, N. B., S. Harper, and M. E. Young (2012) · 2012
Cited alongside, same era.
Estimating treatment effect heterogeneity in randomized program evaluation
Imai, K. and M. Ratkovic (2013) · 2013
Cited alongside, same era.
What’s the risk: Differentiating risk ratios, odds ratios, and hazard ratios?
George, A., T. S. Stead, and L. Ganti (2020) · 2020
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Causal Inference: What If
Hernàn, M. and J. Robins (2020) · 2020
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Quasi-oracle estimation of heterogeneous treatment effects
Nie, X. and S. Wager (2020, 09) · 2020
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Generalizing randomized trial findings to a target population using complex survey population data
Ackerman, B., C. R. Lesko, J. Siddique, R. Susukida, and E. A. Stuart (2021) · 2021
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Oral rimegepant for migraine prevention
Edvinsson, L. (2021) · 2021
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Estimating heterogeneous treatment effects for general responses
Gao, Z. and T. Hastie (2021) · 2021
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Shall we count the living or the dead?
Huitfeldt, A., M. P. Fox, E. J. Murray, A. Hróbjartsson, and R. M. Daniel (2021) · 2021
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Covariate balancing sensitivity analysis for extrapolating randomized trials across locations
Nie, X., G. Imbens, and S. Wager (2021) · 2021
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Quasi-oracle estimation of heterogeneous treatment effects
Nie, X. and S. Wager (2021) · 2021
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Robertson, S. E., J. A. Steingrimsson, N. R. Joyce, E. A. Stuart, and I. J. Dahabreh (2021) · 2021
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Appraising screening, making risk in/visible. The medical debate over Non-Rare Thrombophilia (NRT) testing before prescribing the pill
Turrini, M. and C. Bourgain (2021, August) · 2021
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Odds ratios are far from "portable": A call to use realistic models for effect variation in meta-analysis
Xiao, M., H. Chu, S. Cole, Y. Chen, R. MacLehose, D. Richardson, and S. Greenland (2021) · 2021
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Estimation and validation of ratio-based conditional average treatment effects using observational data
Yadlowsky, S., F. Pellegrini, F. Lionetto, S. Braune, and L. Tian (2021) · 2021
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On the logic of collapsibility for causal effect measures
Didelez, V. and M. J. Stensrud (2022) · 2022
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The odds ratio is “portable” across baseline risk but not the relative risk: Time to do away with the log link in binomial regression
Doi, S. A., L. Furuya-Kanamori, C. Xu, T. Chivese, L. Lin, O. A. Musa, G. Hindy, L. Thalib, and F. E. Harrell Jr (2022) · 2022
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Controversy and debate: Questionable utility of the relative risk in clinical research: Paper 1: A call for change to practice
Doi, S. A., L. Furuya-Kanamori, C. Xu, L. Lin, T. Chivese, and L. Thalib (2022) · 2022
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Variable importance measures for heterogeneous causal effects
Hines, O., K. Diaz-Ordaz, and S. Vansteelandt (2022) · 2022
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Reporting risk: from math to meaning
Lapointe-Shaw, L., G. Babe, P. C. Austin, A. P. Costa, and A. Jones (2022, Nov.) · 2022
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Rejoinder for discussions on correct and logical causal inference for binary and time-to-event outcomes in randomized controlled trials
Liu, Y., B. Wang, H. Tian, and J. C. Hsu (2022) · 2022
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Correct and logical causal inference for binary and time-to-event outcomes in randomized controlled trials
Liu, Y., B. Wang, M. Yang, J. Hui, H. Xu, S. Kil, and J. C. Hsu (2022) · 2022
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On the homogeneity of measures for binary associations
Wang, L. (2022) · 2022
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Is or “portable” in meta-analysis? time to consider bivariate generalized linear mixed model
Xiao, M., Y. Chen, S. Cole, R. MacLehose, D. Richardson, and H. Chu (2022) · 2022
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Variable importance for causal forests: breaking down the heterogeneity of treatment effects
Bénard, C. and J. Josse (2023) · 2023
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A review of generalizability and transportability
Degtiar, I. and S. Rose (2023) · 2023
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Design sensitivity and its implications for weighted observational studies
Huang, M., D. Soriano, and S. D. Pimentel (2023) · 2023
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Causal inference on distribution functions
Lin, Z., D. Kong, and L. Wang (2023) · 2023
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Causal inference methods for combining randomized trials and observational studies: a review
Colnet, B., I. Mayer, G. Chen, A. Dieng, R. Li, G. Varoquaux, J.-P. Vert, J. Josse, and S. Yang (2024) · 2024
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Causal interpretation of the hazard ratio in randomized clinical trials
Fay, M. P. and F. Li (2024, October) · 2024
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Interpretable causal inference for analyzing wearable, sensor, and distributional data
Katta, S., H. Parikh, C. Rudin, and A. Volfovsky (2024) · 2024
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Quantifying treatment effects: Estimating risk ratios via observational studies
Boughdiri, A., J. Josse, and E. Scornet (2025) · 2025
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Generalizing causal effects with noncompliance: Application to deep canvassing experiments
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How hazard ratios can mislead and why it matters in practice
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Overlap violations in external validity: Application to ugandan cash transfer programs
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