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A Tutorial on Methods to Estimating Clinically and Policy-Meaningful Measures of Treatment Effects in Prospective Observational Studies: A Review

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  • Austin Peter C
  • Laupacis Andreas

Abstract

In randomized controlled trials (RCTs), treatment assignment is unconfounded with baseline covariates, allowing outcomes to be directly compared between treatment arms. When outcomes are binary, the effect of treatment can be summarized using relative risks, absolute risk reductions and the number needed to treat (NNT). When outcomes are time-to-event in nature, the effect of treatment on the absolute reduction of the risk of an event occurring within a specified duration of follow-up and the associated NNT can be estimated. In observational studies of the effect of treatments on health outcomes, treatment is frequently confounded with baseline covariates. Regression adjustment is commonly used to estimate the adjusted effect of treatment on outcomes. We highlight several limitations of measures of treatment effect that are directly obtained from regression models. We illustrate how both regression-based approaches and propensity-score based approaches allow one to estimate the same measures of treatment effect as those that are commonly reported in RCTs. The CONSORT statement recommends that both relative and absolute measures of treatment effects be reported for RCTs with dichotomous outcomes. The methods described in this paper will allow for similar reporting in observational studies.

Suggested Citation

  • Austin Peter C & Laupacis Andreas, 2011. "A Tutorial on Methods to Estimating Clinically and Policy-Meaningful Measures of Treatment Effects in Prospective Observational Studies: A Review," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-32, January.
    • Handle: RePEc:bpj:ijbist:v:7:y:2011:i:1:n:6
    DOI: 10.2202/1557-4679.1285
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    References listed on IDEAS

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    1. Kenneth F Schulz & Douglas G Altman & David Moher & for the CONSORT Group, 2010. "CONSORT 2010 Statement: Updated Guidelines for Reporting Parallel Group Randomised Trials," PLOS Medicine, Public Library of Science, vol. 7(3), pages 1-7, March.
    2. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
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    1. Radice Rosalba & Ramsahai Roland & Grieve Richard & Kreif Noemi & Sadique Zia & Sekhon Jasjeet S., 2012. "Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-45, August.
    2. Malay Biswas, 2017. "Are They Efficient in the Middle? Using Propensity Score Estimation for Modeling Middlemen in Indian Corporate Corruption," Journal of Business Ethics, Springer, vol. 141(3), pages 563-586, March.
    3. Habimana, Dominique & Haughton, Jonathan & Nkurunziza, Joseph & Haughton, Dominique Marie-Annick, 2021. "Measuring the impact of unconditional cash transfers on consumption and poverty in Rwanda," World Development Perspectives, Elsevier, vol. 23(C).
    4. Shrestha, Samjhana & Kestens, Yan & Thomas, Frédérique & El Aarbaoui, Tarik & Chaix, Basile, 2019. "Spatial access to sport facilities from the multiple places visited and sport practice: Assessing and correcting biases related to selective daily mobility," Social Science & Medicine, Elsevier, vol. 236(C), pages 1-1.
    5. repec:plo:pone00:0111479 is not listed on IDEAS

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