Harlan Campbell

Harlan Campbell

Vancouver, British Columbia, Canada
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University of British Columbia

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Publications

  • The World of Research Has Gone Berserk: Modeling the Consequences of Requiring “Greater Statistical Stringency” for Scientific Publication

    The American Statistician

    In response to growing concern about the reliability and reproducibility of published science, researchers have proposed adopting measures of “greater statistical stringency,” including suggestions to require larger sample sizes and to lower the highly criticized “p < 0.05” significance threshold. While pros and cons are vigorously debated, there has been little to no modeling of how adopting these measures might affect what type of science is published. In this article, we develop a novel…

    In response to growing concern about the reliability and reproducibility of published science, researchers have proposed adopting measures of “greater statistical stringency,” including suggestions to require larger sample sizes and to lower the highly criticized “p < 0.05” significance threshold. While pros and cons are vigorously debated, there has been little to no modeling of how adopting these measures might affect what type of science is published. In this article, we develop a novel optimality model that, given current incentives to publish, predicts a researcher’s most rational use of resources in terms of the number of studies to undertake, the statistical power to devote to each study, and the desirable prestudy odds to pursue. We then develop a methodology that allows one to estimate the reliability of published research by considering a distribution of preferred research strategies. Using this approach, we investigate the merits of adopting measures of “greater statistical stringency” with the goal of informing the ongoing debate.

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  • The Validity and Efficiency of Hypothesis Testing in Observational Studies with Time-Varying Exposures

    Observational Studies

    The fundamental obstacle of observational studies is that of unmeasured confounding. If all potential confounders are measured within the data, and treatment occurs at but a single time-point, conventional regression adjustment methods provide consistent estimates and allow for valid hypothesis testing in a relatively straightforward manner. However, in situations for which treatment occurs at several successive timepoints, another type of confounding is also problematic: time-dependent…

    The fundamental obstacle of observational studies is that of unmeasured confounding. If all potential confounders are measured within the data, and treatment occurs at but a single time-point, conventional regression adjustment methods provide consistent estimates and allow for valid hypothesis testing in a relatively straightforward manner. However, in situations for which treatment occurs at several successive timepoints, another type of confounding is also problematic: time-dependent confounding may bias estimates and invalidate testing due to collider-stratification. While “causal inference methods” can adequately adjust for time-dependent confounding, these methods require strong and unverifiable assumptions. Alternatively, instrumental variable analysis can be used. This paper sheds light on the issues involved when considering the relative merits of these two approaches for the purpose of hypothesis testing in the presence of time-dependent confounding.

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  • Conditional equivalence testing: An alternative remedy for publication bias

    PLOS ONE

    We introduce a publication policy that incorporates “conditional equivalence testing” (CET), a two-stage testing scheme in which standard NHST is followed conditionally by testing for equivalence. The idea of CET is carefully considered as it has the potential to address recent concerns about reproducibility and the limited publication of null results. In this paper we detail the implementation of CET, investigate similarities with a Bayesian testing scheme, and outline the basis for how a…

    We introduce a publication policy that incorporates “conditional equivalence testing” (CET), a two-stage testing scheme in which standard NHST is followed conditionally by testing for equivalence. The idea of CET is carefully considered as it has the potential to address recent concerns about reproducibility and the limited publication of null results. In this paper we detail the implementation of CET, investigate similarities with a Bayesian testing scheme, and outline the basis for how a scientific journal could proceed to reduce publication bias while remaining relevant.

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  • Comparison of reducing epicardial fat by exercise, diet or bariatric surgery weight loss strategies: A systematic review and meta-analysis

    Obesity Reviews

  • The consequences of proportional hazards based model selection

    Statistics in Medicine

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