42 citations · 63 across the 17 of their papers we have counts for
17 papers
Don't Disregard the Data for Lack of a Likelihood: Bayesian Synthetic Likelihood for Enhanced Multilevel Network Meta-Regression
Harlan Campbell, Charles C. Margossian, Jeroen P. Jansen +1
Multilevel network meta-regression (ML-NMR) enables population-adjusted indirect treatment comparisons by combining individual patient data (IPD) with aggregate data. When individu…
A fully Bayesian approach for the imputation and analysis of derived outcome variables with missingness
Harlan Campbell, Tim Morris, Paul Gustafson
Derived variables are variables that are constructed from one or more source variables through established mathematical operations or algorithms. For example, body mass index (BMI)…
Integrating representative and non-representative survey data for efficient inference
Nathaniel Dyrkton, Paul Gustafson, Harlan Campbell
Non-representative surveys are commonly used and widely available but suffer from selection bias that generally cannot be entirely eliminated using weighting techniques. Instead, w…
Defining a credible interval is not always possible with "point-null'' priors: A lesser-known correlate of the Jeffreys-Lindley paradox
Harlan Campbell, Paul Gustafson
In many common situations, a Bayesian credible interval will be, given the same data, very similar to a frequentist confidence interval, and researchers will interpret these interv…
Bayes factors and posterior estimation: Two sides of the very same coin
Harlan Campbell, Paul Gustafson
Recently, several researchers have claimed that conclusions obtained from a Bayes factor (or the posterior odds) may contradict those obtained from Bayesian posterior estimation. I…
Adjusting for misclassification of an exposure in an individual participant data meta-analysis
Valentijn M. T. de Jong, Harlan Campbell, Lauren Maxwell +3
A common problem in the analysis of multiple data sources, including individual participant data meta-analysis (IPD-MA), is the misclassification of binary variables. Misclassifica…
re:Linde et al. (2021): The Bayes factor, HDI-ROPE and frequentist equivalence tests can all be reverse engineered -- almost exactly -- from one another
Harlan Campbell, Paul Gustafson
Following an extensive simulation study comparing the operating characteristics of three different procedures used for establishing equivalence (the frequentist "TOST", the Bayesia…
Measurement Error in Meta-Analysis (MEMA) -- a Bayesian framework for continuous outcome data
Harlan Campbell, Valentijn M. T. de Jong, Lauren Maxwell +3
Ideally, a meta-analysis will summarize data from several unbiased studies. Here we consider the less than ideal situation in which contributing studies may be compromised by measu…
Bayesian adjustment for preferential testing in estimating the COVID-19 infection fatality rate
Harlan Campbell, Perry de Valpine, Lauren Maxwell +4
A key challenge in estimating the infection fatality rate (IFR) -- and its relation with various factors of interest -- is determining the total number of cases. The total number o…
Equivalence testing for linear regression
Harlan Campbell
We introduce equivalence testing procedures for linear regression analyses. Such tests can be very useful for confirming the lack of a meaningful association between a continuous o…
A non-inferiority test for R-squared with random regressors
Harlan Campbell
Determining the lack of association between an outcome variable and a number of different explanatory variables is frequently necessary in order to disregard a proposed model. This…
The consequences of checking for zero-inflation and overdispersion in the analysis of count data
Harlan Campbell
Count data are ubiquitous in ecology and the Poisson generalized linear model (GLM) is commonly used to model the association between counts and explanatory variables of interest.…
Can we disregard the whole model? Omnibus non-inferiority testing for in multivariable linear regression and in ANOVA
Harlan Campbell, Daniël Lakens
Determining a lack of association between an outcome variable and a number of different explanatory variables is frequently necessary in order to disregard a proposed model (i.e.,…
What to make of non-inferiority and equivalence testing with a post-specified margin?
Harlan Campbell, Paul Gustafson
In order to determine whether or not an effect is absent based on a statistical test, the recommended frequentist tool is the equivalence test. Typically, it is expected that an ap…
The world of research has gone berserk: modeling the consequences of requiring "greater statistical stringency" for scientific publication
Harlan Campbell, Paul Gustafson
In response to growing concern about the reliability and reproducibility of published science, researchers have proposed adopting measures of greater statistical stringency, includ…
Is it even rainier in North Vancouver? A non-parametric rank-based test for semicontinuous longitudinal data
Harlan Campbell
When the outcome of interest is semicontinuous and collected longitudinally, efficient testing can be difficult. Daily rainfall data is an excellent example which we use to illustr…
Conditional Equivalence Testing: an alternative remedy for publication bias
Harlan Campbell, Paul Gustafson
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 fo…