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\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage[english]{babel}
\usetikzlibrary{decorations.pathreplacing,calligraphy} % for tikz curly braces
% \definecolor{col1}{HTML}{D92102}
% \definecolor{col2}{HTML}{273B81}
\definecolor{col1}{HTML}{140e09}
\definecolor{col2}{HTML}{4daf4a}
\title{Replication of ``null results'' -- Absence of evidence or evidence of
absence?}
\author[1*\authfn{1}]{Samuel Pawel}
\author[1\authfn{1}]{Rachel Heyard}
\author[1]{Charlotte Micheloud}
\author[1]{Leonhard Held}
\affil[1]{Epidemiology, Biostatistics and Prevention Institute, Center for Reproducible Science, University of Zurich, Switzerland}
\corr{samuel.pawel@uzh.ch}{SP}
\contrib[\authfn{1}]{Contributed equally}
% %% Disclaimer that a preprint
% \vspace{-3em}
% \begin{center}
% {\color{red}This is a preprint which has not yet been peer reviewed.}
% \end{center}
## knitr options
library(knitr)
opts_chunk$set(fig.height = 4,
echo = FALSE,
warning = FALSE,
message = FALSE,
cache = FALSE,
eval = TRUE)
## should sessionInfo be printed at the end?
library(reporttools) # reporting of p-values
## not show scientific notation for small numbers
options("scipen" = 10)
## the replication Bayes factor under normality
BFr <- function(to, tr, so, sr) {
bf <- dnorm(x = tr, mean = 0, sd = so) /
dnorm(x = tr, mean = to, sd = sqrt(so^2 + sr^2))
return(bf)
}
formatBF. <- function(BF) {
if (is.na(BF)) {
BFform <- NA
} else if (BF > 1) {
if (BF > 1000) {
BFform <- "> 1000"
} else {
BFform <- as.character(signif(BF, 2))
}
} else {
if (BF < 1/1000) {
BFform <- "< 1/1000"
} else {
BFform <- paste0("1/", signif(1/BF, 2))
}
}
if (!is.na(BFform) && BFform == "1/1") {
return("1")
} else {
return(BFform)
}
}
formatBF <- Vectorize(FUN = formatBF.)
## Bayes factor under normality with unit-information prior under alternative
BF01 <- function(estimate, se, null = 0, unitvar = 4) {
bf <- dnorm(x = estimate, mean = null, sd = se) /
dnorm(x = estimate, mean = null, sd = sqrt(se^2 + unitvar))
return(bf)
}
\begin{abstract}
In several large-scale replication projects, statistically non-significant
results in both the original and the replication study have been interpreted
as a ``replication success''. Here we discuss the logical problems with this
approach: Non-significance in both studies does not ensure that the studies
provide evidence for the absence of an effect and ``replication success'' can
virtually always be achieved if the sample sizes are small enough. In addition,
the relevant error rates are not controlled. We show how methods, such as
equivalence testing and Bayes factors, can be used to adequately quantify the
evidence for the absence of an effect and how they can be applied in the
replication setting. Using data from the Reproducibility Project: Cancer
Biology we illustrate that many original and replication studies with ``null
results'' are in fact inconclusive, and that their replicability is lower than
suggested by the non-significance approach. We conclude that it is important
to also replicate studies with statistically non-significant results, but that
they should be designed, analyzed, and interpreted appropriately.
% \rule{\textwidth}{0.5pt} \emph{Keywords}: Bayesian hypothesis testing,
% equivalence testing, meta-research, null hypothesis, replication success}
\textit{Absence of evidence is not evidence of absence} -- the title of the 1995
paper by Douglas Altman and Martin Bland has since become a mantra in the
statistical and medical literature \citep{Altman1995}. Yet, the misconception
that a statistically non-significant result indicates evidence for the absence
of an effect is unfortunately still widespread \citep{Makin2019}. Such a ``null
result'' -- typically characterized by a \textit{p}-value of $p > 0.05$ for the
null hypothesis of an absent effect -- may also occur if an effect is actually
present. For example, if the sample size of a study is chosen to detect an
assumed effect with a power of $80\%$, null results will incorrectly occur
$20\%$ of the time when the assumed effect is actually present. If the power of
the study is lower, null results will occur more often. In general, the lower
the power of a study, the greater the ambiguity of a null result. To put a null
result in context, it is therefore critical to know whether the study was
adequately powered and under what assumed effect the power was calculated
\citep{Hoenig2001, Greenland2012}. However, if the goal of a study is to
explicitly quantify the evidence for the absence of an effect, more appropriate
methods designed for this task, such as equivalence testing
\citep{Senn2008,Wellek2010,Lakens2017} or Bayes factors \citep{Kass1995,
Goodman1999}, should be used from the outset.
% two systematic reviews that I found which show that animal studies are very
% much underpowered on average \citep{Jennions2003,Carneiro2018}
The interpretation of null results becomes even more complicated in the setting
of replication studies. In a replication study, researchers attempt to repeat an
original study as closely as possible in order to assess whether consistent
results can be obtained with new data \citep{NSF2019}. In the last decade,
various large-scale replication projects have been conducted in diverse fields,
from the biomedical to the social sciences \citep[among
others]{Prinz2011,Begley2012,Klein2014,Opensc2015,Camerer2016,Camerer2018,Klein2018,Cova2018,Errington2021}.
Most of these projects reported alarmingly low replicability rates across a
broad spectrum of criteria for quantifying replicability. While most of these
projects restricted their focus on original studies with statistically
significant results (``positive results''), the \emph{Reproducibility Project:
Psychology} \citep[RPP,][]{Opensc2015}, the \emph{Reproducibility Project:
Experimental Philosophy} \citep[RPEP,][]{Cova2018}, and the
\emph{Reproducibility Project: Cancer Biology} \citep[RPCB,][]{Errington2021}
also attempted to replicate some original studies with null results -- either
non-significant or interpreted as showing no evidence for a meaningful effect by
the original authors.
While the RPEP and RPP interpreted non-significant results in both original and
replication study as a ``replication success'' for some individual replications
(see, for example, the replication of \citet[replication report:
\url{https://osf.io/wcm7n}]{McCann2005} or the replication of \citet[replication
report:
\url{https://osf.io/9xt25}]{Ranganath2008}), % and \url{https://osf.io/fkcn5})
they excluded the original null results in the calculation of an overall
replicability rate based on significance. In contrast, the RPCB explicitly
defined null results in both the original and the replication study as a
criterion for ``replication success''. According to this criterion,
$11/15 = \Sexpr{round(11/15*100, 0)}\%$ replications of original null effects
were successful.
We believe that there are several logical problems with this
``non-significance'' criterion. First, if the original study had low statistical
power, a non-significant result is highly inconclusive and does not provide
evidence for the absence of an effect. It is then unclear what exactly the goal
of the replication should be -- to replicate the inconclusiveness of the
original result? On the other hand, if the original study was adequately
powered, a non-significant result may indeed provide some evidence for the
absence of an effect when analyzed with appropriate methods, so that the goal of
the replication is clearer. However, the criterion by itself does not
distinguish between these two cases. Second, with this criterion researchers can
virtually always achieve replication success by conducting a replication study
with a very small sample size, such that the \textit{p}-value is non-significant
and the result are inconclusive. This is because the null hypothesis under which
the \textit{p}-value is computed is misaligned with the goal of inference, which
is to quantify the evidence for the absence of an effect. We will discuss
methods that are better aligned with this inferential goal. Third, the criterion
does not control the error of falsely claiming the absence of an effect at some
predetermined rate. This is in contrast to the standard replication success
criterion of requiring significance from both studies \citep[also known as the
two-trials rule, see Section 12.2.8 in][]{Senn2008}, which ensures that the
error of falsely claiming the presence of an effect is controlled at a rate
equal to the squared significance level (for example, $5\% \times 5\% = 0.25\%$
for a $5\%$ significance level). The non-significance criterion may be intended
to complement the two-trials rule for null results. However, it fails to do so
in this respect, which may be required by regulators and funders.
The aim of this paper is to present two principled approaches for analyzing
replication studies of null results, which can address the limitations of the
non-significance criterion. In the following, we will use the null results
replicated in the RPCB to illustrate the problems of the non-significance
criterion. We then explain and illustrate how both frequentist equivalence
testing and Bayesian hypothesis testing can be used to overcome them. It is
important to note that it is not our intent to diminish the enormously important
contributions of the RPCB, but rather to build on their work and provide
recommendations for future replication researchers.
rpcbRaw <- read.csv(file = "../data/rpcb-effect-level.csv")
rpcb <- rpcbRaw %>%
mutate(
## recompute one-sided p-values based on normality
## (in direction of original effect estimate)
zo = smdo/so,
zr = smdr/sr,
po1 = pnorm(q = abs(zo), lower.tail = FALSE),
pr1 = pnorm(q = abs(zr), lower.tail = ifelse(sign(zo) < 0, TRUE, FALSE)),
## compute some other quantities
c = so^2/sr^2, # variance ratio
d = smdr/smdo, # relative effect size
po2 = 2*(1 - pnorm(q = abs(zo))), # two-sided original p-value
pr2 = 2*(1 - pnorm(q = abs(zr))), # two-sided replication p-value
sm = 1/sqrt(1/so^2 + 1/sr^2), # standard error of fixed effect estimate
smdm = (smdo/so^2 + smdr/sr^2)*sm^2, # fixed effect estimate
pm2 = 2*(1 - pnorm(q = abs(smdm/sm))), # two-sided fixed effect p-value
Q = (smdo - smdr)^2/(so^2 + sr^2), # Q-statistic
pQ = pchisq(q = Q, df = 1, lower.tail = FALSE), # p-value from Q-test
BForig = BF01(estimate = smdo, se = so), # unit-information BF for original
BForigformat = formatBF(BF = BForig),
BFrep = BF01(estimate = smdr, se = sr), # unit-information BF for replication
BFrepformat = formatBF(BF = BFrep)
study1 <- "(20, 1, 1)" # evidence of absence
study2 <- "(29, 2, 2)" # absence of evidence
plotDF1 <- rpcbNull %>%
filter(id %in% c(study1, study2)) %>%
mutate(label = ifelse(id == study1,
"Goetz et al. (2011)\nEvidence of absence",
"Dawson et al. (2011)\nAbsence of evidence"))
@
\section{Null results from the Reproducibility Project: Cancer Biology}
\label{sec:rpcb}
Figure~\ref{fig:2examples} shows effect estimates on standardized mean
difference (SMD) scale with $\Sexpr{round(100*conflevel, 2)}\%$ confidence
intervals from two RPCB study pairs. In both study pairs, the original and
replications studies are ``null results'' and therefore meet the
non-significance criterion for replication success (the two-sided
\textit{p}-values are greater than $0.05$ in both the original and the
replication study). However, intuition would suggest that the conclusions in the
two pairs are very different.
The original study from \citet{Dawson2011} and its replication both show large
effect estimates in magnitude, but due to the very small sample sizes, the
uncertainty of these estimates is large, too. With such low sample sizes, the
results seem inconclusive. In contrast, the effect estimates from
\citet{Goetz2011} and its replication are much smaller in magnitude and their
uncertainty is also smaller because the studies used larger sample sizes.
Intuitively, the results seem to provide more evidence for a zero (or negligibly
small) effect. While these two examples show the qualitative difference between
absence of evidence and evidence of absence, we will now discuss how the two can
be quantitatively distinguished.
\begin{figure}[!htb]
<< "2-example-studies", fig.height = 3 >>=
## create plot showing two example study pairs with null results
ggplot(data = plotDF1) +
facet_wrap(~ label) +
geom_hline(yintercept = 0, lty = 2, alpha = 0.3) +
geom_pointrange(aes(x = "Original", y = smdo,
ymin = smdo - qnorm(p = (1 + conflevel)/2)*so,
ymax = smdo + qnorm(p = (1 + conflevel)/2)*so), fatten = 3) +
geom_pointrange(aes(x = "Replication", y = smdr,
ymin = smdr - qnorm(p = (1 + conflevel)/2)*sr,
ymax = smdr + qnorm(p = (1 + conflevel)/2)*sr), fatten = 3) +
geom_text(aes(x = 1.05, y = 2.5,
label = paste("italic(n) ==", no)), col = "darkblue",
parse = TRUE, size = 3.8, hjust = 0) +
label = paste("italic(n) ==", nr)), col = "darkblue",
parse = TRUE, size = 3.8, hjust = 0) +
label = paste("italic(p) ==", formatPval(po))), col = "darkblue",
label = paste("italic(p) ==", formatPval(pr))), col = "darkblue",
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
strip.text = element_text(size = 12, margin = margin(4), vjust = 1.5),
strip.background = element_rect(fill = alpha("tan", 0.4)),
axis.text = element_text(size = 12))
@
\caption{\label{fig:2examples} Two examples of original and replication study
pairs which meet the non-significance replication success criterion from the
Reproducibility Project: Cancer Biology \citep{Errington2021}. Shown are
standardized mean difference effect estimates with
$\Sexpr{round(conflevel*100, 2)}\%$ confidence intervals, sample sizes $n$,
and two-sided \textit{p}-values $p$ for the null hypothesis that the effect is
absent.}
\section{Methods for assessing replicability of null results}
\label{sec:methods}
There are both frequentist and Bayesian methods that can be used for assessing
evidence for the absence of an effect. \citet{Anderson2016} provide an excellent
summary in the context of replication studies in psychology. We now briefly
discuss two possible approaches -- frequentist equivalence testing and Bayesian
hypothesis testing -- and their application to the RPCB data.
Equivalence testing was developed in the context of clinical trials to assess
whether a new treatment -- typically cheaper or with fewer side effects than the
established treatment -- is practically equivalent to the established treatment
\citep{Wellek2010}. The method can also be used to assess whether an effect is
practically equivalent to an absent effect, usually zero. Using equivalence
testing as a way to put non-significant results into context has been suggested
by several authors \citep{Hauck1986, Campbell2018}. The main challenge is to
specify the margin $\Delta > 0$ that defines an equivalence range
$[-\Delta, +\Delta]$ in which an effect is considered as absent for practical
purposes. The goal is then to reject the % composite %% maybe too technical?
null hypothesis that the true effect is outside the equivalence range. This is
in contrast to the usual null hypothesis of superiority tests which state that
the effect is zero or smaller than zero, see Figure~\ref{fig:hypotheses} for an
illustration.
To ensure that the null hypothesis is falsely rejected at most
$\alpha \times 100\%$ of the time, the standard approach is to declare
equivalence if the $(1-2\alpha)\times 100\%$ confidence interval for the effect
is contained within the equivalence range, for example, a $90\%$ confidence
interval for $\alpha = 5\%$ \citep{Westlake1972}. This procedure is equivalent
to declaring equivalence when two one-sided tests (TOST) for the null hypotheses
of the effect being greater/smaller than $+\Delta$ and $-\Delta$, are both
significant at level $\alpha$ \citep{Schuirmann1987}. A quantitative measure of
evidence for the absence of an effect is then given by the maximum of the two
one-sided \textit{p}-values (the TOST \textit{p}-value). A reasonable
replication success criterion for null results may therefore be to require that
both the original and the replication TOST \textit{p}-values be smaller than
some level $\alpha$ (conventionally $0.05$), or, equivalently, that their
$(1-2\alpha)\times 100\%$ confidence intervals are included in the equivalence
region. In contrast to the non-significance criterion, this criterion controls
the error of falsely claiming replication success at level $\alpha^{2}$ when
there is a true effect outside the equivalence margin, thus complementing the
usual two-trials rule in drug regulation \citep[section 12.2.8]{Senn2008}.
\begin{center}
\begin{tikzpicture}[ultra thick]
\draw[stealth-stealth] (0,0) -- (6,0);
\node[text width=4.5cm, align=center] at (3,-1) {Effect size};
\draw (2,0.2) -- (2,-0.2) node[below]{$-\Delta$};
\draw (3,0.2) -- (3,-0.2) node[below]{$0$};
\draw (4,0.2) -- (4,-0.2) node[below]{$+\Delta$};
\node[text width=5cm, align=left] at (0,1) {\textbf{Equivalence}};
\draw [draw={col1},decorate,decoration={brace,amplitude=5pt}]
(2.05,0.75) -- (3.95,0.75) node[midway,yshift=1.5em]{\textcolor{col1}{$H_1$}};
\draw [draw={col2},decorate,decoration={brace,amplitude=5pt,aspect=0.6}]
(0,0.75) -- (1.95,0.75) node[pos=0.6,yshift=1.5em]{\textcolor{col2}{$H_0$}};
\draw [draw={col2},decorate,decoration={brace,amplitude=5pt,aspect=0.4}]
(4.05,0.75) -- (6,0.75) node[pos=0.4,yshift=1.5em]{\textcolor{col2}{$H_0$}};
\node[text width=5cm, align=left] at (0,2.15) {\textbf{Superiority}\\(two-sided)};
(3,2) -- (3,2) node[midway,yshift=1.5em]{\textcolor{col2}{$H_0$}};
\draw[col2] (3,1.95) -- (3,2.2);
\draw [draw={col1},decorate,decoration={brace,amplitude=5pt,aspect=0.6}]
(0,2) -- (2.95,2) node[pos=0.6,yshift=1.5em]{\textcolor{col1}{$H_1$}};
\draw [draw={col1},decorate,decoration={brace,amplitude=5pt,aspect=0.4}]
(3.05,2) -- (6,2) node[pos=0.4,yshift=1.5em]{\textcolor{col1}{$H_1$}};
\node[text width=5cm, align=left] at (0,3.45) {\textbf{Superiority}\\(one-sided)};
\draw [draw={col1},decorate,decoration={brace,amplitude=5pt,aspect=0.4}]
(3.05,3.25) -- (6,3.25) node[pos=0.4,yshift=1.5em]{\textcolor{col1}{$H_1$}};
\draw [draw={col2},decorate,decoration={brace,amplitude=5pt,aspect=0.6}]
(0,3.25) -- (3,3.25) node[pos=0.6,yshift=1.5em]{\textcolor{col2}{$H_0$}};
\draw [dashed] (2,0) -- (2,0.75);
\draw [dashed] (4,0) -- (4,0.75);
\draw [dashed] (3,0) -- (3,0.75);
\draw [dashed] (3,1.5) -- (3,1.9);
\draw [dashed] (3,2.8) -- (3,3.2);
\end{tikzpicture}
\end{center}
\caption{Null hypothesis ($H_0$) and alternative hypothesis ($H_1$) for
superiority and equivalence tests (with equivalence margin $\Delta > 0$).}
\begin{figure}
\begin{fullwidth}
<< "plot-null-findings-rpcb", fig.height = 8.25, fig.width = "0.95\\linewidth" >>=
## Wellek (2010): strict - 0.36 # liberal - .74
# Cohen: small - 0.3 # medium - 0.5 # large - 0.8
## 80-125% convention for AUC and Cmax FDA/EMA
## 1.3 for oncology OR/HR -> log(1.3)*sqrt(3)/pi = 0.1446
margin <- 0.74
rpcbNull$ptosto <- with(rpcbNull, pmax(pnorm(q = smdo, mean = margin, sd = so,
lower.tail = TRUE),
pnorm(q = smdo, mean = -margin, sd = so,
lower.tail = FALSE)))
rpcbNull$ptostr <- with(rpcbNull, pmax(pnorm(q = smdr, mean = margin, sd = sr,
lower.tail = TRUE),
pnorm(q = smdr, mean = -margin, sd = sr,
lower.tail = FALSE)))
ex1 <- "(20, 1, 1)"
ind1 <- which(rpcbNull$id == ex1)
ex2 <- "(29, 2, 2)"
ind2 <- which(rpcbNull$id == ex2)
rpcbNull$id <- ifelse(rpcbNull$id == ex1,
"(20, 1, 1) - Goetz et al. (2011)", rpcbNull$id)
"(29, 2, 2) - Dawson et al. (2011)", rpcbNull$id)
## create plots of all study pairs with null results in original study
ggplot(data = rpcbNull) +
## order in ascending original paper order and label with id variable
facet_wrap(~ paper + experiment + effect + id,
labeller = label_bquote(.(id)), scales = "free", ncol = 3) +
geom_hline(yintercept = 0, lty = 2, alpha = 0.25) +
## equivalence margin
geom_hline(yintercept = c(-margin, margin), lty = 3, col = 2, alpha = 0.9) +
## ## also show the 95% CIs
## geom_linerange(aes(x = "Original", y = smdo,
## ymin = smdo - qnorm(p = (1 + 0.95)/2)*so,
## ymax = smdo + qnorm(p = (1 + 0.95)/2)*so), size = 0.2, alpha = 0.6) +
## geom_linerange(aes(x = "Replication", y = smdr,
## ymin = smdr - qnorm(p = (1 + 0.95)/2)*sr,
## ymax = smdr + qnorm(p = (1 + 0.95)/2)*sr), size = 0.2, alpha = 0.6) +
## 90% CIs
geom_pointrange(aes(x = "Original", y = smdo,
ymin = smdo - qnorm(p = (1 + conflevel)/2)*so,
geom_pointrange(aes(x = "Replication", y = smdr,
ymin = smdr - qnorm(p = (1 + conflevel)/2)*sr,
annotate(geom = "ribbon", x = seq(0, 3, 0.01), ymin = -margin, ymax = margin,
alpha = 0.05, fill = 2) +
geom_text(aes(x = 1.05, y = pmax(smdo + 2.5*so, smdr + 2.5*sr, 1.1*margin),
label = paste("italic(n) ==", no)), col = "darkblue",
parse = TRUE, size = 2.3, hjust = 0, vjust = 2) +
geom_text(aes(x = 2.05, y = pmax(smdo + 2.5*so, smdr + 2.5*sr, 1.1*margin),
label = paste("italic(n) ==", nr)), col = "darkblue",
parse = TRUE, size = 2.3, hjust = 0, vjust = 2) +
geom_text(aes(x = 1.05, y = pmax(smdo + 2.5*so, smdr + 2.5*sr, 1.1*margin),
label = paste("italic(p)",
ifelse(po < 0.0001, "", "=="),
formatPval(po))), col = "darkblue",
parse = TRUE, size = 2.3, hjust = 0) +
geom_text(aes(x = 2.05, y = pmax(smdo + 2.5*so, smdr + 2.5*sr, 1.1*margin),
label = paste("italic(p)",
ifelse(pr < 0.0001, "", "=="),
formatPval(pr))), col = "darkblue",
parse = TRUE, size = 2.3, hjust = 0) +
geom_text(aes(x = 1.05, y = pmax(smdo + 2.5*so, smdr + 2.5*sr, 1.1*margin),
label = paste("italic(p)['TOST']",
ifelse(ptosto < 0.0001, "", "=="),
col = "darkblue", parse = TRUE, size = 2.3, hjust = 0, vjust = 3) +
geom_text(aes(x = 2.05, y = pmax(smdo + 2.5*so, smdr + 2.5*sr, 1.1*margin),
label = paste("italic(p)['TOST']",
ifelse(ptostr < 0.0001, "", "=="),
col = "darkblue", parse = TRUE, size = 2.3, hjust = 0, vjust = 3) +
geom_text(aes(x = 1.05, y = pmax(smdo + 2.5*so, smdr + 2.5*sr, 1.1*margin),
label = paste("BF['01']", ifelse(BForig <= 1/1000, "", "=="),
size = 2.3, hjust = 0, vjust = 4.5) +
geom_text(aes(x = 2.05, y = pmax(smdo + 2.5*so, smdr + 2.5*sr, 1.1*margin),
label = paste("BF['01']", ifelse(BFrep <= 1/1000, "", "=="),
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
strip.text = element_text(size = 8, margin = margin(3), vjust = 2),
strip.background = element_rect(fill = alpha("tan", 0.4)),
\caption{Effect estimates on standardized mean difference (SMD) scale with
$\Sexpr{round(conflevel*100, 2)}\%$ confidence interval for the ``null
results'' and their replication studies from the Reproducibility Project:
Cancer Biology \citep{Errington2021}. The identifier above each plot indicates
(original paper number, experiment number, effect number). Two original effect
estimates from original paper 48 were statistically significant at $p < 0.05$,
but were interpreted as null results by the original authors and therefore
treated as null results by the RPCB. The two examples from
Figure~\ref{fig:2examples} are indicated in the plot titles. The dashed gray
line represents the value of no effect ($\text{SMD} = 0$), while the dotted
red lines represent the equivalence range with a margin of
$\Delta = \Sexpr{margin}$, classified as ``liberal'' by \citet[Table
1.1]{Wellek2010}. The \textit{p}-value $p_{\text{TOST}}$ is the maximum of the
two one-sided \textit{p}-values for the null hypotheses of the effect being
greater/less than $+\Delta$ and $-\Delta$, respectively. The Bayes factor
$\BF_{01}$ quantifies the evidence for the null hypothesis
$H_{0} \colon \text{SMD} = 0$ against the alternative
$H_{1} \colon \text{SMD} \neq 0$ with normal unit-information prior assigned
to the SMD under $H_{1}$.}
<< "successes-RPCB" >>=
ntotal <- nrow(rpcbNull)
## successes non-significance criterion
nullSuccesses <- sum(rpcbNull$po > 0.05 & rpcbNull$pr > 0.05)
## success equivalence testing criterion
equivalenceSuccesses <- sum(rpcbNull$ptosto <= 0.05 & rpcbNull$ptostr <= 0.05)
ptosto1 <- rpcbNull$ptosto[ind1]
ptostr1 <- rpcbNull$ptostr[ind1]
ptosto2 <- rpcbNull$ptosto[ind2]
ptostr2 <- rpcbNull$ptostr[ind2]
## success BF criterion
bfSuccesses <- sum(rpcbNull$BForig > 3 & rpcbNull$BFrep > 3)
BForig1 <- rpcbNull$BForig[ind1]
BFrep1 <- rpcbNull$BFrep[ind1]
BForig2 <- rpcbNull$BForig[ind2]
BFrep2 <- rpcbNull$BFrep[ind2]
Returning to the RPCB data, Figure~\ref{fig:nullfindings} shows the standardized
mean difference effect estimates with $\Sexpr{round(conflevel*100, 2)}\%$
confidence intervals for all 15 effects which were treated as quantitative null
results by the RPCB.\footnote{There are four original studies with null effects
for which two or three ``internal'' replication studies were conducted,
leading in total to 20 replications of null effects. As in the RPCB main
analysis \citep{Errington2021}, we aggregated their SMD estimates into a
single SMD estimate with fixed-effect meta-analysis and recomputed the
replication \textit{p}-value based on a normal approximation. For the original
studies and the single replication studies we report the \textit{p}-values as
provided by the RPCB.} Most of them showed non-significant \textit{p}-values
($p > 0.05$) in the original study. It is, however, noteworthy that two effects
from the second experiment from the original paper 48 were regarded as null
results despite their statistical significance. We see that there are
$\Sexpr{nullSuccesses}$ ``successes'' according to the non-significance
criterion (with $p > 0.05$ in original and replication study) out of total
$\Sexpr{ntotal}$ null effects, as reported in Table 1
from~\citet{Errington2021}.
We will now apply equivalence testing to the RPCB data. The dotted red lines in
Figure~\ref{fig:nullfindings} represent an equivalence range for the margin
$\Delta = \Sexpr{margin}$, which \citet[Table 1.1]{Wellek2010} classifies as
``liberal''. However, even with this generous margin, only
$\Sexpr{equivalenceSuccesses}$ of the $\Sexpr{ntotal}$ study pairs are able to
establish replication success at the $5\%$ level, in the sense that both the
original and the replication $90\%$ confidence interval fall within the
equivalence range (or, equivalently, that their TOST \textit{p}-values are
smaller than $0.05$). For the remaining $\Sexpr{ntotal - equivalenceSuccesses}$
studies, the situation remains inconclusive and there is no evidence for the
absence or the presence of the effect. For instance, the previously discussed
example from \citet{Goetz2011} marginally fails the criterion
($p_{\text{TOST}} = \Sexpr{formatPval(ptosto1)}$ in the original study and
$p_{\text{TOST}} = \Sexpr{formatPval(ptostr1)}$ in the replication), while the
example from \citet{Dawson2011} is a clearer failure
($p_{\text{TOST}} = \Sexpr{formatPval(ptosto2)}$ in the original study and
$p_{\text{TOST}} = \Sexpr{formatPval(ptostr2)}$ in the replication) as both
effect estimates even lie outside the equivalence margin.
The post-hoc specification of equivalence margins is controversial. Ideally, the
margin should be specified on a case-by-case basis in a pre-registered protocol
before the studies are conducted by researchers familiar with the subject
matter. In the social and medical sciences, the conventions of \citet{Cohen1992}
are typically used to classify SMD effect sizes ($\text{SMD} = 0.2$ small,
$\text{SMD} = 0.5$ medium, $\text{SMD} = 0.8$ large). While effect sizes are
typically larger in preclinical research, it seems unrealistic to specify
margins larger than $1$ on SMD scale to represent effect sizes that are absent
for practical purposes. It could also be argued that the chosen margin
$\Delta = \Sexpr{margin}$ is too lax compared to margins commonly used in
clinical research; for instance, in oncology, a margin of $\Delta = \log(1.3)$
is commonly used for log odds/hazard ratios, whereas in bioequivalence studies a
margin of \mbox{$\Delta = \log(1.25) % = \Sexpr{round(log(1.25), 2)}
$} is the convention \citep[chapter 22]{Senn2008}. These margins would
translate into much more stringent margins of $\Delta
= % \log(1.3)\sqrt{3}/\pi =
\Sexpr{round(log(1.3)*sqrt(3)/pi, 2)}$ and $\Delta = % \log(1.25)\sqrt{3}/\pi =
\Sexpr{round(log(1.25)*sqrt(3)/pi, 2)}$ on the SMD scale, respectively, using
the $\text{SMD} = (\surd{3} / \pi) \log\text{OR}$ conversion \citep[p.
233]{Cooper2019}. Therefore, we report a sensitivity analysis in
Figure~\ref{fig:sensitivity}. The top plot shows the number of successful
replications as a function of the margin $\Delta$ and for different TOST
\textit{p}-value thresholds. Such an ``equivalence curve'' approach was first
proposed by \citet{Hauck1986}. We see that for realistic margins between $0$ and
$1$, the proportion of replication successes remains below $50\%$ for the
conventional $\alpha = 0.05$ level. To achieve a success rate of
$11/15 = \Sexpr{round(11/15*100, 0)}\%$, as was achieved with the
non-significance criterion from the RPCB, unrealistic margins of $\Delta > 2$
are required, highlighting the paucity of evidence provided by these studies.
Changing the success criterion to a more lenient level ($\alpha = 0.1$) or a
more stringent level ($\alpha = 0.01$) hardly changes this conclusion.
\begin{figure}[!htb]
<< "sensitivity", fig.height = 6.5 >>=
## compute number of successful replications as a function of the equivalence margin
marginseq <- seq(0.01, 4.5, 0.01)
sensitivityGrid <- expand.grid(m = marginseq, a = alphaseq)
equivalenceDF <- lapply(X = seq(1, nrow(sensitivityGrid)), FUN = function(i) {
m <- sensitivityGrid$m[i]
a <- sensitivityGrid$a[i]
rpcbNull$ptosto <- with(rpcbNull, pmax(pnorm(q = smdo, mean = m, sd = so,
lower.tail = TRUE),
pnorm(q = smdo, mean = -m, sd = so,
lower.tail = FALSE)))
rpcbNull$ptostr <- with(rpcbNull, pmax(pnorm(q = smdr, mean = m, sd = sr,
lower.tail = TRUE),
pnorm(q = smdr, mean = -m, sd = sr,
lower.tail = FALSE)))
successes <- sum(rpcbNull$ptosto <= a & rpcbNull$ptostr <= a)
data.frame(margin = m, alpha = a,
successes = successes, proportion = successes/nrow(rpcbNull))
}) %>%
bind_rows()
## plot number of successes as a function of margin
nmax <- nrow(rpcbNull)
bks <- c(0, 3, 6, 9, 11, 15)
labs <- paste0(bks, " (", round(bks/nmax*100, 0), "%)")
rpcbSuccesses <- 11
marbks <- c(0, margin, 1, 2, 3, 4)
plotA <- ggplot(data = equivalenceDF,
aes(x = margin, y = successes,
color = factor(alpha, ordered = TRUE, levels = rev(alphaseq)))) +
facet_wrap(~ 'italic("p")["TOST"] <= alpha ~ "in original and replication study"',
labeller = label_parsed) +
geom_vline(xintercept = margin, lty = 3, alpha = 0.4) +
annotate(geom = "segment", x = margin + 0.25, xend = margin + 0.01, y = 2, yend = 2,
arrow = arrow(type = "closed", length = unit(0.02, "npc")), alpha = 0.9,
color = "darkgrey") +
annotate(geom = "text", x = margin + 0.28, y = 2, color = "darkgrey",
label = "margin used in main analysis",
size = 3, alpha = 0.9, hjust = 0) +
geom_hline(yintercept = rpcbSuccesses, lty = 2, alpha = 0.4) +
annotate(geom = "segment", x = 0.1, xend = 0.1, y = 13, yend = 11.2,
arrow = arrow(type = "closed", length = unit(0.02, "npc")), alpha = 0.9,
color = "darkgrey") +
annotate(geom = "text", x = -0.04, y = 13.5, color = "darkgrey",
label = "non-significance criterion successes",
size = 3, alpha = 0.9, hjust = 0) +
geom_step(alpha = 0.8, linewidth = 0.8) +
scale_y_continuous(breaks = bks, labels = labs) +
scale_x_continuous(breaks = marbks) +
coord_cartesian(xlim = c(0, max(equivalenceDF$margin))) +
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labs(x = bquote("Equivalence margin" ~ Delta),
y = "Successful replications",
color = bquote("threshold" ~ alpha)) +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
strip.background = element_rect(fill = alpha("tan", 0.4)),
strip.text = element_text(size = 12),
legend.position = c(0.85, 0.25),
plot.background = element_rect(fill = "transparent", color = NA),
legend.box.background = element_rect(fill = "transparent", colour = NA))
## compute number of successful replications as a function of the prior scale
priorsdseq <- seq(0, 40, 0.1)
bfThreshseq <- c(3, 6, 10)
sensitivityGrid2 <- expand.grid(s = priorsdseq, thresh = bfThreshseq)
bfDF <- lapply(X = seq(1, nrow(sensitivityGrid2)), FUN = function(i) {
priorsd <- sensitivityGrid2$s[i]
thresh <- sensitivityGrid2$thresh[i]
rpcbNull$BForig <- with(rpcbNull, BF01(estimate = smdo, se = so, unitvar = priorsd^2))
rpcbNull$BFrep <- with(rpcbNull, BF01(estimate = smdr, se = sr, unitvar = priorsd^2))
successes <- sum(rpcbNull$BForig >= thresh & rpcbNull$BFrep >= thresh)
data.frame(priorsd = priorsd, thresh = thresh,
successes = successes, proportion = successes/nrow(rpcbNull))
}) %>%
bind_rows()
## plot number of successes as a function of prior sd
plotB <- ggplot(data = bfDF,
aes(x = priorsd, y = successes, color = factor(thresh, ordered = TRUE))) +
facet_wrap(~ '"BF"["01"] >= gamma ~ "in original and replication study"',
labeller = label_parsed) +
geom_vline(xintercept = 2, lty = 3, alpha = 0.4) +
geom_hline(yintercept = rpcbSuccesses, lty = 2, alpha = 0.4) +
annotate(geom = "segment", x = 7, xend = 2 + 0.2, y = 0.5, yend = 0.5,
arrow = arrow(type = "closed", length = unit(0.02, "npc")), alpha = 0.9,
color = "darkgrey") +
annotate(geom = "text", x = 7.5, y = 0.5, color = "darkgrey",
label = "standard deviation used in main analysis",
size = 3, alpha = 0.9, hjust = 0) +
annotate(geom = "segment", x = 0.5, xend = 0.5, y = 13, yend = 11.2,
arrow = arrow(type = "closed", length = unit(0.02, "npc")), alpha = 0.9,
color = "darkgrey") +
annotate(geom = "text", x = 0.05, y = 13.5, color = "darkgrey",
label = "non-significance criterion successes",
size = 3, alpha = 0.9, hjust = 0) +
geom_step(alpha = 0.8, linewidth = 0.8) +
scale_y_continuous(breaks = bks, labels = labs, limits = c(0, nmax)) +
scale_x_continuous(breaks = priorbks) +
coord_cartesian(xlim = c(0, max(bfDF$priorsd))) +
labs(x = "Prior standard deviation",
y = "Successful replications ",
color = bquote("threshold" ~ gamma)) +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
strip.background = element_rect(fill = alpha("tan", 0.4)),
strip.text = element_text(size = 12),
legend.position = c(0.85, 0.25),
plot.background = element_rect(fill = "transparent", color = NA),
legend.box.background = element_rect(fill = "transparent", colour = NA))
grid.arrange(plotA, plotB, ncol = 1)
@
\caption{Number of successful replications of original null results in the RPCB
as a function of the margin $\Delta$ of the equivalence test
($p_{\text{TOST}} \leq \alpha$ in both studies for
$\alpha = \Sexpr{rev(alphaseq)}$) or the standard deviation of the zero-mean
normal prior distribution for the SMD effect size under the alternative
$H_{1}$ of the Bayes factor test ($\BF_{01} \geq \gamma$ in both studies for
The distinction between absence of evidence and evidence of absence is naturally
built into the Bayesian approach to hypothesis testing. A central measure of
evidence is the Bayes factor \citep{Kass1995}, which is the updating factor of
the prior odds to the posterior odds of the null hypothesis $H_{0}$ versus the
\begin{align*}
\underbrace{\frac{\Pr(H_{0} \given \mathrm{data})}{\Pr(H_{1} \given
\mathrm{data})}}_{\mathrm{Posterior~odds}}
= \underbrace{\frac{\Pr(H_{0})}{\Pr(H_{1})}}_{\mathrm{Prior~odds}}
\times \underbrace{\frac{p(\mathrm{data} \given H_{0})}{p(\mathrm{data}
\given H_{1})}}_{\mathrm{Bayes~factor}~\BF_{01}}.
\end{align*}
The Bayes factor quantifies how much the observed data have increased or
decreased the probability of the null hypothesis $H_{0}$ relative to the
alternative $H_{1}$. If the null hypothesis states the absence of an effect, a
Bayes factor greater than one (\mbox{$\BF_{01} > 1$}) indicates evidence for the
absence of the effect and a Bayes factor smaller than one indicates evidence for
the presence of the effect (\mbox{$\BF_{01} < 1$}), whereas a Bayes factor not
much different from one indicates absence of evidence for either hypothesis
(\mbox{$\BF_{01} \approx 1$}). A reasonable criterion for successful replication
of a null result may hence be to require a Bayes factor larger than some level
$\gamma > 1$ from both studies, for example, $\gamma = 3$ or $\gamma = 10$ which
are conventional levels for ``substantial'' and ``strong'' evidence,
respectively \citep{Jeffreys1961}. In contrast to the non-significance
criterion, this criterion provides a genuine measure of evidence that can
distinguish absence of evidence from evidence of absence.
When the observed data are dichotomized into positive (\mbox{$p < 0.05$}) or
null results (\mbox{$p > 0.05$}), the Bayes factor based on a null result is the
probability of observing \mbox{$p > 0.05$} when the effect is indeed absent
(which is $95\%$) divided by the probability of observing $p > 0.05$ when the
effect is indeed present (which is one minus the power of the study). For
\mbox{$\BF_{01} = 95\%/10\% = \Sexpr{round(0.95/0.1, 2)}$} indicating almost ten
times more evidence for the absence of the effect than for its presence. On the
other hand, if the power is only $50\%$, we have
\mbox{$\BF_{01} = 95\%/50\% = \Sexpr{round(0.95/0.5,2)}$} indicating only
slightly more evidence for the absence of the effect. This example also
highlights the main challenge with Bayes factors -- the specification of the
alternative hypothesis $H_{1}$. The assumed effect under $H_{1}$ is directly
related to the power of the study, and researchers who assume different effects
under $H_{1}$ will end up with different Bayes factors. Instead of specifying a
single effect, one therefore typically specifies a ``prior distribution'' of
plausible effects. Importantly, the prior distribution, like the equivalence
margin, should be determined by researchers with subject knowledge and before
In practice, the observed data should not be dichotomized into positive or null
results, as this leads to a loss of information. Therefore, to compute the Bayes
factors for the RPCB null results, we used the observed effect estimates as the
data and assumed a normal sampling distribution for them, as typically done in a
meta-analysis. The Bayes factors $\BF_{01}$ shown in
Figure~\ref{fig:nullfindings} then quantify the evidence for the null hypothesis
of no effect ($H_{0} \colon \text{SMD} = 0$) against the alternative hypothesis
that there is an effect ($H_{1} \colon \text{SMD} \neq 0$) using a normal
``unit-information'' prior distribution\footnote{For SMD effect sizes, a normal
unit-information prior is a normal distribution centered around the value of
no effect with a standard deviation corresponding to one observation. Assuming
that the group means are normally distributed
\mbox{$\overline{X}_{1} \sim \Nor(\theta_{1}, 2\sigma^{2}/n)$} and
\mbox{$\overline{X}_{2} \sim \Nor(\theta_{2}, 2\sigma^{2}/n)$} with $n$ the
total sample size and $\sigma$ the known data standard deviation, the
distribution of the SMD is
\mbox{$\text{SMD} = (\overline{X}_{1} - \overline{X}_{2})/\sigma \sim \Nor\{(\theta_{1} - \theta_{2})/\sigma, 4/n\}$}.
The standard deviation of the SMD based on one unit ($n = 1$) is hence $2$,
just as the unit standard deviation for log hazard/odds/rate ratio effect
sizes \citep[Section 2.4]{Spiegelhalter2004}.} \citep{Kass1995b} for the
effect size under the alternative $H_{1}$. We see that in most cases there is no
substantial evidence for either the absence or the presence of an effect, as
with the equivalence tests. For instance, with a lenient Bayes factor threshold
of $3$, only $\Sexpr{bfSuccesses}$ of the $\Sexpr{ntotal}$ replications are
successful, in the sense of having $\BF_{01} > 3$ in both the original and the
replication study. The Bayes factors for the two previously discussed examples
are consistent with our intuitions -- in the \citet{Goetz2011} example there is
indeed substantial evidence for the absence of an effect
($\BF_{01} = \Sexpr{formatBF(BForig1)}$ in the original study and
$\BF_{01} = \Sexpr{formatBF(BFrep1)}$ in the replication), while in the
\citet{Dawson2011} example there is even weak evidence for the \emph{presence}
of an effect, though the Bayes factors are very close to one due to the small
sample sizes ($\BF_{01} = \Sexpr{formatBF(BForig2)}$ in the original study and
$\BF_{01} = \Sexpr{formatBF(BFrep2)}$ in the replication).
As with the equivalence margin, the choice of the prior distribution for the SMD
under the alternative $H_{1}$ is debatable. The normal unit-information prior
seems to be a reasonable default choice, as it implies that small to large
effects are plausible under the alternative, but other normal priors with
smaller/larger standard deviations could have been considered to make the test
more sensitive to smaller/larger true effect sizes.
% There are also several more advanced prior distributions that could be used
% here \citep{Johnson2010,Morey2011}, and any prior distribution should ideally
% be specified for each effect individually based on domain knowledge.
We therefore report a sensitivity analysis with respect to the choice of the
prior standard deviation and the Bayes factor threshold in the bottom plot of
Figure~\ref{fig:sensitivity}. It is uncommon to specify prior standard
deviations larger than the unit-information standard deviation of $2$, as this
corresponds to the assumption of very large effect sizes under the alternatives.
However, to achieve replication success for a larger proportion of replications
than the observed
$\Sexpr{bfSuccesses}/\Sexpr{ntotal} = \Sexpr{round(bfSuccesses/ntotal*100, 0)}\%$,
unreasonably large prior standard deviations have to be specified. For instance,
a standard deviation of roughly $5$ is required to achieve replication success
in $50\%$ of the replications at a lenient Bayes factor threshold of
$\gamma = 3$. The standard deviation needs to be almost $20$ so that the same
success rate $11/15 = \Sexpr{round(11/15*100, 0)}\%$ as with the
non-significance criterion is achieved. The necessary standard deviations are
even higher for stricter Bayes factor threshold, such as $\gamma = 6$ or
$\gamma = 10$.
studyInteresting <- filter(rpcbNull, id == "(48, 2, 4)")
noInteresting <- studyInteresting$no
nrInteresting <- studyInteresting$nr
@
Of note, among the $\Sexpr{ntotal}$ RPCB null results, there are three
interesting cases (the three effects from original paper 48) where the Bayes
factor is qualitatively different from the equivalence test, revealing a
fundamental difference between the two approaches. The Bayes factor is concerned
with testing whether the effect is \emph{exactly zero}, whereas the equivalence
test is concerned with whether the effect is within an \emph{interval around
zero}. Due to the very large sample size in the original study
($n = \Sexpr{noInteresting}$) and the replication
($n = \Sexpr{prettyNum(nrInteresting, big.mark = "'")}$), the data are
incompatible with an exactly zero effect, but compatible with effects within the
equivalence range. Apart from this example, however, both approaches lead to the
same qualitative conclusion -- most RPCB null results are highly ambiguous.
We showed that in most of the RPCB studies with original ``null results'',
neither the original nor the replication study provided conclusive evidence for
the presence or absence of an effect. It seems logically questionable to declare
an inconclusive replication of an inconclusive original study as a replication
success. While it is important to replicate original studies with null results,
our analysis highlights that they should be analyzed and interpreted
appropriately. Box~\hyperref[box:recommendations]{1} summarizes our
recommendations.
\caption*{Box 1: Recommendations for the analysis of replication studies of
original null results. Calculations are based on effect estimates
$\hat{\theta}_{i}$ with standard errors $\sigma_{i}$ for $i \in \{o, r\}$
from an original study (subscript $o$) and its replication (subscript $r$).
Both effect estimates are assumed to be normally distributed around the true
effect size $\theta$ with known variance $\sigma^{2}$. The effect size
$\theta_{0}$ represents the value of no effect, typically $\theta_{0} = 0$.}
\label{box:recommendations}
\fbox{
\begin{tabular}{p{0.875\textwidth}}
% \toprule
\textbf{Equivalence test}
\begin{enumerate}
\item Specify a margin $\Delta > 0$ that defines an equivalence range
$[\theta_{0} - \Delta, \theta_{0} + \Delta]$ in which effects are
considered absent for practical purposes.
\item Compute the TOST $p$-values for original and replication data
$$p_{\text{TOST},i}
= \max\left\{\Phi\left(\frac{\hat{\theta}_{i} - \theta_{0} - \Delta}{\sigma_{i}}\right),
1 - \Phi\left(\frac{\hat{\theta}_{i} - \theta_{0} + \Delta}{\sigma_{i}}\right)\right\},
~ i \in \{o, r\}$$
with $\Phi(\cdot)$ the cumulative distribution function of the
standard normal distribution.
\item Declare replication success at level $\alpha$ if
$p_{\text{TOST},o} \leq \alpha$ and $p_{\text{TOST},r} \leq \alpha$,
conventionally $\alpha = 0.05$.
\item Perform a sensitivity analysis with respect to the margin $\Delta$.
For example, visualize the TOST $p$-values for different margins to
assess the robustness of the conclusions.
\end{enumerate} \\
% \midrule
\textbf{Bayes factor}
\begin{enumerate}
\item Specify a prior distribution for the effect size $\theta$ that
represents plausible values under the alternative hypothesis that
there is an effect ($H_{1}\colon \theta \neq \theta_{0})$. For
example, specify the mean $m$ and variance $v$ of a normal
distribution $\theta \given H_{1} \sim \Nor(m ,v)$.
\item Compute the Bayes factors contrasting
$H_{0} \colon \theta = \theta_{0}$ to
$H_{1} \colon \theta \neq \theta_{0}$ for original and replication
data. Assuming a normal prior distribution,
% $\theta \given H_{1} \sim \Nor(m ,v)$,
the Bayes factor is
= \sqrt{1 + \frac{v}{\sigma^{2}_{i}}} \, \exp\left[-\frac{1}{2} \left\{\frac{(\hat{\theta}_{i} -
\theta_{0})^{2}}{\sigma^{2}_{i}} - \frac{(\hat{\theta}_{i} - m)^{2}}{\sigma^{2}_{i} + v}
\item Declare replication success at level $\gamma > 1$ if
$\BF_{01,o} \geq \gamma$ and $\BF_{01,r} \geq \gamma$, conventionally
$\gamma = 3$ (substantial evidence) or $\gamma = 10$ (strong
evidence).
\item Perform a sensitivity analysis with respect to the prior
distribution. For example, visualize the Bayes factors for different
prior standard deviations to assess the robustness of the
conclusions.
\end{enumerate}
% \\ \bottomrule
\end{tabular}
}
When analyzed with equivalence tests or Bayes factors, the conclusions are far
less optimistic than those of the RPCB investigators, who state that ``original
null results were twice as likely as original positive results to mostly
replicate successfully (80\% vs. 40\%)'' \citep[p.16]{Errington2021}. While the
exact success rate depends on the equivalence margin and the prior distribution,
sensitivity analyses showed that even with unrealistically liberal choices, the
success rate remains below 40\%. This is not unexpected, as a study typically
requires larger sample sizes to detect the absence of an effect than to detect
its presence \citep[section 11.5.3]{Matthews2006}. However, the RPCB sample
sizes were only chosen so that each replication had at least 80\% power to
detect the original effect estimate. The design of replication studies should
ideally align with the planned analysis \citep{Anderson2017, Anderson2022,
Micheloud2020, Pawel2022c}. If the goal of the study is to find evidence for
the absence of an effect, the replication sample size should also be determined
so that the study has adequate power to make conclusive inferences regarding the
absence of the effect.
For both the equivalence test and the Bayes factor approach, it is critical that
the equivalence margin and the prior distribution are specified independently of
the data, ideally before the original and replication studies are conducted.
Typically, however, the original studies were designed to find evidence for the
presence of an effect, and the goal of replicating the ``null result'' was
formulated only after failure to do so. It is therefore important that margins
and prior distributions are motivated from historical data and/or field
conventions \citep{Campbell2021}, and that sensitivity analyses regarding their
choice are reported.
Researchers may also ask which of the two approaches is ``better''. We believe
that this is the wrong question to ask, because both methods address slightly