Represents the issue of chronological bias in a clinical trial.
chronBias(type, theta, method, saltus, alpha = 0.05)character string, should be one of "linT", "logT", or "stepT",
see Details.
factor of the time trend for further details see type.
character string, should be one of "sim" or "exact", see Description.
integer or missing specifying the patient index (i.e. position)
of the step in case of step time trend.
significance level
S4 object of class chronBias, a formal representation of the
issue of chronological bias in a clinical trial.
Chronological bias can be an issue in the design of a clinical trial. The
chronBias function is a constructor function
for an S4 object of the class chronBias representing the issue of
chronological bias, s.a. time trends, in a clinical trial. It supports two possible modes,
method="sim" and method="exact", and three different types of trend.
If method="sim", the object represents the simulated type-I-error rate given
the level alpha, the selection effect eta and the biasing
strategy type. When calling assess for a chronBias object
with method="sim", one test decision is computed for each sequence of
randSeq. The type-I-error rate (power) is the proportion of falsely
(correctly) rejected null hypotheses.
If method="exact", the object represents the exact type-I-error proabability
given the level alpha, the selection effect eta and the
biasing strategy type. When calling assess for a chronBias
object with method="exact", the exact p-value of each
randomization sequence is computed. So far, this is only supported for
normal endpoints. Then the type-I-error probability is
the sum of the corresponding quantiles of the doubly noncentral t-distribution.
type = "linT"Represents linear time trend. Linear time trend means that the expected response
of the patients increases evenly by theta with
every patient included in the study, until reaching N theta after N patients.
Linear time trend may occur as a result of gradually relaxing in- or exlusion criteria
throughout the trial.
It can be presented by the formula: $$f(i) = i \theta$$
type = "logT"Represents logistic time trend. Logistic time trend means that the expected response
of the patients increases logistically in the patient index by theta with
every patient included in the study, until reaching log(N) theta after N patients.
Logistic time trend may occur as a result of a learning curve, i.e. in a surgical trial.
It can be presented by the formula:
$$\log(i) \theta$$
type = "stepT"Represents step trend. Step trend means that the expected response of the patients increases
by theta after a given point ("saltus") in the allocation process.
Step trend may occur if a new device is used after the point \(c\) = "saltus", or if
the medical personal changes after after this point.
Step time trend can be presented by the formula:
$$f(i) = 1_{c \leq i \leq N} \theta$$
G. K. Rosenkranz (2011) The impact of randomization on the analysis of clinical trials. Statistics in Medicine, 30, 3475-87.
M. Tamm and R.-D. Hilgers (2014) Chronological bias in randomized clinical trials under different types of unobserved time trends. Methods of Information in Medicine, 53, 501-10.
Other issues: combineBias,
corGuess, imbal,
issue, selBias,
setPower