Micro-randomized trials (MRTs) are a novel experimental design for developing mobile health interventions. Participants are repeatedly randomized in an MRT, resulting in longitudinal data with time-va...
Konopiński (2022) suggests that when averaging nucleotide diversity over a sequence, ignoring per-site sample size variation (i.e., using an unweighted mean) offers an improvement in precision (lower ...
Randomized test-treatment studies aim to evaluate the clinical utility of diagnostic tests by providing evidence on their impact on patient health. However, the sample size calculation is affected by ...
An adaptive design with a blinded sample size recalculation in a randomized test-treatment study based on the prevalence is proposed and evaluated by a simulation study. The results of the adaptive de...
The adaptive design achieves the desired theoretical power, under the assumption that all other nuisance parameters have been specified correctly, while wrong assumptions regarding the prevalence may ...
The consideration of a blinded recalculation of the sample size already during the planning of the study may be advisable in order to increase the possibility of success as well as an enhanced process...
Estimation of the standard radiation dose at each imaging facility is required for radiation dose management, including establishment and utilization of the diagnostic reference levels. We investigate...
Stratified random sampling is an effective sampling technique for estimating the population characteristics. The determination of strata boundaries and the allocation of sample size to the strata are ...
In clinical trials, the determination of an adequate sample size is a challenging task, mainly due to the uncertainty about the value of the effect size and nuisance parameters. One method to deal wit...
In our research, we address sample size recalculation in three-stage trials, i.e. trials with two pre-planned interim analyses. We show how recalculation rules from two-stage trials can be modified to...
While we observe a notable favorable effect in terms of power and expected sample size by using three-stage designs compared to two-stage designs, the benefits of recalculation rules appear less clear...
Sample size recalculation is also applicable in three-stage designs. However, the extent to which recalculation brings benefits depends on which trial characteristics are most important to the applica...
In the management of most chronic conditions characterized by the lack of universally effective treatments, adaptive treatment strategies (ATSs) have grown in popularity as they offer a more individua...
Statistical tests of mediation are important for advancing implementation science; however, little research has examined the sample sizes needed to detect mediation in 3-level designs (e.g., organizat...
Statistical power was estimated for 17,496 3-level mediation designs in which the independent variable (X) resided at the highest cluster level (e.g., organization), the mediator (M) resided at the in...
For 2-sided tests, statistical power to detect mediation was sufficient (≥0.8) in only 463 designs (2.6%) estimated using MVM and 228 designs (1.3%) estimated using MSEM; the minimum number of highest...
While our analysis has important limitations, results suggest many of the 3-level mediation designs that can realistically be conducted in implementation research lack statistical power to detect medi...
Although books and articles guiding the methods of sample size calculation for prevalence studies are available, we aim to guide, assist and report sample size calculation using the present calculator...
We present and discuss four parameters (namely level of confidence, precision, variability of the data, and anticipated loss) required for sample size calculation for prevalence studies. Choosing corr...
Two calculators can be used with free software (Spreadsheet and RStudio) that benefit researchers with limited resources. It will, hopefully, minimize the errors in parameter selection, calculation, a...
