High-quality real-world data can be transformed into scientific
    real-world evidence for regulatory and healthcare decision-making
    using proven analytical methods and techniques. For example, propensity
    score (PS) methodology can be applied to select a subset of real-world
    data containing patients that are similar to those in the current clinical
    study in terms of baseline covariates, and to stratify the selected
    patients together with those in the current study into more homogeneous
    strata. Then, statistical methods such as the power prior approach or
    composite likelihood approach can be applied in each stratum to draw
    inference for the parameters of interest. This package provides
    functions that implement the PS-integrated real-world 
    evidence analysis methods such as
    Wang et al. (2019) <doi:10.1080/10543406.2019.1657133>,
    Wang et al. (2020) <doi:10.1080/10543406.2019.1684309>, and
    Chen et al. (2020) <doi:10.1080/10543406.2020.1730877>.

Power and Sample Size for Health Researchers is a Shiny application that brings together a 
    series of functions  related to sample size and power calculations for common analysis in the healthcare 
    field. There are functionalities to calculate the power, sample size to estimate or test hypotheses for means and 
    proportions (including test for correlated groups, equivalence, non-inferiority and superiority), association, correlations coefficients, 
    regression coefficients (linear, logistic, gamma, and Cox), linear mixed model, 
    Cronbach's alpha, interobserver agreement, intraclass correlation coefficients,
    limit of agreement on Bland-Altman plots,
    area under the curve, sensitivity and specificity incorporating the prevalence of disease. 
    You can also use the online version at <https://hcpa-unidade-bioestatistica.shinyapps.io/PSS_Health/>.

Test-based Image structural similarity measure and test of independence. 
    This package implements the key functions of two tasks: (1) computing
    image structural similarity measure PSSIM of Wang, Maldonado and
    Silwal (2011) <DOI:10.1016/j.csda.2011.04.021>;
    and (2) test of independence between a response and 
    a covariate in presence of heteroscedastic treatment effects 
    proposed by Wang, Tolos, and Wang (2010) <DOI:10.1002/cjs.10068>.