of GSDs on the sample space, bias, uncertainty measures and the amount of information; see @WHITEHEAD1986, @liu1999, @liu2006, @Brannath2006, @schou2013meta, @milanzi2015, @Graf2016, @shimura2017 and @Marschner2018.

@WHITEHEAD1986 investigated bias in GSDs and suggested a correction. @liu1999 and @liu2006 recognized change in support in the one-parameter exponential family, and investigated unbiased estimation. The adaptation-driven change in support is critical for derivation MLEs’ distributions in Section \[normality\]. @milanzi2015 developed a likelihood approach that applies when the early stopping rule does not depend on the parameter of interest. @schou2013meta recognized presence of truncation in the joint distribution of stage-specific test statistics. @Brannath2006 and @Graf2016 investigated bias and MSEs in sample size modification problems. A comprehensive simulation study comparing various GSD estimators of a parameter of interest is reported in @shimura2017. @Marschner2018 have found information in conditional and unconditional GSD MLEs and quantified loss associated with conditioning. As it is shown in Section \[normality\], unconditional and conditional information measures differ from what they reported in supplementary material to their manuscript, but the information loss is the same.

However, the normality and asymptotic normality assumption continues to be directly used with non-normally distributed statistics. @mehta2007 built repeated confidence intervals. @koopmeiners2012 explored MLEs conditional on stopping but 