Change from Baseline and Analysis of Covariance Revisited. Senn, S. Stat Med, 25:4334-4344, 2006.
  title = {Change from Baseline and Analysis of Covariance Revisited},
  volume = {25},
  journal = {Stat Med},
  author = {Senn, Stephen},
  year = {2006},
  keywords = {repeated-measures,change-score,lords-paradox,ancova,change,change-from-baseline,analysis-of-covariance,baselines},
  pages = {4334-4344},
  citeulike-article-id = {13265683},
  posted-at = {2014-07-14 14:10:02},
  priority = {0},
  annote = {shows that claims that in a 2-arm study it is not true that ANCOVA requires the population means at baseline to be identical;refutes some claims of lia00lon;problems with counterfactuals;temporal additivity ("amounts to supposing that despite the fact that groups are difference at baseline they would show the same evolution over time");causal additivity;is difficult to design trials for which simple analysis of change scores is unbiased, ANCOVA is biased, and a causal interpretation can be given;temporally and logically, a "baseline cannot be a {$<$}i{$>$}response{$<$}/i{$>$} to treatment", so baseline and response cannot be modeled in an integrated framework as Laird and Ware's model has been used;"one should focus clearly on `outcomes' as being the only values that can be influenced by treatment and examine critically any schemes that assume that these are linked in some rigid and deterministic view to `baseline' values. An alternative tradition sees a baseline as being merely one of a number of measurements capable of improving predictions of outcomes and models it in this way.";"You cannot establish necessary conditions for an estimator to be valid by nominating a model and seeing what the model implies unless the model is universally agreed to be impeccable. On the contrary it is appropriate to start with the estimator and see what assumptions are implied by valid conclusions.";this is in distinction to lia00lon}
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