Controversies Concerning Randomization and Additivity in Clinical Trials. Senn, S. Stat Med, 23:3729-3753, 2004.
doi  bibtex   
  title = {Controversies Concerning Randomization and Additivity in Clinical Trials},
  volume = {23},
  journal = {Stat Med},
  doi = {10.1002/sim.2074},
  author = {Senn, Stephen},
  year = {2004},
  keywords = {rct,study-design,randomization,covariate-adjustment,covariable-adjustment,baseline,additivity},
  pages = {3729-3753},
  citeulike-article-id = {13265395},
  citeulike-linkout-0 = {},
  posted-at = {2014-07-14 14:09:55},
  priority = {0},
  annote = {p. 3735: "in the pharmaceutical industry, in analyzing the data, if a linear model is employed, it is usual to fit centre as a factor but unusual to fit block.";p. 3739: a large trial "is not less vulnerable to chance covariate imbalance";p. 3741:"There is no place, in my view, for classical minimization" (vs. the method of Atkinson);"If an investigator uses such [allocation based on covariates] schemes, she or he is honour bound, in my opinion, as a very minimum, to adjust for the factors used to balance, since the fact that they are being used to balance is an implicit declaration that they will have prognostic value.";"The point of view is sometimes defended that analyses that ignore covariates are superior because they are simpler. I do not accept this. A value of {$\pi$}=3 is a simple one and accurate to one significant figure ... However very few would seriously maintain that if should generally be adopted by engineers.";p. 3742: "as Fisher pointed out ... if we balance by a predictive covariate but do not fit the covariate in the model, not only do we not exploit the covariate, we actually increase the expected declared standard error."; p. 3744:"I would like to see standard errors for group means abolished."; p. 3744:"A common habit, however, in analyzing trials with three or more arms is to pool the variances from all arms when calculating the standard error of a given contrast. In my view this is a curious practice ... it relies on an assumption of additivity of {$<$}i{$>$}all{$<$}/all{$>$} treatments when comparing only {$<$}i{$>$}two{$<$}/i{$>$}. ... a classical t-test is robust to heteroscedasticity provide that sample sizes are equal in the groups being compared and that the variance is internal to those two groups but is not robust where an external estimate is being used."; p. 3745: "By adjusting main effects for interactions a type III analysis is similarly illogical to Neyman's hypothesis test."; "Guyatt {$<$}i{$>$}et al.{$<$}/i{$>$} ... found a 'method for estimating the proportion of patients who benefit from a treatment ... In fact they had done no such thing."; p. 3746: "When I checked the Web of Science on 29 June 2003, the paper by Horwitz {$<$}i{$>$}et al.{$<$}/i{$>$} had been cited 28 times and that by Guyatt {$<$}i{$>$}et al.{$<$}/i{$>$} had been cited 79 times. The letters pointing out the fallacies had been cited only 8 and 5 times respectively."; "if we pool heterogeneous strata, the odds ratio of the treatment effect will be different from that in every stratum, even if from stratum to stratum it does not vary."; p. 3747: "Part of the problem with Poisson, proportional hazard and logistic regression approaches is that they use a single parameter, the linear predictor, with no equivalent of the variance parameter in the Normal case. This means that lack of fit impacts on the estimate of the predictor. ... what is the value of randomization if, in all except the Normal case, we cannot guarantee to have unbiased estimates. My view ... was that the form of analysis envisaged (that is to say, which factors and covariates should be fitted) justified the allocation and {$<$}i{$>$}not vice versa{$<$}/i{$>$}."; "use the additive measure at the point of analysis and transform to the relevant scale at the point of implementation. This transformation at the point of medical decision-making will require auxiliary information on the level of background risk of the patient."; p. 3748:"The decision to fit prognostic factors has a far more dramatic effect on the precision of our inferences than the choice of an allocation based on covariates or randomization approach and one of my chief objections to the allocation based on covariates approach is that trialists have tended to use the fact that they have balanced as an excuse for not fitting. This is a grave mistake."}

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