Commentary on Alan M Batterham Sportscience 14, 13-14, 2010
(sportsci.org/2010/amb.htm) |

This article and the
accompanying spreadsheets will be very useful additions to the researcher’s
toolbox. The article details a series
of scenarios/study designs for which minimisation is advisable in all cases,
even for crossover designs based on order of interventions. This is sound advice
and the points are well taken. I am interested in
comparing Will’s spreadsheets to existing freeware such as the Minim program. Minim permits up to four
groups, different proportions of patients in each group, any number of
prognostic factors and categories for each factor (subject to a total of 100
categories for all factors together), and different weights for each
prognostic factor if required, so that some factors can be treated as more
important to balance for than others. On the issue of weighting, I note that
for the scenario in which characteristics for all participants are known
before allocation, Will’s method assigns primary weighting to one factor and
equal importance to secondary factors, whereas for allocating participants as
they are recruited equal weighting is assigned to all factors. I am unsure
what influence this difference would have on resulting estimates of effects
if one wished to weight factors more finely. However, I have never been in
possession of sufficient a priori information to decide fine-tuned weighting
between multiple prognostic factors, and in practice equal importance is the
default assumption, so I doubt there is any practical advantage of Minim in
this regard. In any event, Will proposes a neat side-step of this potential
problem which allows one to ‘double-weight’ a variable by including it twice
with identical values. Will’s method is an
advance on Minim in that it may be applied in situations in which characteristics
for all people to be allocated are known in advance, rather than solely for
scenarios in which participants are allocated as they are recruited. A
further advantage over Minim is the ability to include variables measured on
a continuous scale, though the article notes that the influence on outcome is
likely to be small. I was pleased to
read that subject characteristics need to be included in the analysis. Other statisticians have also made this
often neglected point; for example Senn (2007) stated that "the factors that we
considered to be important in the first place and led us to adopt minimization…
cannot be regarded as irrelevant. So,
for example, if we sought to balance by sex, we must include sex as a factor
in the analysis." The benefit of including subject characteristics with
minimisation is better precision than that with simple random allocation.
This is perhaps one reason why minimisation has been described as the "platinum
standard" for trials–that is, better than the gold standard of randomisation
(Treasure and MacRae, 1998). The examples used to
illustrate the effect of differences between sample and population means
(e.g., Figure 1 and associated text) are instructive. Further, deriving a
standardised difference between the sample and population means via the
standard error of the active intervention mean is a neat way of anchoring minimum
sample size to the default for the minimum important difference (MID) of 0.2
standard deviations. This reveals that the standardised mean difference
between a sample mean and the population mean is typically >MID for a
group sample size <25. The alternative
analysis method to account for minimisation that Will proposes for the
situation is which assignment is performed after all participants have been
recruited has echoes of propensity score matching–a method used in observational/controlled
before-and-after studies (non-randomised). There is a long-standing debate in
the propensity score matching literature on whether the matched samples
should be treated as paired or as independent groups. Will proposes the
former to increase precision and backs it up with simulations. I need to examine
more closely any parallels between Will’s method and propensity score
matching with respect to this issue. Interestingly, Will notes in the article
that using the pre-post crossover spreadsheet to analyse data from groups minimised
on baseline values of the dependent variable produced confidence intervals
that were too narrow. Senn S (2007). A
personal view of some controversies in allocating treatment to patients in
clinical trials. Statistics in medicine 14, 2661-2674 Treasure T, MacRae KD (1998).
Minimisation: the platinum standard for trials? British Medical Journal 317,
362-363 Published May 2010 |