{smcl}
{* 23dec2014}{...}
{title:Title}
{p2colset 5 18 20 2}{...}
{p2col :{hi:artbindlg} {hline 2}}ART (Binary Outcomes) - Sample Size and Power dialog{p_end}
{p2colreset}{...}
{title:Definitions and usage}
{p 0 4}
{cmd:Number of groups} (default 2, max. 6) Number of arms in the clinical trial.
{p_end}
{p 0 4}
{cmd:Allocation ratios} By default, all groups are assumed of equal size, so
the allocation ratios (more precisely, weights) are
all equal to 1. You can very this, e.g. 1 2 2 would
specify that groups 2 and 3 should have twice as many
patients allocated as group 1.
{p_end}
{p 0 4}
{cmd:Proportions} These are the target event probabilities in the arms
of the trial. Values must all be in the range (0,1).
{p_end}
{p 0 4}
{cmd:Trend} Allows a linear trend test across the groups, with
scores 1, 2, 3,... attached to the groups. A trend
test may be more powerful than a general comparison
between the groups. See also ^dose^.
{p_end}
{p 0 4}
{cmd:Dose} A quantity assigned to each group which represents the
dose of some medication or other intervention received
by the subjects in that group. If you specify a dose
level for any group, you must specify a level for
every group.
{p_end}
{p 0 4}
{cmd:Alpha} (default 0.05 two-sided) Alpha is the Type I error probability.
{p_end}
{p 0 4}
{cmd:Power or N} Power is the power of the trial, N is the total
sample size (all groups combined). The radio buttons
allow you to choose whether the program will display
the power for given N or the N for specified power.
{p_end}
{p 0 4}
{cmd:Local alternatives} Under local alternatives (the default option), the
program uses the covariance matrix appropriate to the
null hypothesis of no difference among the proportions
under both the null and the alternative hypotheses.
This approach is reasonable if the odds ratio(s)
under the alternative hypothesis are between about
0.5 and 2. For two-group studies, the sample sizes
tend to be somewhat larger with local alternatives
than with global (non-local) alternatives.
{p_end}
{p 0 4}
{cmd:Distant alternatives} Under non-local alternatives, different covariance
matrices are assumed according to the proportions
proposed under the alternative hypothesis.
{p_end}
{p 0 4}
{cmd:Unconditional test} The unconditional test (the default) with local
alternatives (also the default, see above) is the
usual Pearson chisquare test. For two-group studies,
the sample sizes may be anywhere from slightly larger
to considerably larger with the unconditional test
than with the conditional test.
{p_end}
{p 0 4}
{cmd:Conditional test (Peto)} The test is conditional on the total number of
observed events and is based on Peto's approximation
to the log odds ratio. It is available with local
alternatives only. It gives smaller sample sizes,
but perhaps should not be used unless you are willing
to analyse the results of the study using the same
test.
{p_end}
{p 0 4}
{cmd:Non-inferiority design} In a non-inferiority design one wishes to test whether the effects of the
control and experimental treatments differ by no more than a
a prespecified amount. In the calculations the roles of the null
and alternative (alternate) hypotheses are reversed. That is,
the sample size is calculated with signficance level equal to
1-power and power equal to 1-alpha. A side-effect of this reversal is that
the program is not able to compute the power of a non-inferiority
design for a given sample size. However, the power can still be determined
by trial and error, by repeatedly entering alpha and power until the desired
sample size is achieved.
{p_end}
{p 0 4}
{cmd:One-sided alpha} (default two-sided) With one-sided alpha the significance level
used by the program is doubled, resulting in a larger power or
smaller sample size. This option should be used with caution.
{p_end}
{title:Examples}
{hi:Example 1}
Number of groups 2 Allocation ratios [Default]
Proportions 0.2 0.3
Trend No Dose
Alpha 0.05 Power or N 0.8
Specify power Yes Specify sample size No
Local alternatives Yes Global (non-local) alt No
Unconditional test Yes Conditional test No
Non-inferiority design No One-sided alpha No
{hi:Result}
. artbin, pr(0.2 0.3) ngroups(2) aratios(1 1) distant(0) alpha(0.05) power(0.8) onesided(0) ni(0)
ART - ANALYSIS OF RESOURCES FOR TRIALS (version 1.1.0 12feb2014)
------------------------------------------------------------------------------
A sample size program by Abdel Babiker, Patrick Royston & Friederike Barthel,
MRC Clinical Trials Unit at UCL, London WC2B 6NH, UK.
------------------------------------------------------------------------------
Type of trial Superiority - binary outcome
Statistical test assumed Unconditional comparison of 2
binomial proportions
Number of groups 2
Allocation ratio Equal group sizes
Anticipated event probabilities 0.200, 0.300
Alpha 0.050 (two-sided)
Power (designed) 0.800
Total sample size (calculated) 589
Expected total number of events 148
------------------------------------------------------------------------------
Machin & Campbell (Table 3.1, p. 24) give n = 294 per group.
{hi:Example 2}
Number of groups 4 Allocation ratios [Default]
Proportions 0.1 0.2 0.3 0.4
Trend No Dose
Alpha 0.05 Power or N 0.9
Specify power Yes Specify sample size No
Local alternatives Yes Global (non-local) alt No
Unconditional test Yes Conditional test No
Non-inferiority design No One-sided alpha No
{hi:Result}
. artbin, pr(0.1 0.2 0.3 0.4) ngroups(2) distant(0) alpha(0.05) power(0.9) onesided(0) ni(0)
ART - ANALYSIS OF RESOURCES FOR TRIALS (version 1.1.0 12feb2014)
------------------------------------------------------------------------------
A sample size program by Abdel Babiker, Patrick Royston & Friederike Barthel,
MRC Clinical Trials Unit at UCL, London WC2B 6NH, UK.
------------------------------------------------------------------------------
Type of trial Superiority - binary outcome
Statistical test assumed Unconditional comparison of 4
binomial proportions
Number of groups 4
Allocation ratio Equal group sizes
Anticipated event probabilities 0.100, 0.200, 0.300, 0.400
Alpha 0.050 (two-sided)
Power (designed) 0.900
Total sample size (calculated) 213
Expected total number of events 54
------------------------------------------------------------------------------
{hi:Example 3}
As Example 2 but with Trend checked (doses unspecified)
. artbin, pr(0.1 0.2 0.3 0.4) ngroups(4) distant(0) alpha(0.05) power(0.9) trend onesided(0) ni(0)
ART - ANALYSIS OF RESOURCES FOR TRIALS (version 1.1.0 12feb2014)
------------------------------------------------------------------------------
A sample size program by Abdel Babiker, Patrick Royston & Friederike Barthel,
MRC Clinical Trials Unit at UCL, London WC2B 6NH, UK.
------------------------------------------------------------------------------
Type of trial Superiority - binary outcome
Statistical test assumed Unconditional comparison of 4
binomial proportions
Number of groups 4
Allocation ratio Equal group sizes
Linear trend test: doses are 1,...,4
Anticipated event probabilities 0.100, 0.200, 0.300, 0.400
Alpha 0.050 (two-sided)
Power (designed) 0.900
Total sample size (calculated) 158
Expected total number of events 40
------------------------------------------------------------------------------
{hi:Example 4}
As Example 1 but assuming a non-inferiority design
. artbin, pr(0.2 0.3) ngroups(2) distant(0) alpha(0.05) power(0.8) onesided(0) ni(1)
ART - ANALYSIS OF RESOURCES FOR TRIALS (version 1.1.0 12feb2014)
------------------------------------------------------------------------------
A sample size program by Abdel Babiker, Patrick Royston & Friederike Barthel,
MRC Clinical Trials Unit at UCL, London WC2B 6NH, UK.
------------------------------------------------------------------------------
Type of trial Non-inferiority - binary outcome
Statistical test assumed Comparison of 2 binomial proportions
P1 and P2.
Null hypothesis H0: P2-P1 = 0.100
Alternative hypothesis H1: P2-P1 = 0.000
Null variance estimation method Sample estimate
Number of groups 2
Allocation ratio Equal group sizes
Anticipated event probabilities 0.200, 0.200
Alpha 0.050 (two-sided)
Power (designed) 0.800
Total sample size (calculated) 504
Expected total number of events 101
------------------------------------------------------------------------------
{title:Authors}
{pstd}Abdel Babiker, MRC Clinical Trials Unit at UCL{break}
{browse "mailto:a.babiker@ucl.ac.uk":Ab Babiker}
{pstd}Friederike Maria-Sophie Barthel, formerly MRC Clinical Trials Unit{break}
{browse "mailto:sophie@fm-sbarthel.de":Sophie Barthel}
{pstd}Babak Choodari-Oskooei, MRC Clinical Trials Unit at UCL{break}
{browse "mailto:b.choodari-oskooei@ucl.ac.uk":Babak Oskooei}
{pstd}Patrick Royston, MRC Clinical Trials Unit at UCL{break}
{browse "mailto:j.royston@ucl.ac.uk":Patrick Royston}
{title:Also see}
{psee}
Manual: {hi:[R] sampsi}
{psee}
Online: help for {help artmenu}, {help artbin}, {help artbindlg}, {help artbindlg}