{smcl}
{* *! version 1.1.0 15Apr2022}
{cmd:help power swgee}
{hline}
{title:Title}
{p2colset 1 15 17 2}{...}
{p2col :{hi:power swgee} {hline 2}}Computes power (under both a Z and t distribution) for cluster randomized stepped wedge designs assuming analysis is performed using a generalized estimating equations (GEE) model
{marker syntax}{...}
{title:Syntax}
{p 8 14 2}
{cmd:power swgee, } {cmd:es({it:real}) {cmdab:nclust:ers(}{it:{help integer}}) {cmdab:nper:iods(}{it:integer}) {cmdab:n(}{it:integer}) [{it:other options} {it:power_options}]}
{synoptset 30 tabbed}{...}
{synopthdr}
{synoptline}
{syntab:Required}
{synopt:{cmdab:es(}{it:real})}Specifies the effect size. Note that this is not the standardized effect size but the effect size on the scale of the outcome. Specifically, specify a difference for identity link,
an odds ratio for logit link and a risk or rate ratio for log link (for binary or count outcomes, respectively).{p_end}
{synopt:{cmdab:nclust:ers(}{it:integer})}Specifies the number of clusters in the stepped wedge design. Note that if only the
required options are specified, then the number of clusters must be a multiple of {cmdab:nper:iods}-1{p_end}
{synopt:{cmdab:nper:iods(}{it:integer})}Number of time periods in the study{p_end}
{synopt:{cmdab:n(}{it:integer})}Number of individuals in each cluster at each time period. Depednign on the design
(cross sectional or cohort) these may be the same individuals within the same cluster in different time periods, or
these may be different people in each cluster in each time period{p_end}
{pstd}
If only the required options are specified, a complete design is assumed and all clusters start in the control condition
so that the number of steps is {cmdab:nper:iods}-1.
{syntab:Optional}
{synopt:{cmdab:mu0(}{it:{help numlist}})}Mean/probability/rate of outcome in control at baseline (time period 0). Must specify a
probability/rate between 0 and 1 (but not including 0 or 1), which is then converted to the link function scale. Defaults
to 0.1. {p_end}
{synopt:{cmdab:muT(}{it:{help numlist}})}Mean/probability/rate of outcome in control at final time period (time T), even if no
clusters are in the control condition at the final time period. Must specify a probability/rate between 0 and 1 (but not
including 0 or 1), which is then converted to the link function scale. Defaults to be equal to {cmd:mu0}. See description
section for more details on period effects.{p_end}
{synopt:{cmdab:mus(}{it:{help numlist}})}A list of numbers indicating the control outcome probabilities/rates across
time (the time trend). If not supplied, then defaults to period effects being specified by {cmd:mu0} and {cmd:muT}.
Otherwise, if {cmd:mus} is specified, {cmd:mu0} and {cmd:muT} will be overridden if they are also specified.{p_end}
{synopt:{cmdab:design(}{it:{help varlist}})}The design option allows the user to insert a list of variables corresponding
to the design, where those variables are from a data set that is currently loaded in Stata.
{synopt:{cmdab:alpha(}{it:real})}Two-tailed type I error rate; defaults to 0.05{p_end}
{synopt:{cmdab:working_ind(}{it:integer})}Specifes whether to use the robust sandwich variance assuming
working independence (1) or the model based variance where the working correlation corresponds to the true correlation structure (0) to estimate standard errors of the mean model parameters. The default is the
model based variance where the working correlation corresponds to the true correlation structure (0).{p_end}
{synopt:{cmdab:corstr(}{it:{help string}})}Gives the structure of the true correlation matrix, which is also the
working correlation matrix if working_ind=0. The following options are available
for corstr in the context of SW-CRTs. The default is nested exchangeable. Note that the underline in the table indicates
that the option can be specified more succinctly with only the first word of each structure. For example, nested exchangeable
can be specified by only including the word "nested" in the option.{p_end}
{center:{opt corstr} options}
{center:{hline 63}}
{center: Option Design Type Correlation Parameters}
{center:{hline 63}}
{center:{ul:nested} exchangeable Cross-sectional tau0, tau1}
{center: {ul:block} exchangeable Cohort tau0, tau1, tau2}
{center:{ul:exponential} decay Cross-sectional tau0, rho1}
{center: {ul:proportional} decay Cohort tau0, rho1, rho2}
{center:{hline 63}}
{synopt:{cmdab:family(}{it:string})}Specifies the distribution family; default is gaussian, which can also be specified as normal.{p_end}
{synopt:{cmdab:link(}{it:string})}Specifies the link function. The default is logit for binomial family; log for Poisson family; and identity for Gaussian family. The following options are available for {cmdab:family} and {cmdab:link}.{p_end}
{center:Family Link }
{center:{hline 20}}
{center:binomial logit }
{center:binomial log }
{center:binomial identity}
{center:poisson log }
{center:poisson identity}
{center:gaussian log }
{center:gaussian identity}
{center:{hline 20}}
{synopt:{cmdab:phi(}{it:real})}Dispersion parameter; defaults to 1{p_end}
{synopt:{cmdab:df(}{it:real})}Degrees of freedom for the t-test; defaults to {cmdab:nclust:ers} - 2{p_end}
{synopt:{cmdab:tau0(}{it:real})}Within-period ICC (required for all four correlation structures); must be specified by user{p_end}
{synopt:{cmdab:tau1(}{it:real})}Constant between-period ICC (for nested and block exchangeable correlation structures); defaults to be equal to {cmdab:tau0}{p_end}
{synopt:{cmdab:tau2(}{it:real})}Constant repeated-measures ICC (for block exchangeable correlation structure); defaults to be equal to {cmdab:tau1}{p_end}
{synopt:{cmdab:rho1(}{it:real})}Between-period ICC decay parameter (for exponential and proportional decay correlation structures); defaults to be equal to 1, indicating no decay{p_end}
{synopt:{cmdab:rho2(}{it:real})}Repeated-measures ICC decay parameter (for proportional decay correlation structure); defaults to be equal to {cmdab:rho1}{p_end}
{pstd}
{it:power_options} indicates that other options are passed to the power command. For example, the option {cmd:table} requests an output table, and {cmd:graph()} can be used to specify parameters to graph.
However, certain options, such as {cmd:onesided}, have no effect on this custom power program.
{synoptline}
{p2colreset}{...}
{marker description}{...}
{title:Description}
{pstd}
{cmd:power swgee} computes power calculations for stepped wedge designs in the generalized estimating equations (GEE) framework. The theory and background behind the methods are published in {help power_cmd_powersw##Li2018:Li et al. (2018)} and {help power_cmd_powersw##Li2019:Li (2020)}. Further description can be found in an article currently under review at Stata Journal. The submitted article can be downloaded {browse "https://sites.duke.edu/johngallis/stata-packages/":here}.
{marker options}{...}
{title:Options}
{pstd}
If the {cmdab:design} option is not specified, then the design defaults to a complete design defined by {cmdab:nclust:ers} and {cmdab:nper:iods}. For example, if {cmdab:nclust:ers}=5 and {cmdab:nper:iods}=6, then there are 5 steps
with 1 cluster per step (because all clusters are assumed to start in the control condition and end in the treatment condition and an equal number of clusters is assumed in each step). The design is given by:
{center:{txt}1 2 3 4 5 6}
{center:{c TLC}{hline 25}{c TRC}}
{center:1 {c |} {res}0 1 1 1 1 1{txt} {c |}}
{center:2 {c |} {res}0 0 1 1 1 1{txt} {c |}}
{center:3 {c |} {res}0 0 0 1 1 1{txt} {c |}}
{center:4 {c |} {res}0 0 0 0 1 1{txt} {c |}}
{center:5 {c |} {res}0 0 0 0 0 1{txt} {c |}}
{center:{c BLC}{hline 25}{c BRC}{txt}}
{pstd}
Period effects are specified using either the {cmd:mu0} and {cmd:muT} options, or the {cmd:mus} option. The {cmd:mu0} and {cmd:muT} options are used by default, and will be overridden if the {cmd:mus} option is specified.
{pstd}
If using the {cmd:mu0} and {cmd:muT} options, you specify the prevalence/rate of outcome in control at baseline and final time period. (Note that for continuous outcomes with identity link, these will
be undefined. If you enter values anyway, they are ignored in the calculation.) (If muT is not specified, it will default to be equal to mu0, which is equivalent to stating that there is no time
trend in the outcome.) Then the program will create a linear trend on the link function scale based on these prevalences/rates and the number of time periods. For example, suppose you specify 0.1 as {cmd:mu0} and 0.2 as {cmd:muT},
with four time periods and using a log link. First, the program will convert the endpoints on the link function scale (log(0.1)=-2.30 and log(0.2)=-1.61), then create period effects for the other two time periods in equally spaced
intervals between these two periods (in this case, -2.07 and -1.84).
{pstd}
For researchers who want even more fine-tuned control, the {cmd:mus} option will allow you to enter a set of probabilities/rates equal to the number of periods, which will then be
converted to the link function scale for the power function.
{marker example}{...}
{title:Examples}
{pstd}
Create dataset corresponding to the design
{phang2}{cmd:. clear}{p_end}
{phang2}{cmd:. qui set obs 15}{p_end}
{phang2}{cmd:. forvalues i=1/4 {c -(}}{p_end}
{phang2}{cmd:. gen var`i'=0}{p_end}
{phang2}{cmd:. {c )-}}{p_end}
{phang2}{cmd:. replace var4 = 1}{p_end}
{phang2}{cmd:. replace var3 = 1 in 1/10}{p_end}
{phang2}{cmd:. replace var2 = 1 in 1/5}{p_end}
Run power command with proportional decay correlation structure, varying tau0
{phang2}{cmd:. power swgee, mu0(0.1) muT(0.2) es(2) design(var1-var4) nclust(15) nper(4) n(100) family(poisson) link(log)}
{cmd:corstr(proportional decay) tau0(0.04(0.01)0.06) rho1(0.02) rho2(0.7) alpha(0.05) table graph(xdimension(tau0)}
{cmd:ydimension(t_power))}{p_end}
{it:({stata "power_swgee_examples power_swgee_examples_1":click to run})}
Example without design matrix
{phang2}{cmd:. clear}{p_end}
{phang2}{cmd:. power swgee, mu0(0.1) muT(0.2) es(2) nclust(15) nper(4) n(100) family(binomial) link(logit)}
{cmd:corstr(exponential decay) tau0(0.04(0.01)0.06) rho1(0.02) alpha(0.05) table graph(xdimension(tau0) ydimension(t_power))}{p_end}
{it:({stata "power_swgee_examples power_swgee_examples_2":click to run})}
{marker results}{...}
{title:Stored results}
{pstd}
{cmd:power swgee} stores the following in {cmd:r()}:
{synoptset 23 tabbed}{...}
{p2col 5 23 26 2: Scalars}{p_end}
{synopt:{cmd:r(onesided)}}1 for a one-sided test, 0 otherwise; {cmd:defunct; not used in the program}{p_end}
{synopt:{cmd:r(N)}}number of individuals; {cmd:defunct; not used in the program} {p_end}
{synopt:{cmd:r(separator)}}number of lines between separator lines in the table{p_end}
{synopt:{cmd:r(divider)}}1 if divider is requested in the table, 0 otherwise{p_end}
{synopt:{cmd:r(alpha)}}significance level (type I error); {cmd:defunct; not used in the program, see r(size)}{p_end}
{synopt:{cmd:r(beta)}}probability of a type II error {cmd:defunct; not used in the program}{p_end}
{synopt:{cmd:r(power)}}power {cmd:defunct; not used in the program; see power_t and power_z in the output table.}{p_end}
{synopt:{cmd:r(working_ind)}}1 if working_ind option set to 1; 0 otherwise{p_end}
{phang2}Other items in the return scalars are for the last iteration of the power program (last row of the output table). All
items for all iterations can be extracted from the table matrix ({cmd:r(pss_table)}).{p_end}
{p2col 5 23 26 2: Macros}{p_end}
{synopt:{cmd:r(corstr)}}Correlation structure chosen{p_end}
{synopt:{cmd:r(family)}}Distributional family chosen{p_end}
{synopt:{cmd:r(link)}}Link function chosen{p_end}
{phang2}Other macros are common to the power command. See {it:{help power}} for more information.{p_end}
{p2col 5 23 26 2: Matrices}{p_end}
{synopt:{cmd:r(pss_table)}}table of results{p_end}
{synopt:{cmd:r(design)}}design matrix; if user-entered and varied, this is the design matrix of the last iteration of the program
(i.e., the last row in the table){p_end}
{synopt:{cmd:r(mus)}}Matrix of probabilities/rates entered in the {cmd:mus} option or the {cmd:mu0} and {cmd:muT} option;
will be missing if outcome is continuous{p_end}
{synopt:{cmd:r(betas)}}Matrix of period effects on the link function scale{p_end}
{p2colreset}{...}
{marker reference}{...}
{title:References}
{marker Li2018}{...}
{phang}
Li, F., Forbes, A. B., Turner, E. L., & Preisser, J.S. (2018). Sample size determination for GEE analyses of stepped wedge cluster randomized trials. {it:Biometrics}, 74(4), 1450-1458.
{p_end}
{marker Li2019}{...}
{phang}
Li, F. (2020). Design and analysis considerations for cohort stepped wedge cluster randomized trials with a decay correlation structure. {it:Statistics in Medicine}, 39(4), 438-455.
{p_end}
{marker author}{...}
{title:Authors}
John A. Gallis
Duke University Department of Biostatistics and Bioinformatics
Duke Global Health Institute
Durham, NC
john.gallis@duke.edu
Xueqi Wang
Duke University Department of Biostatistics and Bioinformatics
Duke Global Health Institute
Durham, NC
xueqi.wang@duke.edu
Paul J. Rathouz
Department of Population Health
University of Texas at Austin
Dell Medical School
Austin, TX
paul.rathouz@austin.utexas.edu
John S. Preisser
Department of Biosttistics
University of North Carolina at Chapel Hill
Gillings School of Global Public Health
Chapel Hill, NC
jpreisse@bios.unc.edu
Fan Li
Yale School of Public Health
Center for Methods in Implementation and Prevention Science
New Haven, CT
fan.f.li@yale.edu
Elizabeth L. Turner
Duke University Department of Biostatistics and Bioinformatics
Duke Global Health Institute
Durham, NC
liz.turner@duke.edu
{marker acknowledgements}{...}
{title:Acknowledgements}
The authors would like to thank Alyssa Platt of the Duke Global Health Institute Research Design and Analysis Core for testing and providing feedback on the programs.