.- help for ^stcascoh^ (STB-59: sbe41) .- Create a dataset suitable for case-cohort analysis -------------------------------------------------- ^stcascoh^ [varlist] [^if^ exp] [^in^ range] ^, a^lpha^(^#^)^ [ ^gr^oup^(^varnames^)^ ^gen^erate^(^varlist^)^ ^ep^s^(^#^) ^se^ed(#) ^nosh^ow ] ^stcascoh^ is for use with survival-time data; see help @st@. You must ^stset^ data with an ^id()^ variable before using this command; see help @stset@. Description ----------- ^stcascoh^ is used to create the appropriate dataset for a case-cohort analysis by sampling the cohort at the time of entry (subcohort) and including all failures, whether they occur in the random sample or not (nonsubcohort cases). To this aim, ^stcascoh^ expands the observations who fail in two parts: (1) time interval (t0,t-eps] and (2) time interval (t-eps,t]. The following variables are added to the dataset: ^ _subco^ coded 0 for subcohort member with no failure 1 for subcohort members who failed 2 for nonsubcohort cases ^ _wBarlow^ log-weights of records as in Barlow method ^ _wSelPre^ log-weights of records as in Self and Prentice method The names of the new variables can be changed by specifying the ^gen()^ option. varlist defines the variables to be retained in the final dataset. If varlist is not specified, all variables are carried over into the resulting dataset. ^stcascoh^ shows two summary tables: the first one describes the subcohort membership vs. failure in the cohort; the second displays the risk sets with three controls or less to check whether the subcohort becomes small due to many failures or censorings. In the new dataset, nonsubcohort cases cannot rely on the original ^stset^ declaration. At the end of the module, ^stset^ is invoked to fix entry and exit time to the present _t0 and _t variables. Randomness in the sampling is obtained using Stata ^uniform()^ function. Seed can be specified by a ^seed()^ option. Observations not meeting ^if^ and ^in^ criteria are dropped even if they fail. The resulting dataset can be analyzed using ^stselpre^ or ^stcox^. ^stselpre^ fits proportional hazards model according with Prentice and Self--Prentice methods. Self--Prentice model based variance is estimated. When using ^stcox^, ^robust^ option is needed to estimate the approximate variance as proposed by Lin and Ying and Barlow. In this case, analysis can be performed using three methods: 1- Prentice: ^stcox varlist, robust^ 2- Self and Prentice: ^stcox varlist, offset(_wSelPre) robust^ 3- Barlow: ^stcox varlist, offset(_wBarlow) robust^ Options ------- ^alpha(^#^)^ specifies the sampling fraction. The sampling fraction can be expressed as real or integer. ^group(^varlist^)^ specifies that the alpha sample is to be drawn within each set of values of varlist, thus maintaining the proportion of each group. ^generate(^varlist^)^ specifies other variable names for three generated variables. ^eps(^#^)^ specifies a typically small number so that a case that is in the risk set at time t is represented in the expanded data by an "infinitesimal" episode (t-eps,t]. ^eps^ should be set to a number that is small compared to the measurement unit of time. ^eps^ defaults to 1E-3. ^seed(^#^)^ specifies seed for random sampling. ^noshow^ prevents ^stcascoh^ from showing the names of the key ^st^ variables. Examples -------- . ^stcascoh, alpha(20)^ . ^stcascoh afe yfe ln_exp, alpha(0.3) gen(mycohort) group(race) seed(987654321)^ Also see -------- Manual: ^[R] stcox [R] sttocc^ On-line: help for @stcox@ @stselpre@ @sttocc@ Reference --------- Barlow WE, Ichicawa L, Rosner D, and Izumi S: Analysis of Case-Cohort Designs. Journal Clinical Epidemiology 1999; 52: 1165-1172. Author ------ Enzo Coviello Unita' di Epidemiologia e Statistica Az. USL BA/1 70053 Andria (Bari) Italy coviello@@mythnet.it