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** stcox      **  Cox proportional hazards regression
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  Overview:  To model survival as a function of covariates the Cox model
             may be used.  This model focuses on estimates of hazard 
             ratios.  


  Usage:     "stset ftime, failure(status)"
             "stcox tx"
             "stcox tx wbc"

             Where - ftime is the follow-up time
                     dvar is the censoring/death indicator (dvar=1 if
                       the event time was observed and 0 if censored)

             Where - "tx wbc" are the covariates

  Summaries: STATA will return estimates of the hazard ratios (exponentiated
             coefficients), standard errors, and confidence intervals.


  Options:   (1) "stcox tx wbc, nohr" -- the "nohr" option means that 
                    regression coefficients (log hazard ratios) will be
                    reported rather than hazard ratios.

             (2) "stcox tx wbc, basesurv( s0 )" -- this option saves the 
                    estimate of the baseline survival as the variable "s0".

                  NOTE:  if you are saving s0, then this is for X=0 (all of
                    them).  So,  you might want to center your predictors
                    so that the value X=0 is meaningful.

             (3) "stcox tx wbc"
                 "est store modTxWbc" -- this option is used similar to
                    logistic regression for computing likelihood ratio tests.

             (4) "stcox tx, strata(Svar)" -- this means a separate baseline 
                    hazard is assumed for every level of "Svar" but hazard
                    ratios within these strata are assumed to be equal 
                    (common).  A single "tx" HR is estimated.

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** stcoxkm    **  Cox proportional hazards model compared to Kaplan-Meier
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  Overview:  This compares Kaplan-Meier curves to the estimated survival
             curves based on the Cox regression model.


  Usage:     "stset ftime, failure(status)"
             "stcoxkm, by(tx)"

             Where - ftime is the follow-up time
                     dvar is the censoring/death indicator (dvar=1 if
                       the event time was observed and 0 if censored)

             Where - "tx " is the covariates

  Summaries: STATA will create a KM plot and overlay the fitted Cox
             model estimates of survival curves.

  Options:   (1) The "by(Svar)" is required.



OTHER COMMENTS:

(*) Some graphical output after regression:

     This is based on the command "sts graph"  Example:

          sts graph, strata(tx) adjustfor( wbc )

     This will create survival function estimates for each level of tx
     after adjusting for wbc.  What this means is that we use a Cox
     regression with

     log[ h(t,X) ] = log[h0(t)] * tx + b2 wbc

     and then looks at the fitted survival curves for different 
     levels of tx (ie. tx=1 and tx=0) for a fixed value of the 
     adjustment variables (wbc=0 in this case, and in general
     X_j=0 for all of the adjustment variables).

     This is not exactly what I had hoped for...  In the end we obtain 
     separate baseline survival estimates for each level of "tx".

     options:

       by(tx) -- a separate Cox regression for each level of tx.  This 
                 means that any adjustment variable is included as a
                 main effect *and* interaction with tx.

                 i.e. log[h(t,X)] = log[h0(t)]*tx + newlwbc*tx

       strata(tx) -- now adjustment is calculated by using a stratified
                     Cox regression, so that a common regression estimate
                     is used for the adjustment variable.

                 i.e. log[h(t,X)] = log[h0(t)]*tx + newlwbc