Description Usage Arguments Value Note Author(s) Examples

This function returns a summary of two group comparison in terms of effect size, lower and upper limits of CI, and p-value. Three classes of "outcome.var" variable can be analyzed using this function: 1) Cox proportional hazards model for survival outcome.var using coxph() 2) T-test for continuous outcome.var using lm() 3) Z-test for binary outcome.var using prop.test().

1 2 3 4 5 |

`outcome.var` |
a vector specifying the outcome variable. For 'binary' outcome.var, it should be a vector of 1 or 0. In case of a 'survival' variable, this will be a matrix of two columns: 1) time to event 2) censorship. |

`subgroup.var` |
a vector of row index specifying the subgroup to be included for the analysis. If NULL (default), all data will be used. |

`treatment.var` |
the name of the treatment variable. |

`placebo.code` |
the name of the control group within the treatment variable. |

`active.code` |
the name of the treatment/experimental group within the treatment variable. |

`outcome.class` |
the outcome class of the 'outcome.var' variable. One of the 3 values - "survival", "binary", or "continuous". |

`alpha` |
the confidence level (CI) for point estimate, i.e. 0.05 (default) for 95 percent CI. |

`surv.conf.type` |
confidence interval type. See conf.type in survfit. Default is "plain" |

`ties` |
Default is "efron". To match internal sas results, use "exact". See parameter "ties" in coxph. |

`covariate.var` |
a vector specifying the covariate variables. This can be added to adjust for in the analysis for survival and continuous outcome.var variable classes. Default is NULL. |

`strat.factor.var` |
a vector specifying the stratification variables. This can be added for the survival outcome.var variable class. Default is NULL. |

`return.fit` |
if TRUE, returns a table of summary statistics. Default is FALSE. |

`fit.para` |
a list of fitting parameters. Currently only |

A named vector of following entries: if binary - Effect.Size (Proportion Difference), Lower, Upper, P, Rsp.Placebo, Rsp.Active, N.Placebo, N.Active, nRsp.Placebo, nRsp.Active; if survival - [Events, N, Median Suvival Time] for each group, Effect.Size (Hazard Ratio), Lower, Upper, Wald P; if continuous - Effect.Size (Mean Difference), Lower, Upper, P.

This function requires "survival" package to call the coxph() function. Two treatment arms are required. Treatment group variable can be forced into a factor. Censorship variable is 1 if an event happened, 0 if censored.

Alexey Pronin [email protected], Ning Leng [email protected], and previous team members (see DESCRIPTION)

1 2 | ```
data(input)
SummaryTwoGroups(outcome.var = input$OS, treatment.var = input$Arm, placebo.code = "CTRL", active.code = "TRT", outcome.class = "continuous",surv.conf.type="plain")
``` |

lengning/gClinBiomarker documentation built on May 9, 2019, 2:55 p.m.

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