# Note on potential discrepancies between simtrial and survdiff

#### Larry Leon

## Overview

    library(gsDesign)
    library(gsDesign2)
    library(dplyr)
    library(gt)
    library(simtrial)
    library(tidyr)
    library(survival)

In the survival (base R) package, the log-rank and Cox estimation procedures apply (by default) a correction to “fix” roundoff errors. These are implemented with the `timefix` option (by default `timefix = TRUE`) via the `aeqSurv()` function. However, in the simtrial package, (and also Hmisc), such a correction is not implemented; Consequently, there can be discrepancies between simtrial and base R survival (`survdiff()`, `coxph()`, and `survfit()`).

For details on the `aeqSurv()` function, see Therneau, 2016 and the `?aeqSurv` function documentation.

In the following, we describe a simulation scenario where a discrepancy is generated and illustrate how discrepancies can be resolved (if desired) by pre-processing survival times with `aeqSurv()` and thus replicating `survdiff()` and `coxph()` default calculations.

In the simulated dataset, two observations are generated:

- Observation \(i=464\) with survival time \(Y=0.306132722582\).
- Observation \(i=516\) with survival time \(Y=0.306132604679\).
- Per `aeqSurv()`, these times are tied and set to \(Y=0.306132604679\).
- The log-rank and Cox estimates can therefore differ between other approaches without the “timefix” correction.

## Scenario definitions

We define various true data generating model scenarios and convert for use in gsDesign2. Here, we are using a single scenario where discrepancies were found. This is just for illustration to inform the user of simtrial that discrepancies can occur and how to resolve via `aeqSurv()`, if desired.

    survival_at_24_months <- 0.35
    hr <- log(.35) / log(.25)
    control_median <- 12
    control_rate <- c(log(2) / control_median, (log(.25) - log(.2)) / 12)

    scenarios <- tribble(
      ~Scenario, ~Name,           ~Period, ~duration, ~Survival,
      0,         "Control",       0,       0,         1,
      0,         "Control",       1,       24,        .25,
      0,         "Control",       2,       12,        .2,
      1,         "PH",            0,       0,         1,
      1,         "PH",            1,       24,        .35,
      1,         "PH",            2,       12,        .2^hr,
      2,         "3-month delay", 0,       0,         1,
      2,         "3-month delay", 1,       3,         exp(-3 * control_rate[1]),
      2,         "3-month delay", 2,       21,        .35,
      2,         "3-month delay", 3,       12,        .2^hr,
      3,         "6-month delay", 0,       0,         1,
      3,         "6-month delay", 1,       6,         exp(-6 * control_rate[1]),
      3,         "6-month delay", 2,       18,        .35,
      3,         "6-month delay", 3,       12,        .2^hr,
      4,         "Crossing",      0,       0,         1,
      4,         "Crossing",      1,       3,         exp(-3 * control_rate[1] * 1.3),
      4,         "Crossing",      2,       21,        .35,
      4,         "Crossing",      3,       12,        .2^hr,
      5,         "Weak null",     0,       0,         1,
      5,         "Weak null",     1,       24,        .25,
      5,         "Weak null",     2,       12,        .2,
      6,         "Strong null",   0,       0,         1,
      6,         "Strong null",   1,       3,         exp(-3 * control_rate[1] * 1.5),
      6,         "Strong null",   2,       3,         exp(-6 * control_rate[1]),
      6,         "Strong null",   3,       18,        .25,
      6,         "Strong null",   4,       12,        .2,
    )
    # scenarios |> gt()

    fr <- scenarios |>
      group_by(Scenario) |>
      #  filter(Scenario == 2) |>
      mutate(
        Month = cumsum(duration),
        x_rate = -(log(Survival) - log(lag(Survival, default = 1))) /
          duration,
        rate = ifelse(Month > 24, control_rate[2], control_rate[1]),
        hr = x_rate / rate
      ) |>
      select(-x_rate) |>
      filter(Period > 0, Scenario > 0) |>
      ungroup()
    # fr |> gt() |> fmt_number(columns = everything(), decimals = 2)

    fr <- fr |> mutate(fail_rate = rate, dropout_rate = 0.001, stratum = "All")

    # MWLR
    mwlr <- fixed_design_mb(
      tau = 12,
      enroll_rate = define_enroll_rate(duration = 12, rate = 1),
      fail_rate = fr |> filter(Scenario == 2),
      alpha = 0.025, power = .85, ratio = 1,
      study_duration = 36
    ) |> to_integer()

    er <- mwlr$enroll_rate

## A scenario that generates a discrepancy

    set.seed(3219)

    dgm <- fr[c(14:17), ]

    fail_rate <- data.frame(
      stratum = rep("All", 2 * nrow(dgm)),
      period = rep(dgm$Period, 2),
      treatment = c(
        rep("control", nrow(dgm)),
        rep("experimental", nrow(dgm))
      ),
      duration = rep(dgm$duration, 2),
      rate = c(dgm$rate, dgm$rate * dgm$hr)
    )

    dgm$stratum <- "All"
    # Constant dropout rate for both treatment arms and all scenarios
    dropout_rate <- data.frame(
      stratum = rep("All", 2),
      period = rep(1, 2),
      treatment = c("control", "experimental"),
      duration = rep(100, 2),
      rate = rep(.001, 2)
    )

Simulated dataset with discrepancy between logrank test of `simtrial::wlr()` and `survdiff()` (also compare to score test of `coxph()` [same as `survdiff()` with default `timefix = TRUE`]).

    ss <- 395

    set.seed(8316951 + ss * 1000)

    # Generate a dataset
    dat <- sim_pw_surv(
      n = 698,
      enroll_rate = er,
      fail_rate = fail_rate,
      dropout_rate = dropout_rate
    )

    analysis_data <- cut_data_by_date(dat, 36)

    dfa <- analysis_data

    dfa$treat <- ifelse(dfa$treatment == "experimental", 1, 0)

    z1 <- dfa |> wlr(weight = fh(rho = 0, gamma = 0))

    check <- survdiff(Surv(tte, event) ~ treat, data = dfa)

    # Note, for `coxph()`, use
    # cph.score <- summary(coxph(Surv(tte, event) ~ treat, data = dfa, control = coxph.control(timefix = TRUE)))$sctest

    cat("Log-rank wlr() vs survdiff()", c(z1$z^2, check$chisq), "\n")

    ## Log-rank wlr() vs survdiff() 0.1577428 0.1577954

Verify that `timefix = FALSE` in `coxph()` agrees with `wlr()`:

    cph.score <- summary(coxph(
      Surv(tte, event) ~ treat,
      data = dfa,
      control = coxph.control(timefix = FALSE)
    ))$sctest
    cat("Log-rank wlr() vs Cox score z^2", c(z1$z^2, cph.score["test"]), "\n")

    ## Log-rank wlr() vs Cox score z^2 0.1577428 0.1577428

Pre-processing survival times with `aeqSurv()` to implement `timefix = TRUE` procedure.

Verify `wlr()` and `survdiff()` now agree.

    Y <- dfa[, "tte"]
    Delta <- dfa[, "event"]

    tfixed <- aeqSurv(Surv(Y, Delta))
    Y <- tfixed[, "time"]
    Delta <- tfixed[, "status"]
    # Use aeqSurv version
    dfa$tte2 <- Y
    dfa$event2 <- Delta

    # wlr() after "timefix"
    dfa2 <- dfa
    dfa2$tte <- dfa2$tte2
    dfa2$event <- dfa2$event2
    z1new <- dfa2 |> wlr(weight = fh(rho = 0, gamma = 0))
    cat("Log-rank wlr() with timefix vs survdiff() z^2", c(z1new$z^2, check$chisq), "\n")

    ## Log-rank wlr() with timefix vs survdiff() z^2 0.1577954 0.1577954

Where do they differ (`tte2` are times after `aeqSurv()`)?

    dfa <- dfa[order(dfa$tte2), ]

    id <- seq(1, nrow(dfa))

    diff <- exp(dfa$tte) - exp(dfa$tte2)
    id_diff <- which(abs(diff) > 0)

    tolook <- seq(id_diff - 2, id_diff + 2)

    dfcheck <- dfa[tolook, c("tte", "tte2", "event", "event2", "treatment")]
    print(dfcheck, digits = 12)

    ##                tte           tte2 event event2    treatment
    ## 13  0.276251560170 0.276251560170     1      1 experimental
    ## 143 0.298789385712 0.298789385712     1      1      control
    ## 464 0.306132722582 0.306132604679     1      1      control
    ## 516 0.306132604679 0.306132604679     1      1 experimental
    ## 605 0.336489970678 0.336489970678     1      1 experimental

Verify `coxph()` (default) and `coxph()` with `aeqSurv()` pre-processing (using `tte2` as outcome and setting `timefix = FALSE`) are identical:

Also note that here ties do not have impact because in separate arms.

    # Check Cox with ties
    cox_breslow <- summary(coxph(Surv(tte, event) ~ treatment, data = dfa, ties = "breslow"))$conf.int
    cox_efron <- summary(coxph(Surv(tte, event) ~ treatment, data = dfa, ties = "efron"))$conf.int
    cat("Cox Breslow and Efron hr (tte, timefix=TRUE):", c(cox_breslow[1], cox_efron[1]), "\n")

    ## Cox Breslow and Efron hr (tte, timefix=TRUE): 0.9657106 0.9657106

    # Here ties do not have impact because in separate arms
    cox_breslow <- summary(coxph(Surv(tte2, event2) ~ treatment, data = dfa, ties = "breslow", control = coxph.control(timefix = FALSE)))$conf.int
    cox_efron <- summary(coxph(Surv(tte2, event2) ~ treatment, data = dfa, ties = "efron", control = coxph.control(timefix = FALSE)))$conf.int
    cat("Cox Breslow and Efron hr (tte2, timefix=FALSE):", c(cox_breslow[1], cox_efron[1]), "\n")

    ## Cox Breslow and Efron hr (tte2, timefix=FALSE): 0.9657106 0.9657106

So here there is a difference between `tte` and `tte2` times, but there is not an impact of ties for Cox between `"breslow"` and `"efron"` because the ties (single tie in `tte2`) are in separate arms.

Lastly, artificially change treatment so that two observations are tied within the same treatment arm which generates difference between `"breslow"` and `"efron"` options for `ties`:

    # Create tie within treatment arm by changing treatment
    dfa3 <- dfa
    dfa3[19, "treat"] <- 1.0

    cox_breslow <- summary(coxph(Surv(tte, event) ~ treat, data = dfa3, ties = "breslow", control = coxph.control(timefix = TRUE)))$conf.int
    cox_efron <- summary(coxph(Surv(tte, event) ~ treat, data = dfa3, ties = "efron", control = coxph.control(timefix = TRUE)))$conf.int
    cat("Cox Breslow and Efron hr (tte, timefix=TRUE)=", c(cox_breslow[1], cox_efron[1]), "\n")

    ## Cox Breslow and Efron hr (tte, timefix=TRUE)= 0.9729723 0.9729778

Same as

    cox_breslow <- summary(coxph(Surv(tte2, event2) ~ treat, data = dfa3, ties = "breslow", control = coxph.control(timefix = FALSE)))$conf.int
    cox_efron <- summary(coxph(Surv(tte2, event2) ~ treat, data = dfa3, ties = "efron", control = coxph.control(timefix = FALSE)))$conf.int
    cat("Cox Breslow and Efron hr (tte2, timefix=FALSE)=", c(cox_breslow[1], cox_efron[1]), "\n")

    ## Cox Breslow and Efron hr (tte2, timefix=FALSE)= 0.9729723 0.9729778