The `parmsurvfit`

package executes basic parametric survival analysis techniques similar to those in ‘Minitab’. Among these are fitting right-censored data, assessing fit, plotting survival functions, and summary statistics and probabilities.

The `fit_data`

function produces maximum likelihood estimates (MLE) for right censored data based on a specified distribution. Here,

`time`

: time-to-event variable`censor`

: censoring status variable (0 = right-censored; 1 = complete)

Common survival distributions include: Weibull (`weibull`

), log-normal (`lnorm`

), exponential (`exp`

), and logistic (`logis`

).

Assess fit graphically with histograms and overlaid density curves or numerically with the Anderson Darling adjusted test statistic.

All time to event data are plotted regardless of censoring status.

creates a percent-percent plot of right-censored data given that it follows a specified distribution. Points are plotted according to the median rank method to accommodate the right-censored values.

The Anderson-Darling (AD) test statistic provides a numerical measure of fit such that lower values indicate a better fit. Computation of the test statistic adhered to Minitab’s documentation, utilizing the median rank plotting method.

The survival function \(S(t)\) estimates the proportion of subjects that survive beyond a specified time \(t\).

The hazard function, denoted \(h(t)\), estimates the conditional risk that a subject will experience the event of interest in the next instant of time, given that the subject has survived beyond a certain time \(t\).

The cumulative hazard function, denoted \(H(t)\), is the total accumulated risk of experiencing an event up to time \(t\).

A survival probability estimates the probability that a subject survives (does not experience the event of interest) beyond a specified time \(t\).

```
surv_prob(data = firstdrink,
dist = "weibull",
x = 30,
lower.tail = F,
time = "age",
censor = "censor",
by = "gender")
#>
#> For level = 1
#> P(T > 30) = 0.02488195
#>
#> For level = 2
#> P(T > 30) = 0.08227309
#>
#> For all levels
#> P(T > 30) = 0.05439142
```

Various summary statistics, including mean, median, standard deviation, and percentiles of survival time. All summary statistics from the class `fitdistcens`

are provided. If the distribution supplied is one of normal, lognormal, exponential, weibull, or logistic then the standard deviation reported is an exact computation from parameter estimates; however, if a user specifies a distribution other than that from this list, then the standard deviation is estimated from 1,000 randomly generated values from the distribution.

```
surv_summary(data = firstdrink,
dist = "weibull",
time = "age",
censor = "censor",
by = "gender")
#>
#>
#> For level = 1
#> shape 2.637645
#> scale 18.2804
#> Log Liklihood -1425.271
#> AIC 2854.541
#> BIC 2862.808
#> Mean 16.24398
#> StDev 6.625303
#> First Quantile 11.39844
#> Median 15.90884
#> Third Quantile 20.6903
#>
#> For level = 2
#> shape 2.516025
#> scale 20.85053
#> Log Liklihood -1730.273
#> AIC 3464.546
#> BIC 3473.126
#> Mean 18.50288
#> StDev 7.872356
#> First Quantile 12.70752
#> Median 18.02407
#> Third Quantile 23.74094
```