event_prediction_KM: Event prediction using a lifetable and a recruitment RCurve

Description Usage Arguments Value Author(s) Examples

View source: R/deprecated.R

Description

This is a function to perform event prediction using summary-level data. As of 1.4.0 this function is deprecated as event_prediction gives improved accuracy.
It uses the fit_KM function to perform non-linear regression of Weibull and log-normal curves to the provided survival data.
It creates an event Curve object from this, and combines it with a recruitment RCurve and an optional dropout(censoring) Curve.
Using the same numerical integration approach as nph_curve_trajectories it performs an unconditional event prediction.
If a conditioning time, event number (and preferably number at risk) are provided, a conditional event prediction is also calculated.

Usage

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event_prediction_KM(
  KMcurve,
  Survival = "Survival",
  Time = "Time",
  weighting = FALSE,
  Weights = "Weights",
  Weight_power = 1,
  rcurve,
  max_time = 100,
  dcurve = Blank(),
  type = c("automatic", "Weibull", "Lognormal", "Exponential"),
  startbeta = 1,
  startsigma = 1,
  condition = FALSE,
  cond_Events = 0,
  cond_NatRisk = NULL,
  cond_Time = 0,
  units = c("Days", "Months"),
  discountHR = 1
)

Arguments

KMcurve

The dataframe object containing the survival data

Survival

The column name for the survival function (i.e. the probabilities). Default is "Survival"

Time

The column name for the times. Default is "Time" Alternatively accepts "Weibull" or "Lognormal" to force the type.

weighting

Boolean for whether to use weighting. Default=TRUE as it greatly improves curve fitting.

Weights

Name of Weights column. Default="Weights". Optional if weighting=FALSE. Recommended to use number at risk or remaining.

Weight_power

Power to raise the weights to. Useful in large trials to give added weight to later points where numbers may still be high. Default=1 (Use weights as specified).

rcurve

Observed and/or expected recruitment distribution as an RCurve object. This should typically be of PieceR type (piecewise linear recruitment).

max_time

Maximum time to predict events up to.

dcurve

Dropout/censoring distribution as a Curve object. This is Blank() by default, i.e. no dropout.

type

Type of event curve to fit. Default is "Automatic", fitting both Weibull and Log-normal curves.

startbeta

Starting value for the Weibull beta (shape) parameter to be used in the non-linear regression. Default=1 (exponential).

startsigma

Starting value for the Lognormal sigma (sd) parameter to be used in the non-linear regression. Default=1.

condition

Boolean whether to also do a conditional event prediction. Default=FALSE Note that If all conditioning options are left as defaults, conditioned calculation will equal the unconditional one.

cond_Events

Number of events to condition on. Default=0. Optional unless condition=TRUE.

cond_NatRisk

Number of patients at risk to condition on. By default, the program will estimate the number at risk assuming no censoring. It is highly recommended to specify this if conditioning. Default=NULL(takes value of N - cond_Events). Optional unless condition=TRUE.

cond_Time

Time, in months, to condition on. Default=0. Optional unless condition=TRUE.

units

Units that the KM-curve is specified in. Accepts "Days", "Months". Default="Days".

discountHR

Hazard ratio for discounting events e.g. used to predict adjudicated events from unadjudicated data where patients remain 'at risk' after an event is adjudicated not to have occurred. Values below 1 indicate fewer events will occur than predicted by the curve-fitting. Note that changing this argument is only allowed if type="Weibull" since log-normal curves are not compatible with proportional hazards. Default=1 (No discounting)

Value

Returns a list object with the fitted ecurve, the dcurve, the rcurve, the fitting details, and a summary table with one row per month up to max_time containing the following columns:

Author(s)

James Bell

Examples

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recruit <- PieceR(matrix(c(rep(1,12),10,15,25,30,45,60,55,50,65,60,55,30),ncol=2),1)
example_data_short <- simulate_trials(active_ecurve=Weibull(50,0.8),control_ecurve=Weibull(50,0.8),
rcurve=recruit, assess=10,iterations=1,seed=12345,detailed_output=TRUE)

library(survival)

temp1 <- summary(survfit(Surv(example_data_short[,"Time"],1-example_data_short[,"Censored"])~ 1,
error="greenwood"))
out1 <- cbind(temp1$time,temp1$n.risk,temp1$surv,temp1$std.err)
out1 <- rbind(c(0,out1[1,2],1,0),out1)
colnames(out1) <- c("Time","NAR","Survival","Std.Err")
x1 <- ceiling(max(out1[,"Time"]))
example_lifetable <- out1[findInterval(0:x1,out1[,"Time"]),]
example_lifetable[,"Time"] <- 0:x1

event_prediction_KM(KMcurve=example_lifetable, weighting=TRUE, Weights="NAR", rcurve=recruit,
max_time=60, type="automatic", condition=TRUE, cond_Events=49, cond_NatRisk=451, cond_Time=10, 
units="Months")

gestate documentation built on Feb. 20, 2020, 5:08 p.m.