Confidence bands for the survival function

In several medical reports, the survival function is graphed along with the confidence intervals. The endpoints of the confidence intervals are usually connected to draw an area where the entire survival curve is contained with a given confidence. Confidence intervals are pointwise, i.e., they refer to the survival probability at a single time, but they are not valid for all the estimates of the entire survival curve. To this aim, the appropriate measure is confidence bands, not yet available within Stata. Two methods are usually employed to construct these confidence bands. The first was proposed by Hall and Wellner (1980), and the second was proposed by Nair (1984). The latter produces the so-called equal precision (EP) confidence bands. For both methods, log-minus-log and arcsine square-root transformed versions have been proposed. stcband is a new Stata command that allows the user to graph the survival function, together with the confidence bands constructed according to the Hall–Wellner and EP methodologies. The available options allow the user to a) specify the lower and upper limits of the time where the bands are to be estimated; b) choose the linear, log, or arcsine transformation; c) set the confidence level at 90, 95, or 99%; d) save the estimates; and e) manage the aspect of the graph. A further option allows the user to estimate the confidence bands of the cumulative hazard function. Using an example, I illustrate * the results obtained by using stcband and the corresponding R function; * the use of the command and the differences of the estimates of its confidence bands versus the usual pointwise confidence intervals. Finally, I will compare the coverage probabilities of the confidence bands estimated according to the above mentioned approaches by using simulated data with various survival distributions. I will perform simulations using Stata’s bootstrap command. The new command also has an accompanying help file in which the user is able to run an example, taken from the second edition of Klein and Moeschberger’s Survival Analysis Techniques for Censored and Truncated Data (p. 109–117), by clicking on the Viewer window. stcband is available for download from the SSC Archives.

STCOXGOF: Stata module to produce goodness-of-fit test and plot after a Cox model

stcoxgof is a post-estimation command testing the goodness of fit after a Cox model. So you must use this command after stcox. To compute Gronnesby and Borgan test and to obtain Arjas like plots Martingale residuals must also be saved specifying stcox's mgale() option; see help stcox. stcoxgof calls scoretest_cox, written by Isabel Canette of StataCorp, to compute score test statistics.Cox model, goodness of fit, Gronnesby and Borgan test, Arjas plot

STCOMPET: Stata module to generate cumulative incidence in presence of competing events

In survival or cohort studies the failure of an individual may be one of several distinct failure types. In such a situation we observe an event of interest and one or more competing events whose occurrence precludes or alters the probability of occurence of the first one. stcompet creates variables containing Cumulative Incidence, a function that in this case appropriately estimates the probability of occurrence of each endpoint, corresponding Standard Error and Confidence Bounds. The values in numlist of the previous stset are assumed as occurrence of event of interest. In compet() options you can specify numlist relating to the occurrence of up to six competing events. This version has been updated from that published in Stata Journal, 4:2.survival data, cumulative incidence, competing events

STEXPECT: Stata module to compute expected survival

stexpect implements computation of expected survival probability after merging the data with a suitable file of "standard" rates. You must specify in ratevar(varname) a reference rate variable and in output(filename) a file where the estimates will be saved.survival data, expected survival

STARJAS: Stata module to produce Arjas plot to check proportional hazards assumption

Arjas plots allow to check if a categorical covariate should be included in a proportional hazards model and if this covariate has proportional hazards. For each level a line is plotted. If the covariate does not need to be in the model then for each value a roughly 45° line through origin should be plotted. Otherwise Arjas plots will give curves which are approximately linear but with slope differing from 1. If a covariate has a nonproportional effect on the hazard rate then the curves will differ nonlinearly from 45° line. Assume an indicator variable having only two values 0 and 1, the curve for the level 1 should be concave if the hazard ratio is increasing in t whereas the curve for the level 0 should be convex.proportional hazards, Arjas plot

STKERHAZ: Stata module to produce baseline hazard estimates via kernel smoother and plots

stkerhaz computes nonparametric estimates of the baseline hazard or baseline SMR and draws the graph of the results. This command can be used after stcox. In this case it requires that you previously specified stcox's basech option. Otherwise you can specify varname storing cumulative baseline hazard.survival data, kernel smoothing, baseline hazard

STCASCOH: Stata module to create dataset suitable for case-cohort analysis

stcascoh is used to create an appropriate dataset for analysis as case-cohort study, sampling the cohort at time of entry and including all failures whether they occur in the random sample or not. To this aim stcascoh expands observations who fail in two parts: (1) time interval (t0,t-eps] and (2) time interval (t-eps,t]. This is version 1.2.1 of the software, revised to prepare data for use with stselpre (q.v.)survival data, cohort analysis

STQUANT: Stata module to estimate quantiles for survival time

stquant presents point and interval estimates of quantiles for survival time. Confidence intervals are obtained by two methods: large sample normal distribution of the estimated quantile and that one proposed by Brookmeyer and Crowley. Both show limits. The first one has not yet been fully studied and I noted some inconsistencies for small samples in the right tail of the survivorship function. The latter method is recommended in the literature to be employed when there are no tied survival times. This is version 2.01 of the software.survival analysis, quantile estimation

STCOMPADJ: Stata module to estimate the covariate-adjusted cumulative incidence function in the presence of competing risks

stcompadj estimates the adjusted cumulative incidence function based on a Cox or a flexible parametric regression model in the presence of competing risks. Cox regression in the presence of competing risks is usually performed by fitting separate models for each failure type. It is possible to obtain the same results by using a single analysis after appropriately adapting the data set. In short this consists of expanding each observation for each cause of failure, creating a stratum indicator taking on a value of 1 for the first n records, 2 for the following n records and so on, and modifying the failure indicator so that it attains the value 1 for each observation of death caused by the main event in the first stratum, for each observation of death caused by the competing event in the second stratum and so on. This way of representing data (expanded format) allows to model both identical and different effects of the same covariate on the main and competing events.cumulative incidence function, competing risks, Cox regression, flexible parametric regression, risk

STPEPEMORI: Stata module to test the equality of cumulative incidences across two groups in the presence of competing risks

stpepemori tests the equality of cumulative incidences or conditional probabilities across two groups. So varname specifying the groups to be compared can take just two values.Pepe, Mori, cumulative incidence, conditional probability