inference.SMR: Inference about the standardized mortality ratio (SMR) when...

In InferenceSMR: Inference About the Standardized Mortality Ratio when Evaluating the Effect of a Screening Program on Survival

Inference about the standardized mortality ratio (SMR) when evaltuating the effect of a screening program on survival.

Description

This function estimates the expected number of deaths, its variance, the SMR and confidence intervals about the SMR.

Usage

inference.SMR(obs.death, normal = "log-smr", alpha = 0.05, contribution,
incid, cox, fuzz = 0.01, Poisson = FALSE, covnames)

Arguments

obs.death	The observed number of deaths for the people participating in the screening program. A numeric value.
normal	Indicates at which level should the normality assumption be made, either at the SMR level, at the log-SMR level or at the root-SMR level. A character vector containing one or many of the following elements “smr”, “log-smr” and “root-smr”.
alpha	The nominal error rate of the confidence intervals. A numeric value between 0 and 1, e.g. 0.05 to obtain a 95
contribution	An object of contributions produced by the function contrib.
incid	A matrix containing: the incidences, the value of the covariates and the person-years at risk, in that order. It can be obtained with the function incidences.
cox	An oject of class coxph containing the model that was used to estimate the survival in the cohort of non-participants.
fuzz	Numerical precision is problematic when it comes to test equality between objects. The option fuzz is used to consider objects not differing by more than fuzz to be equal. The fuzz option should be chosen to be a small positive number, for instance 0.0001.
Poisson	Indicates whether the incidences' variance should be estimated with a Poisson distribution (TRUE) or a binomial distribution (FALSE). The default is FALSE.
covnames	An alphanumeric vector containing the names of the covariates used to estimate the survival in the cohort of non-participants, that is, the names of the covariates used to obtain the cox object.

Details

The inference.SMR function estimates the expected number of deaths as in Sasieni (2003), estimates the variance of the expected number of deaths and builds confidence intervals as in Talbot et al. (2011). As suggested in the latter, the variance of the observed number of deaths is estimated by the observed number of deaths.

Value

expected	The expected number of deaths
obs.death	The observed number of deaths
variance	The variance of the expected number of deaths
smr	The standardized mortality ratio
smr.var	The variance of the SMR. Only returned if “smr” was given in the normal argument.
smr.ci	A 1-alpha confidence interval for the SMR. Only returned if “smr” was given in the normal argument.
logSMR.var	The variance of the natural logarithm of the SMR. Only returned if “log-smr” was given in the normal argument.
logSMR.ci	A 1-alpha confidence interval for the log-SMR. Only returned if “log-smr” was given in the normal argument.
rootSMR.var	The variance of the square root of the SMR. Only returned if “root-smr” was given in the normal argument.
rootSMR.ci	A 1-alpha confidence interval for the root-SMR. Only returned if “root-smr” was given in the normal argument.

Note

A complete example of usage is provided in the help page of the screening dataset.

Author(s)

Denis Talbot, Thierry Duchesne, Jacques Brisson, Nathalie Vandal.

References

Sasieni P. (2003) On the expected number of cancer deaths during follow-up of an initially cancer-free cohort. Epidemiology, 14, 108-110.

Talbot, D., Duchesne, T., Brisson, J., Vandal, N. (2011) Variance estimation and confidence intervals for the standardized mortality ratio with application to the assessment of a cancer screening program, Statistics in Medicine, 30, 3024-3037.

See Also

est.expDeath, var.expDeath, screening

Examples

#This example uses pre-built objects and shows the simple usage
#of the est.expDeath function when those objects already exist.
#For an example of how to build those objects, refer to the 
#help page of the screening dataset.

#Estimating the variance can be very long even in this small sample example, e.g. a few hours.
#Remove "#" to run example :
#data(req.objects);
#cox.data = req.objects$cox.data;
#results = inference.SMR(obs.death = sum(screening$deathSCN),
# normal = c("smr", "log-smr", "root-smr"),
#	 alpha = 0.05, req.objects$contribution, req.objects$incid,
#	 cox = req.objects$cox, fuzz = 0.01, Poisson = TRUE, req.objects$covnames);


#********  INFERENCE ABOUT THE SMR  ********* 
#
#Observed =  18  Expected =  33.44264 
#Obs.var. =  18  Exp.var. =  39.38153 
#SMR =  0.5382351 
#
# 95 % Confidence intervals with normality assumption at : 
#
#The SMR level : ( 0.2204119 0.8560583 )
#
#The log-SMR level : ( 0.2982118 0.9714471 )
#
#The root-SMR level : ( 0.2673299 0.9029762 )

#results
#
#$expected
#[1] 33.44264
#
#$obs.death
#[1] 18
#
#$variance
#            2
#[1,] 39.38153
#
#$smr
#[1] 0.5400112
#
#$smr.var
#              2
#[1,] 0.02629511
#
#$smr.ci
#[1] 0.2204119 0.8560583
#
#$logSMR.var
#              2
#[1,] 0.09076763
#
#$logSMR.ci
#[1] 0.2982118 0.9714471
#
#$rootSMR.var
#              2
#[1,] 0.01221358
#
#$rootSMR.ci
#[1] 0.2673299 0.9029762

InferenceSMR documentation built on May 9, 2022, 9:06 a.m.