Keywords: Expected Survival; Epidemiologic Methods; Follow- up Studies; Life Tables; One- sample log-rank test; Standardized Mortality Ratio; Survival Analysis Corresponding author: Adelino F. Leite-Moreira amoreira@med.up.pt Confict of interest: The authors declare no confict of interests. First published: 22JUN2021 EXTENDED ABSTRACT © 2020 The Authors. This is an open access article distributed under CC BY license, whis license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use (https://creativecommons.org/licenses/by/4.0/). Open Access Publication J. Stat. Health Decis. 2021;3(1):52-53 | https://doi.org/10.34624/jshd.v3i1.24853 52 A16 Comparing one sample’s observed survival with the expected in the general population or in specifc high- risk subgroups Raquel Moreira 1 , Francisca A. Saraiva 1 , Rui J. Cerqueira 1,2 , Ana F. Ferreira 1 , Mário J. Amorim 1,2 , António S. Barros 1 , Paulo Pinho 2 , André P. Lourenço 1,3 , Adelino F. Leite-Moreira 1,2 1 Cardiovascular Research and Development Center, Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, Porto, Portugal; 2 Department of Cardiothoracic Surgery, Centro Hospitalar Universitário São João, Porto, Portugal; 3 Department of Anesthesiology, Centro Hospitalar Universitário São João, Porto, Portugal Introduction There are doubts if a certain therapy is safe in some diseases. End-stage renal disease (ESRD) is an example for which patient’s life expectancy is reduced. The high rate of comorbidities and therapy adjust- ments has been a major concern. Whether these patients would beneft from an invasive procedure needs to be evaluated since their fragile clinical status may result in reduced survival, regardless of the intervention. The knowledge about which/when would the outcomes of high-risk patients be observed is of utmost relev- ance, using previous studies or registries. The most frequently reported method to compare the observed patient’s survival with the expected in the general population uses life tables and a one-sample log-rank test (1, 2). However, life tables available in each country limit the analysis to comparisons with the general population. It is diffcult to extrapolate about the impact of an intervention in specifc patients, e.g. ESRD, to a population with the same disease but with no intervention, since there are no adequate registries in those subgroups. To surpass this caveat, we considered the paper from Guyot et al. who depicted the process of digitising Kaplan-Meier (KM) curves to extract survival statistics (3). The aim of this study was to explore two methods of comparing the observed survival of our ESRD patients submitted to coronary artery bypass grafting (CABG) with (i) the expected survival in the general population, age and sex-matched; and (ii) the expected survival in a haemodialysis (HD) population not submitted to CABG. Methods Single-centre retrospective study including consecutive HD patients submitted to CABG. To compare the observed survival of the sample with the expected in general population we used the one-sample log- rank test, available at http://biostatistics.mgh.harvard.edu/biostatistics/resources.html as an excel spread- sheet with a supplement (4). Data from the observed sample, (age at operation, gender, race and follow-up (FUP) time) were introduced. To determine the time-to-event in the general population, the annual death rate for each age during the FUP time, adjusted for age, gender and race, obtained through https:// www.ine.pt/ was inserted. The survival rate in the sample at each year after diagnosis results from dividing the sum of N survival rates at each time by the number of patients .The expected number of deaths is calculated by cumulative death rates at last FUP for all patients and for each year after diagnosis: .This expected number of deaths (E) results from adding the cumulative death rate at the last age of FUP (t i ) over the sample size . The software cal- culates the expected survival for a similar subject in the population and a standardized mortality ratio (SMR). Using GetData Graph Digitizer 2.26 (http://getdata-graph-digitizer.com/), we imported and digitised the curve from Almeida et al. (5) reporting the survival of Portuguese HD patients; and the KM curve from our sample. A delineation of each curve was done and two ASCII (text) fles, were exported. An event table was built considering the number of patients at risk provided for each year. These fles were imported by an R script to read the number at risk at each time point and calculate approximations of number of censored on each interval, i; adjusting the total number at risk and number of events within each i according to KM estimates from curves. It obtains individual patient data (time, event and group). Finally, the coxph formula to estimate hazard ratio (HR) and confdence intervals through Cox proportional hazard regression was applied.