Page 88 - ISMCON souvenir 2021

ISMSCON - 2021

OS63: COMPETING RISK ANALYSIS IN PREPUCE CANCER

Sunil K. Yadav, Jitendra R. Gawde, Rajashree Dey, Soutik Halder, Sanjay D. Talole &
Atanu Bhattacharjee
Section of Biostatistics, Centre for Cancer Epidemiology, Tata Memorial Centre, Kharghar, Navi Mumbai 410210,
India (Email Id: ysunilkumar40@gmail.com)

Abstract
Background: In survival analysis, our main aim is to find out the follow-up time until an event occurs.
Competing risks are those types of risks that are defined as death due to other causes. It may happen
that a cancer survival patient died due to other causes i.e. competing risks. For competing risk regression
models there are mainly two concepts – Cause-specific hazard models: This model tells that which
covariates affect the rate at which events occur. Sub distributions hazards ratios: This model tells that
which covariates affect the probability of an event occurring over time.
Methods and Illustrations: To find out cause-specific hazard models here use the conventional Cox PH
model by treating competing events as censoring. To fit cumulative incidence function, apply Fine-Gray’s
method. Fine-Gray’s method is used for sub distributions hazards ratios. Gray’s test and the CIF curves
suggest that the two groups are similar. Hence we say that Gray’s test for equality of CIFs.
Interpretations: The cumulative incidence function gives the summary curve which shows the cumulative
failures rates over time due to particular cases. It also allows for groups comparison and visualization of
estimated CIFs.
Keywords: Cause specific hazard, Competing risk, Cox proportional hazard, Cumulative incidence
function, Fine-Gray’s method.

OS64: A study on mortality Pattern in a tertiary care hospital of

Ranchi, Jharkhand.

Author’s- Dr Asha kiran, Dr Dewesh kumar, Dr Vidya Sagar Dr Vivek Kashyap, Dr S B Singh,
Dr Shalini Sunderam, Dr Anit Kujur, Dr Santosh Kr Soren
Presenting Author: Dr Surendra Sahu
Department of Preventive and Social Medicine, RIMS, Ranchi.

ABSTRACT
Introduction: Cause of death statistics from hospitals are considered along with mortality data from
other sources to constitute the essential statistics on the health of a population. Mortality data is used
to periodically review health priorities, set research agendas and monitor progress towards national and
global health and development goals. This study was done to know the socio demographic profile and
pattern of causes of death in a Tertiary Care Teaching Hospital in Ranchi, Jharkhand, India.

Methodology: A retrospective study was done with Death records from medical record section of patients
in Rajendra Institute of Medical Sciences, Ranchi, Jharkhand over a 1 year period. Cause of death was
classified using ICD-10 (international classification of diseases).
Results: Total number of death was 4528, male (58.23%) more than female (41.77%) and Non
communicable diseases (88.09%) more than communicable disease (11.92%).The leading cause of
death in last one year was CVA(16.96%) & MI followed by RTA(12.10%), CKD, CLD, and pulmonary
tuberculosis(11.92%).

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