Kaplan Meire
Dr Athar Khan
MBBS,DPH,DCPS-HCSM(MPH),MBA, MCPS,PGD-Statistics,DCPS-HPE. PhD Scholar
Associate Professor
Department of Community Medicine
Liaquat College of Medicine & Dentistry
Karachi,Pakistan.
matharm@yahoo.com
Introduction to survival analysis Providing intuition of hazard function, survival function, cumulative failure function. Life table, KM and log-rank test
Introduction to survival analysis Providing intuition of hazard function, survival function, cumulative failure function. Life table, KM and log-rank test
Application of Survival Data Analysis- Introduction and Discussion (存活数据分析及应用- 简介和讨论), will give an overview of survival data analysis, including parametric and non-parametric approaches and proportional hazard model, providing a real life example of survival data-based field return analysis. Several common issues in survival data analysis will also be discussed.
These annotated slides will help you interpret an OR or RR in clinical terms. Please download these slides and view them in PowerPoint so you can view the annotations describing each slide.
Application of Survival Data Analysis- Introduction and Discussion (存活数据分析及应用- 简介和讨论), will give an overview of survival data analysis, including parametric and non-parametric approaches and proportional hazard model, providing a real life example of survival data-based field return analysis. Several common issues in survival data analysis will also be discussed.
These annotated slides will help you interpret an OR or RR in clinical terms. Please download these slides and view them in PowerPoint so you can view the annotations describing each slide.
Cohort, case control & survival studies-2014Ramnath Takiar
The presentation discusses about Cohort, Case-control and Survival studies. The concept of Cohort and Case-control studies is explained with the help of diagrams as perceived by me. Some discussion is also there about survival and relative survival. Appropriate data is also provided to explain about survival and relative survival.
Study Designs Cohort StudiesChapter 7Learning.docxdeanmtaylor1545
Study Designs: Cohort
Studies
Chapter 7
Learning Objectives
• Differentiate cohort studies from other
study designs
• List main characteristics, advantages, and
disadvantages of cohort studies
• Describe three research questions that
lend themselves to cohort studies
• Calculate and interpret a relative risk
• Give three examples of published studies
discussed in this chapter
Temporality
• Temporality refers to the timing of
information about cause and effect.
• Did the information about cause and effect
refer to the same point in time?
• Or, was the information about the cause
garnered before or after the information
about the effect?
Limitations of Other Study
Designs
• Demonstrating temporality is a
difficulty of most observational
studies.
Limitations of Other Study
Designs (cont’d)
• Cross-sectional and case-control study
designs are based on exposure and
disease information that is collected at the
same time.
• Advantage: Efficient for generating and
testing hypotheses.
• Disadvantage: Leads to challenges
regarding interpretation of results.
Limitations of Other Study
Designs (cont’d)
• Cross-sectional studies:
– Present difficulties in distinguishing the
exposures from the outcomes of the disease,
especially if the outcome marker is a
biological or physiological parameter.
Limitations of Other Study
Designs (cont’d)
• Case-control studies:
– Raise concerns that recall of past
exposures differs between cases and
controls.
Limitations of Other Study
Designs (cont’d)
• There has been no actual lapse of time
between measurement of exposure and
disease.
• None of the previous study designs is well
suited for uncommon exposures.
What is a cohort?
• A cohort is defined as a population group,
or subset thereof, that is followed over a
period of time.
• The term cohort is said to originate from
the Latin cohors, which referred to one of
ten divisions of an ancient Roman legion.
What is a cohort? (cont’d)
• Cohort group members experience a
common exposure associated with a
specific setting (e.g., an occupational
cohort or a school cohort) or they share a
non-specific exposure associated with a
general classification (e.g., a birth
cohort—being born in the same year or
era).
Cohort Effect
• The influence of membership in a particular
cohort.
• Example: Tobacco use in the U.S.
– Fewer than 5% of population smoked around the
early 1900s.
– Free cigarettes for WWI troops increased
prevalence of smoking in the population.
– During WWI, age of onset varied greatly; then
people began smoking earlier in life.
– One net effect was a shift in the distribution of the
age of onset of lung cancer.
Cohort Analysis
• The tabulation and analysis of morbidity or
mortality rates in relationship to the ages
of a specific group of people (cohort)
identified at a particular period of time and
followed as they pass thro.
This brief paper will help you to understand survival analysis. This type of analysis is important when the time between exposure and event is of clinical interest. The misconception that mortality and survival are interchangeable comes from the lay use of the terms. Learn more and let me know if you have any questions.
In this presentation i tried to explain in detail about cohort studies, their types, how to conduct them, their outcomes, and how to calculate sample size of these studies.
What are the five critical elements ensuring the program planning success?
1) Mobilizing the community
2) Collecting and organizing data
3) Choosing health priorities
4) Developing a comprehensive intervention plan
5) Evaluating PATCH
The four Multiple Determinants of Chronic Disease?
1) Behavioral determinants
2) Healthcare determinants
3) Environmental determinants
4) Social determinants.
What is Epidemiology?
distribution and determinants of health-related states in specified populations, and the application of this study to the control of health problems
compare between person analyzes and Time analyses?
Person: distribution of a disease or condition varies in the population according to personal characteristics, such as age, race, or gender
Time: surveillance systems monitor the trends in occurrence of chronic disease rates through utilizing the epidemic curve to detect outbreaks
4 elements for Health Believe Model
1) Perceived suscssibility
2) Perceived severity
3) Perceived benefits
4) Perceived barrier
5) Cuss action
6) Self-efficacy
cause of tobacco use?
1) Societal and individual factors
2) Advertising and promotion (tobacco” Safer)
3) Access
4) Social norms
5) Individual psychosocial factors
6) Continued tobacco use
7) Inadequate understanding
8) Lower price
elements of a chronic disease surveillance system:
1) Notifiable Disease Systems
2) statistics vital
3) Sentinel Surveillance
4) Chronic Disease Registries
5) Health Surveys
6) Administrative Data Collection Systems
7) Census Data
- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
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Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
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Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
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Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
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18. Survival analysis, or more generally, time-to-event analysis,
refers to a set of methods for analyzing the length of time until
the occurrence of a well-defined end point of interest.
START
Time to event is outcome variable
Binary outcome (event has occurred versus it has not occurred)
EVENT
Death/Occurrence/Reoccurrence
/Survival after treatment
4/20/2020 DR ATHAR KHAN 18
TIME
Survival time, time-to-event, time to-
detection, failure time, relapse-free
survival time (also called disease-free survival
time), event-free survival.
19. Survival analysis is a statistical method which analyses data to
predict the time of occurrence of one or more event.
Survival time is time from the start of the study till the
occurrence of event is called survival time.
Survival in this context is remaining free of a particular outcome
over time.
Event-free survival which is the proportion of subjects who have
not yet experienced an event.
START EVENT
Complete remission/Relapse
4/20/2020 DR ATHAR KHAN 19
TIME
Survival time, time-to-event, event time, time
to-detection and failure time
20. Cancer Studies e.g. Leukemia patients [time in remission]
(weeks)
Disease-free cohort [time until heart disease] (years)
Elderly (60+) population [time until death] (years)
Heart transplants [time until death] (months)
START
The time to event or survival time can be measured in days,
weeks, years, etc.
EVENT
Death/Occurrence/Reoccurrence
/Survival after treatment
4/20/2020 DR ATHAR KHAN 20
TIME
Survival time, time-to-event, time to-
detection and failure time
21. Whether or not a participant suffers the event of interest during
the study period (i.e., a dichotomous variable) often coded as
1=event occurred or 0=event did not occur during the study
observation period.
Survival analysis focuses on two important pieces of information
4/20/2020 DR ATHAR KHAN 21
The follow up time for each individual being followed.
Follow Up Time: Time zero, or the time origin, is the time at
which participants are considered at-risk for the outcome of
interest.
24. Types of Survival Analysis
▪ Comparing survival distributions
▪ The log-rank test (also known as the Mantel log-rank test,
the Cox Mantel log-rank test, and the MantelHaenszel test)
is the most commonly used test for comparing survival
distributions.
▪ Breslow’s test (also known as Gehan’s generalised
Wilcoxon test)
4/20/2020 DR ATHAR KHAN 24
25. Types of Survival Analysis
▪ Survival models
▪ Survival models are used to quantify the effect of one or
more explanatory variables on failure time. This involves
specification of a linear-like model for the log hazard.
▪ Cox proportional hazards model
4/20/2020 DR ATHAR KHAN 25
26. Censoring
• Observations are called censored when the information
about their survival time is incomplete.
• There are three main types of censoring: right, left, and
interval.
• The most common is called right censoring.
• This can occur when a participant drops out before the
study ends (the participants observed time is less than the
length of the follow-up).
4/20/2020 DR ATHAR KHAN 26
27. Censoring
• When a participant is event free at the end of the
observation period (the participant's observed time is
equal to the length of the follow-up period).
4/20/2020 DR ATHAR KHAN 27
28. An observation is left-censored if its initial time at risk is unknown. This will
occur if we do not know when a participant experienced for the first time the
condition of interest. For example, when an individual contracted a disease.
4/20/2020 DR ATHAR KHAN 28
30. INTERVAL CENSORING In many applications, the time of the
event may be known only up to a time interval, especially when
the time is established by periodical examinations
4/20/2020 DR ATHAR KHAN 30
32. During the study period, three participants suffer myocardial infarction (MI), one
dies, two drop out of the study (for unknown reasons), and four complete the 10-
year follow-up without suffering MI.
4/20/2020 DR ATHAR KHAN 32
33. • An important assumption is made to make appropriate use
of the censored data. Specifically, we assume that
censoring is independent or unrelated to the likelihood of
developing the event of interest.
• This is called non-informative censoring and essentially
assumes that the participants whose data are censored
would have the same distribution of failure times (or times
to event) if they were actually observed.
4/20/2020 DR ATHAR KHAN 33
34. In survival analysis we analyze not only the numbers of participants who suffer
the event of interest (a dichotomous indicator of event status), but also the times
at which the events occur.
What is the likelihood that a participant will suffer an MI over 10
years? 3/10 = 30%
4/20/2020 DR ATHAR KHAN 34
38. The Kaplan-Meier Assumptions
• The event status should consist of two mutually exclusive( 2
events cannot both occur at the same time) and collectively
exhaustive states (at least one of the events must occur)
• The event status is mutually exclusive because the outcome for
a case can either be censored or the event has occurred. It
cannot be both.
• The time to an event or censorship (known as the "survival
time") should be clearly defined and precisely measured.
4/20/2020 DR ATHAR KHAN 38
39. The Kaplan-Meier Assumptions
• Where possible, left-censoring should be minimized or
avoided.
• There should be independence of censoring and the event. This
means that the reason why cases are censored does not relate
to the event i.e. non informative censoring
• There should be a similar amount and pattern of censorship per
group.
4/20/2020 DR ATHAR KHAN 39
40. months 07 to 140 cutoff.
4/20/2020 DR ATHAR KHAN 40
47. Video Link
YouTube: How to Use SPSS-Kaplan-Meier Survival
Curve
https://www.youtube.com/watch?v=f4X5csxtJkE
4/20/2020 DR ATHAR KHAN 47
48. H o = normality
If you accept, then assume normality
If you reject, then do not assume normality
If p < then 0.05, reject the H0
Use Kaplan Meier Test
4/20/2020 DR ATHAR KHAN 48
49. ▪ Overall censoring was 47/200(23.5%).
▪ Resumption of smoking was 153/200 (76.5%)
▪ Resumption of smoking in Hypnotherapy group was 79/104 (76%).
▪ Resumption of smoking in Nicotine patch group was 74/96(77%).
4/20/2020 DR ATHAR KHAN 49
52. ▪ Mean resumption of smoking time was 60 ± 3 months.
▪ In group-HP, mean resumption time was 58.4 ± 4.31 months.
▪ On the other hand in group-NP, mean resumption time was
62.2 ± 4.2 months.
▪ Median resumption of smoking time was 46.8 months.
▪ In group-HP, median resumption time was 44.4 months.
▪ On the other hand in group-NP, median resumption time was
49.2 months.
4/20/2020 DR ATHAR KHAN 52
53. Confidence interval overlapping – No difference
22.6 75.7
35.7 51.2
Since there is a lot of overlap in the confidence intervals, it is unlikely that there is much
difference in the "average" survival time.
If confidence intervals do not overlap between levels, differences in effect on time to event
can be inferred.4/20/2020 DR ATHAR KHAN 53
55. ▪ The horizontal axis shows the time to event.
▪ In this plot, drops in the survival curve occur whenever the
participant resume smoking.
▪ The vertical axis shows the probability of survival (probability
of resuming smoking).
▪ In survival analysis the survival probabilities are usually
reported at certain time points on the curve (e.g. 1 year and 5
year survival); otherwise the median survival time (the time at
which 50% of the subjects have reached the event) can be
reported.
4/20/2020 DR ATHAR KHAN 55
56. ▪ Cumulative survival proportion appears to be higher in the
nicotine patch group compared to the hypnotherapy group.
▪ Hypnotherapy programme prolongs the time until participants
resume smoking (i.e., the event) compared to the other
interventions.
4/20/2020 DR ATHAR KHAN 56
58. ▪ Survival curves cross each other (i.e., whether there is an
"interaction" between survival distributions).
▪ Survival curves are similarly shaped, even if they are above or
below one another.
▪ As such, a group survival curve that appears "above" another
group's survival curve is usually considered to be
demonstrating a beneficial/advantageous effect.
▪ Smooth curves are better than step down pattern curves.
4/20/2020 DR ATHAR KHAN 58
63. The p-value (sig) is the probability of getting a test statistic of at
least 0.379 if there really is no difference in survival times for
treatment groups. As the p-value = 0.538 and is greater than 0.05,
conclude that there is no significant evidence of a difference in
survival times for treatment groups. The estimated time until
resumption is 44.4 months for HP and 49.2 months for NP this
difference is statistically NOT significant (p=0.538) therefore,
both groups have similar time for start of smoking again.
4/20/2020 DR ATHAR KHAN 63
64. The log rank test
▪ The log-rank test tests the hypothesis that there is no difference
in survival times between the groups studied at all time points in
the study.
▪ The log rank rest for the data in our example was P = 0.538;
thus the two curves are not statistically significantly different.
4/20/2020 DR ATHAR KHAN 64
65. ▪ Log-rank test: what happens later in time.
▪ Breslow: what happens later in time.
▪ Tarone: what happens middle in time.
▪ All three test p-value <0.05 – significant results
▪ All three test p-value > 0.05 – insignificant results
▪ If mix – at certain points significant
4/20/2020 DR ATHAR KHAN 65
80. 4/20/2020 DR ATHAR KHAN 80
DR ATHAR KHAN
MBBS, MCPS, DPH, DCPS-HCSM, DCPS-HPE, MBA, PGD-
STATISTICS, CCRP
ASSOCIATE PROFESSOR
DEPARTMENT OF COMMUNITY MEDICINE
LIAQUAT COLLEGE OF MEDICINE & DENTISTRY
KARACHI, PAKISTAN
0092-3232135932