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Cancer Epidemiology - Science topic

Cancer Epidemiology is a forum for discussions about the epidemiology of cancer.
Questions related to Cancer Epidemiology
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The BEIR VII Phase 2 Report presents the lifetime attributable cancer risk for incidence and mortality:
BEIR VII Report: https://nap.nationalacademies.org/download/11340 (You can choose Download as guest), Tables 12D-1, 12D-2 and 12D-3, pages 311-312
The values for mortality from lung cancer are slightly greater than the values for incidence. How is this possible? Thanks for answers.
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I agree with @James Leigh; three might be an error in the modeling.
The other possible explanation could be the fact that the attributable factor for lung cancer mortality is greater than the lung cancer diagnosis . . . .
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A few days ago a colleague of mine made me think about the impact the COVID19 crisis will have on cohort studies. Especially those focused on causes of mortality and the elderly will be deeply impacted by the number of deaths due to the pandemic.
Is this something manageable?
How big is this matter in your mind?
How can this be handled?
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Well, the cohort study is highly suggested in covid pendamic. however, the covid-19 is no more a cohort situation it is done for observation and per and post-test by training and other medication.
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At fiften minutes past eight in the morning, on August 6, 1945, Japanese time, an atomic bomb was detonated above Hiroshima. Most of the city was destroyed, and by the end of that year 90,000–166,000 inhabitants had died as a result of the blast and its short-term effects. Epidemiological studies have documented increased disease burdens for malignant conditions among survivors including those exposed in utero, as well as risks for some noncancer diseases The psychosocial effects and consequences are less well studied, but remain substantial to this day. August 6, 2020 will be an opportunity for global remembrance of this human catastrophe.
DISCUSSION TOPIC: What as human beings and as scientists have we learned?
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Today, August 6th, 2020 is the 75th anniversary of this tragic event, part of the even greater tragedy of the 2nd World War. I am surprised that no RG follower has offered a response to the question posed, given the significance of the event. On this day however, it is noteworthy that Hiroshima's mayor took the opportunity to warn the world about the rise of "self-centered nationalism" and appealed for more international cooperation to overcome the Covid-19 pandemic. Apparently, memorial events have been drastically scaled back because of the pandemic.
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The event of death by metastasis or recurrence is very common, many researchers have linked the tendency of some tumors to re-appear to the presence of occult or dormant cancer cells that may have a phenotype that allows them to remain "hidden" from the immune response. However, the understanding of these cells and the mechanisms that they use to achieve evasion remain mostly unknown. It is critical to understand these cells better and to be able to detect their presence for they are of great therapeutic importance.
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Hi
LGR5 is the best biomarker for detection of cancer stem cells in colorectal patients .
u can browse our my profile and see our paper about that
Regards
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In the information age, there is more data out there than can be analyzed in a lifetime!  As a faculty member, I have been often asked about readily available and wanted to compile a list of readily available data.  I am sure, many of you have encountered the same question from your students.  Here is a compilation of medical data available for download.  I have personally worked with many of these and have found them very helpful.  These are mostly exclusive to the United States and would like input about international datasets which are freely accessible as well.  Navigating through some of these datasets may take some getting used to:
Name:
National Health and Nutrition Examination Survey(NHANES)
Description:
The NHANES interview includes demographic, socioeconomic, dietary, and health-related questions. The examination component consists of medical, dental, and physiological measurements, as well as laboratory tests administered by highly trained medical personnel.
URL
Description:
The following link is a link that will bring you to many sets of data outlined below.  These are very helpful.  Some are self-reported data (NHANES), while others are performed by health care professionals (i.e NAMCS data).  There is some longitudinal data, and others have longitudinal data if you incorporate the mortality linkage files.  These are excellent for Cox-proportional models.
URL:
For other datasets available from the national center for health statistics (NCHS) please see below:
This page allows you to search the CDC and NCHS sites. NCHS is the Federal Government's principal vital and health statistics agency. NCHS data systems include data on vital events as well as information on health status, lifestyle and exposure to unhealthy influences, the onset and diagnosis of illness and disability, and the use of health care. Some of the NCHS data systems and surveys are ongoing annual systems while others are conducted periodically. NCHS has two major types of data systems: systems based on populations, containing data collected through personal interviews or examinations; and systems based on records, containing data collected from vital and medical records. Data include: National Health Interview Survey, National Immunization Survey, National Survey of Family Growth, National Health Care Survey , National Employer Health Insurance Survey, National Vital Statistics System, and Mortality Data. Research activities include: Aging, AIDS, Classification of Diseases, Data on America's Children, Evaluation of Certificates, Healthy People 2000, International Activities, Minority Health, National Death Index, Nutrition Monitoring, and Public Health Conference on Records and Statistics. The National Center for Health Statistics (NCHS) is a part of the Centers for Disease Control and Prevention, U.S. Department of Health and Human Services. NCHS is located in Hyattsville, Maryland, with offices in Research Triangle Park, North Carolina, and with a CDC-liaison office in Atlanta, Georgia.
Name:
BRFSS
Behavioral Risk Factor Surveillance System
Description:
The name explains what the scope of this database.  There is wonderful data physical activity, cardiovascular disease, chronic pulmonary diseases, and other self-reported data.  More than 500,000 interviews were conducted in 2011, making the BRFSS the largest telephone survey in the world. Also in 2011, new weighting methodology—raking, or iterative proportional fitting—replaced the post stratification weighting method that had been used with previous BRFSS data sets.
URL:
Name:
Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute
This database works to provide information on cancer statistics in an effort to reduce the burden of cancer among the U.S. population.  This is an excellent resource to study risk factors of cancer and longitudinal mortality studies.
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Hi! I am looking for a statistic website where there will be some valuable information about the percentage of types of breast tumours diagnosed each year.
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Whenever you intend to study cancer, anyone, just try GLOBOCAN. It is no1 site most referenced and updated with all cancer info including types, incidence, mortality, year-by-year (even up to this moment), globally, intercontinental, nation-by-nation etc. You can download datasheets with statistics. You everything going on about all cancer types in the globe with free access. Check the link and the sample test of what to expect below;
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We are currently designing a nested case-control study based on retrospective registry healthcare data available over a 4-year period. We have identified all cases, and are now looking into the selection of controls.
We want to use incidence density or risk set sampling, so selected controls should still be eligible as controls for future cases, and can be selected twice or more.
How should we select controls exactly when selecting from a retrospective registry dataset? We believe that if we select a control for a case occurring at time x, we should add all registry data we have available for this control up to time x, and delete all registry data available for this control after x. However, this control should still be available as a control for a case occurring at time x+y. If we select this control again for another case at time x+y, should we just add all available registry between x and y data to that control's data in the dataset? Or should we end up with two separate rows in our dataset by duplicating the control, and add data up to time x for the first duplicate, and up to time y for the second duplicate?
We believe the former makes more sense but do not find guidance on this in the literature.
Any advice in this would be greatly appreciated!
Philippe
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Hi Philipe,
I tend to agree with James, the first approach of selecting the controls with data up to a specific timepoint seems much easier.
Regards,
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Over the past three decades, adenocarcinoma of the oesophagus has increased in incidence more than any other tumour. The cancer is thought to be the result of gastroesophageal reflux damaging and inflaming the distal oesophagus and causing its squamous mucosa to undergo columnar metaplasia . This Barrett’s mucosa has an increased risk of progressing to dysplasia and adenocarcinoma.
What factors are responsible for the rapid rise?
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Hello, quality of food is decreasing due to know how technologies food additives, new pesticide residues, new chemicals and harm environments.
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Can anyone please help me to find any short term certified course in cancer epidemiology and biostatistics.
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Hi Aparna,
Short courses on cancer epidemiology and biostatistics vary depending on what you have in mind (online / taught / duration) and how much you are willing to pay.
The international agency for research on cancer run summer schools on cancer epidemiology and the next one is 17th June - 5th July 2019 in Lyon, France with various modules: https://training.iarc.fr/summer-school-in-lyon/
There are also regular STATA Summer schools in London with various modules covering medical statistics and meta-analysis: https://www.stata.com/news/stata-summer-school-london-2018/
I attended one of these and found it very useful as well as learning to use STATA.
You can also explore various specific University modules offered in most places if you wanted a taught short course or even free online modules using MOOC - Massive Open Online Courses: https://www.mooc-list.com
I undertook an online course on medical statistics at the Stanford University and it was free and excellent. https://online.stanford.edu/courses/som-y0007-statistics-medicine Professor Kristin Sainani was awesome in delivering the lectures and you could always study at your pace and review video lectures.
Hope you find these suggestions useful.
Kind regards,
Oladejo Olaleye
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With some evidence emerging of NCD onset at earlier ages in LMICs compared to HICs, and some evidence about possible shifts in SES gradients in both NCDs and NCD risk factor profiles - are these coalescing into transitions in the traditional SES gradients/patterns for these conditions and risks in both lower and higher income countries?
See for example, Global Health Watch 4: An Alternative World Health Report. or Remais, et al., IJE 2012. https://academic.oup.com/ije/article-lookup/doi/10.1093/ije/dys135
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The evidence for a shift or transition in the age risk of NCDs in LMICs need careful examination. A big international team solicitored by the WHO could be very helpful. Impressions are growing that NCDs risk is increasing with "extension"  not necessary shift towards younger age group. The main determinant is this contesxt is likely to be the  massive change in lifestyle behaviour. However, aretificial cause of such rise and shift or extension should not be ignored. All countries including LMICs ones are witnessing development in their health care systems with more chances to earlier diagnosis, better treatment and improved recording of events.  These last factors could explain part of the apparent risk and extension. True rise remains, however, a real contributor and  this needs extensive work to support, quantify and explain.
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Hello scientists,
When I think about genetic diseases like cystic fibrosis, sickle cell anemia, diabetes, and various forms of cancer I don't imagine that those can be easily treated by surgery because the issue is that the wrong protein(s) are written into a patient's genetic code. I imagine that if one were to look at the protein expression map of a diseased individual and a normal individual there should be statistical differences most of which are insignificant, but one upregulated or downregulated protein in a large pathway has a critical mutation.
 
So what would be the most efficient means to figure out the culprit protein and the gene that codes for it? I consider myself more proficient in bioinformatics than experimental wet-lab biochemistry. But I'm probably overthinking it as scientists usually collect a crude sample, filter it as much as they can, and send it off for a sequencing lab to analyze, and then collect the results and analyze them, right?
 
Once the malformed protein and its pathway is discovered, perhaps from biostatistics of control and diseased groups, how does one fix the gene for which bad insertions, deletions, or frameshifts happened in? I know from my reading research papers that viruses often leave fragments of their dna in their host, even if their host's immune system manages to suppress them or wipe them out, so probably the least harmful virus available could function as a vector to fix the point mutation. But how do you know that it will do what you calculate?   
Perhaps CRISPR is the best way to go about it. I imagine that correcting the genetic diseases might still be a difficult task given that biochemists often work with cell lysates in vitro, but the cure in vivo isn't supposed to lyse the cells.
 
Can drugs be used to treat genetic diseases permanently after a few doses? Many drugs are inhibitors of some protein pathway but they are gradually eliminated from the body, but is there any evidence that some drugs can change protein pathways permanently after x number of administration doses?  Perhaps they can if drugs change the behavior of immune system cells, and those cells then change other cells' pathways, then there might be a way to treat genetic diseases. 
I can sort of imagine solutions on computers. But those solutions have to work in the complicated matrix which is the chambers of the human body. 
How do you think we scientists can treat genetic diseases?  
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You're asking a question that the pharmaceutical industry, and biomedical science in general, has struggled with for decades.  Indeed, target discovery and validation is every bit as important as drug discovery when it comes to treating diseases, be they genetically driven or not.
Many people expected the Human Genome Project to essentially answer the questions you're asking - clearly it didn't.  While fabulously useful, the HGP resulted in very, very few simple answers or advances.  Similarly, to take just cancer: it doesn't have *a* genetic cause, it has a bunch of disparate causes.  Consider Weinberg and Hanahan's reviews, where they lay out an argument for cancer being caused by 12 key pathways - that's still 12 genetic pathways, that can be altered at multiple points to offer the same effect.  Or Celiac disease: is it caused by a problem with tissue transglutaminase?  For some people yes, that seems to play a big role.  For others, it's something else entirely.
Regarding drug treatment: curing chronic conditions is difficult to impossible, hence them being chronic conditions.  But just because a condition is genetic doesn't make it chronic.  Take cancer again as an example: somebody can have a mutation to their Ras gene that normally leads to cancer, but never develop a tumor.  So if somebody with that mutation DOES develop a tumor, we imagine we could cure it, and not have the disease come back.  We are, frankly, pretty bad at this right now - even the best cancer chemical therapies have low absolute success rates, but we get better each year.
Now, something like, say, celiac disease?  Or depression?  Or hormonal disorders?  It's unlikely those will be curable, at least without constant chemical treatment.  It all comes down to what the base state is - is the problem just in a specific place (ie: a tumor) or is it all over (ie: celiac disease and the whole intestine).  If you can't imagine just removing a few (or a lot of) cells and having the person both be cured AND still be alive, chances are drugs won't cure them either, since that's metaphorically all drugs do - remove the bad cells from play.
As for CRISPR/Gene editing in general: that's certainly a research track.  But it's an incredibly controversial pathway in general too - with a drug, you can wash it out of the system if something goes wrong.  But a gene edit?  It's very, very hard to undo what you did.  And while a protein (again, say Ras) might have a mutation that leads to cancer...  what ELSE is that mutation doing?  How much of the rest of the host is mutated to work with the mutant protein?  What good is it to cure the cancer if the person's skin stops regrowing as it sheds, for instance?  But, of course, as you note: a gene edit *could* fix the problem, then and there, simply and safely.  Then you've got the issue of what counts as the *correct* form of a protein, and who decides.  We've generally hesitated to release genetically modified organisms into the wild in case there are unplanned effects (and there are ALWAYS unplanned effects).  Now, we can sterilize corn, or salmon, or whatnot - but we can't ethically sterilize a person for daring to get gene alteration therapy.  So we could only alter them to 'normal', right?  But what's normal?  Who defines it?  And if we CAN alter your genes to 'normal', then what's to stop you from altering them to 'better'?  If we can equate a gene with height, or high IQ, or blue eyes, or whatever...  why shouldn't people be allowed to get cosmetic genetic surgery, if others are allowed healing genetic surgery?  It's an ethically difficult field in a way that more traditional drug treatment is not (and even that is changing - I saw a report at one point that there are active levels of Prozac in the Thames.  How ethical is it to treat people with drugs if they'll stay in the environment, essentially poisoning other people, for decades?)
But if you want to dig into genetic causes, cancer is probably one of the best places to start.  Not that it's intrinsically more genetically based than any other genetic disease, but there are a LOT of publicly available resources out there.  In particular, take a look at the Cancer Genome Atlas, and the Cancer Cell Line Encyclopedia.  You can get raw data, or use various online tools, to poke around and see if you can find something nobody else has ever noticed.
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Hi,
I need help in merging two SAS data sets:
(1) data set 1 (matched): cases & controls matched 1:2 on age groups, chemo, and radiation
(2) data set 2 (main): main data set containing all patients and their characteristics.
Data set 1 (matched) looks like this:
caseid controlid agegrp chemo rad num
0001   00052     45+         1        1     1
0001   00082     45+         1        1     2
0002   00045    25-30      1        0     1
0002   00036    25-30      1        0     2
Data set 2 (main) looks like this:
id        stage   er   pr    status
0001     1        0    1       0
0002     2        1    0       1
How do I merge data set 1 with the data set 2 to incorporate other characteristics of patients?
Thank you for your help!
Javaid
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Just rename the caseId to Id and then sort both dataset by Id and follow these codes:
data merged_data;
   merge set1 (in=a) set2;
   by Id;
  if a ;
run;
I hope it works!
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I am analyzing the incidence of cancer among Iraqi Kurds in Northern Iraq/ abroad and looking for resources or expertise in this area. Any advice would be greatly appreciated. 
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Great, thank you so much for this information Dr. Hughson, this is extremely helpful. 
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When reading articles using the Charlson Comorbidity Score (Charlson ME et al J Chron Dis. 1987;40:373–383) to describe the comorbidity of a cohort of patients with cancer, for instance head neck cancer, I have the impression that some authors include the primary tumor into the calculation (Charlson's category "any tumor" with 2 points) and others not (only when the patients have at the same time or within last 5 years another type of cancer). The same problem I see with Charlson's category "solid metastatic tumor = 6 points). Some authors seem to include the primary tumor if metastatic (M+) into the calculation, others not.
What is correct?
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Lieber Orlando,
da es ein Comorbidity Score ist, sollte die Grunderkrankung nicht hinzugerechnet werden.
Liebe Grüße und ein Frohes Neues Jahr
Christoph
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I want to know the number of cases of occupational bladder cancer required in case-control study of risk factors for bladder cancer?
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The size of the study (no. of cases and controls) will depend on which causal effects you wish to detect, which degree of increased risk you wish to detect at which alpha level with which power. There are numerous reference to methods in RG answers including mine.
I assume you will be looking at things like aniline dyes, HIV, PAH, tobacco, alcohol, radiation therapy to other organs, general radiation and other chemicals..
you will also need some a priori estimates of likely effects for power studies., based on the literature.
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I have three time periods starting from year 1970. All of them have follow up till 2015. As you can imagine the first time period has longer survival curve than the other two survival curves. When I run the log rank test, I get a significant value. But I feel that the value is affected by the fact that the first time period has longer follow up and survival. I hope to find a significantly increasing survival trend between the three time periods. Which test should I use for the same in SPSS?
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This is a good question.  Proper survival analysis (Kaplan-Meier curves, log-rank test, and Cox proportional hazards models) does account for the discrepancy in follow-up time. 
However, I think you can make a reasonable argument that the additional follow-up time for patients in the older era adds no value to a comparison of the three eras (i.e. if you're looking at survival for cases diagnosed from 1970-1979, 1980-1989, and 1990-1999, the events occurring 25+ years after diagnosis in the 1970-79 and 1980-89 cohort are more-or-less meaningless in a comparison against the 1990-99 era).
I think that it does make intuitive sense, in the type of problem you're describing, to truncate your analysis at the longest follow-up time for the shortest era (so in my example above, truncate your analysis at 25 years after diagnosis, since patients from the "latest" era of 1990-1999 could have as much as 25 years of follow-up, and at least theoretically you can compute an estimate of 25-year survival in that group; consider all patients from the 1970-79 and 1980-89 era who survived >25 years to be censored and still alive at 25 years).  Then you should be able to produce a KM curve going out to 25 years, and create a Cox proportional-hazards model with the "era" as a covariate in the model.
I also think it would be worth reporting some pre-specified time points (1-year survival, 5-year survival, 10-year survival) which obviously also does not have this issue.
For what it's worth, I have been working with a transplant surgeon on a similar question comparing survival after heart transplant in older eras vs. more recently, and have been amused at his seeming inability to understand this.  He has asked me to calculate things like "10-year survival" in a cohort of patients transplanted from 2010-2014 to compare it to prior eras (clearly not understanding that we cannot estimate 10-year survival in patients who have not been followed for 10 years).  So although this may sound elementary, you need not feel bad for asking the question; this is definitely something that confuses researchers across different fields.
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I have a group of postoperative oncologic patients at 5 years follow-up which I divided into four subgroups according to the alive status (dead or alive) and recurrence (disease-free, recurrent). What are the subgroups when calculating the disease-free survival at 5 years of follow-up? Any theoretical literature background on how the calculation is performed is appreciated. Thanks.
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Dear,
On simple note, you need following variables : Survival time, Events 
Parameters required
- cut off date for follow up or censoring date, or study end point date. (A)
-date of remission/ or date of surgery(B)
-date of diagnosis(C)
For DFS, survival time:  A-B
For OS survival time: A-C
Designate your relapsed patients 1 and rest 0,( For DFS) Event  will be 1
Designate your died patients 1 and rest 0, (For OS) Event will be 1.
Be careful to exclude the death other than disease burden.
Subsiquntly use any statistical package to get Kaplan Meier curves followed by log rank test  to compare between the groups
Best Wishes..
Surender
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Future scope of pharmacoepidemiology in India.
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Thanks dear Bhandari
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I've just looked through De Vita's Prime Molecular Biology Cancer, but not sure it works for me. 
Another one is principle for cancer epidemiology, which is really old (around 1991). 
I need a book describing both the key concepts in molecular cancer epidemiology and cutting edge lab and statistical techniques in this field.
Thanks!
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Hi Wang,
The link that Jignasa provided seems to have been truncated and doesn't work. The book description is on the IARC website at: http://goo.gl/iSh2Es
You should be able to find a copy for around 90USD: http://goo.gl/nYcRWQ
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The process to calculate upregulated genes if given a cancer gene.
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I don't even understand the question.   Are you asking about genes that are co-regulated with a specific oncogene or tumor suppressor gene?   Are you looking for all gene expression changes in a cancer, compared to normal tissue?  What do you mean by "given a cancer gene"?   Lastly, are you familiar with Oncomine?  
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What should be the next evidence-based approach in clinical management?
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dear  Victor.
we wait the final pathology !
thank you!
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SEER database has epidemiological cancer data from US. It has its own software that gets incidence rates from the database and also gives the survival graph from the database. 
After getting survival graphs for two different study cohorts I am not sure how to statistically compare them and get a p-value for the comparison.
can anybody please help and give me some suggestions.
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Babu: In SEER Survival Session you need to check the box "Case Listing" in the "Parameters" tab. Than you need to define all your analysis variables as required by your project (site, age, sex etc...) and hit the "Execute" button and you will get the table with single line for every individual. The most important columns from the table will be "Survival Months" and "Vital Status Recode". You can use highlight all-copy/paste commands to get the data into Excel, save it in whichever format is needed and process by a statistical SW of your choice for tests that I mentioned above (after coding for dead/censored individuals vs time between diagnosis and follow-up).
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Rdeently I operated on a young girl of 16 yrs,with huge mass in the Abdomen.on CT scan revealed of Large ovarian tumour extending all over abdomen probably of neoplastic etiology.all tumour markers were in normal range.huge rt.ovarian tumour of 4.5 kg taken out.frozen showed cystadenoma of ovary provisionally benign.final histopath awaited.how often one can say such huge ovarian tumours in Adolesent girls ?
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sir Asharaf tumours histopathology report came as Benign ovarian tumour.patient went home attending school.
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Need some prevalence data on Burkitts Lymphoma
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here's a start:
Burkitt's lymphoma in Africa, a review of the epidemiology and etiology http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2269718/
Rainey et al Spatial distribution of Burkitt’s lymphoma in Kenya and
association with malaria risk. Tropical Medicine and International Health 2007: 12(8): 936–943 doi:10.1111/j.1365-3156.2007.01875.x
Alex
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Is there anyone that can give me some suggestions? I normally use Stata for my analyses. Thank you.
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I Guess you might have seen this already http://www.stata.com/meeting/spain13/abstracts/materials/sp13_zlotnik.pdf In short there is a way in Stata to create nomograms based on regression results but this presentation at least does not point to a ready-made package to download for sure. (Nomograms can vary a lot in how it looks and how many variables etc are used.)
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Methodology question: I am trying to figure out how to measure the degree to which a risk-related problem has become a public issue. The metric needs to be easily analyzed, accessible, quantitative, and stable over at least five years (from 2015 to 2020) and have high face validity. The measurement should be robust – it does not have to be particularly refined or capable of resolving small differences.
The application for this metric is that I am developing a project that has to do with factors (such as Peter Sandeman’s “outrage”) that determine which risk-related (environmental, health, sustainability) issues become public issues over time. I need a way to measure outcomes and compare them against predictions.
Social media is the most obvious approach. One metric would be Google hits, which is convenient, free, and cumulative, and which almost certainly will be around in five years in roughly the present form without too much bias from algorithm changes introduced over the period. On the other hand, I am concerned about Twitter because I’m not sure it will be as stable a platform over the time period and I’m not sure how much people tweet about issues as opposed to people and events. Newspaper inches in a journal of record (such as the New York Times), which used to be an old standby, might be completely obsolete by 2020.
I would be grateful for practical ideas.
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We once contemplated a study of the salience of public health issues (in our case stress related) that were loaded up and viewed on u-tube. This can be monitored if you have a good tech person. Another metric to think about Tee.
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I want to adjust for smoking in an analysis and want to go beyond the simple current, former and never categorization of smokers, which seems to be a bit too crude for my purposes. Of course, the choice of how to parameterize smoking status depends on the outcome of interest (here, my outcome is composite cardiovascular disease). I am thinking that I need to account for dose/duration and perhaps recency of quitting. I am wondering what advice people have and if there are any key references that specifically address this issue.
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Agree that smoking duration is more important than pack years. In my view the deleterious effects of smoking on CVD pertain to artherothrombotic effects and are cumulative. But those effects diminish over time with stopping. So classify smokers as current (smoking during the period of follow up for CVD events) or past (quit smoking before the period of follow up for CVD events) or recent (quit smoking during the period of follow up for CVD events). Add another variable that gives the duration of smoking (eg 5 years). If you have a large enough sample size, you could also add a variable for age at onset of smoking.
As an example, I smoked for 6 years, I began smoking at age 18, I am now 63.
So class me as past smoker , 6yrs duration, age of smoking onset 18. These variables would exert very little effect on my current risk for CVD, which is what you wish (ie dose response). A current smoker, who smoked for 20 years, who started at 18 and is currently 38 should exhibit a much higher risk for CVD than a non smoker of similar age
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Writing a book and one chapter is on how managing sustainability seems to be turning into a profession, with full-time managers doing this in business. What do professionals in sustainability need to know? Is there a distinct science underlying the field? If so, what does it involve? Are environmental sciences and studies programs providing adequate grounding in this science?
Please concentrate on the questions, not the definition of sustainability. There are lots of different definitions of sustainability (my book will offer another one), but for the purpose of this discussion, please assume that "sustainability" means doing business and managing enterprises in a way that works toward the goal that there is minimum impact on the environment, good prospects for the future, no degradation that would compromise the future, and that protects health and a decent life.
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for an insightful framing of the scientific questions, look at The Natural Step's "system conditions" for sustainability. The Natural Step seems to be somewhat out of fashion among Americans working in this area, but the framework is immensely valuable conceptually.
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To what extent have lifestyle, diet, patient, treatment and tumour factors influenced this change (if at all)?
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Gurdeep:
You are right to suspect a residual mammographic-independent component of increased DCIS incidence:
The effect of screening programs on incidence of DCIS (per 1,000 screening mammograms) was examined using data from the BCSC (Breast Cancer Surveillance Consortium) and the NBCCEDP (National Breast and Cervical Cancer Early Detection Program) [1,2,3], finding (1) that the incidence of screen-detected DCIS was greater than the incidence of nonscreen-detected DCIS; (2) the Incidence of DCIS in the United States increased over time regardless of the definition used. But most relevant was: (3) that the data revealed greater increases over time in incidence per 100,000 population than per 1,000 screened, that is, that the incidence of DCIS increased over time, even when the rate of mammography was constant, suggesting that although the clear preponderance of cases accounting for the continued increase in DCIS incidence was secondary to upswings in mammographic screening (as supported in eight population-based trials of mammography screening), not all of this increased incidence could be so accounted for. In addition, note that we have differential incidence dependent on morphology or histological subtype: the incidence of non-comedo DCIS, that is tumors without comedo necrosis (these tumors not being associated with subsequent DCIS or invasive cancer) has generally increased across all age groups, whereas rates of comedo DCIS (a type of DCIS which is associated with subsequent DCIS or invasive cancer), has held constant or decreased [4,5].
So data support that there is a residual and non-trivial incidence of increased DCIS not dependent on the changing dynamics of mammographic screening. We are only now learning that DCIS exhibits some substantively different clinical behavior as well as natural history depending on not only histology and morphology but also on the subtype of DCIS (endocrine-positive, HER2-positive, or basal-like), and it is clear that we need more, and more mature, data, as to the differential dynamics of DCIS subtypes to clarify both the dependencies of incidence patterns and any clinically relevant consequences for therapeutic intervention.
References
1. Ernster VL, Ballard-Barbash R, Barlow WE, et al. Detection of ductal carcinoma in situ in women undergoing screening mammography. J Natl Cancer Inst 2002 Oct 16; 94(20):1546-54.
2. Smith-Bindman R, Chu PW, Miglioretti DL, et al. Comparison of screening mammography in the United States and the United kingdom. JAMA 2003 Oct 22; 290(16):2129-37.
3. Smith-Bindman R, Ballard-Barbash R, Miglioretti DL, et al. Comparing the performance of mammography screening in the USA and the UK. J Med Screen 2005; 12(1):50-4.
4. Li C, Daling J, Malone K. Age-specific incidence rates of in situ breast carcinomas by histologic type, 1980 to 2001. Cancer Epidemiol Biomarkers Prev 2005;14(4):1008-1011.
5. Virnig BA, Wang SY, Shamilyan T, Kane RL, Tuttle TM. Ductal carcinoma in situ: risk factors and impact of screening. J Natl Cancer Inst Monogr 2010; 2010(41):113-6.
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I am looking for a standard way to calculate risk-factor-specific incidence rate from overall population incidence rate. The available data are overall population incidence rate of a disease, prevalence of a risk factor, and relative risk of that disease according to the risk factor. What I want to do is to estimate the incidence rate of people with or without that risk factor. My image is to allocate the overall incidence rate into two groups (risk factor holders and non-holders), keeping consistency with the overall incidence rate. Are there any good references?
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Thanks Freddy! That's exactly what I was looking for.
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I am synthesizing some data from a few observational studies for a meta analysis later, I calculated a weighted odds ratio using the M.H method, however I am not sure how to calculate the confidence intervals. I am not using any statistical software at the moment, just doing calculations and putting in the data into an excel file. Can I just calculate the standard error by adding all exposed and unexposed groups in the cases and controls, and then using the standard formula for standard error calculation and so on for C.I ? Or is there any specific method for calculating C.I of the M.H odds ratios.
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A specific method for calculating confidence interval of Mantel-Haenszel Odds Ratio was first described in Clayton D. & Hills M. (1993) Statistical Methods in Epidemiology. Oxford University Press, Oxford. It is also reproduced in Page 183 of Essential Medical Statistics by Betty Kirkwood and Jonathan E. Sterne. The following steps should be followed to calculate the Mantel-Haenszel CI :
i) Here 95% CI ranges from OR/EF to OR*EF, where OR is Mantel-Haenszel Odds ratio and EF is the exposure factor;
ii) EF = exp (1.96* SE) , where exp is exponential and SE is the Standard error of the Mantel-Haenszel Odds Ratio;
iii) SE = Sqrt [V/(Q*R)];
iv) Now just think about a, b, c, d as the values the four cells of a 2 X 2 table of each stratum (Remember that using this approach we usually describe the simple Odds Ratio as (a*d)/ (b*c) ). Thus ai, bi, ci ,di represent the a, b, c, d values of the i-th stratum. Similarly ni is the sum of ai, bi, ci and di in the i-th stratum;
v) Using the above notational style,
Q = Sigma (i.e. summation of) [(ai*bi) / ni]
R = Sigma [(ci*di) / ni]
V = Sigma [(ai +bi)* (ci+di)*(ai+ci)*(bi+di)]/ [ni*ni*(ni-1)].
You can easily generate the 95% Ci in Excel itself provided you carefully translate the above to Excel formula. If you are still not sure, you can directly consult the books mentioned in the beginning.
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At least since 2008 IAD has been considered an option that may be offered to men with metastatic prostate cancer but I have no idea about actual application of this strategy. Any feedback or publications would be welcomed.
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Higher limit of C.I - lower limit of C.I / 3.92 is the formula I used, my question is that this gives me a standard error of the regular odds ratio as opposed as the Log odds ratio, so should I just calculate the natural log of the standard errors I get through the above formula or should I first convert the limits of the confidence intervals into their natural log and calculate the S.E then?
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For your formula, higher limit and lower limit of Ci should be taken natural log first
e.g. CI of OR (2, 5), after taking natural log, it is (0.693, 1.609),
SE=(1.609-0.693)/3.92=0.2337
remark: 3.92 is 1.96*2
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I am trying to look at the association between a polymorphism and risk of cancer incidence by a meta-analysis. However for some studies we do not have the complete genotype information for the same. We noted allele distribution from the publications and tried to contact authors to obtain the complete genotype data. Some of them, however, haven't responded. So we have done a primary analysis including only studies that have the complete genotype data. Now I wish to do a sensitivity analysis by including the omitted studies and perform the comparisons with the available data. Is this approach correct? or unnecessary?
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The question is how will you manage to add the studies, from which results are not available? From my perspective the best thing you can do is to discuss possbile limitation of publication bias. You are aware that the estimation done by metaanalysis could be different because of unpublished results. The issue has to be discussed and explained. Stats Direct shows a plot, from whoch possible publication bias can be assessed, although you know for shure that there is not available data, because you encountered such studies. On the other hand, if the result of the metaanalysis shows a heterogenity test p<= 0,1, that could mean possible heterogenity of the studies included to metaanalysis. Then a sensitivity analysis is a must taking into account discriminating factors. More on that topic here: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1767262/
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How to determine cut off value of independent variables (e.g. pack year of smoking, parity, amount of alcohol etc.) to consider as risk while analyzing OR in case control study.
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It is true that using continuous variables generally helps you get the most information out of your data, but there are times when using a cutoff is necessary, for example to establish treatment guidelines. Blood pressure is an example of this: Anything over 140 systolic BP is considered high blood pressure because BP over this level is associated with adverse health outcomes. It may also be used to guide treatment with BP medications. The 140 figure used to be higher until epidemiologic studies showed that systolic BP as low as 140 was associated with bad outcomes too.
The cutpoint at where to categorize your data variable can be based on many things. But first, if a statistical method (such as linear regression) can be used that uses the continous data, by all means use it. Logistic regression will let you use either continous or binary variables. One way you can tweak the cutpoints when categorizing your data in logistic regression is to do a sensitivity and specificity, and positive predictive value / negative predictive value with the results, to see how your regression model performs at predicting outcome at various cutpoints.
Also for putting your results into practical terms, consider a NNT (number needed to treat) analysis.
If you can use more than one cutpoint, you may wish to stratify your data and do a stratified analysis. This will (hopefully) for example establish an increasing risk of the outcome with increasing exposure---the classic example being increasing mortality rates as cigarettes smoked per day increases. (you can do this using one cutpoint too, but using three or more makes a stronger case for a dose-response effect).
Some studies use the bottom ten percent of a value for the cutpoint, for example birthweight, if you can find normative data with which to classify your data. Or you can convert your continous data to Z-scores or a percentile rank and use, for example, the lower 25% or upper 10% as a cutoff. It depends what your data variable is, of course.
In short, you just have to play with the numbers. But as others have said, try to find some basis for your cutpoint in the literature, or something that makes biological sense (physiology frequently exhibits thresholds, for example muscle fatigue or the transition of cells into carcinoma). Or consider cutpoints that have practical value, such as for diagnosing or treating disease, or a population-level health impact of the risk factor, including economic health impacts.
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As a possibly pathway to understanding the role the immune system plays in attempt to control cancer growth, an interesting test would be to perform an epidemiological study of those with autoimmunity diseases and find the incidence of cancer in that sub-population.
Are cancer rates lower in those with some form of autoimmunity? Have studies been carried out to test this?
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Yes, there is a clear inverse correlation between vitiligo and melanoma. Those with vitiligo have a 3-fold decreased risk of melanoma (Teulings, Br J Derm 2013). This is not surprising, since vitiligo results from a cytotoxic response directed against melanocytes (van den Boorn, J Invest Derm 2009), which are the neoplastic cells in melanoma. Also, the appearance of vitiligo in a patient with late-stage melanoma is a good prognostic sign (Nordlund JAAD 1983), with some developing complete remission. Identical antigens are targeted in both vitiligo and melanoma, however the affinity of the T cell receptor (TCR) is greater on clones isolated from vitiligo patients compared to melanoma patients (Palermo Eur J Imm 2005). Some have proposed to use vitiligo TCR expression as a therapy for melanoma patients (Circosta Hum Gene Ther 2009). There is also support for this inverse correlation from genome-wide association studies (GWAS) on vitiligo and melanoma patients. The tyrosinase risk allele for vitiligo is protective against melanoma, and vice-versa (Jin NEJM 2010).
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What do you think abut the USPHS task force recommendations on mammography and prostate caner screening?
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Thanks. Yes, I would be interested in a citable PDF version so that I can send it to some course leaders and students and start a debate on this in the medical school.
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I've been studying in some books and found differents values, none consensus.
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The best studies to answer to this question come from cancer registries. In western countries, the SEER is the most accurate. Tumors are located with the same rate in the right colon, left colon and rectum.
(right colon 28%, sigmoid 23%, rectum 25%, rectosigmoid 10%: Anatomic subsite of primary colon colorectal cancer.... Cancer 2013)
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We are planning to evaluate the quality of our cancer registry data for its completeness by using 3 sources of data, is there any one who would like to help us in capture-recapture analysis?
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I think the main problem with capture-recapture methods is meeting the necessary assumption of the heterogeneity of mixing in the population, so that the recapture is independent of the first capture.
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Thyroid cancer
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This is the most reliable database for cancer epidemiology.