Survival bias in prostate cancer 5k,ford f-150 svt raptor jump,2014 ford edge vehicle weight ontario - Good Point

About a month ago, I wrote a post congratulating Sharon Begley for her well reasoned reporting on a paper in Health Affairs that argues that the extra money we spend on cancer care is worth it because it buys us increased survival time. This criticism is somewhat puzzling since we also examine mortality trends in our paper, and the same story holds.
Their results were dependent on the data in the Appendix, yet the errors in it suggest that it was not critically reviewed. Even so, I’ll concede that we may have lower mortality rates than many other countries when it comes to breast cancer. Ultimately though, people with cancer—and their physicians—are most concerned about their survival chances once they are diagnosed. This study showed that survival rates are increased for two cancers that are massively screened for in the US.
Death rates and survival rates are tricky things to consider because, as they say, life is 100% fatal, no one gets out of here alive, etc. We don’t really care how long we live after the cancer has been diagnosed, but rather how long we live since we actually got the cancer. The reason is that the longer you live the higher your chances that something else will kill you. The Lancet study looked at 3 (or 4) different cancers, breast cancer in women, prostate cancer and colorectal cancer (sometimes separately colon cancer and rectal cancer).
The difference in survival between most of the top countries is small, but despite that the US is clearly in the top ranks. While it’s hard to say that the US is #1 or #2 in this measure since, statistically many countries are tied for the #1 and #2 positions, it is the case that of the 31 countries studied the US has better cancer survival rates than at least 25 of them.
That’s right, many comments in the news merely state that the US has the best cancer survival rate in the world. Who cares if we have a high survival rate for cancers that don’t tend to kill people or that not many people contract in the first place?
Combining the genders where appropriate to get total mortality, lung cancer is the leading cause of death by cancer in developed countries (all developed countries combined). The cited study in Lancet Oncology is the first to compare cancer survival statistics across many different countries. The implied question here is, would our #1 ranking in cancer survival be significantly harmed as a side-effect of health care reform?
I didn’t see any reference in the article to the US portion being more or less skewed toward those with insurance. I’m not suggesting that the US is not ahead for a large section of its population, thanks to aggressive screening and top notch comprehensive cancer centres.
And overall a key point: from what I can see, much of the difference between the US and elsewhere is down to access and use of diagnostics, not treatment.
First let me say that I don’t know why your 2nd comment required my approval to be seen, so apologies for the delay in posting it. Next, you should note that we’re arguing the same point but merely from different angles. There are many cancers that are slow growing, symptomless and would not kill a person before something else would (many prostrate cancers are of this kind). The assertions of this study, showing the us coming out ahead seem to be contradicted by the OECD study among developed nations from 2000. If we go by number of overall cancer deaths per 100000, the US comes out at a solid middle among the developed world, but actually well behind Britain and Scandinavia. Also, even if we take these studies showing the USA doing awesomely at face value, the blogs claim that just because we deal with cancer better than most nations doesn’t really speak much about any inherent awesomeness in the US system.
Any claims on this board or others, attesting to the superiority of the US healthcare system are highly dubious. When treatment before symptoms occur is more effective than treatment that is delayed until symptoms appear.
By these criteria, blood pressure screening to detect and treat hypertension is an ideal circumstance for screening. Even when lung cancer is detected by screening, earlier treatment does not seem to prolong survival substantially. There is controversy about the role of PSA (prostate-specific antigen) testing to identify prostate cancer.
Test validity is the ability of a screening test to accurately identify diseased and non-disease individuals. A 2 x 2 table, or contingency table, is also used when testing the validity of a screening test, but note that this is a different contingency table than the ones used for summarizing cohort studies, randomized clinical trials, and case-control studies. The contingency table for evaluating a screening test lists the true disease status in the columns, and the observed screening test results are listed in the rows. The column with diseased subjects is emphasized, since sensitivity focuses on the probability that the test will correctly identify diseased subjects.
What was the probability that the screening test would correctly indicate disease in this subset? Specificity focuses on the accuracy of the screening test in correctly classifying truly non-diseased people. Question: In the above example, what was the prevalence of disease among the 64,810 people in the study population? One problem is that a decision must be made about what test value will be used to distinguish normal versus abnormal results. If we move the cut-off to the left, we can increase the sensitivity, but the specificity will be worse. ROC curves provide a means of defining the criterion of positivity that maximizes test accuracy when the test values in diseased and non-diseased subjects overlap. I could then plot the true positive rate (the sensitivity) as a function of the false positive rate (1-specificity), and the plot would look like the figure below.
Note that the true positive and false positive rates obtained with the three different cut points (criteria) are are shown by the three blue points representing true positive and false positive rates using the three different criteria of positivity.
This provides a standard way of assessing test accuracy, but perhaps another approach might be to consider the seriousness of the consequences of a false negative test. When evaluating the feasibility or the success of a screening program, one should also consider the positive and negative predictive values. Positive predictive value is the probability that subjects with a positive screening test truly have the disease.
Negative predictive value is the probability that subjects with a negative screening test truly don't have the disease.
One way to avoid confusing this with sensitivity and specificity is to imagine that you are a patient and you have just received the results of your screening test (or imagine you are the physician telling a patient about their screening test results.
Conversely, if it is good news, and the screening test was negative, how reassured should the patient be? Another way that helps me keep this straight is to always orient my contingency table with the gold standard at the top and the true disease status listed in the columns.
If a test subject has an abnormal screening test (i.e., it's positive), what is the probability that the subject really has the disease?
Positive predictive value focuses on subjects with a positive screening test in order to ask the probability of disease for those subjects.
Negative predictive value: If a test subject has a negative screening test, what is the probability that the subject really does not have the disease? Negative predictive value focuses on subjects with a negative screening test in order to ask the probability that subjects with a negative test are truly not diseased. This widget will compute sensitivity, specificity, and positive and negative predictive value for you. Sensitivity and specificity are characteristics of the test and are only influenced by the test characteristics and the criterion of positivity that is selected.
To illustrate the effect of prevalence on positive predictive value, consider the yield that would be obtained for HIV testing in three different settings. These three scenarios all illustrate the consequences of HIV testing using a test that is 100% sensitive and 99.5% specific. The 1st scenario illustrates the yield if the screening program were conducted in female blood donors, in whom the prevalence of disease is only 0.01%. The 2nd scenario illustrates the yield if the screening program were conducted in males in a clinic for sexually transmitted infections, in whom the prevalence of disease is 4%.
This 3rd scenario illustrates the yield if the screening program were conducted in users of intravenous drugs, in whom the prevalence of disease is 20%. At first glance screening would seem to be a good thing to do, but there are consequences to screening that carry a cost, and the potential benefits of screening need to be weighed against the risks, especially in subsets of the population that have low prevalence of disease! Specifically, one needs to consider what happens to the people who had a positive screening test but turned out not to have the disease (false positives). For a very relevant look at this, see the following brief article from the New York Times on the potential harms of screening for prostate cancer. There is concern among some that there is an inordinate emphasis on early diagnosis of disease and that the increasingly aggressive pursuit of abnormalities among people without symptoms is leading to actually harm and great cost without reaping any benefits. Even if a test accurately and efficiently identifies people with pre-clinical disease, its effectiveness is ultimately measured by its ability to reduce morbidity and mortality of the disease.
These include correlational studies that examine trends in disease-specific mortality over time, correlating them with the frequency of screening in a population.
Case-control and cohort studies are frequently used to evaluate screening, but their chief limitation is that the study groups may not be comparable because of confounders, volunteer bias, lead-time bias, and length-time bias. Because of these limitations, the optimal means of evaluating efficacy of a screening program is to conduct a randomized clinical trial (RCT) with a large enough sample to ensure control of potential confounding factors.
People who choose to participate in screening programs tend to be healthier, have healthier lifestyles, and they tend to adhere to therapy better, and their outcomes tend to be better because of this. The premise of screening is that it allows you to identify disease earlier, so you can initiate treatment at an early stage in order to effect cure or at least longer survival. In the next figure two patients again have identical biologic onset and detectable pre-clinical phases.



To illustrate consider a hypothetical randomized trial in which half of the subjects were screened and the other half were not. For an nice summary of lead time bias, and length time bias follow this link: Primer on Lead-Time, Length, and Overdiagnosis Bias. Down syndrome is a spectrum of abnormalities that generally result from an error during gametogenesis in the ovary that results in the birth of a child with three copies of chromosome 21 (trisomy 21) instead of the normal two copies. Prior to 2014 the most up-to-date screening method during pregnancy was a combined approach during the first trimester that was conducted in two steps during week 11 to 13 during pregnancy.
Ultrasound (the nuchal translucency screening test) in which US is used to measure a specific region on the back of a baby's neck looking for fluid accumulations that occur with abnormalities.
In late 2011 cell-free DNA sequencing (cfDNA testing) of maternal plasma was introduced as a new screening modality in the US. This link below will allow you to listen to a report about the study on National Public Radio (NPR).
The tables below summarize the evaluations of the "Standard Test for Down syndrome and the results obtained with the newer DNA sequencing technique. Interpretation: The probability of biopsy-proven prostate cancer among men with a positive PSA test was 42%.
Interpretation: The probability of not having prostate cancer among men with a negative PSA test was 81%. I have no idea if they’re responding to my criticism, but I wanted to address some of their points anyway.
Moreover, the differences in mortality rate aren’t nearly as big as the differences in survival rates.
While mortality rates in the population may be a focus of epidemiological research, they are not the statistics of greatest interest to those diagnosed with cancer.  Once diagnosed, a patient and her physician care more about how long she will live—hence our choice of survival as the primary endpoint. We have massive screening programs for prostate cancer and breast cancer, but not for most of those other cancers. Then it declared, in the manuscript’s discussion, that these differences are likely due to the increased health care spending in the US, specifically citing pharmaceuticals. Do you know what is commonly done to control for factors affecting incidence rates such as behavior, genetics, etc. Many bloggers (site, site, site, and site) and research institutes have discussed the results of a 2007 article in the medical journal Lancet Oncology that compared cancer survival rates in several different countries. So if more people are dying from other causes and fewer people are dying from this cancer it’s probable that the updated detection and treatment are indeed leading toward longer life.
So the articles that cite the US’s superior cancer survival rates are accurate, at least for these cancers. The cancers studied in the Lancet article are ranked #2, #4 and #6 in terms of total mortality in developed nations.
The cancers they chose were a reasonable cross section of the most significant cancers among developed nations.
When you have a big cause and a big effect most folks take the conservative route and assume cause & effect until proven otherwise. Now in the latest study it’s up to 42%, and the effect of adding more registries has been to decrease survival rates.
Theirs identified the countries that had 100% coverage from those that didn’t, and as you say the US had only 42% coverage. However either it or other articles I researched also pointed out that the US is much more heterogeneous than the average European country.
While I believe you are correct in all that you say, my point for this site is to showcase that a deep dive into the facts is not required for understanding, merely a different dive, one with charts and numbers. And, while there were regional differences, overall the fraction of blacks in the registries was about the US average.
It appears to me that these error bars are the standard statistical thing and do not represent any additional information about the quality of data in the registries. Namely how much of the US ranking here is due to social and cultural features and not directly tied to the health care system? I am interested in revisiting this topic in a future article and will undoubtedly use the articles you cite when I do so. Smokeless cigarettes are proper increasingly more hot magnitude smokers and promptly to be quitters. I’m curious as to the methodology of the people doing the survey showing the survival rate as 90%, what are they doing about people who cannot afford treatment? These blogs tend to ignore the fact that multiple studies have consistently shown the US falling short in other health-related areas such as respiratory disease, infant mortality, and others. AmericanHealthJournal is a medical content site which contains a large library of high quality medicine videos. However, disease frequently begins long before symptoms occur, and even in the absence of symptoms there may be a point at which the disease could be detected by a screening test. If they become symptomatic, the gallbladder can be removed, and the delayed treatment generally causes no problem.
Variability in the measurement can be the result of physiologic variation or the result of variables related to the method of testing.
An ideal screening test is exquisitely sensitive (high probability of detecting disease) and extremely specific (high probability that those without the disease will screen negative).
The 2 x 2 table below shows the results of the evaluation of a screening test for breast cancer among 64,810 subjects. However, only 132 of these were found to actually have disease, based on the gold standard test. When thinking about sensitivity, focus on the individuals who, in fact, really were diseased - in this case, the left hand column.
It is the probability that non-diseased subjects will be classified as normal by the screening test. Specificity focuses on the probability that the screening test will correctly identify non-diseased subjects.
Unfortunately, when we compare the distributions of screening measurements in subjects with and without disease, we find that there is almost always some overlap, as shown in the figure to the right.
If we move the cut-off to the right, the specificity will improve, but the sensitivity will be worse. As the previous figure demonstrates, one could select several different criteria of positivity and compute the sensitivity and specificity that would result from each cut point. This is a receiver-operator characteristic curve that assesses test accuracy by looking at how true positive and false positive rates change when different criteria of positivity are used. For example, failing to identify diabetes right away from a dip stick test of urine would not necessarily have any serious consequences in the long run, but failing to identify a condition that was more rapidly fatal or had serious disabling consequences would be much worse. David Felson from the Boston University School of Medicine discusses sensitivity and specificity of screening tests and diagnostic tests. These are also computed from the same 2 x 2 contingency table, but the perspective is entirely different. If the test was positive, the patient will want to know the probability that they really have the disease, i.e., how worried should they be?
The illustrations used earlier for sensitivity and specificity emphasized a focus on the numbers in the left column for sensitivity and the right column for specificity. In the same example, there were 63,895 subjects whose screening test was negative, and 63,650 of these were, in fact, free of disease. David Felson is a Professor of Medicine in the Boston University School of Medicine, and he teaches a course in Clinical Epidemiology at the BU School of Public Health. In contrast, the positive predictive value of a test, or the yield, is very dependent on the prevalence of the disease in the population being tested. Serological testing for HIV is extremely sensitive (100%) and specific (99.5%), but the positive predictive value of HIV testing will vary markedly depending on the prevalence of pre-clinical disease in the population being tested. Even with 100% sensitivity and 95% specificity, the positive predictive value (yield) is only 1.9%. The most definitive measure of efficacy is the difference in cause-specific mortality between those diagnosed by screening versus those diagnosed by symptoms.
However, the costs and ethical problems associated with RCTs for screening can be substantial, and much data will continue to come from observational studies. In this case the screened patient lives longer than the unscreened patient, but his survival time is still exaggerated by the lead time from earlier diagnosis. Prostate cancer, for example, is a very slow growing tumor in many men, but very rapidly progressing and lethal in others. Because we assigned subjects randomly, the DPCPs are more or less equally distributed in the two groups.
The screened subjects who are identified as having disease will tend to have longer survival times, because they have, on average, a less aggressive form of cancer. Compute the sensitivity, specificity, and positive predictive value of each screening test and comment on the utility of the newer DNA test compared to the previous standard testing. The question gives us the total number of subjects and the prevalence of biopsy-proven prostate cancer.
However, as suggested by the NPR broadcast, the specificity of the new test that used DNA sequencing was better and resulted on only 6 false positive screening tests compared to 69 false positive tests with the older standard test. Other rare complications of amniocentesis include Injury to the baby or mother, infection, and pre-term labor. If they’d done their calculations on these differences, the cost might not have appeared to be worth it.
Put another way, researchers do not abandon measuring survival in oncology trials because there is a well-recognized issue of attrition bias. Do you really think that has nothing to do with the massive differences in results between those two cancers and the others? The blogs and news sites claim the US is the best in the world for cancer survival and the medical journal article seems to back that up. Cancer researchers use two different statistics to track this: mortality rate and survival rate, each has its own strengths and weaknesses.


The benefit of this is that if one gets lung cancer (for example) and dies from pneumonia because ones lungs were weakened by the cancer then it seems correct to attribute this death as being caused by the cancer. People in different countries die at different rates due to heart attacks, automobile accidents, pneumonia and other causes. One problem with this statistic is it doesn’t account for cancers that weaken but do not kill you. The American Cancer society has a report that lists the top causes of death due to cancer around the world.
Additionally, breast cancer is #2 for women (#4 over all), and prostate cancer (#6 over all) is #3 for men. The other countries with less than 100% coverage were Canada, Japan, France, Italy, Spain, Netherlands, Switzerland, Germany, Austria, Portugal, Poland, Czech Republic, Brazil and Algeria, about half of the countries. For example, the US fraction included Atlanta but not Georgia, all of Colorado and all of California with additional detail for Los Angeles and San Francisco, just to name a few. While this may weigh more favorably for the US (we’re #1 despite having a more heterogeneous population), I chose to present the original Lancet article data as is.
It’s well known that the Scots, for example, drink and smoke themselves to early graves and avoid doctors but there are superb hospitals in Scotland. Specifically some US commentators have cited the Lancet report and merely state that the US is #1 in cancer survival. But when the data with the error bars is included it’s easy to see that the US is merely tied for first place with about 4-5 other countries (something I mentioned in my article).
Your example of the Scots drinking and smoking themselves to death and the US possibly having a higher screening rate are things that are not going to change under the proposed bills currently being considered in the US.
There are apparently many different types of cancers each of which can attack different organs.
I will be simply a usual visitor on your site (more like addict :P) to your website however experienced a challenge.
We are looking for individuals who may be interested in writing guest articles to our site. There were 177 women who were ultimately found to have had breast cancer, and 64,633 women remained free of breast cancer during the study.
Also note that 63,695 people had a negative screening test, suggesting that they did not have the disease, BUT, in fact 45 of these people were actually diseased.
In the example above, suppose I computed the sensitivity and specificity that would result if I used cut points of 2, 4, or 6. Consequently, a common sense approach might be to select a criterion that maximizes sensitivity and accept the if the higher false positive rate that goes with that if the condition is very serious and would benefit the patient if diagnosed early. If this orientation is used consistently, the focus for predictive value is on what is going on within each row in the 2 x 2 table, as you will see below. The higher the prevalence of disease is in the population being screened, the higher the positive predictive values (and the yield).
The examples below show how drastically the predicative value varies among three groups of test subjects. The only thing that is different among these three populations is the prevalence of previously undiagnosed HIV. Diagnostic measures included the area under the receiver-operating characteristic curve, sensitivity, specificity, and likelihood ratios. They may also undergo invasive diagnostic tests such as needle biopsy and surgical biopsy unnecessarily. There are several study designs which can potentially be used to evaluate the efficacy of screening. The two subjects to the right have the same age, same time of disease onset, the same DPCP, and the same time of death. These differences in DPCP exaggerate the apparent benefit of screening, because there is a greater chance that screening will detect subjects with long DPCPs, and therefore, more benign disease.
If we conduct a screening in half of the subjects at a specific point in time, there is a greater probability that those who screen positive will have longer DPCPs on average, because they are detectable by screening, but their disease has not progressed to the stage of causing symptoms or death yet.
It also gives us the sensitivity and specificity of the the PSA test that they used, so we can construct the contingency table from this information, and then compute the positive predictive value. Since women with positive screening tests are recommended to undergo amniocentesis for definitive diagnosis, false positive tests in this setting represent cases in which unnecessary amniocentesis was done, placing the fetus at risk. The bottom line is that looking at mortality also supports our finding of a widening gap between the United States and the European countries we investigated. But the United States has the lowest longevity of the industrialized nations, so there’s some cause for skepticism. The lung cancer-pneumonia example above would be counted as a death due to pneumonia by this measure.
So the cancers studied are a reasonable cross section of the most significant cancers that affect the developed world. But stated in plain English, without the benefit of charts or graphs, it can be misleading. The reason is that there are many countries that are very close to the US in cancer survival rates and these countries have some form of government involvement in health care.
In contrast, the article did mention that the portion of Italy studied was a more affluent part than the country as a whole and it mentioned Cuba as having some quality control issues with their registries (and for that reason I excluded Cuba from my version of their graphs).
After all, there are only 2 ways to account for this, either compare countries as a whole (what was done) or compare the predominant race in each country to that of other countries (eg, Caucasian US to Caucasian France).
But until you see the data that supports that (the bar charts here) the reader doesn’t really get a sense of what that means. I noticed that these studies that put the US ahead tend to base it off of 5 year + survival rates, so it probably matters what overall cancer mortality is. I’m not really likely certain when it is the correct site to ask, and you have zero spam comments. We hope that detection of disease in the DPCP will lead to earlier treatment and that this, in turn, will lead to a better outcome. If there were no definitive tests that were feasible or if the gold standard diagnosis was invasive, such as a surgical excision, the true disease status might only be determined by following the subjects for a period of time to determine which patients ultimately developed the disease. Among the 177 women with breast cancer, 132 had a positive screening test (true positives), but 45 had negative tests (false negatives). Consequently, the primary means of increasing the yield of a screening program is to target the test to groups of people who are at higher risk of developing the disease.
In the case of fecal blood testing for colorectal cancer, patients with positive screening tests will undergo colonoscopy, which is expensive, inconvenient, and uncomfortable, and it carries its own risks such as accidental perforation of the colon.
However, if we compare survival time from the point of diagnosis, the subject whose disease was identified through screening appears to survive longer, but only because their disease was identified earlier. If the study consisted of 2,620 men and 930 had cancer, then there must have been 2,620-930= 1690 men without cancer. If you diagnose a cancer earlier in one country than in another, almost by definition survival time is increased, even if they die at the same rate at the same time. We may have inferior survival rates for other cancers and furthermore the cancers studied above may not be the most significant cancers in the US or world.
The graphs above, largely the same graphs as published in Lancet Oncology, clearly show many countries are also very close to the US in terms of cancer survival.
Fortunately data wasn’t available for the latter comparison and besides it seems off-topic to consider race specific data when trying to set policy for an entire country. Assuming the article is true, something that the lay public has to do, the article itself indicates that the US’s lead is not that substantial.
Survival rates and other statistics may be more related to this underlying type of cancer than it is with the location of the occurrence. For example, the accuracy of mammography for breast cancer would have to be determined by following the subjects for several years to see whether a cancer was actually present. Among the 64,633 women without breast cancer, 63,650 appropriately had negative screening tests (true negatives), but 983 incorrectly had positive screening tests (false positives). The diagonal blue line illustrates the ROC curve for a useless test for which the true positive rate and the false positive rate are equal regardless of the criterion of positivity that is used - in other words the distribution of test values for disease and non-diseased people overlap entirely.
Survival estimates absolutely will be subject to lead time bias when there are different methods of diagnosis. If improved screening allowed doctors to detect it four years earlier then suddenly there would be a much higher five year survival rate as everyone now survives 7 years after detection. It’s hard to believe that such a change would significantly affect the survival ranking. When you reflect on this lead in terms of the dollars the US spends on health care, this slim lead hardly seems worth the effort. There has been much controversy regarding the age at which routine mammography screening should begin in order to screen for breast cancer. So, the closer the ROC curve is to the blue star, the better it is, and the closer it is to the diagonally blue line, the worse it is.
The other problem is false negatives, who will be reassured that they don't have disease, when they really do.
Therefore, the number of men without prostate cancer who had positive tests must have been 1690-558=1132. Indeed, there is some recent data that supports that, regarding prostate cancer at least, the US may be over-treating (and hence over-spending on) cancer. More recently, there has been controversy about whether PSA (prostate-specific antigen) screening should be used at all in men. The earlier detection didn’t actually change the course of the disease, the doctors just detected the cancer earlier.



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