I usually don't use my blog to take on or pick apart published pieces - my aim with the blog is to create a diversity of ideas and viewpoints to the reader. There is plenty of intelligent writing in the Web that is thought-provoking and worth bringing to the attention of readers interested in the general ideas of statistics and machine learning. But I came across this learning recently that - I have to admit - caused a fair amount of angst and therefore an urge-to-act. This was the Judgment Deficit by Amar Bhide, a professor of Finance at Tufts University.
The journal article from HBS talks about how machines or computers can make decisions in certain types of situations and human judgment needs to come in at other places. Fair enough. The article then bemoans the recent Great Recession and lays part of the blame on statistical models used in Finance. Specifically the author says
In recent times, though, a new form of centralized control has taken root: mechanistic decision making based on top-down statistical models and algorithms. This has been especially true in finance, where risk models have replaced the judgments of thousands of individual bankers and investors, to disastrous effect.This kind of thinking is not only delusional but also dangerous. (Another part of the article that didn't necessarily get me singing from the rooftops was the lengthy encomium heaped on the economics of Freidrich Hayek, the libertarian economist and the founder of the famous Austrian Economists school. I am still not clear how is that related to the topic at hand.)
The fundamental reason why banks took the risks they took were because there were incentives to do so and there was not enough of an appreciation of the downside. Bankers thought the spiral of rising home-prices, the ability to take the assets off balance sheet and maintain minimal capital reserves, was an unending one and were unable to either spot the inevitable edge of the cliff or were too late to pull back once they spotted it. Also the desire to have these activities as unregulated as possible (to allow free pursuit of profit, or to make 'markets efficient' as Wall Street would argue) led to a number of opacities (about risk) developing in the system which lead to situations where high-schools in Norway were exposed to the collapse of Bear Stearns. So lets not put the blame on top-down statistical models and algorithms. If the alternative that Bhide suggests, having manual underwriters take more of the decisions, were to have happened, I am not sure whether the conclusions reached by these underwriters would have been any different. Apart from a few economists, fund managers and people like Nourini and Taleb (who have made an image of themselves as Cassandras of Doom and therefore have to say anything to maintain that image), nobody - let me say that again - nobody saw this edifice collapsing. No one thought house prices in the US would ever come down. Everyone (human beings and computers alike) were victims of the rear-view mirror bias, i.e. expecting that the future would play out exactly as the past.
So let's go a little bit easy on computers, statistical models and automated decision making.
Showing posts with label statistical inference. Show all posts
Showing posts with label statistical inference. Show all posts
Thursday, August 26, 2010
Tuesday, December 29, 2009
A serious problem - but analytics may have some common-sense solutions
My family and I just got back from a India vacation. As always, we had a great time and as always, the travel was painful. One, because of its length and also because of all the documentation checks at various points in the journey. But in hindsight, I am feeling thankful that we were back in the States before the latest terrorist attack on the NWA jetliner to Detroit took place. A Nigerian man, Umar Farouk AbdulMutallak, tried to set off an explosive device but thankfully did not succeed.
Now apparently, this individual was on the anti-terrorism radar for a while. He was on the terrorist watch-list but not on the official no-fly list. Hence, he was allowed to board the flight going from Amsterdam to Detroit, where he tried to perpetrate his misdeed. The events have raised a number of valid questions on the job the TSA (the agency in charge of ensuring safe air travel within and to/from the US) is doing in spotting these kinds of threats. There were a number of red flags in this case. A passenger who had visited Yemen - a place as bad as Pakistan when it comes to providing a safe haven for terrorists. A ticket paid in cash. Just one carry-on bag and no bags checked in. A warning coming from this individual's family, no less. A denied British visa - another country that has as much to fear from terrorism as the US. The question I have is: could more have been done? Could analytics have been deployed more effectively to identify and isolate the perpetrator? And how could all of this be achieved without giving a very overt impression of profiling? A few ideas come to mind.
First, a scoring system to constantly upgrade the threat level of individuals and provide a greater amount of precision in understanding the threat posed by an individual at a certain point in time. A terror list of 555,000 is too bloated and is likely to contain a fair number of false positives. This model would use latest information about the traveler, all of which can be gathered at the time of travel or before travel. Is the traveler a US citizen or a citizen of a friendly country? (US Citizen or Perm Resident = 1, Citizen of US ally = 2, Other countries = 3, Known terrorist nation = 5) Has the person bought the ticket in cash or by electronic payment? (Electronic payment = 1, Physical instrument such as a cheque = 2, Cash = 5) Does the person have a US contact? Is the contact a US citizen or a permanent resident? Is the person traveling to a valid residential address? What are the countries the individual has visited in the last 24 months? And so on. You get the idea. Now the weights that have been attached are quite arbitrary to start, but they can always be adjusted as the perception of these risk factors change and our understanding evolves.
Now what needs to be done is to update the parameters of this model every 3-6 months or so. Then every individual on the database as well as very person traveling needs to be scored using this model and high scorers (high risk of either having connections to terrorist network or traveling with some nefarious intent) can be identified for additional screening and scrutiny. These are the types of common-sense solutions that can be deployed to solve these types of ticklish problems. When the size of the problem has been reduced from 555,000 people on whom you need to spend the same amount of time, to one where the amount of scrutiny can be sloped based on the propensity to cause trouble, the problem suddenly becomes a lot more tractable.
Now apparently, this individual was on the anti-terrorism radar for a while. He was on the terrorist watch-list but not on the official no-fly list. Hence, he was allowed to board the flight going from Amsterdam to Detroit, where he tried to perpetrate his misdeed. The events have raised a number of valid questions on the job the TSA (the agency in charge of ensuring safe air travel within and to/from the US) is doing in spotting these kinds of threats. There were a number of red flags in this case. A passenger who had visited Yemen - a place as bad as Pakistan when it comes to providing a safe haven for terrorists. A ticket paid in cash. Just one carry-on bag and no bags checked in. A warning coming from this individual's family, no less. A denied British visa - another country that has as much to fear from terrorism as the US. The question I have is: could more have been done? Could analytics have been deployed more effectively to identify and isolate the perpetrator? And how could all of this be achieved without giving a very overt impression of profiling? A few ideas come to mind.
First, a scoring system to constantly upgrade the threat level of individuals and provide a greater amount of precision in understanding the threat posed by an individual at a certain point in time. A terror list of 555,000 is too bloated and is likely to contain a fair number of false positives. This model would use latest information about the traveler, all of which can be gathered at the time of travel or before travel. Is the traveler a US citizen or a citizen of a friendly country? (US Citizen or Perm Resident = 1, Citizen of US ally = 2, Other countries = 3, Known terrorist nation = 5) Has the person bought the ticket in cash or by electronic payment? (Electronic payment = 1, Physical instrument such as a cheque = 2, Cash = 5) Does the person have a US contact? Is the contact a US citizen or a permanent resident? Is the person traveling to a valid residential address? What are the countries the individual has visited in the last 24 months? And so on. You get the idea. Now the weights that have been attached are quite arbitrary to start, but they can always be adjusted as the perception of these risk factors change and our understanding evolves.
Now what needs to be done is to update the parameters of this model every 3-6 months or so. Then every individual on the database as well as very person traveling needs to be scored using this model and high scorers (high risk of either having connections to terrorist network or traveling with some nefarious intent) can be identified for additional screening and scrutiny. These are the types of common-sense solutions that can be deployed to solve these types of ticklish problems. When the size of the problem has been reduced from 555,000 people on whom you need to spend the same amount of time, to one where the amount of scrutiny can be sloped based on the propensity to cause trouble, the problem suddenly becomes a lot more tractable.
Monday, December 28, 2009
Some end of year reading
1. Krugman on the America's own lost decade - link here
The usual Krugman rant on how things are going downhill and accelerating.
2. The Freakonomics blog on the practice of not inflation-adjusting stock returns - link here
Stock returns are seldom adjusted for inflation, transaction costs and taxes. While usually savvy investors do account for these factors, it is easy to get misled unless one reads the fine print.
3. How did buy and hold do in the last 10 years? link here
It is unfair asking a stock picker to pick just one stock. Makes for good headlines but does not really allow the stock picker to demonstrate their skills. The probability that any one company could be impacted by freak events is usually pretty high.
4. Health Statistics and the mammogram controversy - link here (from the WSJ) and here from the Numbers Guy blog
Reading any kind of reporting coming from the US (except sports, maybe) has become a painful drag through the ideology of the author.
5. Happiness - State of mind or state of body? - link here
An interesting 'light' piece of reading. Turns out that they are one and the same thing.
The usual Krugman rant on how things are going downhill and accelerating.
2. The Freakonomics blog on the practice of not inflation-adjusting stock returns - link here
Stock returns are seldom adjusted for inflation, transaction costs and taxes. While usually savvy investors do account for these factors, it is easy to get misled unless one reads the fine print.
3. How did buy and hold do in the last 10 years? link here
It is unfair asking a stock picker to pick just one stock. Makes for good headlines but does not really allow the stock picker to demonstrate their skills. The probability that any one company could be impacted by freak events is usually pretty high.
4. Health Statistics and the mammogram controversy - link here (from the WSJ) and here from the Numbers Guy blog
Reading any kind of reporting coming from the US (except sports, maybe) has become a painful drag through the ideology of the author.
5. Happiness - State of mind or state of body? - link here
An interesting 'light' piece of reading. Turns out that they are one and the same thing.
Monday, August 3, 2009
Why individual level data analysis is difficult
I recently completed a piece of data analysis using individual level data. The project was one of the more challenging pieces of analysis I have undertaken at work, and I was (happily, for myself and everyone else who worked on it) able to turn it into something useful. And there were some good lessons learned at the end of it all, which I want to share in today's post.
So, what is unique and interesting about individual level data and why is it valuable?
- With any dataset that you want to derive insights from, there are a number of attributes about the unit being analyzed (individual, group, state, country) and one or more attributes that you are trying to explain. Let's call the first group predictors and the second group target variables. Individual level data has a much wider range of predictor and target variables. There is also a much wider range of interactions between these various predictors. For example, while on an average, older people tend to be wealthier, individual level data reveals that there are older people who are broke and younger people who are millionaires. As a result of these wide ranges of data and the different types of interactions between these variables (H-L, M-M, M-H, L-H ... you get the picture), it is possible to understand fundamental relationships between the predictors and the targets and interactions between the predictors. Digging a little deeper into the people vs wealth data, what this might tell you is that what really matters for your level of wealth is your education levels, the type of job you do, etc. This level of variation is not available with the group level data. In other words, the group level data is just not as rich.
- Now, along with the upside comes downside. The richness of the individual level predictors means that data occassionally is messy. What is messy? Messy means having wrong values at an individual level, sometimes missing or null values at an individual level. At a group level, many of the mistakes average themselves out, especially if the errors are distributed evenly around zero. But at the individual levels, the likelihood of errors has to be managed as part of the analysis work. With missing data, the challenge is magnified. Is missing data truly missing? Or did it get dropped during some data gathering step? Is there something systematic to missing data, or is it random? Should missing data be treated as missing or should it be imputed to some average value? Or should it be imputed to a most likely value? These are all things that can materially impact the analysis and therefore should be given due consideration.
Now to the analysis itself. What were some of my important lessons?
- Problem formulation has to be crystal clear and that in turn should drive the technique.
Problem formulation is the most important step of the problem solving exercise. What are we trying to do with the data? Are we trying to build a predictive model with all the data? Are we examining interactions between predictors? Are we studying the relationship between predictors one at a time and the target? All of these outcomes require different analytical approaches. Sometimes, analysts learn a technique and then look around for a nail to hit. But judgment is needed to make sure the appropriate technique is used. The analyst needs to have the desire to learn to use an technique that he/she is not aware of. By the same token, discipline to use a simpler technique where appropriate.
- Spending time to understand the data is a big investment that is completely worth it.
You cannot spend too much time understanding the data. Let me repeat that for effect. You cannot spend too much time understanding the data. And I have come to realize that far from being a drudge, understanding the data is one of the most fulfilling and value added pieces of any type of analysis. The most interesting part of understanding data (for me) is the sheer number of data points that are located so far away from the mean or median of the sample. So if you are looking at people with mortgages and the average mortgage amount is $150,000, the number of cases where the mortgage amount exceeds $1,000,000 lends a completely new perspective of the type of people in your sample.
- Explaining the results in a well-rounded manner is a critical close-out at the end.
The result of a statistical analysis is usually a set of predictors which have met the criteria for significance. Or it could be a simple two variable correlation that is above a certain threshold. But whatever be the results of the analysis, it is important to base the analysis result in real-life insights that can be understood by the audience. So, if the insight reveals that people with large mortgages have a higher propensity to pay off their loans, further clarification will be useful around the income level of these people, their education levels, the types of jobs they hold, etc. All these ancillary data points are ways of closing out the profile of the "thing" that has been revealed by the analysis.
- Speed is critical to get the results in front of the right people before they lose interest.
And finally, if you are going to spend a lot of everyone's (and your) precious time doing a lot of the above, the results need to be driven in extra-short time for people to keep their interest in what you are doing. In today's information-saturated world, it only takes the next headline in the WSJ for people to start talking about something else. So, you need to basically do the analysis in a smart manner, and also it needs to be super-fast. Delivered yesterday, as the cliche goes.
In hindsight, it gives me an appreciation of why data analysis or statistical analysis using individual level data is one of the more challenging analytical exercises. And why it is so difficult to get it right.
So, what is unique and interesting about individual level data and why is it valuable?
- With any dataset that you want to derive insights from, there are a number of attributes about the unit being analyzed (individual, group, state, country) and one or more attributes that you are trying to explain. Let's call the first group predictors and the second group target variables. Individual level data has a much wider range of predictor and target variables. There is also a much wider range of interactions between these various predictors. For example, while on an average, older people tend to be wealthier, individual level data reveals that there are older people who are broke and younger people who are millionaires. As a result of these wide ranges of data and the different types of interactions between these variables (H-L, M-M, M-H, L-H ... you get the picture), it is possible to understand fundamental relationships between the predictors and the targets and interactions between the predictors. Digging a little deeper into the people vs wealth data, what this might tell you is that what really matters for your level of wealth is your education levels, the type of job you do, etc. This level of variation is not available with the group level data. In other words, the group level data is just not as rich.
- Now, along with the upside comes downside. The richness of the individual level predictors means that data occassionally is messy. What is messy? Messy means having wrong values at an individual level, sometimes missing or null values at an individual level. At a group level, many of the mistakes average themselves out, especially if the errors are distributed evenly around zero. But at the individual levels, the likelihood of errors has to be managed as part of the analysis work. With missing data, the challenge is magnified. Is missing data truly missing? Or did it get dropped during some data gathering step? Is there something systematic to missing data, or is it random? Should missing data be treated as missing or should it be imputed to some average value? Or should it be imputed to a most likely value? These are all things that can materially impact the analysis and therefore should be given due consideration.
Now to the analysis itself. What were some of my important lessons?
- Problem formulation has to be crystal clear and that in turn should drive the technique.
Problem formulation is the most important step of the problem solving exercise. What are we trying to do with the data? Are we trying to build a predictive model with all the data? Are we examining interactions between predictors? Are we studying the relationship between predictors one at a time and the target? All of these outcomes require different analytical approaches. Sometimes, analysts learn a technique and then look around for a nail to hit. But judgment is needed to make sure the appropriate technique is used. The analyst needs to have the desire to learn to use an technique that he/she is not aware of. By the same token, discipline to use a simpler technique where appropriate.
- Spending time to understand the data is a big investment that is completely worth it.
You cannot spend too much time understanding the data. Let me repeat that for effect. You cannot spend too much time understanding the data. And I have come to realize that far from being a drudge, understanding the data is one of the most fulfilling and value added pieces of any type of analysis. The most interesting part of understanding data (for me) is the sheer number of data points that are located so far away from the mean or median of the sample. So if you are looking at people with mortgages and the average mortgage amount is $150,000, the number of cases where the mortgage amount exceeds $1,000,000 lends a completely new perspective of the type of people in your sample.
- Explaining the results in a well-rounded manner is a critical close-out at the end.
The result of a statistical analysis is usually a set of predictors which have met the criteria for significance. Or it could be a simple two variable correlation that is above a certain threshold. But whatever be the results of the analysis, it is important to base the analysis result in real-life insights that can be understood by the audience. So, if the insight reveals that people with large mortgages have a higher propensity to pay off their loans, further clarification will be useful around the income level of these people, their education levels, the types of jobs they hold, etc. All these ancillary data points are ways of closing out the profile of the "thing" that has been revealed by the analysis.
- Speed is critical to get the results in front of the right people before they lose interest.
And finally, if you are going to spend a lot of everyone's (and your) precious time doing a lot of the above, the results need to be driven in extra-short time for people to keep their interest in what you are doing. In today's information-saturated world, it only takes the next headline in the WSJ for people to start talking about something else. So, you need to basically do the analysis in a smart manner, and also it needs to be super-fast. Delivered yesterday, as the cliche goes.
In hindsight, it gives me an appreciation of why data analysis or statistical analysis using individual level data is one of the more challenging analytical exercises. And why it is so difficult to get it right.
Wednesday, June 17, 2009
Why sitting down and talking does not help - (and what it means for the data scientist)
What is the sane person's advise to two people who cannot agree on something? It is usually sit down and resolve their differences. A set of recent recent studies seem to suggest that it doesn't really help.
Some recent studies have shown that when people with strong opposing positions are put together to talk it out, it makes them even more entrenched in their opinions. This is the point put forward by Cass Sunstein in his book Going to Extremes. Even when the groups/ people with opposing views are presented objective evidence, people tend to "see" what they want to believe in the data and ignore the rest.
Another study cited by the Economist struck a similar message. The study was looking at self-help books which stress positive thinking, and their impacts on people. What the study found was that positive thinking only helps for people who are predisposed to thinking positively. The study can be credited to Joanne Wood of the University of Waterloo in Canada and her colleagues. The researchers report in Psychological Science journal that when people with high self-esteem are made to repeat positive reinforcing messages, they do tend to take more positive positions (on standardized tests) than people who do not repeat positive reinforcing messages.
So far so good. It sounds as though positive reinforcing helps. But when the test was done on people with low self-esteem, the results were quite the opposite. People who repeated the positive reinforcing message took less positive positions than the ones that did not repeat the message. So it seems to imply that positive reinforcement actually hurts when applied to people who are inclined to believe otherwise. For me, this sounds like another example of people entrenching towards their own biases. When people with entrenched positions are forced to take a contrary position (or look at objective data), they tend to entrench even further on their original positions.
So what are the implications for the data scientist from all of this?
Mostly that predisposed positions produce a sort of "blindness" to objective data. We all suffer from confirmation bias, we like to believe what we like to believe. It is therefore a great effort for us to actively look at data objectively and take what that data is telling us, vs. putting the appropriate spin that suits us. The data scientist needs to exercise tremendous discipline here. It takes a superhuman effort not to succumb to our biases and to (not) believe what we want to believe, and take a genuine interest in forming an objective opinion.
One of the bigger learning for me in all of this is also the importance of give-and-take in making progress on any issue. Because of the entrenchment bias, people seldom change their views (i.e., come around to your way of thinking) based on objective data and logical persuasion. They only come along when they have skin in the game and for that to happen, there has to be an active element of give-and-take between the two parties. Which makes me even more admiring of GOOD politicians and diplomats. Their ability to keep moving forward on an issue in a bipartisan manner comes out of their skill in give-and-take, and thereby overcoming the entrenchment bias.
Some recent studies have shown that when people with strong opposing positions are put together to talk it out, it makes them even more entrenched in their opinions. This is the point put forward by Cass Sunstein in his book Going to Extremes. Even when the groups/ people with opposing views are presented objective evidence, people tend to "see" what they want to believe in the data and ignore the rest.
Another study cited by the Economist struck a similar message. The study was looking at self-help books which stress positive thinking, and their impacts on people. What the study found was that positive thinking only helps for people who are predisposed to thinking positively. The study can be credited to Joanne Wood of the University of Waterloo in Canada and her colleagues. The researchers report in Psychological Science journal that when people with high self-esteem are made to repeat positive reinforcing messages, they do tend to take more positive positions (on standardized tests) than people who do not repeat positive reinforcing messages.
So far so good. It sounds as though positive reinforcing helps. But when the test was done on people with low self-esteem, the results were quite the opposite. People who repeated the positive reinforcing message took less positive positions than the ones that did not repeat the message. So it seems to imply that positive reinforcement actually hurts when applied to people who are inclined to believe otherwise. For me, this sounds like another example of people entrenching towards their own biases. When people with entrenched positions are forced to take a contrary position (or look at objective data), they tend to entrench even further on their original positions.
So what are the implications for the data scientist from all of this?
Mostly that predisposed positions produce a sort of "blindness" to objective data. We all suffer from confirmation bias, we like to believe what we like to believe. It is therefore a great effort for us to actively look at data objectively and take what that data is telling us, vs. putting the appropriate spin that suits us. The data scientist needs to exercise tremendous discipline here. It takes a superhuman effort not to succumb to our biases and to (not) believe what we want to believe, and take a genuine interest in forming an objective opinion.
One of the bigger learning for me in all of this is also the importance of give-and-take in making progress on any issue. Because of the entrenchment bias, people seldom change their views (i.e., come around to your way of thinking) based on objective data and logical persuasion. They only come along when they have skin in the game and for that to happen, there has to be an active element of give-and-take between the two parties. Which makes me even more admiring of GOOD politicians and diplomats. Their ability to keep moving forward on an issue in a bipartisan manner comes out of their skill in give-and-take, and thereby overcoming the entrenchment bias.
Friday, May 29, 2009
Data handling - the heart of good analysis - Part 1
I have been building consumer behaviour models using regression and classification tree techniques for the last 4 years now. Most of this work has been in SAS. Now, there are a large number of interesting SAS procedures that are only slightly different from one another. Many of them can be interchangeable used, like PROC REG and PROC GLM.
But the single most important learning for me over this period has been that you can't spend enough time understanding and transforming the data. Very many interesting and potentially promising pieces of analysis go nowhere because the researcher has not enough time understanding the data. And then, having understood the data, transformed it into a form that is relevant to the problem at hand.
One of the seminal pieces on understanding data and plotting it in useful ways, is John Tukey's "Exploratory Data Analysis". This paper introduces some unique and important ways of graphing and understanding what the data is trying to say. One of my personal favorite SAS procedures is PROC MEANS and PROC UNIVARIATE. And of course PROC GPLOT. My advice to the budding social scientist and quantitative practitioner is to learn to use these techniques before learning the cooler procedures like LOGISTIC and Linear Models. This was one of the first things I learnt in my own journey as a statistical modeler and I have some very good and experienced colleagues to thank for making sure I learnt the basics first.
Over the next several posts, I am going to share some of my favorite forms of data depiction. The next several days will be a very interesting read, I promise.
But the single most important learning for me over this period has been that you can't spend enough time understanding and transforming the data. Very many interesting and potentially promising pieces of analysis go nowhere because the researcher has not enough time understanding the data. And then, having understood the data, transformed it into a form that is relevant to the problem at hand.
One of the seminal pieces on understanding data and plotting it in useful ways, is John Tukey's "Exploratory Data Analysis". This paper introduces some unique and important ways of graphing and understanding what the data is trying to say. One of my personal favorite SAS procedures is PROC MEANS and PROC UNIVARIATE. And of course PROC GPLOT. My advice to the budding social scientist and quantitative practitioner is to learn to use these techniques before learning the cooler procedures like LOGISTIC and Linear Models. This was one of the first things I learnt in my own journey as a statistical modeler and I have some very good and experienced colleagues to thank for making sure I learnt the basics first.
Over the next several posts, I am going to share some of my favorite forms of data depiction. The next several days will be a very interesting read, I promise.
Sunday, May 24, 2009
Simple regression applications - estimating flu impacts
Came across this interesting piece around the estimation of flu impacts. From Slate. One of my favourite web-sites.
You must have across the new articles which say that flu caused so many thousand deaths in a certain year. Now attributing deaths to flu is not as straight-forward as it would seem. Flu is not the "Cause of Death" that often in a death certificate. Flu usually kills by causing secondary conditions like pneumonia, heart disease, etc. which the enfeebled body is not able to resist. So one can find relatively few cases where the cause of death can be directly attributed to the flu. So how is the estimation done? The answer is simple regression using deaths as the dependent variable and the number of flu cases as the independent variable.
One piece of data is the number of deaths in the US. This can be broken down by week or by month for the flu season. (Approx Oct to Apr) The other piece of data is the number of flu cases tracked by various testing labs across the country. This information is also available broken down by week or by month. The CDC website is a ready source of such morbidity, going back at least to the early 90s! Then it is a matter of running a simple regression to create a link between flu cases and deaths. The regression takes the form: deaths = intercept + co_eff * number of flu cases, the intercept being the number of deaths one can expect due to other baseline causes.
It almost seems too simple to be true. How can you be sure that deaths caused in a certain month can be linked to flu cases from that period? Or does one assume a certain lag for flu to lead to mortality? How do we know we have normalized for everything else? What is the CI of the estimates? Check this paper out by William W. Thompson for more details!
Now with the emergence of the potentially more deadly H1N1 flu, how can one go about estimating its impact?
You must have across the new articles which say that flu caused so many thousand deaths in a certain year. Now attributing deaths to flu is not as straight-forward as it would seem. Flu is not the "Cause of Death" that often in a death certificate. Flu usually kills by causing secondary conditions like pneumonia, heart disease, etc. which the enfeebled body is not able to resist. So one can find relatively few cases where the cause of death can be directly attributed to the flu. So how is the estimation done? The answer is simple regression using deaths as the dependent variable and the number of flu cases as the independent variable.
One piece of data is the number of deaths in the US. This can be broken down by week or by month for the flu season. (Approx Oct to Apr) The other piece of data is the number of flu cases tracked by various testing labs across the country. This information is also available broken down by week or by month. The CDC website is a ready source of such morbidity, going back at least to the early 90s! Then it is a matter of running a simple regression to create a link between flu cases and deaths. The regression takes the form: deaths = intercept + co_eff * number of flu cases, the intercept being the number of deaths one can expect due to other baseline causes.
It almost seems too simple to be true. How can you be sure that deaths caused in a certain month can be linked to flu cases from that period? Or does one assume a certain lag for flu to lead to mortality? How do we know we have normalized for everything else? What is the CI of the estimates? Check this paper out by William W. Thompson for more details!
Now with the emergence of the potentially more deadly H1N1 flu, how can one go about estimating its impact?
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