Friday, July 20, 2012

A Little Time Away

The blog will be taking a week off as I head out to ride my way across Iowa on my bicycle. It is RAGBRAI time; the Register’s Annual Great Bike Ride Across Iowa, now in its 40th year. When it started, it had no more than 300 riders. This year, more than 18,000 will participate and another 10,000 will be present in support roles. Each day’s ride will vary from as few as 49 miles to as many as 85 (with an option to ride 104 that day, for those hardy enough to wish to ride a “Century”). We overnight in small towns along the way, bringing needed tourism dollars to small Iowa farming communities, and basically eating each city out of house and home. There will be pie. Lots of pie. And we will see stalwarts such as Mr. Pork Chop, Pastafari, Tom’s Tender Turkey (Always a good one because you can by a glass of iced tea with actual ice in it, and on a hot day that can be, well, orgasmic), Farm Boys (great breakfast burritos), and shave ice.

 This is an ecumenical ride. Young or old, heavy or thin, people come out and ride. They ride road bikes, mountain bikes, hybrids, fixed-wheel bikes, really old bikes, recumbent bikes, trikes, and crank forward bikes. I bet you did not know there were this many kinds of bikes! There will be tandems, triples and one family riding a bicycle with 5 people on it. There will be the bike that looks like a banana. One that has a sail. There will be $14,000 Pinarello Dogma bikes. And people wearing cow outfits, on bikes that are spotted.

There will be cornfields, with I really, really hope good corn- it is supposed to be hot and this drought is truly troubling. Average high temperatures for the next week may reach 98 degrees. We will sweat, a lot. We will eat lots of bratwurst, and drink our share of beer and Gatorade. More of the latter than the former, of course.

 I learn a lot from riding. Biking has made me healthy, and I have trained. I’ve ridden close to 3000 miles since January 1 and so I am hoping that the daily travels will go well, even if hot. But I have trained, a reminder of the importance of doing so for my other roles here at the college. So, I will end with the old joke: “How is your RAGBRAI training going? “Great! I am up to 4 pieces of pie a day!”

Biker on….

Monday, July 16, 2012

Reliability and Validity Cheat Sheet


2 X 2 Contingency Table

Present

Absent Row Total
Positive a (True +) b (False +) a + b
Negative c (False -) d (True -) c + d
Column Total a + c b + d a + b + c+ d

Sensitivity: a/(a+c) The ability of a test to correctly identify people who have the target disorder.

Specificity: d/(b+d) The ability of a test to correctly identify people who do not have the target disorder

Positive predictive value: a/(a+b) The probability that a positive test will correctly identify people who have the target disorder.

Negative predictive value: d/(c+d) The probability that a negative test will correctly identify people who do not have the target disorder.

 • Likelihood ratio of a positive test: [a/(a+c)/1-{d/(b+d)}] : (Sensitivity)/(1-Specificity) A ratio of the probability of a positive test in a person with the condition compared to the probability of a positive test in a person without the condition.

Likelihood ratio of a negative test: [1-{a)/(a+c)}]/[d/(b+d)] : (1-Sensitivity)/(Specificity) A ratio of the probability of a negative test in a person with the condition compared to the probability of a negative test in a person without the condition.

Prevalence: (a+c)/(a+b+c+d) • Accuracy: (a+d)/ (a+b+c+d)

Monday, July 9, 2012

Epidemiology Cheat Sheet

• Incidence= [Number of new cases in a time period/Population] x 100,000

• Prevalence= [Number of existing cases in a time period/population] x 100,000

• Bradford Hill Criteria of causation
  1. Strength of Association 
  2. Consistency Specificity in the cause 
  3. Temporality 
  4. Dose-response relationship 
  5. Plausibility 
  6. Coherence 
  7. Experimental evidence 
  8. Analogy 

 • Cross-sectional study: assesses both the health status and the exposure levels of individuals within a population at one point in time.

 • Case-control study: a retrospective study that initially identifies two groups of subjects. All individuals in one group have the particular disease or condition under investigation (the cases), whereas everybody in the other group is free from the disease (the controls).

 • Odds ratio (OR): the risk of the odds of developing the disease in the exposed group divided by the odds of developing the disease in the unexposed group.

a= case exposed to risk factor
b= controls exposed to risk factor
c= case not exposed to risk factor
d= controls not exposed to risk factor

OR= (a/c)/(b/d)= ad/bc If exposure if harmful, OR will bew greater than1 If exposure is protective, OR is less than1 If OR=1, no risk can be attributed to the disease

 • Cohort study: Follows a group of subjects forward in time and compares their outcomes after one group is exposed to some known of suspected cause of disease while the other group is not exposed.

• Relative risk: compares the risk of some health-related event occurring in two groups that are included in a prospective study. It is the probability of disease occurring in the exposed group divided by the probability of disease in the unexposed group.

a= those exposed to the risk factor and who have the disease
b= those exposed but who do not have the disease
c= those not exposed to the risk factor and who have the disease
d= those not exposed and who do not have the disease

 RR= [a/(a+b)]/[c/(c+d)] If RR is greater than1, association is positive If RR is less than1, association is negative (or protective) If RR=1, not associated with risk of disease

• Attributable risk: the probability of disease in the exposed group minus the probability of disease in the unexposed group. AR= [a/(a+b)] – [c/(c+d)]

• Absolute risk reduction: the difference in the probability of disease between the treatment and control groups. It is calculated the same as AR, but it tells you the how much of the difference in reduction of disease incidence between the groups is due to the treatment. ARR= [a/(a+b)]- [c/(c+d)]

• Relative risk reduction: the comparative reduction in rates of bad outcomes between the experimental and control groups in an RCT or cohort study. RRR= absolute risk reduction/probability of disease in unexposed group, thus RRR= {a/(a+b)] – [c/(c+d)]}/[c/(c+d)]

 • Number needed to treat: number of patients who would need to be treated in order to prevent one additional bad outcome. NNT= 1/Absolute risk reduction= 1/[a/(a+b)] – (c/(c+d)]