 Research
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Estimating age conditional probability of developing disease from surveillance data
 Michael P Fay^{1, 2}Email author
https://doi.org/10.1186/1478795426
© Fay; licensee BioMed Central Ltd. 2004
 Received: 15 July 2003
 Accepted: 27 July 2004
 Published: 27 July 2004
Abstract
Fay, Pfeiffer, Cronin, Le, and Feuer (Statistics in Medicine 2003; 22; 1837–1848) developed a formula to calculate the ageconditional probability of developing a disease for the first time (ACPDvD) for a hypothetical cohort. The novelty of the formula of Fay et al (2003) is that one need not know the rates of first incidence of disease per personyears alive and diseasefree, but may input the rates of first incidence per personyears alive only. Similarly the formula uses rates of death from disease and death from other causes per personyears alive. The rates per personyears alive are much easier to estimate than per personyears alive and diseasefree. Fay et al (2003) used simple piecewise constant models for all three rate functions which have constant rates within each age group. In this paper, we detail a method for estimating rate functions which does not have jumps at the beginning of age groupings, and need not be constant within age groupings. We call this method the midage group joinpoint (MAJ) model for the rates. The drawback of the MAJ model is that numerical integration must be used to estimate the resulting ACPDvD. To increase computational speed, we offer a piecewise approximation to the MAJ model, which we call the piecewise midage group joinpoint (PMAJ) model. The PMAJ model for the rates input into the formula for ACPDvD described in Fay et al (2003) is the current method used in the freely available DevCan software made available by the National Cancer Institute.
Keywords
 Female Breast Cancer
 Estimate Rate Function
 Piecewise Constant Model
 Segmented Regression Model
 Invasive Cancer Incidence
Background
Fay, Pfeiffer, Cronin, Le, and Feuer [1] showed how to calculate the ageconditional probabilities of developing a disease (ACPDvD) from registry data. Throughout this paper we use "cancer" as our disease of interest, but the method applies to specific types of cancer as well as other diseases where information is collected by population based surveillance methods. Fay et al [1] provided a formula (see equation 1 below) to calculate ACPDvD after inputing the rate function by age of (1) first incidence of cancer per personyears alive, (2) death from cancer per personyears alive, and (3) death from other causes per personyears alive. Fay et al [1] used a simple piecewise constant model for the three rate functions, which have constant rates within each age group.
Notice that the MAJ model gives a more smoothly changing and probably a better modeled rate. The only place where the MAJ model may not perform better than the piecewise constant model is at peaks or valleys, where there may be some bias. In Figure 1 we see that the smoothness of the MAJ appears to produce more plausible estimates for ages 0 through 85 and from ages 90 and above, and the only age group with a noteworthy bias problem is 85 to 90. Thus, for almost all of the age range the MAJ model is more plausible.
A problem with the midage group joinpoint model is that it requires numeric integration for its calculation. The second model uses a series of piecewise constant values to approximate the midage group joinpoint model. We call this second model the PMAJ (piecewise midage group joinpoint) model. The PMAJ does not require numeric integration, so it is much faster than the MAJ model. The PMAJ model is a piecewise constant model that only differs from the piecewise constant model of Fay et al [1] in that the pieces are smaller and the corresponding values of the rates are motivated by the MAJ model. Starting with version 5.0, the freely available DevCan software [3] uses the PMAJ method. (There was a small calculation error in versions 5.0 and 5.1 that has been corrected in version 5.2). DevCan calculates ACPDvD or age conditional probability of dying from a disease for U.S. cancer data or for user supplied data.
The outline of this paper is as follows. The review and overview section reviews the issues in estimating the age conditional probability of developing disease from surveillance data. This section includes a motivation for using this type of statistic to describe population data. The review and overview section additionally gives graphical descriptions of the MAJ and PMAJ methods. The paper is structured so that readers not interested in the details may skip the next two sections and the appendix, which give precise and notationally involved definitions of the MAJ estimators. The examples and discussion section gives examples of the estimator of ACPDvD using three different methods for estimating the rates, the simple piecewise constant method proposed in Fay et al [1], the MAJ method, and the PMAJ method. In supplimental material [see Additional file 1] we compare the PMAJ method with the method of Wun, et al [4], since the latter method was the method used in versions of the DevCan software before version 5.0.
Review and overview
Consider a surveillance program like the SEER program of the U.S. National Cancer Institute. This program attempts to count every incidence of cancer within the catchment area of the program. Because cancer is a disease in which the rates of the disease are highly dependent on age, in order to give interpretability to the counts within the SEER registries, we must somehow account for the age distribution in the popoulation.
One simple and popular statistic is the age adjusted rate or directly standardized rate (DSR). In the SEER Cancer Statistics Review [2] DSRs are used to compare different cancer sites, trends on specific cancer sites over time, and rates by sex and race. The DSR is calculated by a simple weighted sum of the age specific rates for each 5 year age group, where the weights are proportional to the U.S. 2000 population. Thus, the DSR may be interpreted as the rates adjusted as if all the populations being compared had age distributions similar to the U.S. 2000 population. The DSRs are useful for gaining an overall picture of how the incidence and mortality of each cancer effects different populations (e.g., different races, SEER population at different times), while controling for the effect of differing age distributions between populations being compared. A disadvantage of the DSR is that it is hard to relate to an individual's risk. For example, Table I4 of the SEER Cancer Statistics Review, 1975–2000 [2] states that the DSR for breast cancer for females for the years 1996–2000 is 135 per 100,000 personyears. The average American woman may wonder, how does that relate to my risk? Will I be likely to get breast cancer in my lifetime? If I am 40 years old now, what is my risk of getting breast cancer in the next 10 years given that I have survived to this old without getting it? These questions are the motivation for using the age conditional probability of developing disease (ACPDvD), and in order to estimate the ACPDvD for female breast cancer, we require information not only about the rate of female breast cancer but also about the rates of dying from female breast cancer and dying from other causes.
Age Conditional Probability of Developing Different Types of Invasive Cancers (in Percent) from SEER 12, 1998–2000
Start Age  End Age  Model  All Invasive (Both Sexes)  Prostat(Male)  Breast (Female)  Acute Lymphocytic Leukemia (Both Sexes) 

0  20  Piecewise const  0.3158  0.0009  0.0015  0.0669 
PMAJ, interval = .5  0.3260  0.0011  0.0021  0.0633  
MAJ  0.3260  0.0011  0.0021  0.0633  
0  50  Piecewise const  4.0690  0.2002  1.9188  0.0837 
PMAJ, interval = .5  4.1657  0.2550  1.9492  0.0808  
MAJ  4.1657  0.2550  1.9492  0.0808  
40  50  Piecewise const  2.5260  0.2032  1.5131  0.0053 
PMAJ, interval = .5  2.5976  0.2579  1.5169  0.0055  
MAJ  2.5975  0.2579  1.5169  0.0055  
0  Inf  Piecewise const  42.0876  17.4952  13.6471  0.1154 
PMAJ, interval = .5  41.7547  17.3375  13.5477  0.1121  
MAJ  41.7574  17.3389  13.5485  0.1121  
60  61  Piecewise const  1.2340  0.5989  0.3822  0.0009 
PMAJ, interval = .5  1.0852  0.4946  0.3627  0.0009  
MAJ  1.0852  0.4946  0.3627  0.0009  
64  65  Piecewise const  1.2758  0.6131  0.3872  0.0009 
PMAJ, interval = .5  1.4453  0.7440  0.4045  0.0010  
MAJ  1.4453  0.7440  0.4045  0.0010  
60  65  Piecewise const  6.0331  2.9128  1.8777  0.0042 
PMAJ, interval = .5  6.0622  2.9492  1.8758  0.0044  
MAJ  6.0622  2.9492  1.8759  0.0044 
Calculation of the ACPDvD is somewhat complicated, and we describe the complications in relation to the simple DSRs. Consider first the age specific incidence rates which are used to calculate the DSRs. These rates simply count the number of incident cases of a particular disease (e.g., female breast cancer) within each age group and divide by the total number of personyears estimated by the population. For counts of a single year, the personyears are estimated by the midyear population of the catchment area (for sexspecific cancers like prostate cancer or female breast cancer, we only use the population of the appropriate sex). Note that the incident cases may include individuals who have previously been diagnosed with the cancer and have developed a new primary cancer.
For the ACPDvD for any specific disease we would like the rate of first incidence per personyears alive and diseasefree. Thus, there are two difficulties, (1) the usual age specific incidence rates include persons with multiple primary cancers, and (2) the denominators include persons who have previously been diagnosed. Merrill and Feuer [5] discuss both difficulties and adjust for them creating riskadjusted cancer incidence rates. Merrill and Feuer [5] study the effect of these adjustments for several cancer sites. To handle the first difficulty, (similar to [5]) we can remove cases where we have a record of a previous diagnosis of that particular type of cancer. Because the registries in SEER were not all begun at the same time, to avoid bias the DevCan program only searches the records for previous cancers back until the year when the last registry was added. This year is denoted the followback year. (If the disease of interest is any malignant cancer, then the difficulty is handled differently. Although at each cancer record we do not record what specific types of cancers were previously diagnosed for the person, we do know whether any tumors were previously diagnosed. Thus, if the disease of interest is any malignant cancer and if the record states there was a previously diagnosed tumor, then we assume that the previously diagnosed tumor was malignant, and do not count that case as a first incidence.) To handle the second difficulty, the additional personyears in the denominator, Merrill and Feuer [5] adjust the denominator by multiplying the agespecific population by 1 minus an estimate of the prevalence of the disease in the population. Merrill and Feuer [5] also estimate the prevalence of medical procedures which remove individuals from the atrisk population, such as hysterectomy which removes the risk of uterine cancers.
In calculating the ACPDvD we use only first incident of the disease of interest as in [5], but we correct for the denominators in a different way using an assumption and some mathematics from the theory of competing risks. This second correction is detailed with precise mathematical notation in Fay et al [1]; here we give more heuristic arguments.
In the following let the disease of interest be "cancer". The ACPDvD between ages x and y, given alive and cancerfree at age x, may be written as the fraction,
To calculate the numerator, we integrate over the probability that the first cancer occurred at exactly age a. In math notation this probability is
where f _{ c }(a) is a probability function representing the probability that the first cancer occurred at exactly age a. One key result described in Fay et al [1] is that f _{ c }(a) can be written as the product of two functions,
λ _{ c }(a) = the probability that the first cancer occurred at exactly age a, given the individual is alive just before age a, and
S _{ a }(a) = the probability that the individual is alive just before age a.
The function λ _{ c }(a) is known as a causespecific hazard function, and it is estimated by some function of the agespecific rates, such as the piecewise constant model of Fay et al [1] or the MAJ model introduced in this paper (see Figure 1). Using standard results for continuous survival data, we can write S _{ a }(a) as
where λ _{ a }(u) ( = the probability that the individual died at age u, given the individual is alive just before age u) is the usual hazard function. We estimate λ _{ a }(u) using some function of the agespecific rates. Thus, the numerator can be written as
Notation
Random Variables and Parameters  

T = age at death  T* = age at first cancer or death before cancer 
J = type of death  J* = type of event 
(J = d) = death from cancer  (J* = c) = first cancer 
(J = o) = death from other causes  (J* = o) = death before first cancer 
λ _{ c }(t) = rate at t for first cancer given alive  = rate at t for first cancer given alive and cancerfree 
λ _{ o }(t) = rate at t for death before cancer given alive  = rate at t for death before cancer given alive and cancerfree 
λ _{ d }(t) = rate at t for death from cancer given alive  
λ _{ a }(t) = rate at t for death given alive  = rate at t for first cancer or death before first cancer given alive and cancerfree 


Observations  
Within the age interval, [a _{ i }, a _{ i }+1), and within the calendar interval of interest we observe...  
c _{ i }= number of first cancer incident cases  = estimate of personyears alive associated with j = c, d, o (DevCan uses the sum of midyear populations during the calendar interval of interest) 
d _{ i }= number of cancer deaths  
o _{ i }= number of other deaths 
The details of the MAJ and the PMAJ models are given in the next two sections.
Readers only interested in the practical ramifications of the choice in models may skip to the examples and discussion section.
Midage group joinpoint estimator
In Fay et al [1], the rates were estimated by a piecewise constant model. Here we use a midage group joinpoint (MAJ) model, where we draw lines connecting the midpoints of the intervals except the first and last interval. The first interval is constant until the midpoint, and the last interval is constant after a nominal "midpoint". This nominal "midpoint" is half the length of the previous age interval from the beginning of the last interval, and would be the midpoint if the last age interval was the same length as the previous interval.
We introduce new notation for breaking up the ages. Fay et al [1] used 0 = a _{0} <a _{1} < ··· <a _{ k }<a _{ k+1}= ∞. Here we use a joinpoint model with joins at the midpoints (and nominal midpoint),
Let
where j _{ i }is either c _{ i }, d _{ i }, or o _{ i }as defined in Table 1. (Note that , where is the piecewise constant function used in [1]). We define and . For j = a, MAJ estimator for the rate at t _{ i }is
Then for t ∈ [t _{ i }, t _{ i+1}) for i = 1,..., k, we define as the point on the line defined by connecting the points (t _{ i }, ) and (t _{ i+1}, ). In other words,
Where
and
Thus, α _{ j,1}= and β _{ j,1}= 0, and similarly by taking limits as t _{ k+1}→ ∞ then α _{ j,k }= and β _{ j,k }= 0.
Note that (for ℓ = 0,1,..., k)
so that for i = 0,1,...,k,
Also notice that (when u < ∞)
Therefore when u ∈ [t _{ i } , t _{ i+1}),
where R _{ j,h }(t _{ℓ}, v) (for ℓ =  1,0,1,2,..., i and v ≤ t _{ℓ+1}) is defined implicitly (see the Appendix). Then,
Piecewise midage group joinpoint estimator
In the MAJ model we divided up the age line into k + 2 intervals. Here we define those intervals in both the t _{ i }notation and the a _{ i }notation.
In the MAJ model the rates for the first and the last intervals are represented by lines with zero slope, and the rates for the i th interval (i = 1,...,k) for the j th rate type (j = a, c, d, o) is a line defined by connecting the points (t _{ i1}, ) and (t _{ i }, ) (see equations 2 and 3 for definition of ). In the PMAJ model we divide the i th interval into m _{ i }equal sized intervals, and use a piecewise constant estimate on each of those m _{ i }intervals. One way to define m _{ i }is to chose m _{ i }so that each equal sized interval is 1/2 year long. In other words, m _{ i }= 2(t _{ i } t _{ i1}). This is the definition of m _{ i }that we use for the DevCan software (starting with version 5.0, see [3]), but all the following holds for arbitrary m _{ i }. In Figure 2 we show the PMAJ model with halfyear intervals and the piecewise constant model for the US all invasive cancer mortality rates for ages 70 through 90 years.
Here are the details. Consider the h th (for h = 1,..., m _{ i }) of the m _{ i }intervals within interval i (for i = 1,...,k) for rate type j (for j = a, c, d, o). This interval is
For convenience we introduce new notation for the ends of this interval, let
so that t _{ i1,0}= t _{ i1}and = t _{ i }. At the beginning of this interval the value of the rate is
(see equations 4 and 6 for definitions of α _{ j,i1}and β _{ j,i1}). Similarly at the end of this interval the rate is
For the PMAJ model we simply assume a constant rate equal to the average of the beginning and the end values of the rate over this interval. In other words, under the PMAJ model for any t ∈ [t _{ i1,h1} ,t _{ i1,h }) we estimate the rate with
Since the PMAJ model is a piecewise model, we can use Appendix A of [1] to express the estimator of age conditional probability of developing cancer. The only hard part is correctly defining the starting and ending of each piecewise interval. The ends of these intervals are
For convenience write these interval ends with only a single index as
Now we can follow very similar notation to Appendix A of [1]. We now repeat that Appendix with the modifications to notation required for the PMAJ model. Let the estimator of A(x,y) under the PMAJ model be denoted (x,y). Let τ _{ i }≤ x <τ _{ i+1}and τ _{ j }<y ≤ τ _{ j+1}for x <y,i ≤ j, and j ≤ M + 2. For convenience we regroup the ages after inserting group delimiters at x and y. Let the new delimiters be 0 = b _{0} ≤ b _{1} ≤ b _{2} ≤ ··· ≤ b _{ M+3}= ∞ where b _{0} = τ _{0},..., b _{ i }= τ _{ i }, b _{ i+1}= x, b _{ i+2}= τ _{ i+1},..., b _{ j+1}= τ _{ j }, b _{ j+2}= y, b _{ j+3}= τ _{ j+1},..., b _{ M+3}= τ _{ M+1}= ∞. We let
where the case λ = 0 and b _{ℓ+1} = ∞ is one of the "impossible" hypothetical cohorts (see Section 3.1 of [1]). Thus, we obtain,
Examples and discussion
In this section we explore several different methods for estimating the rate functions, all using the formula of Fay et al [1] (e.g., all using equation 1). This comparison explores the differences between the piecewise constant method proposed in Fay et al [1], the PMAJ method, and the MAJ method. A different comparison emphasizing differences between versions of the DevCan software is described in the supplemental material [see Additional file 1].
For all of the examples we use data from 1998–2000 [6]. The incidence data come from the Surveillance, Epidemiology, and End Results (SEER) program of the (U.S.) National Cancer Institute, and mortality data from the (U.S.) National Center for Health Statistics. We use the SEER 12 registries which cover about 14 percent of the U.S. population. We only use the mortality data covering the same area as the SEER 12 registries cover. Because the SEER 12 registries have complete coverage only back through 1992, we only look back in the database until 1992 to delete any incident case that had previously been diagnosed with the cancer of interest. These incident cases are deleted so that they are not counted when estimating the counts of first cancer incidence (the c _{ i }values). The midyear population estimates (the n _{ i }values) come from the sum U.S. Census estimates of midyear populations from 1998, 1999, and 2000 for the SEER 12 catchment areas for the appropriate sex group (e.g., males for prostate cancer).
In Table 2 we show the results for all invasive cancers and acute lymphocytic leukemia for both sexes, prostate cancer for males, and breast cancer for females. We see the PMAJ values approximate the MAJ values very well.
In conclusion, we have described several methods for estimating rates for input into a formula to calculate ACPDvD, and we have shown that the PMAJ method provides fast and reasonable estimators for the rates.
Appendix: Calculation of R function
Recall that R _{ j,h }(t _{ℓ}, v) represents an integral with 4 parameters. We can write it as
To simplify notation substitute let t _{ℓ} = u and α _{ j ℓ} = α _{ j } ,β _{ j ℓ}= b _{ j },α _{ h ℓ}= a _{ h }, and β _{ h ℓ}= b _{ h }.
Thus,
Case 1: b _{ j }= 0 and b _{ h }= 0
For our application, whenever v → ∞ then b _{ j }= 0 and b _{ h }= 0, so this is an important special case.
When b _{ j }= 0 and b _{ h }= 0 and a _{ h }= 0 and we obtain
which goes to ∞ when v → ∞.
When b _{ j }= 0 and b _{ h }= 0 and a _{ h }≠ 0 and we obtain
which goes to a _{ j }/a _{ h }when v → ∞.
Case 2: General Case with v< ∞
To calculate the integral, R(u, v, a _{ j }, b _{ j }, a _{ h }, b _{ h }) for finite v, we can use an adaptive use of Romberg's algorithm for numeric integration (we follow closely Lange [7], pp. 210–211).
Let
Divide the interval [u, v] into n equal subintervals of length (v  u)/n, and let
Then lim_{ n→∞} T _{ n }= R(u, v, a _{ j } , b _{ j }, a _{ h }, b _{ h }).
A more accurate approximation uses Romberg's algorithm,
1. Choose n.
2. Calculate T _{ n }.
3. Calculate T _{2n }.
4. For i = 1 to I _{ max }do:
For example, one could use n = 100 and δ = 10^{5} and I _{ max }= 100.
Declarations
Acknowledgements
I would like to thank Kathy Cronin for suggesting the PMAJ method and thank her and Ram Tiwari for reading and commenting on drafts of this article.
Authors’ Affiliations
References
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