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Power evaluation of disease clustering tests
International Journal of Health Geographics volume 2, Article number: 9 (2003)
Abstract
Background
Many different test statistics have been proposed to test for spatial clustering. Some of these statistics have been widely used in various applications. In this paper, we use an existing collection of 1,220,000 simulated benchmark data, generated under 51 different clustering models, to compare the statistical power of several disease clustering tests. These tests are BesagNewell's R, CuzickEdwards' kNearest Neighbors (kNN), the spatial scan statistic, Tango's Maximized Excess Events Test (MEET), Swartz' entropy test, Whittemore's test, Moran's I and a modification of Moran's I.
Results
Except for Moran's I and Whittemore's test, all other tests have good power for detecting some kind of clustering. The spatial scan statistic is good at detecting localized clusters. Tango's MEET is good at detecting global clustering. With appropriate choice of parameter, BesagNewell's R and CuzickEdwards' kNN also perform well.
Conclusion
The power varies greatly for different test statistics and alternative clustering models. Consideration of the power is important before we decide which test statistic to use.
Background
A large number of tests for spatial randomness that adjust for an uneven background population have been proposed. Such test statistics are used to test whether or not the geographical distribution of disease is random. They are also used in many other areas such as genetics, geomorphology and ecology [1–6].
When we use these test statistics, it is important to know whether they have good power. There have been some studies comparing such test statistics [7–14], but there have been few simultaneous comparisons of three or more tests. When evaluating tests for spatial randomness, the best way is to compare them using the same simulated data sets.
For our study, we use existing benchmark data [10], simulated from the female population in the Northeastern United States, to evaluate the power of different test statistics for various kinds of clusters.
Previous studies have shown that the spatial scan statistic has good power in detecting hot spot clusters, and Tango's MEET has good power in detecting global clustering [10]. We compare the power of these two test statistics with six additional tests: BesagNewell' R, CuzickEdwards' kNN, Swartz' entropy test, Whittemore's test, Moran's I and a modified version of Moran's I. These tests are selected for different reasons. Some tests are widely used, such as Moran's I and CuzickEdwards' kNN. Most of them are published in well reputed statistics journals.
Methods
Benchmark data sets
The benchmark data sets are based on the 1990 female population in the 245 counties and county equivalents in the Northeastern United States, consisting of the states of Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut, New York, New Jersey, Pennsylvania, Delaware, Maryland and the District of Columbia. Each county is represented by a centroid coordinate. The data is available at 'http://www.commed.uchc.edu/biostat/datasets/'. The benchmark data and how it was generated has been described in detail elsewhere [10]. We provide a brief summary here.
Under the null hypothesis of no clustering, 100,000 random data sets were generated by randomly allocating 600 cases to the various counties, with probabilities proportional to the county population. The null data is used to estimate the critical values, which is the cutoff point for the significance. Two kinds of clustering models were evaluated, hot spot clusters and global chain clustering.
Hot spot clusters
Hot spot clusters were generated by setting the relative risk in some counties to be larger than 1. Three different sets of local clusters are constructed in a rural, urban and mixed area respectively. Within each of these three sets, there are five different sized clusters with 1, 2, 4, 8 and 16 counties respectively. The center of the rural cluster is Grand Isle County in Vermont. The center of the mixed cluster is Allegheny County (Pittsburgh) in Pennsylvania. The center of the urban cluster is New York county (Manhattan) in New York. The relative risks and counties included in each cluster are listed in Table 1.
In order to evaluate how the disease clustering tests perform when there are multiple hotspot clusters, the benchmark data also include 15 alternate models with two clusters and 5 models with three clusters by using different combinations of the original clusters. In a model, all clusters had the same number of counties.
Global chain clustering
In the global chain clustering model, every county has the same expected number of cases under the null and alternative hypothesis. The counties are tied together sequentially on a chain that passes through the centroid of each county exactly once, after which it reconnects with the first county on the chain, forming a Hamiltonian cycle. A map of the Hamiltonian cycle used has previously been published [10].
To generate clusters, a certain number of cases are first located randomly on the map, according to the null hypothesis. These original cases then generate other new cases close by. If each original case generates one additional case, it is called twins. If two additional cases are generated, it is called triplets.
A total of 26 chain clustering models are constructed with the distance between the twins (triplets) along the chain being either constant or exponentially distributed with different means. If the distance is zero, the twins (triplets) are in the same county. The chain does not imply that the disease itself spreads around the chain, just that twins and triplets cases are located in either of the two directions, as defined by the chain.
Test statistics
Notation
Denote c_{ i }as the number of cases in county i, n_{ i }as the population size of county i, C as the total number of cases, N as the total population size, H as the total number of counties and d_{ ij }as the distance between county i and j.
Let D_{j(i)}be the total number of cases in county i and its j closest neighbors, and let U_{j(i)}be the population size in county i and its j closest neighbors.
BesagNewell's R
BesagNewell's R statistic [15] has been used to study leukemia in upstate New York [16]. The test statistic is defined as , where M_{ i }is a random variable denoting the minimum number of counties needed to have at least k cases in county i and its M_{ i }closest neighboring counties, m_{ i }is the observed value of M_{ i }, that is, m_{ i }= min{j : (D_{j(i)}+ 1) ≥ k}. k is a parameter set by the user. Usually, a large k is more sensitive to large clusters and a small k is more sensitive to small clusters. I is the indicator function with value 1 when P(M_{ i }≤ m_{ i }) < 0.05 and 0 otherwise. P(M_{ i }≤ m_{ i }) is calculated by
The null hypothesis of no clustering is rejected when R is large.
CuzickEdwards' kNN
CuzickEdwards' kNN (kNearest Neighbors) test [17] has been widely used, for example for leukemias and lymphomas among young people in New Zealand [18] and the association of Ixodes pacificus and quine granulocytic ehrlichiosis in California [19].
This test statistic was originally designed for point data, but can easily be adapted for aggregated data. The test statistic is defined as
where k is a parameter chosen by the user and for each county i, denotes the number of k nearest neighbors which are cases. To be more precise, = D_{(h1)(i)}+ t_{h(i)}where h is decided so that U_{(h1)(i)}≤ k, U_{h(i)}>k and . U_{(1)(i)}is defined as 0.
The null hypothesis of no clustering is rejected when T_{ k }is large.
The spatial scan statistic
The spatial scan statistic [20] has among other things been used to study human granulocytic ehrlichiosis near Lyme in Connecticut [21], softtissue sarcoma and nonHodgkin's lymphoma clusters with high dioxin emission levels [22], childhood mortality in rural Burkina Faso [23], bovine tuberculoisis in Argentina [24] and Toxoplasma gondii infection of southeast sea, otters [25].
The spatial scan statistic imposes a circular window on the map and lets the circle centroid move across the study region. For any given position of the centroid, the radius of the window is changed continuously to take any value between zero and some upper limit.
Let L_{j(i)}be the likelihood under the alternate hypothesis that there is a cluster in county i and its j closest neighbors, and let L_{0} be the likelihood under the null hypothesis. It can then be shown that
As this likelihood ratio is maximized over all circles, it identifies the one that constitutes the most likely cluster. The test statistic is
where I is the indicator function with value 1 when and 0 otherwise. The null hypothesis of no clustering is rejected when T is large.
Tango's Maximized Excess Events Test (MEET)
For a given parameter λ, the Excess Events Test statistic [11] is defined as
The choice of λ relates to the geographical scale of clustering. Large λ makes the test sensitive to geographically large clusters, while small λ will make the test more sensitive to small clusters.
To be able to detect clustering irrespectively of its geographical scale, Tango [12] proposed the Maximized Excess Events Test (MEET)
where eet(λ) is the observed value of the Excess Events Test statistic conditioning on λ, and U is the upper limit on λ. Practical implementation of the test uses 'line search' by discretization on λ, and the MEET statistic is evaluated using Monte Carlo hypothesis testing [26].
The null hypothesis of no clustering is rejected when the test statistic is small.
Swartz' entropy test
Swartz [27] proposed a test for spatial randomness based on the concept of entropy. The test statistic is defined [28] as
The null hypothesis of no clustering is rejected when T is small.
Moran's I
Moran's I [29] was originally proposed to analyze continuous data. Subsequently, this statistic has also been used to analyze count data, such as Lyme disease in New York State [30] and cancer incidence in Canada [31].
The Moran' I statistic is defined as
where
and
We also consider a modified version of Moran's I:
In both cases, we reject the null hypothesis of no clustering when I is large.
Whittemore's test
Whittemore et al. [32] proposed the statistic
We reject the null hypothesis of no clustering when T is small.
Power calculation
For BesagNewell's R, CuzickEdwards' kNN. Swartz' entropy test, Moran's I and Whittemore's test, the power estimate is calculated using C++ code written by the author. For the spatial scan statistic and Tango's MEET, the power estimates are obtained from the paper by Kulldorff et al.[10].
Results
Hot spot clusters
Table 2 shows the estimated power of the test statistics in detecting the hot spot clusters. For each type of hot spot cluster, the highest power is highlighted. The spatial scan statistic has good power in detecting all three kinds of hot spot clusters: rural, mixed and urban clusters, and it performs best for detecting rural clusters. Tango's MEET performs best for the urban clusters, but not very well for the rural clusters.
With the right choice of parameter, BesagNewell's R has the best power for detecting mixed clusters, but its strength is very sensitive to choice of parameter. The power of CuzickEdwards' kNN also depends on the parameter. It has good power in detecting all three kinds of hot spot clusters with the right choice of parameter. The choice of parameter depends on the size of the cluster. Usually, for large clusters, large parameter values perform better, while for small clusters, small parameter values are better.
Swartz' entropy test has good power in detecting the rural clusters, but not very good for mixed or urban clusters. Moran's I can detect the rural clusters except for the cluster with only one county. The modified Moran's I has similar performance to Moran's I, but it performs better for the rural clusters, especially for the cluster with one county. Whittemore's test does not perform as well as the other test statistics in detecting hot spot clusters.
All the test statistics have good power for multiple hot spot clusters except Whittemore's test and Moran's I. The spatial scan statistic, Tango's MEET and CuzickEdwards' kNN perform very well in detecting multiple clusters.
Global chain clustering
Table 3 shows the estimated power of the test statistics for global chain clustering. The highest power for each type of global clustering is highlighted. Note that as the distance between the cases increases, there is less clustering in the data, and all tests have lower power.
For most alternative models, Tango's MEET has the highest power. The spatial scan statistic performs well, but not as well as Tango's MEET. Swartz' entropy test is good when the distance is small, but the power decreases very quickly as the distance increases. BesagNewell's R, Moran's I, Whittemore' test are not very good at detecting global clustering.
With the right choice of parameter, CuzickEdwards' kNN performs very well, especially for clustering with small distances. Large parameter values tend to detect clustering with large distance, while small parameter values perform better for clustering with small distance.
The performance of the test statistics for twins clustering with fixed and exponential distance is similar. All test statistics have better power in detecting triplet clustering since there is more clustering there.
Discussion
Of the evaluated test statistics, BesagNewell's R, CuzickEdwards' kNN, the spatial scan statistic, MEET, Whittemore's test are based on Euclidean distances. Moran's I is based on the adjacencies of counties. Swartz' entropy test does not use the spatial relationship among the counties.
The M statistic [33] proposed by Bonetti and Pagano is a nonparametric test that uses the iriterpoint distance distribution to study the spatial pattern of the data. The M statistic has also been evaluated using the same benchmark data [10]. The M statistic does well for mixed and urban clusters and has good power in detecting multiple clusters. Generally, it does not perform quite as well as the spatial scan statistic and MEET, but it is very competitive compared to the other tests.
BesagNewell's R and CuzickEdwards' kNN are good test statistics, but the power depends a lot on the parameter. Usually large parameter value can make the test statistic more sensitive to large clustering, whereas small parameter value can detect the small clustering better. So if we know the scale of clustering and choose a corresponding parameter, these two test statistics may have good power. In practice, we usually don't know the size of clustering. If we try different parameter values, that will cause multiple testing problems.
Sometimes we need to adjust the analysis for age or other covariate. All the test statistics considered here can incorporate such adjustment except Swartz' entropy test, although it can be modified to do so.
In terms of data resolution, BesagNewell's R, Whittemore's test, Tango's MEET and Swartz' entropy test were originally proposed to analyze aggregated data, while CuzickEdwards' kNN was proposed to analyze point data. The spatial scan statistic was proposed to analyze either aggregated or point data. Moran's I was designed for continuous data, but has been used extensively for aggregated count data as well. It is possible and maybe even likely that these test statistics may perform differently when applied to point data.
A strength of this power evaluation study is that the data is typical of epidemiological applications, and uses actual population and geographical data. The strength of the test statistics will depend not only on the alternative model though, but also on the spatial distribution of the areas and the population size in this area. A limit of the study is that the background population of the benchmark data is from only one particular region, the female population of Northeast United States. Under other alternate models and background population, some test statistics may perform better or worse.
Conclusion
The power varies greatly for different disease clustering test statistics. Consideration of the power is important before deciding which test statistic to use. If the size or scale of clustering is known, it is worth considering the use of BesagNewell's R or CuzickEdwards' kNN. If not, we feel confident recommending the spatial scan statistic for the detection of local clusters and use Tango's MEET for the general evaluation of clustering throughout the map. Other tests may be equally good or better for alternative models not considered in this paper.
Abbreviations
 kNN k:

Nearest Neighbors.
 MEET:

Maximized Excess Events Test.
References
 1.
RuizGarcia M: Genetic relationships among some new cat populations sampled in Europe: A spatial autocorrelation analysis. Journal of Genetics. 1997, 76: 124.
 2.
Gustine DL, Elwinger GF: Spatiotemporal genetic structure within white clover populations in grazed swards. Crop Science. 2003, 43: 337344.
 3.
Aubry P, Piegay H: Spatial autocorrelation analysis in geomorphology: Definitions and tests. Geographic Phisique et Quaternaire. 2001, 55: 111129.
 4.
Meirmans PG, Vlot EC, Den Nijs JCM, Menken SBJ: Spatial ecological and genetic structure of a mixed population of sexual diploid and apomictic triploid dandelions. Journal of Evolutionary Biology. 2003, 16: 343352. 10.1046/j.14209101.2003.00515.x.
 5.
Liebhold AM, Gurevitch J: Integrating the statistical analysis of spatial data in ecology. Ecography. 2002, 25: 553557. 10.1034/j.16000587.2002.250505.x.
 6.
Clark SA, Richardson BJ: Spatial analysis of genetic variation as a rapid assessment tool in the conservation management of narrowrange endemics. Invertebrate Systematics. 2002, 16: 583587. 10.1071/IT01041.
 7.
Rogerson PA: The detection of clusters using a spatial version of the chisquare goodnessoffit statistic. Geographical Analysis. 1999, 31: 130147.
 8.
Kulldorff M, Nagarwalla N: Spatial disease clusters: Detection and inference. Statistics in Medicine. 1995, 14: 799810.
 9.
Oden N: Adjusting Moran' I for population density. Statistics in Medicine. 1995, 14: 1726.
 10.
Kulldorff M, Tango T, Park P: Power comparisons for disease clustering tests. Computational Statistics and Data Analysis. 2003, 42: 665684. 10.1016/S01679473(02)001603.
 11.
Tango T: A class of tests for detecting 'general' and 'focused' clustering of rare diseases. Statistics in Medicine. 1995, 14: 23232334.
 12.
Tango T: A test for spatial disease clustering adjusted for multiple testing. Statistics in Medicine. 2000, 19: 191204. 10.1002/(SICI)10970258(20000130)19:2<191::AIDSIM281>3.0.CO;2Q.
 13.
Vach W: Locally optimal tests on spatial clustering. in New Approaches in Classification and Data Analysis. Edited by: Diday. 1994, Berlin, SpringerVerlag, 161168.
 14.
Tango T: Comparison of general tests for spatial clustering. In Disease Mapping and Risk Assessment for Public Health. Edited by: Lawson, et al. 1999, London, Wiley, 111117.
 15.
Besag J, Newell J: The detection of clusters in rare diseases. Journal of the Royal Statistical Society. 1991, A154: 143155.
 16.
Waller LA, Turnbull BW, Clark LC, Nasca P: Spatial pattern analyses to detect rare disease clusters. In: Case Studies in Biometry. Edited by: Lange N, Ryan L, Billard L, Brillinger D, Conquest L, Greenhourse J. 1994, New York: John Wiley & Sons, 1316.
 17.
Cuzick J, Edwards R: Spatial clustering for inhomogeneous populations. Journal of the Royal Statistical Society. 1990, B52: 73104.
 18.
Dockerty JD, Sharples KJ, Borman B: An assessment of spatial clustering of leukaemias and lymphomas among young people in New Zealand. Journal of Epidemiology and Community Health. 1999, 53: 1548.
 19.
Vredevoe LK, Righter PJ, Madigan JE, Kimsey RB: Association of Ixodes pacificus (Acari: Ixodidae) with the spatial and temporal distribution of equine granulocytic ehrlichiosis in California. Journal of Medical Entomology. 1999, 36: 551561.
 20.
Kulldorff M: A spatial scan statistic. Communications in Statistics: Theory and Methods. 1997, 26: 14811496.
 21.
Chaput EK, Meek JI, Heimer R: Spatial analysis of human granulocytic ehrlichiosis near Lyme, Connecticut. Emerging Infectious Diseases. 2002, 8: 943948.
 22.
Viel JF, Arveux P, Baverel J, Cahn JY: Softtissue sarcoma and nonHodgkin's lymphoma clusters around a municipal solid waste incinerator with high dioxin emission levels. American Journal of Epidemiology. 2000, 152: 1319. 10.1093/aje/152.1.13.
 23.
Sankoh OA, Ye Y, Sauerborn R, Muller O, Becher H: Clustering of childhood mortality in rural Burkina Faso. International Journal of Epidemiology. 2001, 30: 485492. 10.1093/ije/30.3.485.
 24.
Perez AM, Ward MP, Torres P, Ritacco V: Use of spatial statistics and monitoring data to identify clustering of bovine tuberculosis in Argentina. Preventive Veterinary Medicine. 2002, 56: 6374. 10.1016/S01675877(02)001241.
 25.
Miller MA, Gardner IA, Kreuder C, Paradies DM, Worcester KR, Jessup DA, Dodd E, Harris MD, Ames JA, Packham AE, Conrad PA: Coastal freshwater runoff is a risk factor for Toxoplasma gondii infection of southern sea otters (Enhydra lutris nereis). International Journal for Parasitology. 2002, 32: 9971006. 10.1016/S00207519(02)000693.
 26.
Dwass M: Modified randomization tests for nonparametric hypotheses. Annals of Mathematical Statistics. 1957, 28: 181187.
 27.
Swartz JB: An entropybased algorithm for detecting clusters of cases and controls and its comparison with a method using nearest neighbors. Health and Place. 1998, 4: 6777. 10.1016/S13538292(97)000269.
 28.
Kulldorff M: Letter to the editor. Health and Place. 1999, 5: 31310.1016/S13538292(99)000210.
 29.
Moran PAP: Notes on continuous stochastic phenomena. Biometrika. 1950, 37: 1723.
 30.
Glavanakov S, White DJ, Caraco T, Lapenis A, Robinson GR, Szymanski BK, Maniatty WA: Lyme disease in New York State: Spatial pattern at a regional scale. American Journal of Tropical Medicine and Hygiene. 2001, 65: 538545.
 31.
Le ND, Marret LD, Roberson DL, Semenciw RM, Turner D, Walter SD: Canadian Cancer Incidence Atlas. Canadian Government Publishing. 1995
 32.
Whittemore AS, Friend N, Brown BW, Holly EA: A test to detect clusters of disease. Biometrika. 1987, 74: 631635.
 33.
Bonetti M, Pagano M: On detecting clustering. Proceedings of the Biometrics Section American Statistical Association. 2001, 2433.
Acknowledgements
This research was funded by NCI grant number RO1CA09597901.
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CH and MK jointly designed the study and chose the methods for evaluation. CH programmed the C++ code, carried out the power simulations and wrote the first draft of the manuscript. Both authors interpreted the results and wrote the final version of the paper.
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Song, C., Kulldorff, M. Power evaluation of disease clustering tests. Int J Health Geogr 2, 9 (2003). https://doi.org/10.1186/1476072X29
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Keywords
 Spatial statistics
 benchmark data
 power
 cluster detection
 hot spot clusters
 global chain clustering
 test for spatial randomness