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survival -- Discussion of the Astronomical Survival Analysis Package.




[This file is extracted from the documentation of the Astronomy Survival Analaysis (ASURV) package developed by Takashi Isobe, Michael LaValley, and Eric Feigelson at Pennsylvania State University.]

Observational astronomers frequently encounter the situation where they observe a particular property (e.g. far infrared emission, calcium line absorption, CO emission) of a previously defined sample of objects, but fail to detect all of the objects. The data set then contains nondetections as well as detections, preventing the use of simple and familiar statistical techniques in the analysis.

A number of astronomers have recently recognized the existence of statistical methods, or have derived similar methods, to deal with these problems. The methods are collectively called `survival analysis' and nondetections are called `censored' data points. This revision of the survival analysis package provides the maximum likelihood estimator of the censored distribution, several two sample tests, correlation tests and linear regressions as described in our papers in the Astrophysical Journal (Feigelson and Nelson, 1985; Isobe, Feigelson, and Nelson, 1986).

No statistical procedure can magically recover information that was never measured at the telescope. However, there is frequently important information implicit in the failure to detect some objects which can be partially recovered under reasonable assumptions. We purposely provide several two-sample tests, correlation tests and linear regressions--each based on different models of where upper limits truly lie--so that the astronomer can judge the importance of the different assumptions.

There are several reasons to have confidence in these methods. First, the volume of scholarly effort devoted to the problem is impressive. A dozen books and scores of papers have been published during the last decade. Second, people in enterprises involving thousands of lives and billions of dollars appear to believe the methods. Third, our Monte Carlo simulations of a typical astronomical problem (flux-limited satellite observations of a magnitude limited population of uniformly distributed objects) showed several methods to be quite reliable in this context (Isobe et al., 1986). More simulations of astronomical problems are definitely needed.

There are also reasons for not applying these methods. We describe some of their known limitations in section about cautions and caveats.


Statistical methods dealing with censored data have a long and confusing history. It began in the 17th century with the development of actuarial mathematics for the life insurance industry and demographic studies. Astronomer Edmund Halley was one of the founders of this field. In the mid-20th century, statistical analysis grew, along with biomedical and clinical research, into a major field of biometrics. Similar (and sometimes identical) statistical methods were being developed in two other fields: reliability theory with its industrial and engineering applications; and econometrics, with attempts to understand the relations between economic forces in groups of people. Finally, we find the same mathematical problems and some of the same solutions arising in modern astronomy as discussed above.

Let us give an illustration of the use of survival analysis in three disparate fields. Consider a linear regression problem. First, an epidemiologist wishes to determine how the longevity or "survival time" (the dependent variable) depends on the number of cigarettes smoked per day (the independent variable). The experiment lasts 10 years, during which some individuals die and others do not. The survival time of the living individuals is only known to be greater than their age when the experiment ends. These values are therefore "right censored data points." Second, a company manufacturing engines wishes to know the average time between breakdowns as a function of engine speed to determine the optimal operating range. Test engines are set running until 20% of them fail; the average "lifetime" and dependence on speed is then calculated with 80% of the data points censored. Third, an astronomer observes a sample of nearby galaxies in the far- infrared to determine the relation between dust and molecular gas. Half have detected infrared luminosities but half are upper limits ("left censored data points"). The infrared luminosities are compared to CO observations, which themselves may contain censored values.

Astronomers have dealt with their upper limits in several ways. One is to consider only detections in the analysis; while possibly acceptable for some purposes (such as regression), this will clearly bias the results in others (such as estimating luminosity functions). Another way is to consider the ratio of detected objects to observed objects in a given sample. When plotted in a range of luminosity bins, this has been called the "fractional luminosity function" and has been frequently used in extragalactic radio astronomy. This is mathematically the same as actuarial life tables, but it is sometimes used incorrectly in comparing different samples: the detected fraction clearly depends on the (usually uninteresting) distance to the objects as well as their (interesting) luminosity. Also, simple square root of N error bars do not apply in these fractional luminosity functions. A third procedure is to take all of the data, including the exact values of the upper limits, and model the properties of the parent population under certain mathematical constraints, such as maximizing the likelihood that the data fits the model. This technique, which is at the root of much of modern survival analysis, was developed by astrophysicist Yoram Avni (Avni et al., 1980; Avni and Tananbaum 1986) and is now commonly used in observational astronomy.

The advantage of learning and using survival analysis methods developed in biometrics, econometrics, actuarial and reliability mathematics is the wide range of useful techniques developed in these fields. In general, astronomers can have faith in the mathematical validity of survival analysis. Issues of great social importance (e.g., establishing Social Security benefits, strategies for dealing with the AIDS epidemic, and MIL-STD reliability standards) are based on it. In detail, however, astronomers must study the assumptions underlying each model and be aware that some methods at the forefront of survival analysis may not be fully understood.


The statistical methods for dealing with censored data might be divided into a 2 x 2 grid: parametric vs. nonparametric, and univariate vs. multivariate. Parametric methods assume that the censored survival times are drawn from a parent population with a known distribution function (e.g., Gaussian, exponential), and is the principal assumption of industrial reliability models. Nonparametric models make no assumption about the parent population, and frequently rely on maximum likelihood techniques. Univariate methods are devoted to determining the characteristics of the population from which the censored sample is drawn, and comparing such characteristics for two or more censored samples. Bivariate methods measure whether the censored property of the same is correlated with another property of the objects and, if so, to perform a regression that quantifies the relationship between the two variables.

We have chosen to concentrate on nonparametric models, since the underlying distribution of astronomical populations is usually unknown. The linear regression methods however, are either fully parametric (e.g. EM algorithm regression) or semi-parametric (e.g. Cox regression, Buckley-James regression). Most of the methods are described in more detail in the astronomical literature by Schmitt (1985), Feigelson and Nelson (1985) and Isobe et al. (1986). The generalized Spearman's rho is derived by Akritas (1990).

There are two tasks that are univariate in nature: kmestimate and twosampt. The kmestimate task gives the Kaplan-Meier estimator for the distribution function of a randomly censored sample. First derived in 1958, it lies at the root of non-parametric survival analysis. It is the unique, self-consistent, generalized maximum likelihood estimator for the population from which the sample was drawn. When formulated in cumulative form, it has analytic asymptotic (for large N) error bars. The median is always well defined, though the mean is not if the lowest point in the sample is an upper limit. It is identical to the differential "redistribute to the right" algorithm, independently derived by Avni et al. (1980), but the differential form does not have simple analytic error analysis.

The second univariate task is twosampt, giving the variety of ways to test whether two censored samples are drawn from the same parent population. They are mostly generalizations of standard tests for uncensored data, such as the Wilcoxon and logrank nonparametric two-sample tests. They differ in how the censored data are weighted or "scored" in calculating the statistic. Thus each is more sensitive to differences at one end or the other of the distribution. The Gehan and logrank tests are widely used in biometrics, while some of the others are not. Details of the tests are provided in the help file.

There are a number of bivariate methods, including three correlation tests and three linear regression analyses. Cox hazard model (correlation test), EM algorithm, and Buckley-James method (linear regressions) can treat several independent variables if the dependent variable contains only one kind of censoring (i.e., upper or lower limits). Generalized Kendall's tau, Spearman's rank order test (correlation tests) and Schmitt's binned linear regression can treat mixed censoring, including censoring in the independent variable, but can have only one independent variable. A bootstrapping procedure provides error analysis for Schmitt's method.


The Kaplan-Meier estimator works with any underlying distribution (e.g., Gaussian, power law, or bimodal), but only if the censoring is "random." That is, the probability that the measurement of an object is censored can not depend on the value of the censored variable. At first glance, this may seem inapplicable to most astronomical problems: we detect the brighter objects in a sample, so the distribution of upper limits always depends on brightness. However, two factors often serve to randomize the censoring distribution. First, the censored variable may not be correlated with the variable by which the sample was initially identified. Thus, infrared observations of a sample of radio bright objects will be randomly censored if the radio and infrared emission are unrelated to each other. Second, astronomical objects in a sample usually lie at different distances, so that brighter objects are not always the most luminous. In cases where the variable of interest is censored at a particular value (e.g., flux, spectral line equivalent width, stellar rotation rate) rather than randomly censored, then parametric methods appropriate to "Type 1" censoring should be used. These are described by Lawless (1982) and Schneider (1986), but are not included in this package.

The censoring mechanisms of each study MUST be understood individually to judge whether the censoring is, or is not, likely to be random. The appearance of the data, even if the upper limits are clustered at one end of the distribution, is NOT a reliable measure. A frequent (if philosophically distasteful) escape from the difficulty of determining the nature of the censoring in a given experiment is to define the population of interest to be the observed sample. The Kaplan-Meier estimator then always gives a valid redistribution of the upper limits, though the result may not be applicable in wider contexts.

The two-sample tests are somewhat less restrictive than the Kaplan-Meier estimator, since they seek only to compare two samples rather than determine the true underlying distribution. The two versions of the Gehan test in twosampt assume that the censoring patterns of the two samples are the same, but the version with hypergeometric variance is more reliable in case of different censoring patterns. The logrank test results appear to be correct as long as the censoring patterns are not very different. Peto-Prentice seems to be the test least affected by differences in the censoring patterns. There is little known about the limitations of the Peto-Peto test.

Two of the bivariate correlation coefficients, generalized Kendall's tau and Cox regression, are both known to be inaccurate when many tied values are present in the data. Ties are particularly common when the data is binned. Caution should be used in these cases. It is not known how the third correlation method, generalized Spearman's rho, responds to ties in the data. However, there is no reason to believe that it is more accurate than Kendall's tau in this case, and it should also used be with caution in the presence of tied values.

Cox regression, though widely used in biostatistical applications, formally applies only if the "hazard rate" is constant; that is, the cumulative distribution function of the censored variable falls exponentially with increasing values. Astronomical luminosity functions, in contrast, are frequently modeled by power law distributions. It is not clear whether the results of a Cox regression are significantly affected by this difference.

There are several limitations to the three linear regression methods (i.e., the emmethod, buckleyjames, and schmittbin tasks). First, only schmittbin works when censoring is present in both variables. Second, emmethod requires that the residuals about the fitted line follow a Gaussian distribution. buckleyjames and schmittbin are less restrictive, requiring only that the censoring distribution about the fitted line is random. Both we and Schneider (1986) find little difference in the regression coefficients derived from these two methods. Third, there is considerable uncertainty regarding the error analysis of the regression coefficients of all three models. emmethod gives analytic formulas based on asymptotic normality, while Avni and Tananbaum (1986) numerically calculate and examine the likelihood surface. buckleyjames gives an analytic formula for the slope only, but it lies on a weak theoretical foundation. We provide Schmitt's bootstrap error analysis for schmittbin, although this may be subject to high computational expense for large data sets. Fourth, schmittbin is questionable in certain cases with heavy censoring. The user must arbitrarily choose a bin size. If it is too small, many censored points at the end of the distribution will be changed to detected points. If the bins are too large, accuracy in the regression calculation is reduced. Fifth, the Buckley-James method has a defect in that the final solution occasionally oscillates rather than converges smoothly. Users may wish to run each of the methods and evaluate the uncertainty with caution. In our judgment, the most reliable linear regression method may be the Buckley-James regression, and we suggest that Schmitt's regression be reserved for problems with censoring present in both variables.

If we consider the field of survival analysis from a broader perspective, we note a number of deficiencies with respect to censored statistical problems in astronomy. Most importantly, survival analysis assumes the upper limits in a given experiment are precisely known, while in astronomy they frequently represent n times the RMS noise level in the experimental detector, where n=2, 3, 5, or whatever. Methods adopted to this situation are clearly needed. A related deficiency is the absence of weighted means or regressions. Survival analysis also has not yet produced any true multi-variate techniques, such as a Principal Components Analysis that permits censoring. There also seems to be a dearth of nonparametric goodness-of-fit tests like the Kolmogorov-Smirnov test.

Finally, we note that this package is not unique. Nearly a dozen software packages for analyzing censored data have been developed (Wagner and Meeker 1985). Four are part of large multi-purpose commercial statistical software systems: SAS, SPSS, BMDP, and GLIM. These packages are available on many university mainframes. We have found BMDP to be the most useful for astronomers (see Appendices to Feigelson and Nelson 1985, Isobe et al., 1986 for instructions on its use). It provides most of the functions in kmestimate and twosampt as well as a full Cox regression, but no linear regressions. Other packages (CENSOR, DASH, LIMDEP, STATPAC, STAR, SURVAN, SURVREG) were written at various universities, medical institutes and industrial labs, and have not been evaluated by us.


The production and distribution of the ASURV package was principally funded at Penn State by a grant from the Center for Excellence in Space Data and Information Sciences (operated by the Universities Space Research Association in cooperation with NASA), NASA grants NAGW-1917 and NAGW-2120, and NSF grant DMS-9007717. T.I. was supported by NASA grant NAS8-37716. We are grateful to Prof. Michael Akritas (Dept. of Statistics, Penn State) for advice and guidance on mathematical issues, and to Drs. Mark Wardle (Dept. of Physics and Astronomy, Northwestern University), Paul Eskridge (Harvard-Smithsonian Center for Astrophysics), and Eric Smith (Laboratory for Astronomy and Solar Physics, NASA-Goddard Space Flight Center) for generous consultation and assistance on the coding. We would also like to thank Drs. Peter Allan (Rutherford Appleton Laboratory), Simon Morris (Carnegie Observatories), Karen Strom (UMASS), and Marco Lolli (Bologna) for their help in debugging.


Akritas, M.
``Aligned Rank Tests for Regression With Censored Data'', Penn State Dept. of Statistics Technical Report, 1989. [Source for ASURV's generalized Spearman's rho computation.]
Amemiya, T.
Advanced Econometrics (Harvard U. Press:Cambridge MA) 1985. [Reviews econometric "Tobit" regression models including censoring.]
Avni, Y., Soltan, A., Tananbaum, H. and Zamorani, G.
"A Method for Determining Luminosity Functions Incorporating Both Flux Measurement and Flux Upper Limits, with Applications to the Average X-ray to Optical Luminosity Ration for Quasars," Astrophys. J. 235:694 1980. [The discovery paper in the astronomical literature for the differential Kaplan-Meier estimator.]
Avni, Y. and Tananbaum, H.
"X-ray Properties of Optically Selected QSOs." Astrophys. J. 305:57 1986. [The discovery paper in the astronomical literature of the linear regression with censored data for the Gaussian model.]
Bickel, P.J and Ritov, Y.
``Discussion: `Censoring in Astronomical Data Due to Nondetections' by Eric D. Feigelson'', in {Statistical Challenges in Modern Astronomy}, eds. E.D. Feigelson and G.J. Babu, (Springer-Verlag: New York) 1992. [A discussion of the possible inadequacies of survival analysis for treating low signal-to-noise astronomical data.]
Brown, B.J. Jr., Hollander, M., and Korwar, R.M.
"Nonparametric Tests of Independence for Censored Data, with Applications to Heart Transplant Studies" in Reliability and Biometry, eds. F. Proschan and R.J. Serfling (Philadelphia: SIAM) p. 327 1974. [Reference for the generalized Kendall's tau correlation coefficient.]
Buckley, J. and James, I.
"Linear Regression with Censored Data," Biometrika 66:429 1979. [Reference for the linear regression with Kaplan-Meier residuals.]
Feigelson, E. D.
``Censored Data in Astronomy'', { Errors, Bias and Uncertainties in Astronomy}, eds. C. Jaschek and F. Murtagh, (Cambridge U. Press: Cambridge) p. 213 1990. [A recent overview of the field.]
Feigelson, E.D. and Nelson, P.I.
"Statistical Methods for Astronomical Data with Upper Limits: I. Univariate Distributions," Astrophys. J. 293:192 1985. [Our first paper, presenting the procedures in UNIVAR here.]
Isobe, T., Feigelson, E.D., and Nelson, P.I.
"Statistical Methods for Astronomical Data with Upper Limits: II. Correlation and Regression," Astrophys. J. 306:490 1986. [Our second paper, presenting the procedures in BIVAR here.]
Isobe, T. and Feigelson, E. D.
``Survival Analysis, or What To Do with Upper Limits in Astronomical Surveys", { Bull. Inform. Centre Donnees Stellaires}, 31:209 1986. [A precis of the above two papers in the Newsletter of Working Group for Modern Astronomical Methodology.]
Isobe, T. and Feigelson, E. D.
``ASURV'', { Bull. Amer. Astro. Society}, 22:917 1990. [The initial software report for ASURV Rev 1.0.]
Kalbfleisch, J.D. and Prentice, R.L.
The Statistical Analysis of Failure Time Data (Wiley:New York) 1980. [A well-known monograph with particular emphasis on Cox regression.]
Latta, R.
``A Monte Carlo Study of Some Two-Sample Rank Tests With Censored Data'', { Jour. Amer. Stat. Assn.}, 76:713 1981. [A simulation study comparing several two-sample tests available in ASURV.]
LaValley, M., Isobe, T. and Feigelson, E.D.
``ASURV'', { Bull. Amer. Astro. Society} 1992 [The new software report for ASURV Rev. 1.1.]
Lawless, J. F.
{ Statistical Models and Methods for Lifetime Data} (Wiley:New York) 1982. [A very thorough monograph, emphasizing parametric models and engineering applications.]
Lee, E.T.
Statistical Methods for Survival Data Analysis (Lifetime Learning Pub.:Belmont CA) 1980. [Hands-on approach, with many useful examples and FORTRAN programs.]
Magri, C., Haynes, M., Forman, W., Jones, C. and Giovanelli, R.
`The Pattern of Gas Deficiency in Cluster Spirals: The Correlation of H I and X-ray Properties'', { Astrophys. J.} 333:136 1988. [A use of bivariate survival analysis in astronomy, with a good discussion of the applicability of the methods.]
Millard, S. and Deverel, S.
``Nonparametric Statistical Methods for Comparing Two Sites Based on Data With Multiple Nondetect Limits'', { Water Resources Research}, 24:2087 1988. [A good introduction to the two-sample tests used in ASURV, written in nontechnical language.]
Miller, R.G. Jr.
Survival Analysis (Wiley, New York) 1981. [A good introduction to the field; brief and informative lecture notes from a graduate course at Stanford.]
Prentice, R. and Marek, P.
``A Qualitative Discrepancy Between Censored Data Rank Tests'', { Biometrics} 35:861 1979. [A look at some of the problems with the Gehan two-sample test, using data from a cancer experiment.]
Sadler, E. M., Jenkins, C. R. and Kotanyi, C. G..
``Low-Luminosity Radio Sources in Early-Type Galaxies'', { Mon. Not. Royal Astro. Soc.} 240:591 1989. [An astronomical application of survival analysis, with useful discussion of the methods in the Appendices.]
Schmitt, J.H.M.M.
"Statistical Analysis of Astronomical Data Containing Upper Bounds: General Methods and Examples Drawn from X-ray Astronomy." Astrophys. J. 293:178 1985. [Similar to our papers, a presentation of survival analysis for astronomers. Derives schmittbin regression for censoring in both variables.]
Schneider, H.
Truncated and Censored Samples Normal Populations (Dekker:New York) 1986. [Monograph specializing on Gaussian models, with a good discussion of linear regression.]
Wagner, A.E. and Meeker, W.Q. Jr.
"A Survey of Statistical Software for Life Data Analysis," in 1985 Proceedings of the Statistical Computing Section, (Amer. Stat. Assn.:Washington DC), p. 441. [Summarizes capabilities and gives addresses for other software packages.]
Wardle, M. and Knapp, G.R.
"The Statistical Distribution of the Neutral-Hydrogen Content of SO Galaxies," Astron. J. 91:23 1986. [Discusses the differential Kaplan-Meier distribution and its error analysis.]
Wolynetz, M.S.
"Maximum Likelihood Estimation in a Linear Model from Confined and Censored Normal Data," Appl. Stat. 28:195 1979. [Published Fortran code for the EM algorithm linear regression.]

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