Improving the fit of bivariate smoothing splines when estimating longitudinal immunological and virological markers in HIV patients with individual antiretroviral treatment strategies.

CD4+ lymphocyte count and HIV RNA plasma viral load are longitudinally monitored in patients with HIV infection. Because data collection intervals may be unequally spaced and these markers experience high within-patient variability, they may be smoothed before use in subsequent models. Estimation strategies must be able to accommodate the drastic changes in viral load which may occur when an individual's treatment strategy is updated. Because these treatment changes are not regimented, these dynamics cannot be modelled using standard methods. We propose univariate and bivariate cubic smoothing splines to fit CD4+ count and viral load over time. The method is developed using state space equations, and the Kalman filter is used to calculate the log-likelihood. Non-linear optimization is used to obtain the maximum likelihood estimates. A modification of the Kalman filter allows non-informative or diffuse priors at the initial observation. Since treatment changes are expected to alter the shape of the curve, we further extend the Kalman filter to permit greater flexibility in the smoothing spline at these time points. The method produces smoothed estimates of the viral load and CD4+ count curves over time.

Simultaneous smoothing and adjusting mortality rates in U.S. counties: melanoma in white females and white males.

Detecting patterns in health-related data for geographic areas is facilitated with the use of exploratory methods, especially smoothing. In addition, these data often must be adjusted for known prognostic factors such as age and gender. The analysis in this paper focuses on mortality rates due to malignant melanoma in White males and White females; these data are adjusted for both age and latitude, separately for males and females, and then smoothed using (a) a non-linear smoother known as weighted head-banging, and (b) a new method that incorporates the adjustment and the smoothing simultaneously. Maps of the continental United States show regions of high rates, even after having adjusted for age and latitude, and suggest the possibility of other variables that may influence the rates.

Fundamental concepts in statistics: elucidation and illustration.

Fundamental concepts in statistics form the cornerstone of scientific inquiry. If we fail to understand fully these fundamental concepts, then the scientific conclusions we reach are more likely to be wrong. This is more than supposition: for 60 years, statisticians have warned that the scientific literature harbors misunderstandings about basic statistical concepts. Original articles published in 1996 by the American Physiological Society's journals fared no better in their handling of basic statistical concepts. In this review, we summarize the two main scientific uses of statistics: hypothesis testing and estimation. Most scientists use statistics solely for hypothesis testing; often, however, estimation is more useful. We also illustrate the concepts of variability and uncertainty, and we demonstrate the essential distinction between statistical significance and scientific importance. An understanding of concepts such as variability, uncertainty, and significance is necessary, but it is not sufficient; we show also that the numerical results of statistical analyses have limitations.

Geographic trends in prostate cancer mortality: an application of spatial smoothers and the need for adjustment.

Prostate cancer mortality among whites and nonwhites in U.S. counties are analyzed for geographic effects. To better visualize geographical effects, the data are smoothed with a bivariate smoother using age-specific rates. Among nonwhites, an important explanatory variable is the proportion of African Americans. A relationship between the mortality rate and this variable is derived, and the data are adjusted for this variable using this relationship. When the rates are adjusted for age only, among whites there is a north-south gradient: rates are higher in the north, lower in the south. Among nonwhites, the gradient runs east to west: higher in the east, lower in the west. The latter gradient disappears when the rates are further adjusted for African Americans. The study reveals the importance of both smoothing the data to visualize patterns in geography and adjusting the data for an important variable to identify underlying patterns. The additional adjustment permits the identification of other areas of the country with elevated or depressed rates.

Geographic analysis of pertussis infection in an urban area: a tool for health services planning.

OBJECTIVE: This study examined whether incident cases of pertussis cluster in urban census tracts and identified community characteristics that predict high-incidence areas. METHODS: An ecological study design was used. The study population included all persons diagnosed with pertussis from January 1, 1986, through December 31, 1994. Maps of rates were constructed via a geographic information system and clustering was statistically confirmed. Associations between pertussis rates and community characteristics were tested. RESULTS: Mapping and statistical analysis revealed spatial clustering of pertussis. Higher age-adjusted rates of pertussis infection were associated with higher proportions of residents below poverty level. CONCLUSIONS: In urban areas pertussis infection clusters in areas of poverty.

Smoothing geographical data, particularly rates of disease.

This paper proposes a linear smoother for geographically-defined data that consist of standardized rates (for example, adjusted for age). The smoother is viewed as a special case of one that applies to data in the form of ratios, and situations are described under which such a smoother can and cannot be useful. Its application to mortality rates due to prostate cancer in both whites and non-whites demonstrates its potential to highlight features in the data that might otherwise remain obscure. Some open questions concerning inference of apparent trends are discussed.

Urbanicity-related trends in lung cancer mortality in US counties: white females and white males, 1970-1987.

BACKGROUND: The effect of urbanization on age-adjusted lung cancer mortality rates in US counties is investigated. The data come from National Cancer Institute, and urban trends are estimated in time periods 1970-1979 and 1980-1987, for both white males and white females. To account for possibly different gradients in different parts of the country, the 48 contiguous states are divided into seven regions. METHODS: A measure of urbanness, urbanicity, is defined and is used to stratify counties. A multiplicative model is proposed that relates county mortality rates to urbanicity. The residuals from this multiplicative model serve as age- and urban-adjusted rates. RESULTS: Urban-rural gradients are significant for nearly all regions for both white males and white females, diminishing slightly in the latter time period for white males but becoming stronger for white females. CONCLUSIONS: The age- and urban-adjusted rates may be used in mapping to investigate geographical patterns that remain after removal of the urban factor.

A data-analytic approach for estimating lead time and screening benefit based on survival curves in randomized cancer screening trials.

Screening tests are used frequently for control of diseases such as cancer. The increased survival time of screen-detected cases over those that are detected clinically may be due in part to 'lead time', or the length of time by which the disease is diagnosed earlier by screening in the presence or absence of any real extension in survival time. A realistic evaluation of screening needs to assess the true benefit of screening; that is, the length of time by which survival has been extended, beyond merely the time of the advanced diagnosis. The comparison of survival measured from time of entry between cases in a screening arm and in a control arm in randomized studies avoids the lead time bias. If the effects of average lead time and average benefit on survival are additive, these effects can be estimated by recognizing that (a) the difference in survival curves since time of diagnosis confounds benefit and lead time, but (b) the difference in survival curves since time of start of study involves benefit only. The method is evaluated on simulated data for its accuracy and may be used on data from randomized studies.

U.S. cancer death rates: a simple adjustment for urbanization.

PIP: This is an exploratory study of geographical factors affecting cancer mortality in the United States. Data were collected by the National Cancer Institute for the years 1950-1969 and concern mortality from cancer of the trachea, bronchus, and lung combined for white males. The authors discuss differences in mortality by level of urbanization and how these might be affected by differences in smoking patterns. (SUMMARY IN FRE)^ieng

An analysis of AIDS incidence data by clustering trends.

AIDS incidence trends vary greatly among geographic areas in the United States. We define clusters of areas within which AIDS incidence trends are similar, as areas within a cluster may have similar human immunodeficiency virus epidemic patterns and thus may lead to similar prevention/intervention strategies. Methods of exploratory data analysis are used to define such clusters from reported quarterly AIDS incidence to December 1990 (adjusted for estimated reporting delays) in homosexual and bisexual men not using intravenous drugs in 39 metropolitan statistical areas (MSAs) in the United States. After smoothing AIDS incidence in each MSA, we define groups from cluster analysis based on a measure of similarity between pairs of MSAs. A log-linear model gives estimates of the scale factors and the common trend for the MSAs in each group. Alternative metrics and simulated data suggest that the clustering is fairly robust to variations in AIDS incidence data. The resulting clusters separate MSAs with different trends, for example, MSAs in which AIDS incidence shows signs of reaching a plateau are separated from MSAs in which incidence continues to increase rapidly.

Evaluation of a method for detecting aberrations in public health surveillance data.

The detection of unusual patterns in routine public health surveillance data on diseases and injuries presents an important challenge to health workers interested in early identification of epidemics or clues to important risk factors. Each week, state health departments report the numbers of cases of about 50 notifiable diseases to the Centers for Disease Control and Prevention, and these reports are published weekly in the Morbidity and Mortality Weekly Report. A new analytic method and a horizontal bar graph were introduced in July 1989 to facilitate easy identification of unusual numbers of reported cases. Evaluation of the statistical properties of this method indicates that the results are fairly robust to nonnormality and serial correlation of the data. An epidemiologic evaluation of the method after the first 6 months showed that it is useful for detection of specific types of aberrations in public health surveillance.

Analysis of aberrations in public health surveillance data: estimating variances on correlated samples.

The detection of unusual patterns in health data presents an important challenge to health workers interested in early identification of epidemics or important risk factors. A useful procedure for detection of aberrations is the ratio of a current report to some historic baseline. This work addresses the problem of finding the variance of such a ratio when the surveillance reports are correlated. Results show that, when estimating this variance or the variance of the sample mean from a series of observations with an estimated correlation structure, bootstrap and jackknife estimates may be overly optimistic. The delta method or a classical method may be more useful when such model dependence is inappropriate.

Development of microfusion techniques to generate human hybridomas.

The rarity of antigen-specific B cells in peripheral blood and lymphoid tissues is a major limitation in the production of human monoclonal antibodies. This has led to a requirement for techniques capable of fusing small numbers of cells and achieving a higher hybridoma formation efficiency than currently is possible. The approach used in these studies to generate human hybridomas is based on the observation that under hypo-osmolar conditions electric field induced cell fusion or electrofusion is facilitated. Electrofusion parameters have been defined in strongly hypo-osmolar solutions which have resulted in a hybridoma formation efficiency greater than 5 X 10(-3) under optimal conditions. Furthermore, this has been accomplished with total input B cells of 1-2 X 10(5). This is a ten-fold reduction in the required number of input B cells and is associated with a hybridoma formation efficiency at least equal to that achieved with a higher input B cell number. An important factor in the development of this microfusion technique appears to be the duration of exposure to the hypo-osmolar solution by B cells to be immortalized. Other parameters which may affect hybridoma yield include the electrical field strength used for cell alignment and membrane breakdown, ratio of human B cells to fusion partner, washing procedure, post-fusion incubation time, and the elimination of toxic molecules.