In the 1950s and ’60s, a doctoral student in biostatistics could be reasonably expected to acquire a fairly sophisticated knowledge of the whole field before completing the doctoral dissertation. This knowledge would include the fundamentals of probability and mathematical theory of statistical inference, as well as biostatistics proper—that is, the theory and application of statistics to the life and health sciences. Today, biostatistics has grown to the point that no doctoral student in it can become an expert in all of it. Thus, our ambitious title notwithstanding, we can, in this limited space, only aspire to cover part of the current research in biostatistics.
As in many scientific fields, widespread application of new biostatistical methods lags behind their publication by a period ranging from a few years to 2 or 3 decades. For example, the Cox proportional hazards model was proposed in 1972 but became a common practice for survival analysis only in the mid-1980s. As another example, the current popularity of mixed-effects regression models and longitudinal data analysis not only lagged behind their theoretical development by some decades, it also had to await the availability of practical computer software, such as SAS PROC MIXED, first made generally available in the 1990s. To identify, in general terms, where the field may be going, we surveyed recent issues of several leading statistical journals and attempted to distill their contents into main categories. These summaries, we hope, will point the way to some topics that may become common practice in the near future, although we recognize that some others may fall by the wayside.
We sought to summarize influential recent work in journals with high impact factors and that have an orientation overlapping substantially with the disciplines of statistics and biostatistics. In selecting journals for our search, we relied mainly on the journals’ impact factors. We selected the top 10 journals in the field of statistics and probability ( Table 1 ) with 2 exceptions: Econometrica was excluded, since it is a specialized journal in an area far from biostatistics; and although Biometrika is ranked 26th among statistics and probability journals, we included it because of its historical importance and because it is the next-highest ranked statistical journal in the field of biostatistics. We systematically scanned all the articles in the most recent issues of these 10 journals. In a few instances, we excluded special sections that were not related to biostatistics. Although the number of articles per journal varied greatly, we feel comfortable that the 583 articles we reviewed represent a good cross section of the most recent statistical publications in the field of biostatistics.
| Journal Title | Impact Factor | Journal Issues Reviewed | Number of Articles |
|---|---|---|---|
| Biostatistics | 3.394 | October 2008 – April 2009 | 45 |
| Journal of the Royal Statistical Society, Series B: Statistical Methodology | 2.835 | November 2008 – September 2009 | 47 |
| Annals of Applied Statistics | 2.448 | September 2008 – June 2009 | 55 |
| Journal of the American Statistical Association | 2.394 | September 2008 – March 2009 | 92 |
| Annals of Statistics | 2.307 | February 2009 – June 2009 | 52 |
| Statistical Methods in Medical Research | 2.177 | October 2008 – August 2009 | 33 |
| Statistical Science | 2.135 | November 2007 – September 2008 | 17 |
| Statistics in Medicine | 2.111 | January 2009 – May 2009 | 89 |
| Biometrics | 1.970 | September 2008 – March 2009 | 100 |
| Biometrika | 1.405 | September 2008 – March 2009 | 53 |
| Total | 583 |
Classifying articles into categories presented some challenges. For example, an article may use Bayesian ideas to develop a new methodology for generalized linear models (GLM), a broad area that encompasses as special cases such varied topics as analysis of variance and multiple linear, logistic, and Poisson regression models. Should we classify such an article under regression analysis or under Bayesian analysis? In most such cases, we opted for the former since that is where the emphasis usually was. This example illustrates the many decisions we made regarding the overlap of subjects. Such decisions have inevitably affected the relative frequencies of the categories. Table 2 presents the 10 categories we settled on, along with their relative frequencies. They are listed in descending order of frequency, except for the category “other.” Each category includes articles where the main emphasis is on that topic, and several also encompass some subcategories. Several topics were covered in previous editorials in this series. In those cases we simply list the categories below. For categories not covered previously, we present a very brief description.
