Hasil untuk "Statistics"

J. Haltiwanger, Ron S. Jarmin, Javier Miranda

J. B. Tenenbaum, Charles Kemp, Thomas L. Griffiths et al.

H. Uno, T. Cai, M. Pencina et al.

For modern evidence‐based medicine, a well thought‐out risk scoring system for predicting the occurrence of a clinical event plays an important role in selecting prevention and treatment strategies. Such an index system is often established based on the subject's ‘baseline’ genetic or clinical markers via a working parametric or semi‐parametric model. To evaluate the adequacy of such a system, C‐statistics are routinely used in the medical literature to quantify the capacity of the estimated risk score in discriminating among subjects with different event times. The C‐statistic provides a global assessment of a fitted survival model for the continuous event time rather than focussing on the prediction of bit‐year survival for a fixed time. When the event time is possibly censored, however, the population parameters corresponding to the commonly used C‐statistics may depend on the study‐specific censoring distribution. In this article, we present a simple C‐statistic without this shortcoming. The new procedure consistently estimates a conventional concordance measure which is free of censoring. We provide a large sample approximation to the distribution of this estimator for making inferences about the concordance measure. Results from numerical studies suggest that the new procedure performs well in finite sample. Copyright © 2011 John Wiley & Sons, Ltd.

S. Allender, P. Scarborough, Vito Peto et al.

A. Vaart

W. Hays

L. Kupper

Rainer Martin

S. Strebelle

H. Clark

P. Hamill, T. Drizd, C. Johnson et al.

J. Simonoff

Walter L. Smith

E. Connor, E. McCoy

A. Kemp, A. Stuart, J. Ord

K. Stowman

Jessika Weiss

Kristian Kirsch

Valentin Amrhein, D. Trafimow, S. Greenland

Abstract Statistical inference often fails to replicate. One reason is that many results may be selected for drawing inference because some threshold of a statistic like the P-value was crossed, leading to biased reported effect sizes. Nonetheless, considerable non-replication is to be expected even without selective reporting, and generalizations from single studies are rarely if ever warranted. Honestly reported results must vary from replication to replication because of varying assumption violations and random variation; excessive agreement itself would suggest deeper problems, such as failure to publish results in conflict with group expectations or desires. A general perception of a “replication crisis” may thus reflect failure to recognize that statistical tests not only test hypotheses, but countless assumptions and the entire environment in which research takes place. Because of all the uncertain and unknown assumptions that underpin statistical inferences, we should treat inferential statistics as highly unstable local descriptions of relations between assumptions and data, rather than as providing generalizable inferences about hypotheses or models. And that means we should treat statistical results as being much more incomplete and uncertain than is currently the norm. Acknowledging this uncertainty could help reduce the allure of selective reporting: Since a small P-value could be large in a replication study, and a large P-value could be small, there is simply no need to selectively report studies based on statistical results. Rather than focusing our study reports on uncertain conclusions, we should thus focus on describing accurately how the study was conducted, what problems occurred, what data were obtained, what analysis methods were used and why, and what output those methods produced.

A. Vaart, M. Jonker, F. Bijma et al.