Seminars
To receive Biostatistics seminar email announcements, send a blank email to BIOSTATEVENTS-subscribe-request@ITSSRV1.UCSF.EDU.
Seminars marked "Research Program" are informal discussions of research by UCSF faculty. For more information, contact John Neuhaus.
The CAPS Method Core Quantitative Methods Working Group also presents seminars of interest to statisticians.
May 20, 2008 RESEARCH PROGRAM
Ying Lu
Department of Radiology, UCSF
"Who Should Take Which Test" — A Recursive Tree Algorithm for Choosing the Optimum Diagnostic Strategy
Several diagnostic tests are commonly available to clinicians. For example, both dual X-ray absorptieometry (DXA) and quantitative ultrasound (QUS) can be used to diagnose osteoporosis so patients can receive treatments to prevent hip fracture. In general, these diagnostic tests vary in cost and their diagnostic accuracy in predicting adverse outcome (such as hip fracture) depend on subject characteristics. In this talk, we present a new recursive partitioning tree-structured algorithm to determine "who should take which test" according to freely available easily collected risk factors. Our algorithm compares the choices 4 possible actions from two diagnostic tests (test 1 and 2): (A0) no need of diagnostic test and treatment (sufficient low risk of disease); (A1) applying diagnostic test 1 and treating only those with positive testing results; (A2) applying diagnostic test 2 and treating only those with positive testing results; and (A3) treating all subjects without any diagnostic tests. The algorithm assigns subjects into one of the four action groups according to answers of sequential binary questions with regards to their observed risk factors. For continuous or ordered variables, the question is whether a subject has value above or below a threshold. For categorical variables, the question is whether a subject belongs to one category. The risk factors utilized in the tree, the corresponding thresholds, and the order of questions are determined by the data. The splitting criterion is to maximize the gain in cost-effective difference (CED), which is the difference between quality-adjusted-life-year (QALY) gain (in $) and incremental diagnostic and treatment cost, in comparison to the strategy of the parent node. The pruning is based on cross-validation method. Cost parameters and discount of QALY can be obtained from published literature and are assumed to be independent of risk factors. The joint distribution of time to adverse outcome and the subsequent time to death with and without treatment depends on risk factors and should be estimated by different partitioning choices. We provide non-parametric estimation procedures to determine these distributions and CED. The proposed method is applied to determine who should receive DXA versus QUS tests and be treated by Alendronate to prevent hip fractures based on age, height reduction from youth, weight, body mass index, walking speed, etc. Sensitivity analysis also shows the ranges of cost parameters, treatment efficacy, and acceptable cost in dollars of one QALY-gain, within which the resulting decision tree remains appropriate. This method can apply to large-scale cohort studies or treatment clinical trials with multiple diagnostic tests as ancillary components to determine the optimum cost-effective combination of diagnostic tests and treatments.
Joint work with Caixia Li.
June 3, 2008 RESEARCH PROGRAM
Joan Hilton
Division of Biostatistics, UCSF
Noninferiority trial designs for binomial rate differences and odds ratios
For noninferiority trials in which binomial response rates differ under H_0 and are equal under H_A, we show that the marginal response rate and the noninferiority margin are convenient design parameters. We also show that the minimum overall sample size, N, and optimal allocation ratio associated with fixed type-1 and type-2 error rates depend on how the margin is parameterized. Since investigators commonly use the difference between experimental and control response rates (delta) for design and the odds ratio (psi) for analysis, we examine the effects on sample size and power of switching parameterizations of the margin. We also model the sample-size ratio, N_delta/N_psi, as a function of a wide range of design parameters; the regression estimates from this model can be used at the design stage to identify pairs for which the margin’s parameterization should not be interchanged between design and analysis. Finally, we discuss ways to quantify the unknown marginal response rate.