Uno H, Cai T, Tian L, Wei LJ. Evaluating prediction
rules for t-year survivors with censored regression models. Journal of
the American Statistical Association 2007;102(478):527-537.
Uno H, Cai T, Pencina MJ, D’Agostino RB, and Wei
LJ. On the C-Statistics for Evaluating Overall Adequacy of Risk
Prediction Procedures with Censored Survival Data. Statistics in
Medicine. 2011;30(10):1105-116.
Uno H, Cai T, Tian L, Wei LJ. Graphical Procedures
for Evaluating Overall and Subject-Specific Incremental Values from New
Predictors with Censored Event Time Data. Biometrics.
2011;67(4):1389-96.
Uno H, Tian L, Cai T, Kohane IS, Wei LJ. A unified
inference procedure for a class of measures to assess improvement in
risk prediction systems with survival data. Statistics in Medicine 2013;
32(14):2430-2442.
Note: Uno et al. (2011, StatMed) discusses a statistical inference
procedure for concordance probability between the risk estimate and the
outcome, which has been widely used as a performance measure of risk
prediction models. There are several methods to handle censored survival
data in estimating the concordance, but all have the significant
limitation that those estimates depend on the underlying study-specific
censoring distribution in the presence of general random censoring.
Given that censoring time distribution is generally study-specific,
methods without this limitation would represent a more widely applicable
approach. This method has been employed in SAS/PHREG procedure
(CONCORDANCE=UNO option) and has been called “Uno’s C” in some
statistical literature. This paper was ranked in one of the 10 most
cited Statistics in Medicine papers in Year ’12-’13.
Clinical Prediction Models
Below is the list of clinical prediction models that I have
developed.
Kastrinos F, Uno H, Ukaegbu C, et
al. Development and Validation of the PREMM5 Model for Comprehensive
Risk Assessment of Lynch Syndrome. Journal of Clinical Oncology. 2017;
35(19):2165-72. PMC5493047
Uno H, Cronin MA, Wadleigh M, et al. Derivation
and validation of the SEER-Medicare myelodysplastic syndromes risk
score. Leukemia Research, 2014, 30;38(12):1420-24. PMC4314368
Hassett MJ, Uno H, Cronin AM, et al. Detecting
Lung and Colorectal Cancer Recurrence Using Structured
Clinical/Administrative Data to Enable Outcomes Research and Population
Health Management, Medical Care. 2017; 55(12):e88-e98.
PMC4732933
Uno H, Ritzwoller DP, Cronin AM, et
al. Determining the Time of Cancer Recurrence Using Claims or Electronic
Medical Record Data. JCO Clinical Cancer Informatics. 2018; (2):1-10.
PMC6338474
Yurgelun M, Uno H, Furniss C, Ukaegbu C,
Horiguchi M, Yussuf A, LaDuca H, Chittenden A, Garber J, Syngal S.
Development & validation of PREMMplus for hereditary cancer risk
assessment. Journal of Clinical Oncology. 2022;
40(35):4083-4094.
Alternatives to the conventional survival data analysis
Despite the issues of the hazard ratio (HR) estimate, the HR has been
the first choice for many decades. The following papers discuss issues
and concerns of the HR and importance of estimating clinically
interpretable metric using robust statistical inference procedures.
Uno H, Claggett B, Tian L, et al. Moving beyond the
hazard ratio in quantifying the between-group difference in survival
analysis. Journal of Clinical Oncology 2014, 32(22):2380-2385.
Uno H, Wittes J, Fu H, et al. Alternatives to
hazard ratios for comparing efficacy or safety of therapies in
noninferiority studies. Annals of Internal Medicine, 2015,
163(2):127-34.
Uno H, Claggett B, Tian L, et al. Adding a new
analytical procedure with clinical interpretation in the tool box of
survival analysis. Annals of Oncology 2018. PMID: 29617717
Horiguchi M, Hassett MJ, Uno H. How do the accrual
pattern and follow-up duration affect the hazard ratio estimate when the
proportional hazards assumption is violated? The Oncologist.
2018;23:1-5.
Uno H, Horiguchi M, Hassett MJ. Statistical
test/estimation methods used in contemporary phase III cancer randomized
controlled trials with time-to-event outcomes. The Oncologist
2020;25:91–93.
Uno H, Schrag D, Kim DH, Tang D, Tian L, Rugo HS,
Wei LJ. Assessing Clinical Equivalence in Oncology Biosimilar Trials
With Time-to-Event Outcomes. JNCI Cancer Spectr. 2019; 3(4):pkz058. doi:10.1093/jncics/pkz058.
Related to the above, we proposed novel statistical tests to compare
survival time data between two groups in the followingpapers. These
tests can capture various patterns of difference and can be good
alternatives to the logrank test or tests based on the hazard ratio.
Uno H, Tian L, Claggett B, We LJ. A versatile test
for equality of two survival functions based on weighted differences of
Kaplan-Meier curves, Statistics in Medicine, 2015; 34(28):3680-95. doi:10.1002/sim.6591.
Horiguchi M, Cronin A, Takeuchi M, Uno H. A
flexible and coherent test/estimation procedure based on restricted mean
survival times for censored time-to-event data in randomized clinical
trials. Statistics in Medicine 2018;37(15):2307-2320. doi:10.1002/sim.7661.
Horiguchi M, Uno H. On permutation tests for
comparing restricted mean survival time with small sample from
randomized trials. Statistics in Medicine. 2020; doi:10.1002/sim.8565.
Uno H, Horiguchi. Ratio and Difference of Average
Hazard with Survival Weight: New Measures to Quantify Survival Benefit
of New Therapy. Statistics in Medicine. 2023; doi:10.1002/sim.9651.
Horiguchi M, Tian L, Uno H. On assessing survival
benefit of immunotherapy using long-term restricted mean survival time.
Statistics in Medicine. 2023; doi:10.1002/sim.9662.
Collaborative clinical research
Cripe LD, Uno H, Paiettta EP, Litzow MR, Ketterling
RP, Bennett JM, Rowe JM, Lazarus HM, Luger S, Tallman MS. Zosquidar, a
novel modulator of P-gp, does not improve the outcome of older patients
with newly diagnosed acute myeloid leukemia: a randomized,
placebo-controlled, trial of the Eastern Cooperative Oncology Group
(ECOG 3999). Blood. 2010; 116: 4077-85.
Solomon SD, Uno H, Lewis E, Eckardt KW, Burdmann
EA, Cooper ME, de Zeeuw D, Ivanovich P, Levey AS, Parfrey P, Remuzzi G,
Singh AK, Toto R, Huang F, Rossert J, McMurray JV, Pfeffer MA for the
TREAT Investigators. Erythropoietic response and outcomes in kidney
disease and type 2 diabetes. The New England Journal of Medicine. 2010;
363 (12): 1146-55.
Skali H, Uno H, Levey AS, Stevens L, Pfeffer MA,
Solomon SD. Prognostic Assessment of Kidney Function by the new CKD
Epidemiology Collaboration equation vs. the MDRD study equation for
Estimated Glomerular Filtration Rate. American Heart Journal.
2011;162(3):548-54.
McMurray JV, Uno H, Jarolim P, Desai AS, de Zeeuw
D, Eckardt KU, Ivanovich P, Levey AS, Lewis EF, McGill JB, Parfrey P,
Parving HH, Toto RM, Solomon SD, Pfeffer MA. Predictors of fatal and
non-fatal cardiovascular events in patients with type 2 diabetes
mellitus, chronic kidney disease and anaemia: an analysis of the Trial
to Reduce cardiovascular Events with Aranesp Therapy (TREAT). American
Heart Journal. 2011;62(4):748-755.
Kumar S, Uno H, Jacobus S, Wier SV, Ahmann G,
Henderson K, Callander N, Haug J, Siegel D, Greipp P, Fonseca R,
Rajkumar V. Impact of gene expression profiling-based risk
stratification in patients with myeloma receiving initial therapy with
lenalidomide and dexamethasone. Blood. 2011;118(16):4359-62.