China
Africa
Explore chapters and articles related to this topic
Inference—Statistical Tests
Published in Prabhanjan Narayanachar Tattar, H. J. Vaman, Survival Analysis, 2022
Prabhanjan Narayanachar Tattar, H. J. Vaman
The choice of weight function can be differently specified for a rich class of test statistics. Recall that we truly have . As with the one-sample case, we obtain the logrank test for whereby . Why is the test called as the logrank test? As explained in ABGK, Peto and Peto (1972)[90] argue that in the uncensored case, the logrank test becomes the savage test where the scores are approximately linearly related to the logarithm of the ranks of observations. The Gehan's test is obtained for , and this times is also called as Gehan-Breslow test. The Tarone-Ware test is the result of choosing . The Harrington-Fleming class of tests is the extension , for . We will begin the discussion with the logrank test for the PBC dataset and then extend the other tests will be illustrated in the next example using the logrank_test function from the coin package.
Survival Analysis
Published in Trevor F. Cox, Medical Statistics for Cancer Studies, 2022
In the lower plot, the survival curves cross at about three months. The data for Arm 1 have been simulated from an exponential distribution with and for Arm 2, from a Weibull distribution with and . Patients in Arm 2 do better than patients in Arm 1 early on from the time of randomisation, but then patients in Arm 1 do better than patients in Arm 2 later. The median survival times are fairly close at 2.39 and 2.85 months. Clearly the survival curves are different, but we cannot reject the null hypothesis of no difference in the survival curves, based on the log-rank test, as with . So, we must be careful when using the log-rank test. What is happening when these survival curves cross, is that the 's will all tend to be positive for the first part of the survival curves and then all tend to be negative for the second part of the survival curves, thus cancelling each other out and producing a low value of LR. It can be shown that the log-rank test is optimal when the proportional hazards assumption holds, i.e. , is when it is most powerful.
Classical Survival Analysis
Published in Catherine Legrand, Advanced Survival Models, 2021
The most popular test to compare the survival curves between two groups is certainly the logrank test. The idea behind the logrank test is to compare, at each event time, the observed number of events with the expected number of events under the null hypothesis. The information available at each event time is often represented in a contingency table as in Table 2.4.
The prognostic role of lymphocyte to monocyte ratio (LMR) in patients with Myelodysplastic Neoplasms
Published in Hematology, 2023
Chuanyang Lu, Qiuni Chen, Jiaxin Li, Chunling Wang, Liang Yu
The cut-off of LMR was determined by X-Tile (version 3.6.1, Yale University, New Haven, CT, United States) [18]. Statistical Package for social sciences (SPSS, version 23, IBM SPSS Statistics 23 software, IBM Corp., Armonk, NY, USA) was used to perform statistical analysis. Mann–Whitney U-test and chi-square test were utilized to evaluate the difference between the two groups and p-value < 0.05 (2-tailed) demonstrated a statistical significance. Kaplan-Meier method has been applied to assess the correlation between LMR and OS. Furthermore, the corresponding p-value was achieved through the log-rank test. Survival curves were graphed by GraphPad Prism (version 8.0.1, GraphPad Software, San Diego, California, USA). Univariate and multivariate Cox regression analyses were conducted to investigate the prognostic factors affecting OS. In univariate analysis, p-value < 0.05 was considered statistically significant, and the corresponding prognostic factors were included in multivariate Cox regression. P-value < 0.05 were considered statistically significant in multivariate analysis.
Prescription patterns and therapeutic effects of second-line drugs in Japanese patients with type 2 diabetes mellitus: Analysis of claims data for metformin and dipeptidyl peptidase-4 inhibitors as the first-line hypoglycemic agents
Published in Expert Opinion on Pharmacotherapy, 2023
Rimei Nishimura, Tomomi Takeshima, Kosuke Iwasaki, Sumiko Aoi
To investigate the treatment effect, Kaplan-Meier curves of the events were computed for each endpoint after adjusting for confounding factors of the first-line treatment, with metformin or DPP4i, using propensity scores. The propensity scores were calculated using a logistic regression model with first-line metformin prescription as the explained variable, and age, sex, Charlson Comorbidity Index [20,21], hypertension (defined by the prescription of antihypertensive agents coded as C03, C07, C08, or C09 by ATC), dyslipidemia (defined by the prescription of statins or other antihyperlipidemic agents coded as C10 by ATC), and prescription of antithrombotic drugs (such as aspirin, novel oral anticoagulants defined by generic name, or other antithrombotic agents coded as B01 by ATC) at baseline as the explanatory variables. Based on the propensity score, the patients were divided into five quintiles, and the weights were adjusted for each quintile [22]. Statistical significance was defined as p < 0.05 using a log-rank test. The statistical analyses were performed using Microsoft Excel 2010 (Microsoft, Redmond, WA, U.S.A) and SAS version 9.4 (SAS Institute, Cary, NC, U.S.A).
Biochemical recurrence after radical prostatectomy – a large, comprehensive, population-based study with long follow-up
Published in Scandinavian Journal of Urology, 2022
Elin Axén, Johan Stranne, Marianne Månsson, Erik Holmberg, Rebecka Arnsrud Godtman
Kaplan-Meier estimates of failure-free survival after BCR were stratified on time to BCR in four categories; 0-2 years, 2-5 years, 5-10 years and >10 years. Cut-offs were chosen prior to analysis, based on intervals considered as clinically relevant. A supporting competing risk analysis was performed in order to examine if the effect of other deaths on the estimates were of clinical importance [15,16]. Differences between groups were compared with log-rank test, where the level of statistical significance was set at 5%. No adjustment for multiple comparisons was performed. Subgroup analysis on patients having BCR from year 2005 when data on hormonal treatment was first available was made as a sensitivity analysis. An additional analysis of BCR-free survival and failure-free survival was made, stratified on clinical risk groups: ‘Low-risk’ (PSA ≤ 10, Gleason score ≤6, and clinical stage T1), ‘Intermediate risk’ (PSA < 20, Gleason score ≤7, and clinical stage ≤ T2, not qualifying for low risk), or ‘High-risk’ (any of PSA >20, Gleason score ≥8, or clinical stage ≥ T3). All statistical analyses were made in R [17].