| Item Type | Journal Article |
|---|---|
| Author | Markus Neuhäuser |
| Author | Graeme D. Ruxton |
| Abstract | ABSTRACT Pearson's asymptotic χ 2 test is often used to compare binary data between two groups. However, when the sample sizes or expected frequencies are small, the test is usually replaced by Fisher's exact test. Several alternative rules of thumb exist for defining “small” in this context. Replacing one test with another based on the obtained data is unusual in statistical practice. Moreover, this commonly‐used switch is unnecessary because Pearson's χ 2 test can easily be carried out as an exact test for any sample sizes. Therefore, we recommend routinely using an exact test regardless of the obtained data. This change of approach allows prespecifying a particular test and a much less ambiguous and more reliable analysis. |
| Date | 05/2025 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://onlinelibrary.wiley.com/doi/10.1002/pst.70012 |
| Accessed | 3/30/2025, 8:06:57 AM |
| Volume | 24 |
| Pages | e70012 |
| Publication | Pharmaceutical Statistics |
| DOI | 10.1002/pst.70012 |
| Issue | 3 |
| Journal Abbr | Pharmaceutical Statistics |
| ISSN | 1539-1604, 1539-1612 |
| Date Added | 3/30/2025, 8:06:57 AM |
| Modified | 3/30/2025, 8:07:24 AM |
Getting an exact test based on Pearson chi-square
| Item Type | Journal Article |
|---|---|
| Author | Bryan E. Shepherd |
| Author | Chun Li |
| Author | Qi Liu |
| Abstract | Abstract We describe a new residual for general regression models defined as , where y is the observed outcome and is a random variable from the fitted distribution. This probability‐scale residual (PSR) can be written as , whereas the popular observed‐minus‐expected residual can be thought of as . Therefore the PSR is useful in settings where differences are not meaningful or where the expectation of the fitted distribution cannot be calculated. We present several desirable properties of the PSR that make it useful for diagnostics and measuring residual correlation, especially across different outcome types. We demonstrate its utility for continuous, ordered discrete, and censored outcomes, including current status data, and with various models including Cox regression, quantile regression, and ordinal cumulative probability models, for which fully specified distributions are not desirable or needed, and in some cases suitable residuals are not available. The residual is illustrated with simulated data and real data sets from HIV‐infected patients on therapy in the southeastern United States and Latin America. The Canadian Journal of Statistics 44: 463–479; 2016 © 2016 Statistical Society of Canada , Résumé Les auteurs décrivent une nouvelle forme de résidus pour un modèle général de régression définis par , où y est la valeur observée et est une variable aléatoire suivant la distribution prescrite par le modèle ajusté. Lié à une échelle de probabilités, ce résidu peut s’écrire alors que la définition populaire correspond plutôt à . Le résidu proposé est donc utile si la différence entre la valeur observée et espérée de la définition populaire n'a pas de sens interprétable, ou lorsque la valeur espérée selon le modèle n'est pas calculable. Les auteurs présentent de nombreuses propriétés désirables de leurs résidus, rendant cette approche utile pour le diagnostic de modèles et le calcul de corrélations dans les résidus, surtout en présence d'observations de types différents. Ils illustrent son usage pour des données continues, ordonnées discrètes et censurées, y compris des données de statut actuel. Ils considèrent différents modèles dont la régression de Cox, la régression quantile et les modèles ordinaux de probabilités cumulatives. Les distributions implicites de ces modèles n'ont pas besoin d’être complètement définies et, dans certains cas, les résidus habituels sont simplement indisponibles. Ils illustrent leur nouvelle définition des résidus par des simulations et avec un jeu de données réelles portant sur des patients du VIH suivant une thérapie dans le sud‐est des États‐Unis ou en Amérique latine. La revue canadienne de statistique 44: 463–479; 2016 © 2016 Société statistique du Canada |
| Date | 12/2016 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://onlinelibrary.wiley.com/doi/10.1002/cjs.11302 |
| Accessed | 3/13/2025, 7:17:59 AM |
| Rights | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| Volume | 44 |
| Pages | 463-479 |
| Publication | Canadian Journal of Statistics |
| DOI | 10.1002/cjs.11302 |
| Issue | 4 |
| Journal Abbr | Can J Statistics |
| ISSN | 0319-5724, 1708-945X |
| Date Added | 3/13/2025, 7:17:59 AM |
| Modified | 3/13/2025, 7:18:31 AM |
| Item Type | Journal Article |
|---|---|
| Author | Rushani Wijesuriya |
| Author | Margarita Moreno‐Betancur |
| Author | John B. Carlin |
| Author | Ian R. White |
| Author | Matteo Quartagno |
| Author | Katherine J. Lee |
| Abstract | ABSTRACT Longitudinal studies are frequently used in medical research and involve collecting repeated measures on individuals over time. Observations from the same individual are invariably correlated and thus an analytic approach that accounts for this clustering by individual is required. While almost all research suffers from missing data, this can be particularly problematic in longitudinal studies as participation often becomes harder to maintain over time. Multiple imputation (MI) is widely used to handle missing data in such studies. When using MI, it is important that the imputation model is compatible with the proposed analysis model. In a longitudinal analysis, this implies that the clustering considered in the analysis model should be reflected in the imputation process. Several MI approaches have been proposed to impute incomplete longitudinal data, such as treating repeated measurements of the same variable as distinct variables or using generalized linear mixed imputation models. However, the uptake of these methods has been limited, as they require additional data manipulation and use of advanced imputation procedures. In this tutorial, we review the available MI approaches that can be used for handling incomplete longitudinal data, including where individuals are clustered within higher‐level clusters. We illustrate implementation with replicable R and Stata code using a case study from the Childhood to Adolescence Transition Study. |
| Date | 2025-02-10 |
| Language | en |
| Short Title | Multiple Imputation for Longitudinal Data |
| Library Catalog | DOI.org (Crossref) |
| URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.10274 |
| Accessed | 1/30/2025, 8:43:51 AM |
| Volume | 44 |
| Pages | e10274 |
| Publication | Statistics in Medicine |
| DOI | 10.1002/sim.10274 |
| Issue | 3-4 |
| Journal Abbr | Statistics in Medicine |
| ISSN | 0277-6715, 1097-0258 |
| Date Added | 1/30/2025, 8:43:51 AM |
| Modified | 1/30/2025, 8:44:26 AM |
| Item Type | Journal Article |
|---|---|
| Author | Liangcai Zhang |
| Author | George Capuano |
| Author | Vladimir Dragalin |
| Author | John Jezorwski |
| Author | Kim Hung Lo |
| Author | Fei Chen |
| Date | 2025-04-20 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://www.tandfonline.com/doi/full/10.1080/10543406.2025.2489280 |
| Accessed | 4/24/2025, 5:57:16 PM |
| Pages | 1-15 |
| Publication | Journal of Biopharmaceutical Statistics |
| DOI | 10.1080/10543406.2025.2489280 |
| Journal Abbr | Journal of Biopharmaceutical Statistics |
| ISSN | 1054-3406, 1520-5711 |
| Date Added | 4/24/2025, 5:57:16 PM |
| Modified | 4/24/2025, 5:57:44 PM |
| Item Type | Journal Article |
|---|---|
| Author | Mark Rubin |
| Date | 11/2024 |
| Language | en |
| Short Title | Inconsistent multiple testing corrections |
| Library Catalog | DOI.org (Crossref) |
| URL | https://linkinghub.elsevier.com/retrieve/pii/S2590260124000067 |
| Accessed | 1/13/2025, 8:55:45 AM |
| Volume | 10 |
| Pages | 100140 |
| Publication | Methods in Psychology |
| DOI | 10.1016/j.metip.2024.100140 |
| Journal Abbr | Methods in Psychology |
| ISSN | 25902601 |
| Date Added | 1/13/2025, 8:55:45 AM |
| Modified | 1/13/2025, 8:57:47 AM |
| Item Type | Book |
|---|---|
| Author | Ewout W. Steyerberg |
| Date | 2019 |
| Place | New York |
| Publisher | Springer |
| ISBN | 3-030-16398-9 |
| Edition | 2nd |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 5/3/2025, 4:30:51 PM |
| Item Type | Journal Article |
|---|---|
| Author | Pedro Miranda Afonso |
| Author | Dimitris Rizopoulos |
| Author | Anushka K. Palipana |
| Author | Emrah Gecili |
| Author | Cole Brokamp |
| Author | John P. Clancy |
| Author | Rhonda D. Szczesniak |
| Author | Eleni‐Rosalina Andrinopoulou |
| Abstract | ABSTRACT Joint models for longitudinal and survival data have become a popular framework for studying the association between repeatedly measured biomarkers and clinical events. Nevertheless, addressing complex survival data structures, especially handling both recurrent and competing event times within a single model, remains a challenge. This causes important information to be disregarded. Moreover, existing frameworks rely on a Gaussian distribution for continuous markers, which may be unsuitable for bounded biomarkers, resulting in biased estimates of associations. To address these limitations, we propose a Bayesian shared‐parameter joint model that simultaneously accommodates multiple (possibly bounded) longitudinal markers, a recurrent event process, and competing risks. We use the beta distribution to model responses bounded within any interval without sacrificing the interpretability of the association. The model offers various forms of association, discontinuous risk intervals, and both gap and calendar timescales. A simulation study shows that it outperforms simpler joint models. We utilize the US Cystic Fibrosis Foundation Patient Registry to study the associations between changes in lung function and body mass index, and the risk of recurrent pulmonary exacerbations, while accounting for the competing risks of death and lung transplantation. Our efficient implementation allows fast fitting of the model despite its complexity and the large sample size from this patient registry. Our comprehensive approach provides new insights into cystic fibrosis disease progression by quantifying the relationship between the most important clinical markers and events more precisely than has been possible before. The model implementation is available in the R package JMbayes2 . |
| Date | 04/2025 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.70057 |
| Accessed | 4/29/2025, 7:49:11 AM |
| Volume | 44 |
| Pages | e70057 |
| Publication | Statistics in Medicine |
| DOI | 10.1002/sim.70057 |
| Issue | 8-9 |
| Journal Abbr | Statistics in Medicine |
| ISSN | 0277-6715, 1097-0258 |
| Date Added | 4/29/2025, 7:49:11 AM |
| Modified | 4/29/2025, 7:50:05 AM |
| Item Type | Journal Article |
|---|---|
| Author | Florian Klinglmüller |
| Author | Tobias Fellinger |
| Author | Franz König |
| Author | Tim Friede |
| Author | Andrew C. Hooker |
| Author | Harald Heinzl |
| Author | Martina Mittlböck |
| Author | Jonas Brugger |
| Author | Maximilian Bardo |
| Author | Cynthia Huber |
| Author | Norbert Benda |
| Author | Martin Posch |
| Author | Robin Ristl |
| Abstract | ABSTRACT While well‐established methods for time‐to‐event data are available when the proportional hazards assumption holds, there is no consensus on the best inferential approach under non‐proportional hazards (NPH). However, a wide range of parametric and non‐parametric methods for testing and estimation in this scenario have been proposed. To provide recommendations on the statistical analysis of clinical trials where non‐proportional hazards are expected, we conducted a simulation study under different scenarios of non‐proportional hazards, including delayed onset of treatment effect, crossing hazard curves, subgroups with different treatment effects, and changing hazards after disease progression. We assessed type I error rate control, power, and confidence interval coverage, where applicable, for a wide range of methods, including weighted log‐rank tests, the MaxCombo test, summary measures such as the restricted mean survival time (RMST), average hazard ratios, and milestone survival probabilities, as well as accelerated failure time regression models. We found a trade‐off between interpretability and power when choosing an analysis strategy under NPH scenarios. While analysis methods based on weighted logrank tests typically were favorable in terms of power, they do not provide an easily interpretable treatment effect estimate. Also, depending on the weight function, they test a narrow null hypothesis of equal hazard functions, and rejection of this null hypothesis may not allow for a direct conclusion of treatment benefit in terms of the survival function. In contrast, non‐parametric procedures based on well‐interpretable measures like the RMST difference had lower power in most scenarios. Model‐based methods based on specific survival distributions had larger power; however, often gave biased estimates and lower than nominal confidence interval coverage. The application of the studied methods is illustrated in a case study with reconstructed data from a phase III oncologic trial. |
| Date | 2025-02-28 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.70019 |
| Accessed | 3/6/2025, 2:09:37 PM |
| Volume | 44 |
| Pages | e70019 |
| Publication | Statistics in Medicine |
| DOI | 10.1002/sim.70019 |
| Issue | 5 |
| Journal Abbr | Statistics in Medicine |
| ISSN | 0277-6715, 1097-0258 |
| Date Added | 3/6/2025, 2:09:37 PM |
| Modified | 3/6/2025, 2:10:59 PM |
DIfferences in nonparametric RMST had low power, parametric estimates had greater power but need assumptions to hold