| Item Type | Journal Article |
|---|---|
| Author | James F. Troendle |
| Author | Eric S. Leifer |
| Author | Song Yang |
| Author | Neal Jeffries |
| Author | Dong‐Yun Kim |
| Author | Jungnam Joo |
| Author | Christopher M. O'Connor |
| Abstract | Consider the choice of outcome for overall treatment benefit in a clinical trial which measures the first time to each of several clinical events. We describe several new variants of the win ratio that incorporate the time spent in each clinical state over the common follow‐up, where clinical state means the worst clinical event that has occurred by that time. One version allows restriction so that death during follow‐up is most important, while time spent in other clinical states is still accounted for. Three other variants are described; one is based on the average pairwise win time, one creates a continuous outcome for each participant based on expected win times against a reference distribution and another that uses the estimated distributions of clinical state to compare the treatment arms. Finally, a combination testing approach is described to give robust power for detecting treatment benefit across a broad range of alternatives. These new methods are designed to be closer to the overall treatment benefit/harm from a patient's perspective, compared to the ordinary win ratio. The new methods are compared to the composite event approach and the ordinary win ratio. Simulations show that when overall treatment benefit on death is substantial, the variants based on either the participants' expected win times (EWTs) against a reference distribution or estimated clinical state distributions have substantially higher power than either the pairwise comparison or composite event methods. The methods are illustrated by re‐analysis of the trial heart failure: a controlled trial investigating outcomes of exercise training. |
| Date | 2024-02-28 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.10045 |
| Accessed | 3/3/2024, 4:05:52 PM |
| Pages | sim.10045 |
| Publication | Statistics in Medicine |
| DOI | 10.1002/sim.10045 |
| Journal Abbr | Statistics in Medicine |
| ISSN | 0277-6715, 1097-0258 |
| Date Added | 3/3/2024, 4:05:52 PM |
| Modified | 3/3/2024, 4:06:39 PM |
| Item Type | Journal Article |
|---|---|
| Author | Alice-Maria Toader |
| Author | Marion K. Campbell |
| Author | Jennifer K. Quint |
| Author | Michael Robling |
| Author | Matthew R Sydes |
| Author | Joanna Thorn |
| Author | Alexandra Wright-Hughes |
| Author | Ly-Mee Yu |
| Author | Tom. E. F. Abbott |
| Author | Simon Bond |
| Author | Fergus J. Caskey |
| Author | Madeleine Clout |
| Author | Michelle Collinson |
| Author | Bethan Copsey |
| Author | Gwyneth Davies |
| Author | Timothy Driscoll |
| Author | Carrol Gamble |
| Author | Xavier L. Griffin |
| Author | Thomas Hamborg |
| Author | Jessica Harris |
| Author | David A. Harrison |
| Author | Deena Harji |
| Author | Emily J. Henderson |
| Author | Pip Logan |
| Author | Sharon B. Love |
| Author | Laura A. Magee |
| Author | Alastair O’Brien |
| Author | Maria Pufulete |
| Author | Padmanabhan Ramnarayan |
| Author | Athanasios Saratzis |
| Author | Jo Smith |
| Author | Ivonne Solis-Trapala |
| Author | Clive Stubbs |
| Author | Amanda Farrin |
| Author | Paula Williamson |
| Abstract | Abstract Background Healthcare system data (HSD) are increasingly used in clinical trials, augmenting or replacing traditional methods of collecting outcome data. This study, PRIMORANT, set out to identify, in the UK context, issues to be considered before the decision to use HSD for outcome data in a clinical trial is finalised, a methodological question prioritised by the clinical trials community. Methods The PRIMORANT study had three phases. First, an initial workshop was held to scope the issues faced by trialists when considering whether to use HSDs for trial outcomes. Second, a consultation exercise was undertaken with clinical trials unit (CTU) staff, trialists, methodologists, clinicians, funding panels and data providers. Third, a final discussion workshop was held, at which the results of the consultation were fed back, case studies presented, and issues considered in small breakout groups. Results Key topics included in the consultation process were the validity of outcome data, timeliness of data capture, internal pilots, data-sharing, practical issues, and decision-making. A majority of consultation respondents ( n = 78, 95%) considered the development of guidance for trialists to be feasible. Guidance was developed following the discussion workshop, for the five broad areas of terminology, feasibility, internal pilots, onward data sharing, and data archiving. Conclusions We provide guidance to inform decisions about whether or not to use HSDs for outcomes, and if so, to assist trialists in working with registries and other HSD providers to improve the design and delivery of trials. |
| Date | 2024-01-29 |
| Language | en |
| Short Title | Using healthcare systems data for outcomes in clinical trials |
| Library Catalog | DOI.org (Crossref) |
| URL | https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-024-07926-z |
| Accessed | 2/5/2024, 4:27:41 PM |
| Volume | 25 |
| Pages | 94 |
| Publication | Trials |
| DOI | 10.1186/s13063-024-07926-z |
| Issue | 1 |
| Journal Abbr | Trials |
| ISSN | 1745-6215 |
| Date Added | 2/5/2024, 4:27:41 PM |
| Modified | 2/5/2024, 4:28:10 PM |
| Item Type | Journal Article |
|---|---|
| Author | Thomas R. Sullivan |
| Author | Tim P. Morris |
| Author | Brennan C. Kahan |
| Author | Alana R. Cuthbert |
| Author | Lisa N. Yelland |
| Abstract | To obtain valid inference following stratified randomisation, treatment effects should be estimated with adjustment for stratification variables. Stratification sometimes requires categorisation of a continuous prognostic variable (eg, age), which raises the question: should adjustment be based on randomisation categories or underlying continuous values? In practice, adjustment for randomisation categories is more common. We reviewed trials published in general medical journals and found none of the 32 trials that stratified randomisation based on a continuous variable adjusted for continuous values in the primary analysis. Using data simulation, this article evaluates the performance of different adjustment strategies for continuous and binary outcomes where the covariate‐outcome relationship (via the link function) was either linear or non‐linear. Given the utility of covariate adjustment for addressing missing data, we also considered settings with complete or missing outcome data. Analysis methods included linear or logistic regression with no adjustment for the stratification variable, adjustment for randomisation categories, or adjustment for continuous values assuming a linear covariate‐outcome relationship or allowing for non‐linearity using fractional polynomials or restricted cubic splines. Unadjusted analysis performed poorly throughout. Adjustment approaches that misspecified the underlying covariate‐outcome relationship were less powerful and, alarmingly, biased in settings where the stratification variable predicted missing outcome data. Adjustment for randomisation categories tends to involve the highest degree of misspecification, and so should be avoided in practice. To guard against misspecification, we recommend use of flexible approaches such as fractional polynomials and restricted cubic splines when adjusting for continuous stratification variables in randomised trials. |
| Date | 2024-03-15 |
| Language | en |
| Short Title | Categorisation of continuous covariates for stratified randomisation |
| Library Catalog | DOI.org (Crossref) |
| URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.10060 |
| Accessed | 3/19/2024, 9:55:39 AM |
| Pages | sim.10060 |
| Publication | Statistics in Medicine |
| DOI | 10.1002/sim.10060 |
| Journal Abbr | Statistics in Medicine |
| ISSN | 0277-6715, 1097-0258 |
| Date Added | 3/19/2024, 9:55:39 AM |
| Modified | 3/19/2024, 10:06:14 AM |
| Item Type | Journal Article |
|---|---|
| Author | Richard D Riley |
| Author | Lucinda Archer |
| Author | Kym I E Snell |
| Author | Joie Ensor |
| Author | Paula Dhiman |
| Author | Glen P Martin |
| Author | Laura J Bonnett |
| Author | Gary S Collins |
| Date | 2024-01-15 |
| Language | en |
| Short Title | Evaluation of clinical prediction models (part 2) |
| Library Catalog | DOI.org (Crossref) |
| URL | https://www.bmj.com/lookup/doi/10.1136/bmj-2023-074820 |
| Accessed | 1/20/2024, 9:39:54 AM |
| Pages | e074820 |
| Publication | BMJ |
| DOI | 10.1136/bmj-2023-074820 |
| Journal Abbr | BMJ |
| ISSN | 1756-1833 |
| Date Added | 1/20/2024, 9:39:54 AM |
| Modified | 1/20/2024, 9:41:01 AM |
| Item Type | Journal Article |
|---|---|
| Author | Richard D Riley |
| Author | Kym I E Snell |
| Author | Lucinda Archer |
| Author | Joie Ensor |
| Author | Thomas P A Debray |
| Author | Ben Van Calster |
| Author | Maarten Van Smeden |
| Author | Gary S Collins |
| Date | 2024-01-22 |
| Language | en |
| Short Title | Evaluation of clinical prediction models (part 3) |
| Library Catalog | DOI.org (Crossref) |
| URL | https://www.bmj.com/lookup/doi/10.1136/bmj-2023-074821 |
| Accessed | 1/23/2024, 3:11:30 PM |
| Pages | e074821 |
| Publication | BMJ |
| DOI | 10.1136/bmj-2023-074821 |
| Journal Abbr | BMJ |
| ISSN | 1756-1833 |
| Date Added | 1/23/2024, 3:11:30 PM |
| Modified | 1/23/2024, 3:12:01 PM |
| Item Type | Journal Article |
|---|---|
| Author | Kimihiro Noguchi |
| Author | Yulia R. Gel |
| Author | Edgar Brunner |
| Author | Frank Konietschke |
| Date | 2012 |
| Language | en |
| Short Title | <b>nparLD</b> |
| Library Catalog | DOI.org (Crossref) |
| URL | http://www.jstatsoft.org/v50/i12/ |
| Accessed | 1/23/2024, 3:02:15 PM |
| Volume | 50 |
| Publication | Journal of Statistical Software |
| DOI | 10.18637/jss.v050.i12 |
| Issue | 12 |
| Journal Abbr | J. Stat. Soft. |
| ISSN | 1548-7660 |
| Date Added | 1/23/2024, 3:02:15 PM |
| Modified | 1/23/2024, 3:02:55 PM |
Table 1 has formula for the concordance probability (probability index) for a proportional odds model when PO holds (continuous Y with a shift in location for a logistic distribution).
| Item Type | Journal Article |
|---|---|
| Author | Guiomar Mendieta |
| Author | Stuart Pocock |
| Author | Virginia Mass |
| Author | Andrea Moreno |
| Author | Ruth Owen |
| Author | Inés García-Lunar |
| Author | Beatriz López-Melgar |
| Author | Jose J. Fuster |
| Author | Vicente Andres |
| Author | Cristina Pérez-Herreras |
| Author | Hector Bueno |
| Author | Antonio Fernández-Ortiz |
| Author | Javier Sanchez-Gonzalez |
| Author | Ana García-Alvarez |
| Author | Borja Ibáñez |
| Author | Valentin Fuster |
| Date | 11/2023 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://linkinghub.elsevier.com/retrieve/pii/S0735109723076295 |
| Accessed | 2/13/2024, 12:42:14 PM |
| Volume | 82 |
| Pages | 2069-2083 |
| Publication | Journal of the American College of Cardiology |
| DOI | 10.1016/j.jacc.2023.09.814 |
| Issue | 22 |
| Journal Abbr | Journal of the American College of Cardiology |
| ISSN | 07351097 |
| Date Added | 2/13/2024, 12:42:14 PM |
| Modified | 2/13/2024, 12:42:33 PM |
| Item Type | Journal Article |
|---|---|
| Author | Haodong Li |
| Author | Sonali Rosete |
| Author | Jeremy Coyle |
| Author | Rachael V. Phillips |
| Author | Nima S. Hejazi |
| Author | Ivana Malenica |
| Author | Benjamin F. Arnold |
| Author | Jade Benjamin‐Chung |
| Author | Andrew Mertens |
| Author | John M. Colford |
| Author | Mark J. Van Der Laan |
| Author | Alan E. Hubbard |
| Abstract | Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targeted maximum likelihood estimation. There are even more recent augmentations of these procedures that can increase robustness, by adding a layer of cross‐validation (cross‐validated targeted maximum likelihood estimation and double machine learning, as applied to substitution and estimating equation approaches, respectively). While these methods have been evaluated individually on simulated and experimental data sets, a comprehensive analysis of their performance across real data based simulations have yet to be conducted. In this work, we benchmark multiple widely used methods for estimation of the average treatment effect using ten different nutrition intervention studies data. A nonparametric regression method, undersmoothed highly adaptive lasso, is used to generate the simulated distribution which preserves important features from the observed data and reproduces a set of true target parameters. For each simulated data, we apply the methods above to estimate the average treatment effects as well as their standard errors and resulting confidence intervals. Based on the analytic results, a general recommendation is put forth for use of the cross‐validated variants of both substitution and estimating equation estimators. We conclude that the additional layer of cross‐validation helps in avoiding unintentional over‐fitting of nuisance parameter functionals and leads to more robust inferences. |
| Date | 2022-05-30 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.9348 |
| Accessed | 2/5/2024, 4:37:20 PM |
| Volume | 41 |
| Pages | 2132-2165 |
| Publication | Statistics in Medicine |
| DOI | 10.1002/sim.9348 |
| Issue | 12 |
| Journal Abbr | Statistics in Medicine |
| ISSN | 0277-6715, 1097-0258 |
| Date Added | 2/5/2024, 4:37:20 PM |
| Modified | 2/5/2024, 4:37:50 PM |
Need to add an outer loop for targeted MLE to get correct standard errors in presence of overfitting
| Item Type | Journal Article |
|---|---|
| Author | Hannah M. La Roi-Teeuw |
| Author | Florien S. Van Royen |
| Author | Anne De Hond |
| Author | Anum Zahra |
| Author | Sjoerd De Vries |
| Author | Richard Bartels |
| Author | Alex J. Carriero |
| Author | Sander Van Doorn |
| Author | Zoë S. Dunias |
| Author | Ilse Kant |
| Author | Tuur Leeuwenberg |
| Author | Ruben Peters |
| Author | Laura Veerhoek |
| Author | Maarten Van Smeden |
| Author | Kim Luijken |
| Date | 5/2024 |
| Language | en |
| Short Title | Don’t be misled |
| Library Catalog | DOI.org (Crossref) |
| URL | https://linkinghub.elsevier.com/retrieve/pii/S0895435624001422 |
| Accessed | 5/9/2024, 7:50:26 AM |
| Pages | 111387 |
| Publication | Journal of Clinical Epidemiology |
| DOI | 10.1016/j.jclinepi.2024.111387 |
| Journal Abbr | Journal of Clinical Epidemiology |
| ISSN | 08954356 |
| Date Added | 5/9/2024, 7:50:26 AM |
| Modified | 5/9/2024, 7:50:50 AM |
| Item Type | Journal Article |
|---|---|
| Author | Brennan C Kahan |
| Author | Joanna Hindley |
| Author | Mark Edwards |
| Author | Suzie Cro |
| Author | Tim P Morris |
| Date | 2024-01-23 |
| Language | en |
| Short Title | The estimands framework |
| Library Catalog | DOI.org (Crossref) |
| URL | https://www.bmj.com/lookup/doi/10.1136/bmj-2023-076316 |
| Accessed | 1/28/2024, 10:10:53 AM |
| Pages | e076316 |
| Publication | BMJ |
| DOI | 10.1136/bmj-2023-076316 |
| Journal Abbr | BMJ |
| ISSN | 1756-1833 |
| Date Added | 1/28/2024, 10:10:53 AM |
| Modified | 1/28/2024, 10:11:42 AM |
| Item Type | Journal Article |
|---|---|
| Author | Anna Ivanova |
| Author | Geert Molenberghs |
| Author | Geert Verbeke |
| Date | 2016-07-03 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://www.tandfonline.com/doi/full/10.1080/10543406.2015.1052487 |
| Accessed | 4/18/2024, 2:25:57 PM |
| Volume | 26 |
| Pages | 601-618 |
| Publication | Journal of Biopharmaceutical Statistics |
| DOI | 10.1080/10543406.2015.1052487 |
| Issue | 4 |
| Journal Abbr | Journal of Biopharmaceutical Statistics |
| ISSN | 1054-3406, 1520-5711 |
| Date Added | 4/18/2024, 2:25:57 PM |
| Modified | 4/18/2024, 5:43:18 PM |
| Item Type | Journal Article |
|---|---|
| Author | Alexei C. Ionan |
| Author | Jennifer Clark |
| Author | James Travis |
| Author | Anup Amatya |
| Author | John Scott |
| Author | James P. Smith |
| Author | Somesh Chattopadhyay |
| Author | Mary Jo Salerno |
| Author | Mark Rothmann |
| Date | 05/2023 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://link.springer.com/10.1007/s43441-022-00483-0 |
| Accessed | 3/11/2024, 2:49:30 PM |
| Volume | 57 |
| Pages | 436-444 |
| Publication | Therapeutic Innovation & Regulatory Science |
| DOI | 10.1007/s43441-022-00483-0 |
| Issue | 3 |
| Journal Abbr | Ther Innov Regul Sci |
| ISSN | 2168-4790, 2168-4804 |
| Date Added | 3/11/2024, 2:49:30 PM |
| Modified | 3/11/2024, 2:49:43 PM |
| Item Type | Journal Article |
|---|---|
| Author | Gail Hayward |
| Author | Ly-Mee Yu |
| Author | Paul Little |
| Author | Oghenekome Gbinigie |
| Author | Milensu Shanyinde |
| Author | Victoria Harris |
| Author | Jienchi Dorward |
| Author | Benjamin R Saville |
| Author | Nicholas Berry |
| Author | Philip H Evans |
| Author | Nicholas Pb Thomas |
| Author | Mahendra G Patel |
| Author | Duncan Richards |
| Author | Oliver Van Hecke |
| Author | Michelle A Detry |
| Author | Christina Saunders |
| Author | Mark Fitzgerald |
| Author | Jared Robinson |
| Author | Charlotte Latimer-Bell |
| Author | Julie Allen |
| Author | Emma Ogburn |
| Author | Jenna Grabey |
| Author | Simon De Lusignan |
| Author | Fd Richard Hobbs |
| Author | Christopher C Butler |
| Date | 2/2024 |
| Language | en |
| Short Title | Ivermectin for COVID-19 in adults in the community (PRINCIPLE) |
| Library Catalog | DOI.org (Crossref) |
| URL | https://linkinghub.elsevier.com/retrieve/pii/S0163445324000641 |
| Accessed | 3/2/2024, 8:03:08 AM |
| Pages | 106130 |
| Publication | Journal of Infection |
| DOI | 10.1016/j.jinf.2024.106130 |
| Journal Abbr | Journal of Infection |
| ISSN | 01634453 |
| Date Added | 3/2/2024, 8:03:08 AM |
| Modified | 3/2/2024, 8:04:03 AM |
| Item Type | Journal Article |
|---|---|
| Author | John Gregson |
| Author | Gregg W. Stone |
| Author | Deepak L. Bhatt |
| Author | Milton Packer |
| Author | Stefan D. Anker |
| Author | Cordula Zeller |
| Author | Bjorn Redfors |
| Author | Stuart J. Pocock |
| Date | 10/2023 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://linkinghub.elsevier.com/retrieve/pii/S0735109723063829 |
| Accessed | 1/21/2024, 11:35:01 AM |
| Volume | 82 |
| Pages | 1445-1463 |
| Publication | Journal of the American College of Cardiology |
| DOI | 10.1016/j.jacc.2023.07.024 |
| Issue | 14 |
| Journal Abbr | Journal of the American College of Cardiology |
| ISSN | 07351097 |
| Date Added | 1/21/2024, 11:35:01 AM |
| Modified | 1/21/2024, 11:35:36 AM |
| Item Type | Journal Article |
|---|---|
| Author | Anders Granholm |
| Author | Theis Lange |
| Author | Michael O. Harhay |
| Author | Anders Perner |
| Author | Morten Hylander Møller |
| Author | Benjamin Skov Kaas‐Hansen |
| Abstract | Abstract It is unclear how sceptical priors impact adaptive trials. We assessed the influence of priors expressing a spectrum of scepticism on the performance of several Bayesian, multi‐stage, adaptive clinical trial designs using binary outcomes under different clinical scenarios. Simulations were conducted using fixed stopping rules and stopping rules calibrated to keep type 1 error rates at approximately 5%. We assessed total sample sizes, event rates, event counts, probabilities of conclusiveness and selecting the best arm, root mean squared errors (RMSEs) of the estimated treatment effect in the selected arms, and ideal design percentages (IDPs; which combines arm selection probabilities, power, and consequences of selecting inferior arms), with RMSEs and IDPs estimated in conclusive trials only and after selecting the control arm in inconclusive trials. Using fixed stopping rules, increasingly sceptical priors led to larger sample sizes, more events, higher IDPs in simulations ending in superiority, and lower RMSEs, lower probabilities of conclusiveness/selecting the best arm, and lower IDPs when selecting controls in inconclusive simulations. With calibrated stopping rules, the effects of increased scepticism on sample sizes and event counts were attenuated, and increased scepticism increased the probabilities of conclusiveness/selecting the best arm and IDPs when selecting controls in inconclusive simulations without substantially increasing sample sizes. Results from trial designs with gentle adaptation and non‐informative priors resembled those from designs with more aggressive adaptation using weakly‐to‐moderately sceptical priors. In conclusion, the use of somewhat sceptical priors in adaptive trial designs with binary outcomes seems reasonable when considering multiple performance metrics simultaneously. |
| Date | 2024-03-29 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://onlinelibrary.wiley.com/doi/10.1002/pst.2387 |
| Accessed | 4/2/2024, 7:37:04 AM |
| Pages | pst.2387 |
| Publication | Pharmaceutical Statistics |
| DOI | 10.1002/pst.2387 |
| Journal Abbr | Pharmaceutical Statistics |
| ISSN | 1539-1604, 1539-1612 |
| Date Added | 4/2/2024, 7:37:04 AM |
| Modified | 4/2/2024, 7:37:40 AM |
| Item Type | Journal Article |
|---|---|
| Author | Toshiaki A Furukawa |
| Author | Gordon H Guyatt |
| Author | Lauren E Griffith |
| Date | 2/2002 |
| Language | en |
| Short Title | Can we individualize the ‘number needed to treat’? |
| Library Catalog | DOI.org (Crossref) |
| URL | https://academic.oup.com/ije/article-lookup/doi/10.1093/ije/31.1.72 |
| Accessed | 3/3/2024, 3:39:40 PM |
| Volume | 31 |
| Pages | 72-76 |
| Publication | International Journal of Epidemiology |
| DOI | 10.1093/ije/31.1.72 |
| Issue | 1 |
| ISSN | 1464-3685, 0300-5771 |
| Date Added | 3/3/2024, 3:39:40 PM |
| Modified | 3/3/2024, 3:41:20 PM |
| Item Type | Journal Article |
|---|---|
| Author | Michael J Foley |
| Author | Christopher A Rajkumar |
| Author | Fiyyaz Ahmed-Jushuf |
| Author | Florentina A Simader |
| Author | Shayna Chotai |
| Author | Rachel H Pathimagaraj |
| Author | Muhammad Mohsin |
| Author | Ahmed Salih |
| Author | Danqi Wang |
| Author | Prithvi Dixit |
| Author | John R Davies |
| Author | Tom R Keeble |
| Author | Claudia Cosgrove |
| Author | James C Spratt |
| Author | Peter D O’Kane |
| Author | Ranil De Silva |
| Author | Jonathan M Hill |
| Author | Sukhjinder S Nijjer |
| Author | Sayan Sen |
| Author | Ricardo Petraco |
| Author | Ghada W Mikhail |
| Author | Ramzi Khamis |
| Author | Tushar Kotecha |
| Author | Frank E Harrell |
| Author | Peter Kellman |
| Author | Darrel P Francis |
| Author | James P Howard |
| Author | Graham D Cole |
| Author | Matthew J Shun-Shin |
| Author | Rasha K Al-Lamee |
| Date | 4/2024 |
| Language | en |
| Short Title | Coronary sinus reducer for the treatment of refractory angina (ORBITA-COSMIC) |
| Library Catalog | DOI.org (Crossref) |
| URL | https://linkinghub.elsevier.com/retrieve/pii/S0140673624002563 |
| Accessed | 4/11/2024, 7:44:51 AM |
| Pages | S0140673624002563 |
| Publication | The Lancet |
| DOI | 10.1016/S0140-6736(24)00256-3 |
| Journal Abbr | The Lancet |
| ISSN | 01406736 |
| Date Added | 4/11/2024, 7:44:51 AM |
| Modified | 4/11/2024, 7:47:29 AM |
| Item Type | Journal Article |
|---|---|
| Author | Craig K. Enders |
| Date | 2023-03-16 |
| Language | en |
| Short Title | Missing data |
| Library Catalog | DOI.org (Crossref) |
| URL | https://doi.apa.org/doi/10.1037/met0000563 |
| Accessed | 4/29/2024, 3:28:37 PM |
| Rights | http://www.apa.org/pubs/journals/resources/open-access.aspx |
| Publication | Psychological Methods |
| DOI | 10.1037/met0000563 |
| Journal Abbr | Psychological Methods |
| ISSN | 1939-1463, 1082-989X |
| Date Added | 4/29/2024, 3:28:37 PM |
| Modified | 4/29/2024, 3:29:11 PM |
| Item Type | Journal Article |
|---|---|
| Author | Seoyoon Cho |
| Author | Matthew A Psioda |
| Author | Joseph G Ibrahim |
| Abstract | Abstract There is an increasing interest in the use of joint models for the analysis of longitudinal and survival data. While random effects models have been extensively studied, these models can be hard to implement and the fixed effect regression parameters must be interpreted conditional on the random effects. Copulas provide a useful alternative framework for joint modeling. One advantage of using copulas is that practitioners can directly specify marginal models for the outcomes of interest. We develop a joint model using a Gaussian copula to characterize the association between multivariate longitudinal and survival outcomes. Rather than using an unstructured correlation matrix in the copula model to characterize dependence structure as is common, we propose a novel decomposition that allows practitioners to impose structure (e.g., auto-regressive) which provides efficiency gains in small to moderate sample sizes and reduces computational complexity. We develop a Markov chain Monte Carlo model fitting procedure for estimation. We illustrate the method’s value using a simulation study and present a real data analysis of longitudinal quality of life and disease-free survival data from an International Breast Cancer Study Group trial. |
| Date | 2024-04-26 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://academic.oup.com/biostatistics/advance-article/doi/10.1093/biostatistics/kxae009/7658810 |
| Accessed | 4/30/2024, 5:04:13 PM |
| Rights | https://academic.oup.com/pages/standard-publication-reuse-rights |
| Pages | kxae009 |
| Publication | Biostatistics |
| DOI | 10.1093/biostatistics/kxae009 |
| ISSN | 1465-4644, 1468-4357 |
| Date Added | 4/30/2024, 5:04:13 PM |
| Modified | 4/30/2024, 5:04:33 PM |
| Item Type | Journal Article |
|---|---|
| Author | P. M. Aronow |
| Author | James M. Robins |
| Author | Theo Saarinen |
| Author | Fredrik Sävje |
| Author | Jasjeet Sekhon |
| Abstract | We argue that randomized controlled trials (RCTs) are special even among settings where average treatment effects are identified by a nonparametric unconfoundedness assumption. This claim follows from two results of Robins and Ritov (1997): (1) with at least one continuous covariate control, no estimator of the average treatment effect exists which is uniformly consistent without further assumptions, (2) knowledge of the propensity score yields a uniformly consistent estimator and honest confidence intervals that shrink at parametric rates with increasing sample size, regardless of how complicated the propensity score function is. We emphasize the latter point, and note that successfully-conducted RCTs provide knowledge of the propensity score to the researcher. We discuss modern developments in covariate adjustment for RCTs, noting that statistical models and machine learning methods can be used to improve efficiency while preserving finite sample unbiasedness. We conclude that statistical inference has the potential to be fundamentally more difficult in observational settings than it is in RCTs, even when all confounders are measured. |
| Date | 2021 |
| Library Catalog | DOI.org (Datacite) |
| URL | https://arxiv.org/abs/2108.11342 |
| Accessed | 3/3/2024, 3:48:09 PM |
| Rights | arXiv.org perpetual, non-exclusive license |
| Extra | Publisher: [object Object] Version Number: 2 |
| DOI | 10.48550/ARXIV.2108.11342 |
| Date Added | 3/3/2024, 3:48:09 PM |
| Modified | 3/3/2024, 3:50:17 PM |