Accounting for overdispersion and clustering in binomial data from N-of-1 trials | Paper Citation Record

Observation f19ddf30-576f-4835-b5c4-f5d34c8fe139 · outbound

This paper cites Single-patient (n-of-1) trials: a pragmatic clinical decision methodology for patient-centered comparative effectiveness research.J Clin Epidemiol.2013; 66(8 Suppl):S21-8.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Single-patient (n-of-1) trials: a pragmatic clinical decision methodology for patient-centered comparative effectiveness research.J Clin Epidemiol.2013; 66(8 Suppl):S21-8

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Observation c4de8464-b851-43b6-9e99-305b5a2b7862 · outbound

This paper cites The n-of-1 clinical trial: the ultimate strategy for individualizing medicine?Per Med.2011;8(2): 161-173.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials The n-of-1 clinical trial: the ultimate strategy for individualizing medicine?Per Med.2011;8(2): 161-173

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Observation 6ca0ed9c-772a-4774-8831-f189f560199c · outbound

This paper cites Combining single patient (N-of-1) trials to estimate population treatment effects and to evaluate individual patient responses to treatment.J Clin Epidemiol.1997;50(4):401-10.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Combining single patient (N-of-1) trials to estimate population treatment effects and to evaluate individual patient responses to treatment.J Clin Epidemiol.1997;50(4):401-10

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Observation 356e454a-512f-4eed-9623-589e579b5692 · outbound

This paper cites Individual (N-of-1) trials can be combined to give popu- lation comparative treatment effect estimates: methodologic considerations.J Clin Epidemiol.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Individual (N-of-1) trials can be combined to give popu- lation comparative treatment effect estimates: methodologic considerations.J Clin Epidemiol

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Observation 560074b2-1dc7-4dba-8f9c-c7385d3d5ee1 · outbound

This paper cites A statistical model for the “N-of-1” study.J Clin Epidemiol.1990;43(5):499-508.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials A statistical model for the “N-of-1” study.J Clin Epidemiol.1990;43(5):499-508

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Observation 4ecbc168-eab3-4d15-be2c-d0fa96214869 · outbound

This paper cites A comparison of four methods for the analysis of N-of-1 Trials.PLoS ONE 2014;9(2):e87752.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials A comparison of four methods for the analysis of N-of-1 Trials.PLoS ONE 2014;9(2):e87752

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Observation 8c3429f0-a36a-4e1d-8889-58199e53de39 · outbound

This paper cites Understanding Variation in Sets of N-of-1 Trials.PLoS ONE 2016;11(12):e0167167.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Understanding Variation in Sets of N-of-1 Trials.PLoS ONE 2016;11(12):e0167167

Reference 7

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Observation ebcc5a97-315f-4ff0-b3ea-37d43ed7e052 · outbound

This paper cites Single patient (n-of-1) trials with binary treatment preference.Stat Med.2005;24(17):2625-2636.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Single patient (n-of-1) trials with binary treatment preference.Stat Med.2005;24(17):2625-2636

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:36:43.463936Z

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Observation b25bf315-f1ac-4910-ae73-a89ec0262d57 · outbound

This paper cites Beta-binomial model for meta-analysis of odds ratios.Stat Med.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Beta-binomial model for meta-analysis of odds ratios.Stat Med

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Observation e10ca970-5953-407d-ab4b-c29af0ef8fc5 · outbound

This paper cites Overdispersion models for correlated multinomial data: Applications to blinding assessment.Stat Med.2019;38(25):4963-4976.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Overdispersion models for correlated multinomial data: Applications to blinding assessment.Stat Med.2019;38(25):4963-4976

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Observation 6aa8bfd2-4e50-42bf-be85-06e1791e28f2 · outbound

This paper cites Longitudinal data analysis for discrete and continuous outcomes.Bio- metrics.1986;42(1):121-30.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Longitudinal data analysis for discrete and continuous outcomes.Bio- metrics.1986;42(1):121-30

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Observation f1c37697-8c25-49f9-8abd-2a76040eccb6 · outbound

This paper cites An extended random-effects approach to modeling repeated, overdispersed count data.Lifetime Data Anal.2007;13(4):513–531.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials An extended random-effects approach to modeling repeated, overdispersed count data.Lifetime Data Anal.2007;13(4):513–531

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Observation 6a4a75f4-62fc-43eb-b451-4c16f4f3df3f · outbound

This paper cites A family of generalized linear models for repeated measures with normal and conjugate random effects.Stat Sci.2010;3:325-347.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials A family of generalized linear models for repeated measures with normal and conjugate random effects.Stat Sci.2010;3:325-347

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Observation 3dd24092-1bf9-4d9c-be6d-2c1a27223a87 · outbound

This paper cites A combined beta and normal random- effects model for repeated, over-dispersed binary and binomial data.J Multivar Anal.2012; 111:94-109.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials A combined beta and normal random- effects model for repeated, over-dispersed binary and binomial data.J Multivar Anal.2012; 111:94-109

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:36:43.509691Z

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Observation df02f009-19a5-4794-881f-03aef72d7253 · outbound

This paper cites Modeling overdispersed longitudi- nal binary data using a combined beta and normal random-effects model.Arch Public Health.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Modeling overdispersed longitudi- nal binary data using a combined beta and normal random-effects model.Arch Public Health

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Observation 6ced70ea-76b9-4ae3-b987-d66b34a8e1ad · outbound

This paper cites Jackknife Estimators of Variance for Parameter Estimates from Esti- mating Equations with Applications to Clustered Survival Data.Biometrics.1994;50:842–846.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Jackknife Estimators of Variance for Parameter Estimates from Esti- mating Equations with Applications to Clustered Survival Data.Biometrics.1994;50:842–846

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Observation 51c6174d-56e5-45b6-9499-1b46f4486011 · outbound

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Accounting for overdispersion and clustering in binomial data from N-of-1 trials Practical considerations of the jackknife estimator of variance for generalized esti- mating equations.Statistical Papers.1997;38(3):363–369

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:36:43.380341Z

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Observation a8ced855-2971-4e3c-85b6-71f1087ca57f · outbound

This paper cites Estimating intraclass correlation for binary data.Biomet- rics1999;55: 137–48.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Estimating intraclass correlation for binary data.Biomet- rics1999;55: 137–48

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Observation 774b623e-a640-4435-bc28-5276e585875c · outbound

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Accounting for overdispersion and clustering in binomial data from N-of-1 trials The intra-cluster correlation coefficient in cluster randomized trials: a review of definitions.Int

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Observation 333576b3-c58c-44bb-8a6b-77cb04b2c9a8 · outbound

This paper cites Comparison of methods for estimating the intraclass correlation coefficient for binary responses in cancer prevention cluster randomized trials.Contemp.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Comparison of methods for estimating the intraclass correlation coefficient for binary responses in cancer prevention cluster randomized trials.Contemp

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Observation d06d0b30-7e0b-436c-8179-6a77daf37fe2 · outbound

This paper cites Confidence interval estimation of the intraclass correlation coefficient for binary outcome data.Biometrics2004;60: 807-811.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Confidence interval estimation of the intraclass correlation coefficient for binary outcome data.Biometrics2004;60: 807-811

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Observation ceee5a00-29e1-4e79-aa27-0afe59a22248 · outbound

This paper cites Profile likelihood-based confidence interval of the intraclass correlation for binary outcome data sampled from clusters.Stat Med.2018;31(29):3982-4002.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Profile likelihood-based confidence interval of the intraclass correlation for binary outcome data sampled from clusters.Stat Med.2018;31(29):3982-4002

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Observation d54a2ac8-1b5e-487a-964a-63acd93bf33d · outbound

This paper cites an unresolved cited work.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Unresolved cited work

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This paper cites heritability.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials heritability

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Observation 48c97a40-5fa6-4368-9806-c974de5c12b0 · outbound

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Accounting for overdispersion and clustering in binomial data from N-of-1 trials A coefficient of agreement for nominal scales.Educ

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Observation 437ec4af-cd66-4df3-afbe-46afdc09e54f · outbound

This paper cites A review of inference procedures for the intraclass correlation coefficient in the one-way random effects model.Int.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials A review of inference procedures for the intraclass correlation coefficient in the one-way random effects model.Int

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Observation 7befb617-b066-4f8e-b9aa-18ceb598d83e · outbound

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Accounting for overdispersion and clustering in binomial data from N-of-1 trials Sibling and parent-offspring correlation estimation with variable family size.PNAS

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Observation bd7fefb6-abac-4039-9c14-2e76369d3b5c · outbound

This paper cites An extended quasi-likelihood function.Biometrika1987;74, 221–232.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials An extended quasi-likelihood function.Biometrika1987;74, 221–232

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Accounting for overdispersion and clustering in binomial data from N-of-1 trials Quasi-likelihood functions, generalized linear models, and the Gauss—Newton method.Biometrika1974;61(3): 439–447

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Observation bdf13626-b074-47db-b3c0-fd9f9bae63ee · outbound

This paper cites A note on extended quasi-likelihood estimation.JRSS-B.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials A note on extended quasi-likelihood estimation.JRSS-B

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Observation 287c0b8b-b0b3-4573-a999-a9439817d0e4 · outbound

This paper cites Likelihood, Quasi-Likelihood and Pseudolikelihood: Some Comparisons.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Likelihood, Quasi-Likelihood and Pseudolikelihood: Some Comparisons

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Observation 02acbf1d-ef86-4c46-abc3-acce40c98521 · outbound

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Accounting for overdispersion and clustering in binomial data from N-of-1 trials Simpson’s paradox from adding constants in contingency tables as an example of Bayesian noncollapsibility.Amer Statistician2010;64: 340-344

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Observation e27da55d-a375-4b8d-8b77-36fa04092449 · outbound

This paper cites Overdispersion: models and estimation.Comput.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Overdispersion: models and estimation.Comput

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Observation aa788118-1ed3-42da-949b-9fe7651e9073 · outbound

This paper cites Biometrika1986;73583-588.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Biometrika1986;73583-588

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Observation 9b70e4ae-0f7f-4668-82a8-22a4f87264d5 · outbound

This paper cites On Linear and Quadratic Estimating Functions.Biometrika1987;74(3): 591-597.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials On Linear and Quadratic Estimating Functions.Biometrika1987;74(3): 591-597

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Observation 4d5e0c2b-a332-4e02-86fe-c07d4c1560d0 · outbound

This paper cites Joint estimation of the mean and dispersion parameters in the analysis of proportions: a comparison of efficiency and bias.The Canadian Journal of Statistics1998;26: 83–94.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Joint estimation of the mean and dispersion parameters in the analysis of proportions: a comparison of efficiency and bias.The Canadian Journal of Statistics1998;26: 83–94

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Observation 29d98a3c-4a14-4889-8f0c-41e33878f71c · outbound

This paper cites Quadratic estimating equations for the estimation of regression and dispersion pa- rameters in the analysis of proportions.Sankhya B2001;63: 43–55.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Quadratic estimating equations for the estimation of regression and dispersion pa- rameters in the analysis of proportions.Sankhya B2001;63: 43–55

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Observation a660da8e-57ca-48a9-b940-38d271bda7d0 · outbound

This paper cites an unresolved cited work.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-07-10T02:36:43.449722Z

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Observation fa42fa1c-05df-4c24-ae93-03f49739ce8c · outbound

This paper cites Proportions with extraneous variance: Single and independent samples.JASA (1973).6846-54.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Proportions with extraneous variance: Single and independent samples.JASA (1973).6846-54

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Observation d5235b83-f6cb-4baa-ae87-de8e1420905c · outbound

This paper cites R packagedirmultversion 0.1.3-4.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials R packagedirmultversion 0.1.3-4

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:36:43.443320Z

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Observation 8347999e-b6ff-4d21-ad4c-a860eb6dd93d · outbound

This paper cites Confidence intervals for the common intraclass correlation in the analysis of clustered binary responses.Journal of Biopharmaceutical Statistics2018;28(4): 682-697.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Confidence intervals for the common intraclass correlation in the analysis of clustered binary responses.Journal of Biopharmaceutical Statistics2018;28(4): 682-697

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:36:43.438920Z

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Observation f5c910d0-c57c-4fa6-b727-31e8f1b85d2f · outbound

This paper cites Asymptotic Statistics.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Asymptotic Statistics

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Observation c17f443b-3ca8-481f-9fec-20db5ae9abf0 · outbound

This paper cites Generalized, linear, and mixed models.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Generalized, linear, and mixed models

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Observation b862faeb-374b-4c10-8df5-1df2e4de0e61 · outbound

This paper cites lme4: Mixed-effects modeling with R Springer (2010).

Accounting for overdispersion and clustering in binomial data from N-of-1 trials lme4: Mixed-effects modeling with R Springer (2010)

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Observation 8de2a2d4-6b65-49a7-9cda-040a3b6057b0 · outbound

This paper cites Clinical usefulness of amitriptyline in fibromyalgia: the results of 23 N-of-1 randomized controlled trials.J Rheumatol1991;18:447- 451.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Clinical usefulness of amitriptyline in fibromyalgia: the results of 23 N-of-1 randomized controlled trials.J Rheumatol1991;18:447- 451

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:36:43.486501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d2d57b1c-ecbc-4ebb-9a33-39a1d573a810 · outbound

This paper cites Switching from NSAIDs to paracetamol: a series of n of 1 trials for individual patients with osteoarthritis.Ann Rheum Dis.2003;62: 1156-1161.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Switching from NSAIDs to paracetamol: a series of n of 1 trials for individual patients with osteoarthritis.Ann Rheum Dis.2003;62: 1156-1161

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:36:43.503257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fd2d9c1a-305d-4c03-ae2a-4fdb83a46487 · outbound

This paper cites Theo- phylline for irreversible chronic airflow limitation: a randomized study comparing n of 1 trials to standard practice.Chest.1999;115(1):38-48.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Theo- phylline for irreversible chronic airflow limitation: a randomized study comparing n of 1 trials to standard practice.Chest.1999;115(1):38-48

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:36:43.432319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b264237d-03ee-4578-aab3-b65d6205f496 · outbound

This paper cites Comparison of population-averaged and subject-specific approaches for analyzing repeated binary outcomes.Am J Epidemiol.1998; 147(7):694-703.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Comparison of population-averaged and subject-specific approaches for analyzing repeated binary outcomes.Am J Epidemiol.1998; 147(7):694-703

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:36:43.434519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 52515057-ecc8-4a57-8789-37dd09bed535 · outbound

This paper cites A comparison of cluster-specific and population- averaged approaches for analyzing correlated binary data.Int Stat Rev.199159:25–35.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials A comparison of cluster-specific and population- averaged approaches for analyzing correlated binary data.Int Stat Rev.199159:25–35

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:36:43.425424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c8c36bf2-1b64-46c0-8572-865d14e1d5ce · outbound

This paper cites To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health.Epidemiology.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health.Epidemiology

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:36:43.420507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 16eebee4-b6af-4e3a-8784-e424034a0727 · outbound

This paper cites Practical Marginalized Multilevel Models.Stat.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Practical Marginalized Multilevel Models.Stat

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:36:43.422983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1025b806-0ecd-4a5f-acdd-0cedd5414ee8 · outbound

This paper cites A note on marginalization of regression parameters from mixed models of binary outcomes.Biometrics.(2018)74(1):354-361.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials A note on marginalization of regression parameters from mixed models of binary outcomes.Biometrics.(2018)74(1):354-361

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:36:43.427821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7e96259d-0f6a-4669-8c80-8748fd210b56 · outbound

This paper cites an unresolved cited work.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-07-10T02:36:43.430081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ac0e6c8a-f923-4d0d-85b6-9e0d05a349df · outbound

This paper cites Methods for longitudinal data: a generalized estimating equation approach.Biometrics1988;44:1049-60.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Methods for longitudinal data: a generalized estimating equation approach.Biometrics1988;44:1049-60

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:36:43.415874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d03217f6-3575-495b-a5e3-d5ca7a15a36c · outbound

This paper cites Generalized Linear Models.

Accounting for overdispersion and clustering in binomial data from N-of-1 trials Generalized Linear Models

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:36:43.418097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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