Tra My Pham at MRC Clinical Trials Unit at UCL University College London London UK - Research Interests, Publications & Academic Profile

Tra My Pham

University Name

United Kingdom

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Research Interests

Biostatistics

100%

Statistics

90%

Real-world Data

60%

Cohort Study

50%

Missing Data

50%

Propensity Score Analysis

30%

Data Imputation

30%

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Articles11

Comment on Oberman & Vink: Should we fix or simulate the complete data in simulation studies evaluating missing data methods?

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Year:

2023

Rethinking intercurrent events in defining estimands for tuberculosis trials

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Year:

2022

Missing data, part 7. Pitfalls in doing multiple imputation

Article Link

Year:

2022

Missing data, part 6. Choices in doing multiple imputation

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Year:

2022

Missing data, part 5. Introduction to multiple imputation

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Year:

2022

Missing data, part 4. Missing data in the outcomes vs in the covariates

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Year:

2022

Missing data, part 3. How to explore missing data

Article Link

Year:

2022

Missing data, part 2. Missing data mechanisms: Missing completely at random, missing at random, missing not at random, and why they matter

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Year:

2022

Missing data, part 1. Why missing data are a problem

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Year:

2022

Cardiovascular outcomes of type 2 diabetic patients treated with DPP‑4 inhibitors versus sulphonylureas as add-on to metformin in clinical practice

Article Link

Year:

2021

A comparison of methods for analyzing a binary composite endpoint with partially observed components in randomized controlled trials

Article Link

Year:

2021

Collaborators5

Kingshuk Pal

University College London

UNITED KINGDOM

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Gordon Forbes

King’s College London

UNITED KINGDOM

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Ruth L Goodall

University College London

UNITED KINGDOM

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Jonathan Bartlett

Professor in Medical Statistics

London School of Hygiene & Tropical Medicine

UNITED KINGDOM

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Patrick Phillips

Assistant Professor

University of California, San Francisco

UNITED STATES

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Tra My Pham

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Research Interests

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Articles11

Comment on Oberman &amp; Vink: Should we fix or simulate the complete data in simulation studies evaluating missing data methods?

Rethinking intercurrent events in defining estimands for tuberculosis trials

Missing data, part 7. Pitfalls in doing multiple imputation

Missing data, part 6. Choices in doing multiple imputation

Missing data, part 5. Introduction to multiple imputation

Missing data, part 4. Missing data in the outcomes vs in the covariates

Missing data, part 3. How to explore missing data

Missing data, part 2. Missing data mechanisms: Missing completely at random, missing at random, missing not at random, and why they matter

Missing data, part 1. Why missing data are a problem

Cardiovascular outcomes of type 2 diabetic patients treated with DPP‑4 inhibitors versus sulphonylureas as add-on to metformin in clinical practice

A comparison of methods for analyzing a binary composite endpoint with partially observed components in randomized controlled trials

Collaborators5

Kingshuk Pal

Gordon Forbes

Ruth L Goodall

Jonathan Bartlett

Patrick Phillips

How do I reach out?

Comment on Oberman & Vink: Should we fix or simulate the complete data in simulation studies evaluating missing data methods?