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The Stats Geek

thestatsgeek.com

MathScience

’15’20’25
Posts
157 from 30 Jun 2013 to 15 Jul 2026
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now and then (2 in the last year)
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personal blog
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2026 2

  1. Estimating hypothetical estimands using post-ICE data – robustness to model misspecification
  2. How to interpret hazard ratios

2025 4

  1. Research Fellow post at LSHTM – machine learning for missing data
  2. The role of post intercurrent event data in the estimation of hypothetical estimands in clinical trials
  3. Multiple imputation for coarsened (grouped) factor covariates
  4. What is meant by a ‘while on treatment’ estimand?

2024 4

  1. Does a Bernoulli/binomial model really assume everyone has the same probability p?
  2. Multiple imputation with flexible parametric survival models
  3. Multiple imputation for missing covariates in the Fine & Gray model for competing risks
  4. Variance estimation for reference-based multiple imputation – the debate continues

2023 6

  1. Estimating hypothetical estimands with causal inference and missing data estimators in a diabetes trial
  2. Multiple imputation and its application – 2nd edition published
  3. Is the ICH E9 estimand addendum compatible with model-based estimands?
  4. On improving the efficiency of trials via linear adjustment for a prognostic score
  5. G-formula for causal inference via multiple imputation
  6. Multiple imputation for missing baseline covariates in discrete time survival analysis

2022 7

  1. ‘An introduction to covariate adjustment in trials’ – PSI covariate adjustment event
  2. PhD on causal inference for competing risks data
  3. Mixed models repeated measures (mmrm) package for R
  4. Causal (in)validity of the trimmed means estimand
  5. Perfect prediction handling in smcfcs for R
  6. Multiple imputation with splines in R using smcfcs
  7. How many imputations with mice? Assessing Monte-Carlo error after multiple imputation in R

2021 15

  1. Conditional mean reference-based multiple imputation
  2. Summary statistics after imputation with mice
  3. Is stratified randomisation in trials (at least large ones) pointless?
  4. Hypothetical estimands – a unification of causal inference and missing data methods
  5. smcfcs imputation in R – now with parallel functionality
  6. Comments on FDA guidance ‘Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biological Products’
  7. Multiple imputation separately by groups in R and Stata
  8. Does the log rank test assume proportional hazards?
  9. Reference based multiple imputation – what’s the right variance and how to estimate it?
  10. Non-proportional hazards – an introduction to their possible causes and interpretation
  11. From DAGs to potential outcomes via Single World Intervention Graphs
  12. The mean of the residuals in logistic regression is always zero
  13. MMRM vs LME model
  14. Is MAR dropout classified as MNAR according to Mohan and Pearl?
  15. Confounding vs. effect modification

2020 17

  1. Mixed model repeated measures (MMRM) in Stata, SAS and R
  2. What might the true sensitivity be for lateral flow Covid-19 tests?
  3. Auxiliary variables and congeniality in multiple imputation
  4. Convergence plots for smcfcs in R
  5. What does correlation in a Bland-Altman plot mean?
  6. Interpretation of frequentist confidence intervals and Bayesian credible intervals
  7. P-values after multiple imputation using mitools in R
  8. What can we infer from proportional hazards?
  9. Imputation of covariates for Fine & Gray cumulative incidence modelling with competing risks
  10. A simulation introduction to censoring in survival analysis
  11. Conference talk video – Bootstrap Inference for Multiple Imputation Under Uncongeniality and Misspecification
  12. Bootstrapping multiple imputation using multiple cores/processors in R
  13. The hazards of period specific and weighted hazard ratios
  14. Reference based imputation for continuous missing data in R with bootstrap inference
  15. Understanding missing at random dropout using DAGs
  16. 3 year post-doc in Bath – Clinical trial estimands – from definition to estimation
  17. The mean of residuals in linear regression is always zero

2019 8

  1. Independence of sample mean and sample variance
  2. PhD in estimands/causal inference in trials (UK/EU)
  3. What’s the difference between statistics and machine learning?
  4. Setting seeds when running R simulations in parallel
  5. Robustness of ANCOVA in randomised trials with unequal randomisation
  6. ANCOVA in RCTs – model based standard errors are valid even under misspecification
  7. Running simulation studies in R
  8. Critical bug fix for smcfcs in Stata

2018 6

  1. Comment on ‘Conditional estimation and inference to address observed covariate imbalance in randomized clinical trials’
  2. Combining bootstrapping and multiple imputation under uncongeniality
  3. Missing not at random sensitivity analysis with FCS multiple imputation
  4. Multiple imputation when estimating relative risks
  5. Causal interpretation of the hazard ratio from RCTs when proportional hazards holds
  6. smcfcs – non-linear relationships between covariates

2017 9

  1. Maximum likelihood multiple imputation
  2. Randomisation as the basis for inference in trials
  3. Live stream seminar 26th October 2017: Covariate adjustment and prediction of mean response in randomised trials
  4. Testing equality of two survival distributions: log-rank/Cox versus RMST
  5. Covariate adjustment and prediction of mean response in randomised trials
  6. Imputing missing covariates in nested case-control and case cohort studies
  7. Odds ratios, collapsibility, marginal vs. conditional, GEE vs GLMMs
  8. ‘Monte-Carlo sensitivity analysis’ recommended against
  9. Handling competing risks in randomized trials

2016 11

  1. Frequentists should more often consider using Bayesian methods
  2. Prediction intervals after random-effects meta-analysis
  3. Why you shouldn’t use propensity score matching
  4. Confidence intervals for the hazard ratio in RCTs which agree with log rank test
  5. Multiple imputation for informative censoring R package
  6. Estimands and assumptions in clinical trials
  7. Estimating effects when outcomes are truncated by death
  8. Matching analysis to design: stratified randomization in trials
  9. Combining bootstrapping with multiple imputation
  10. Multiple imputation for missing covariates in Poisson regression
  11. On the missing at random assumption in longitudinal trials

2015 27

  1. Running simulations in R using Amazon Web Services
  2. smcfcs in R – updated version 1.1.1 with critical bug fix
  3. Machine learning vs. traditional modelling techniques
  4. Weighting after multiple imputation for MNAR sensitivity analysis not recommended
  5. Missing covariates in competing risks analysis
  6. Multiple imputation followed by deletion of imputed outcomes
  7. Using hazard ratios to estimate causal effects in RCTs
  8. On “The fallacy of placing confidence in confidence intervals”
  9. Automatic convergence checking in Bayesian inference with runjags
  10. Multiarm trials – should we allow for multiplicity?
  11. R squared/correlation depends on variance of predictor
  12. My first foray with Stan
  13. Prof. Alan Agresti on modelling ordinal data, issues with Wald based inferences, and GEE for ordinal data
  14. Estimating causal effects from observational studies
  15. A statistician’s initial experiences of Git/GitHub
  16. Substantive model compatible imputation of covariates – smcfcs in R
  17. Including the outcome in imputation models of covariates
  18. Conditional randomization, standardization, and inverse probability weighting
  19. Bayesian inference: are parameters fixed or random?
  20. Potential outcomes, counterfactuals, causal effects, and randomization
  21. Estimating risk ratios from observational data in Stata
  22. Banning p-values from journals
  23. Interval regression with heteroskedastic errors
  24. Why I think Stata’s old xi: prefix is still useful
  25. When is complete case/records logistic regression unbiased?
  26. Why shouldn’t I use linear regression if my outcome is binary?
  27. Interpreting odds and odds ratios

2014 30

  1. Missing covariates in structural equation models
  2. Comparing predictive ability of two nested logistic regression models
  3. Stata-Mata’s st_view function – use with care!
  4. Adjusting for optimism/overfitting in measures of predictive ability using bootstrapping
  5. Multiple imputation using random forest
  6. Meta-analysis of Scottish independence polls (update 17th September 2014)
  7. Meta-analysis of last week’s Scottish independence polls
  8. Checking functional form in logistic regression using loess plots
  9. Using Stata’s sem to adjust for covariate measurement error
  10. Robustness of linear mixed models
  11. Improving efficiency in RCTs using propensity scores
  12. Is the two sample t-test/ANOVA really biased in RCTs?
  13. Test for Alzheimer’s (allegedly) no better than a coin toss
  14. Robustness to misspecification when adjusting for baseline in RCTs
  15. Clustering in randomized controlled trials
  16. Fixed versus random-effects meta-analysis – efficiency and confidence interval coverage
  17. Multiple imputation with interactions and non-linear terms
  18. Adjusting for covariate misclassification in logistic regression – predictive value weighting
  19. Area under the ROC curve – assessing discrimination in logistic regression
  20. Deviance goodness of fit test for Poisson regression
  21. Wilcoxon-Mann-Whitney as an alternative to the t-test
  22. Interpreting changes in hazard and hazard ratios
  23. Leveraging baseline covariates for improved efficiency in randomized controlled trials
  24. The Hosmer-Lemeshow goodness of fit test for logistic regression
  25. A/B testing – confidence interval for the difference in proportions using R
  26. The robust sandwich variance estimator for linear regression (using R)
  27. Wald vs likelihood ratio test
  28. R squared in logistic regression
  29. Adjusting for baseline covariates in randomized controlled trials
  30. R squared and goodness of fit in linear regression

2013 11

  1. R squared and adjusted R squared
  2. The robust sandwich variance estimator for linear regression (theory)
  3. The t-test and robustness to non-normality
  4. Linear regression with random regressors, part 2
  5. Regression inference assuming predictors are fixed
  6. Assumptions for linear regression
  7. A/B testing and Pearson’s chi-squared test of independence
  8. The difference between the sample mean and the population mean
  9. When is complete case analysis unbiased?
  10. The miracle of the bootstrap
  11. Standard deviation versus standard error