# Statistical Methods and Bayesian Inference

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/statistical-methods-and-bayesian-inference/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on various statistical methods and models for handling missing data in research, including multiple imputation, Bayesian modeling, generalized linear models, longitudinal data analysis, and sensitivity analysis. It covers topics such as model complexity, simulation studies, and the application of these methods in different fields. The cluster also discusses the challenges and best practices for analyzing datasets with missing values. |
| Domain | Physical Sciences |
| Field | Mathematics |
| OpenAlex ID | t10243 |
| Works | 138 |

## Topic papers all

Showing 15 of 138.

- [MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS](https://scholariq.org/papers/multivariable-prognostic-models-issues-in-developing-models-evaluating/)
- [Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group](https://scholariq.org/papers/propensity-score-methods-for-bias-reduction-in-the-comparison-of-a-treatment-to/)
- [Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews](https://scholariq.org/papers/bivariate-analysis-of-sensitivity-and-specificity-produces-informative-summary/)
- [Approximate is Better than “Exact” for Interval Estimation of Binomial Proportions](https://scholariq.org/papers/approximate-is-better-than-exact-for-interval-estimation-of-binomial-proportions/)
- [The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed](https://scholariq.org/papers/the-performance-of-tests-of-publication-bias-and-other-sample-size-effects-in/)
- [Internal validation of predictive models](https://scholariq.org/papers/internal-validation-of-predictive-models/)
- [Introduction to the Analysis of Survival Data in the Presence of Competing Risks](https://scholariq.org/papers/introduction-to-the-analysis-of-survival-data-in-the-presence-of-competing-risks/)
- [Tutorial in biostatistics: competing risks and multi‐state models](https://scholariq.org/papers/tutorial-in-biostatistics-competing-risks-and-multi-state-models/)
- [Methods for Trend Estimation from Summarized Dose-Response Data, with Applications to Meta-Analysis](https://scholariq.org/papers/methods-for-trend-estimation-from-summarized-dose-response-data-with/)
- [Variable Selection for Propensity Score Models](https://scholariq.org/papers/variable-selection-for-propensity-score-models/)
- [Conjoint Analysis Applications in Health—a Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force](https://scholariq.org/papers/conjoint-analysis-applications-in-health-a-checklist-a-report-of-the-ispor-good/)
- [Overadjustment Bias and Unnecessary Adjustment in Epidemiologic Studies](https://scholariq.org/papers/overadjustment-bias-and-unnecessary-adjustment-in-epidemiologic-studies/)
- [Meta-DiSc: a software for meta-analysis of test accuracy data](https://scholariq.org/papers/meta-disc-a-software-for-meta-analysis-of-test-accuracy-data/)
- [Regression modelling strategies for improved prognostic prediction](https://scholariq.org/papers/regression-modelling-strategies-for-improved-prognostic-prediction/)
- [The Importance of the Normality Assumption in Large Public Health Data Sets](https://scholariq.org/papers/the-importance-of-the-normality-assumption-in-large-public-health-data-sets/)

## Topic primary papers

Showing 15 of 40.

- [Internal validation of predictive models](https://scholariq.org/papers/internal-validation-of-predictive-models/)
- [Practitioner’s Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls](https://scholariq.org/papers/practitioner-s-guide-to-latent-class-analysis-methodological-considerations-and/)
- [The JASP guidelines for conducting and reporting a Bayesian analysis](https://scholariq.org/papers/the-jasp-guidelines-for-conducting-and-reporting-a-bayesian-analysis/)
- [The proportion of missing data should not be used to guide decisions on multiple imputation](https://scholariq.org/papers/the-proportion-of-missing-data-should-not-be-used-to-guide-decisions-on-multiple/)
- [Simple sample size calculation for cluster-randomized trials](https://scholariq.org/papers/simple-sample-size-calculation-for-cluster-randomized-trials/)
- [Accounting for missing data in statistical analyses: multiple imputation is not always the answer](https://scholariq.org/papers/accounting-for-missing-data-in-statistical-analyses-multiple-imputation-is-not/)
- [Much Ado About Nothing](https://scholariq.org/papers/much-ado-about-nothing/)
- [Bayesian Estimation of Disease Prevalence and the Parameters of Diagnostic Tests in the Absence of a Gold Standard](https://scholariq.org/papers/bayesian-estimation-of-disease-prevalence-and-the-parameters-of-diagnostic-tests/)
- [Extending the simple linear regression model to account for correlated responses: An introduction to generalized estimating equations and multi-level mixed modelling](https://scholariq.org/papers/extending-the-simple-linear-regression-model-to-account-for-correlated-responses/)
- [<b>flexsurv</b>: A Platform for Parametric Survival Modeling in<i>R</i>](https://scholariq.org/papers/b-flexsurv-b-a-platform-for-parametric-survival-modeling-in-i-r-i/)
- [Bayesian Approaches to Modeling the Conditional Dependence Between Multiple Diagnostic Tests](https://scholariq.org/papers/bayesian-approaches-to-modeling-the-conditional-dependence-between-multiple/)
- [Estimating equations for association structures](https://scholariq.org/papers/estimating-equations-for-association-structures/)
- [The BUGS Book](https://scholariq.org/papers/the-bugs-book/)
- [Variational Bayesian Inference for a Nonlinear Forward Model](https://scholariq.org/papers/variational-bayesian-inference-for-a-nonlinear-forward-model/)
- [Interpreting Parameters in the Logistic Regression Model with Random Effects](https://scholariq.org/papers/interpreting-parameters-in-the-logistic-regression-model-with-random-effects/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.