Developing New Approaches for prevalence estimation to simultaneously reduce selection bias and misclassification bias: Application on mental health and social connectedness of sexual minority men
Bibliographic record
Abstract
Background: Epidemiological studies of mental health among sexual minority men (SMM) show considerable heterogeneity in prevalence estimates. While population-based surveys with probability sampling are considered the gold standard for prevalence estimates, misclassification biases can still occur due to reluctance to accurately share sexual orientation in government surveys. Community-based surveys can reduce misclassification for hard-to-reach populations such as sexual minorities but are prone to selection/participation bias due to nonprobability sampling. The purpose of this thesis is to develop and apply different methods for correcting for selection bias in nonprobability samples and misclassification bias in probability samples, applied to SMM mental health. Study Objectives: 1) estimate the prevalence of mental health and social connectedness among SMM using the adjusted logistic propensity (ALP) method that corrects for selection bias due to nonprobability sampling, 2) evaluate the performance and statistical properties of a newly proposed two-step method that can simultaneously correct for selection bias in a nonprobability sample and misclassification bias in a probability sample with a simulation study, 3) apply the two-step method to estimate prevalence of social connectedness among SMM and improve the prevalence estimation of some outcomes obtained in Objective 1. Data Sources: Canadian Community Health Survey (CCHS) 2015-2018 and the community-based Sex Now 2019 survey Results: The ALP resulted in prevalence estimates that fell between the Sex Now and CCHS estimates, reducing heterogeneity in between-survey estimates. The simulation showed that the two-step method produced estimates with the smallest relative bias and the best coverage probability compared to other methods of prevalence estimation, including ALP alone. Applying the two-step method resulted in minimal changes in prevalence estimates compared to the ALP-weighted estimates. Conclusion: The two-step method is the most effective tool in reducing biases in the analytic stage for hard-to-reach populations. Our findings suggest that the biases in our SMM data were minimal, providing more confidence in the robustness of previous analyses with these data. Better recruitment and data collection strategies are still the optimal approach to reducing biases. However, in the presence of selection or misclassification bias, the analytical methods proposed in this thesis provide improved approaches for prevalence estimation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.068 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".