Periodic Testing and Estimation of STD-HIV Association
Bibliographic record
Abstract
Abstract Background The validity of measures used in follow-up studies to estimate the magnitude of the HIV-STD association will be the focus of this paper. A recent simulation study by Boily et al [1] based on a model of HIV and STD transmission showed that the relative risk ( RR ), estimated by the hazard rate ratio ( HRR ) obtained by the Cox model had poor validity, either in absence or in presence of a real association between HIV and STD. The HRR tends to underestimate the true magnitude of a non-null association. These results were obtained from simulated follow-up studies where HIV was periodicaly tested every three months and every month for the STD. Aims and Methods This paper extends the above results by investigating the impact of using different periodic testing intervals on the validity of HRR estimates. Issues regarding the definition of exposure to STDs in this context are explored. A stochastic model for the transmission of HIV and other STDs is used to simulate follow-up studies with different periodic testing intervals. HRR estimates obtained with the Cox model with a time-dependent STD exposure covariate are compared to the true magnitude of the HIV-STD association. In addition, real data are reanalysed using the STD exposure definition described in this paper. The data from Laga et al [2] are used for this purpose. Results (1) Simulated data: independently of the magnitude of the true association, we observed a greater reduction of the bias when increasing the frequency of HIV testing than that of the STD testing. (2) Real data: The STD exposure definition can create substantial differences in the estimation of the HIV-STD association. Laga et al [2] have found a HRR of 2.5 (1.1 - 6.4) for the association between HIV and genital ulcer disease compared to an estimate of 3.5 (1.5 - 8.3) with our improved definition of exposure. Conclusions Results on the simulated data have an important impact on the design of field studies. For instance when choosing between two designs; one where both HIV and STD are screened every 3 months versus one where HIV and STD are screened every 3 months and monthly, respectively. The latter design is more expensive and involves more complicated logistics. Furthermore, this increment in cost may not be justified considering the relatively small gain in terms of validity and variability.
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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 | 0.022 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".