Survival analysis of time to quit attempt using data from the international tobacco control survey in Canada (2002–2014)
Bibliographic record
Abstract
This study uses longitudinal data from the International Tobacco Control (ITC) Survey. The data were collected from 2002 to 2014. There were 9 waves including 8 wave-to-wave transition periods. The data can be used to explore predictors of time until quit attempt among Canadian adults who smoke. To assess the strength of certain potential predictors of the time of making the first "most recent quit attempt" among people who smoke cigarettes in the ITC Canada Survey participating in at least two waves between Wave 1 and Wave 9, and thereby to illustrate the possibility of applying a survival analysis to these longitudinal survey data using complex survey software in the R language. A longitudinal survey was conducted with a representative sample of adult people who smoke cigarettes from the provinces of Canada, with Wave 1 occurring in 2002 and Wave 9 in 2014. An analysis was carried out to determine the factors that were significantly associated with time of making the first "most recent quit attempt" after joining the sample. We used both the Cox proportional hazard model and non-parametric methods for survival analysis, with and without taking account of the complex survey design. Variables expected to be predictors of the time until making a quit attempt included sex and age group at recruitment, both fixed over time, and previous wave indicators/values of advice on quitting from a doctor or other healthcare provider, intention to quit, and cigarettes used per day. In the survival analysis with fixed covariates and assuming simple random sampling, sex and age group were not significant. When the complex design was accounted for, the results were similar. When the time-varying covariates were added, those aged 55 + were more likely to have made a first quit attempt, although age group was no longer signficant. The square root of previous wave cigarettes per day and previous wave intention to quit were found to affect the time until making the first "most recent quit attempt" significantly, the first associated with a later time and the second with an earlier time. Not receiving advice from a doctor or healthcare provider was associated with a later time but was significant only when the complex design was not accounted for. An international longitudinal study provides an opportunity to study smoking cessation among people who smoke cigarettes and the relationship of time of cessation attempt to predictors such as age, sex, advice from a doctor, intention to quit and cigarettes used per day. Previous wave values of cigarettes used per day and intention to quit were strongly associated with time until making a quit attempt.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".