Estimaciones usadas en diseños muestrales complejos: aplicaciones en la encuesta de salud cubana del año 2001
Bibliographic record
Abstract
OBJECTIVES: To look at the individual features of three different methods used to estimate simple parameters--means, totals, and percentages, as well as their standard errors--and of logistic regression models, and to describe how such methods can be used for analyzing data obtained from complex samples. METHODS: Data from Cuba's Second National Survey of Risk Factors and Non-Communicable Chronic Ailments [Segunda Encuesta Nacional de Factores de Riesgo y Afecciones Crónicas No Transmisibles], which was conducted in 2001, were studied. A complex, stratified multi-stage cluster sampling design was used. Cuba's 14 provinces and the municipality of Isla de la Juventud served as the strata, while the clusters consisted of sampled geographic areas (SGA), blocks, and sectors. Samples were weighted in inverse proportion to their probability of being selected, and estimates were performed by sex and age group (15-34, 35-54, 55-74, and 75 or more years). Taylor approximations were used to estimate variances. Three statistical methods were compared: conventional analysis, which assumes all data were obtained through simple random sampling; weighted analysis, which only takes into account the weight of the samples when performing estimates; and adjusted analysis, which looks at all aspects of the sampling design (namely, the disparity in the probability of being included in the sample and the effect of clustering on the data). RESULTS: The point estimates obtained with the three different types of analytic methods were similar. Standard error (SE) estimates for the prevalence of overweight and of arterial hypertension that were obtained by conventional analysis were underestimated by 19.3% and by more than 11.5%, respectively, when such estimates were compared to those obtained with the other two analytic methods. On the other hand, weighted analysis generated SE values that were much smaller than those obtained with the other two types of analyses. The same pattern was noted when odds ratios were calculated using the different methods. CONCLUSIONS: Analytic methods that take into account the way the data are structured as well as the study design give a more realistic picture of the problem under study and provide more exact estimates of the study parameters and their SE than conventional analytic methods. Because data from epidemiologic and public health research are often obtained through complex sampling designs, the methods described in this paper and the statistical packages that utilize them should be used more widely.
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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 | 0.023 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".