Timing of pitch movements and perceived vowel duration

The hypothesis was tested that the timing of accent-lending pitch movements influences the perceived duration of a vowel. Dutch subjects were asked to adjust the physical duration of a vowel so as to fit into the temporal structure of a sentence. The vowel occurred in a monosyllabic word embedded in a carrier sentence. Three pitch movements on the vowel were used, a rise, a rise-fall, and a fall. Two opposite trends were found: the earlier the fall, the longer the duration of the target vowel was adjusted, the earlier the rise or rise-fall, the shorter its duration was adjusted. Control experiments indicated that the results should be interpreted in terms of a trade-off between the effects on prominence of timing of pitch movements and physical segment duration. It is concluded that late timing of pitch movements enhances the perceived vowel duration, but that this effect depends on the kind of pitch movement: the effect is cancelled in the case of late rises and rise-falls, whereas it is enhanced in the case of late falls by virtue of the enhancing effect on prominence of the accented syllable

Measuring balance and model selection in propensity score methods

Background: Propensity score (PS) methods focus on balancing confounders between groups to estimate an unbiased treatment or exposure effect. However, there is lack of attention in actually measuring, reporting and using the information on balance, for instance for model selection. Objectives: To describe and evaluate measures for balance in PS methods: the overlapping coefficient, the Kolmogorov- Smirnov distance, and the Lévy distance, and mean based measures for balance. Methods: We performed simulation studies to estimate the association between these three and several mean based measures for balance and bias (i.e., discrepancy between the true and the estimated treatment effect). Results: For large sample sizes (n=2000) the average Pearson's correlation coefficients between bias and Kolmogorov- Smirnov distance (r=0.89), the Lévy distance (r=0.89) and the absolute standardized mean difference (r=0.90) were similar, whereas this was lower for the overlapping coefficient (r= -0.42). When sample size decreased to 400, mean based measures of balance had stronger correlations with bias. Models including all confounding variables, their squares and interaction terms resulted in smaller bias than models that included only main terms for confounding variables. Conclusions: We conclude that measures for balance are useful for reporting the amount of balance reached in propensity score analysis and can be helpful in selecting the final PS model

Measuring balance and model selection in propensity score methods

Background: Propensity score (PS) methods focus on balancing confounders between groups to estimate an unbiased treatment or exposure effect. However, there is lack of attention in actually measuring, reporting and using the information on balance, for instance for model selection. Objectives: To describe and evaluate measures for balance in PS methods: the overlapping coefficient, the Kolmogorov- Smirnov distance, and the Lévy distance, and mean based measures for balance. Methods: We performed simulation studies to estimate the association between these three and several mean based measures for balance and bias (i.e., discrepancy between the true and the estimated treatment effect). Results: For large sample sizes (n=2000) the average Pearson's correlation coefficients between bias and Kolmogorov- Smirnov distance (r=0.89), the Lévy distance (r=0.89) and the absolute standardized mean difference (r=0.90) were similar, whereas this was lower for the overlapping coefficient (r= -0.42). When sample size decreased to 400, mean based measures of balance had stronger correlations with bias. Models including all confounding variables, their squares and interaction terms resulted in smaller bias than models that included only main terms for confounding variables. Conclusions: We conclude that measures for balance are useful for reporting the amount of balance reached in propensity score analysis and can be helpful in selecting the final PS model

Time-dependent propensity score and collider-stratification bias: Inhaled beta2-agonist and risk of coronary heart disease

Background: In observational studies of time-varying exposure and confounders, the use of propensity score (PS) is limited to assigning weights as in marginal structural models (MSMs). Stratification and conditioning on time-varying cofounders which are also intermediates can induce collider-stratification bias and adjust-away the (indirect) effect of exposure. Similar bias could be expected when one conditions on time-dependent PS. Objectives: We explored collider-stratification and confounding bias due to conditioning or stratifying on timedependent PS in a clinical example on the effect of inhaled short and long-acting beta2-agonist use (SABA and LABA, respectively) on coronary heart disease (CHD). Methods: A cohort of patients with an indication for SABA and/or LABA use was extracted from the Netherlands University Medical Center Utrecht General Practitioner Research Network. Information from 1995 to 2005 was used. SABA and LABA use and potential confounders were ascertained on 3 month intervals. Follow-up began the first day of diagnosis of bronchitis, asthma, or COPD and ended at the occurrence of CHD, death, unregistration with the GP, or end of the study, whichever occurred first. HR were estimated using PS stratification as well as covariate adjustment and compared with those of MSMs in both SABA and LABA separately. In MSMs, censoring was accounted for by including inverse probability of censoring weights. Results: The crude HR of CHD was 0.90 [95% CI: 0.63, 1.28] and 1.55 [95% CI: 1.06, 2.62] in SABA and LABA users respectively. When PS stratification, adjustment using PS, and MSMs were used, the HRs were 1.09 [95%CI: 0.74, 1.61], 1.07 [95% CI: 0.72, 1.60], and 0.86 [95% CI: 0.55, 1.34] for SABA, and 1.09 [95%CI: 0.74, 1.62], 1.13 [95%CI: 0.76, 1.67], 0.77 [95% CI: 0.45, 1.33] for LABA, respectively. Conclusions: Results were similar for different PS methods, but systematically higher than those of MSMs. When treatment and confounders vary during follow-up, conditioning or stratification on time-dependent PS may induce substantial collider-stratification or confounding bias. Hence, the use of methods such as MSMs is recommended

Time-dependent propensity score and collider-stratification bias: Inhaled beta2-agonist and risk of coronary heart disease

Background: In observational studies of time-varying exposure and confounders, the use of propensity score (PS) is limited to assigning weights as in marginal structural models (MSMs). Stratification and conditioning on time-varying cofounders which are also intermediates can induce collider-stratification bias and adjust-away the (indirect) effect of exposure. Similar bias could be expected when one conditions on time-dependent PS. Objectives: We explored collider-stratification and confounding bias due to conditioning or stratifying on timedependent PS in a clinical example on the effect of inhaled short and long-acting beta2-agonist use (SABA and LABA, respectively) on coronary heart disease (CHD). Methods: A cohort of patients with an indication for SABA and/or LABA use was extracted from the Netherlands University Medical Center Utrecht General Practitioner Research Network. Information from 1995 to 2005 was used. SABA and LABA use and potential confounders were ascertained on 3 month intervals. Follow-up began the first day of diagnosis of bronchitis, asthma, or COPD and ended at the occurrence of CHD, death, unregistration with the GP, or end of the study, whichever occurred first. HR were estimated using PS stratification as well as covariate adjustment and compared with those of MSMs in both SABA and LABA separately. In MSMs, censoring was accounted for by including inverse probability of censoring weights. Results: The crude HR of CHD was 0.90 [95% CI: 0.63, 1.28] and 1.55 [95% CI: 1.06, 2.62] in SABA and LABA users respectively. When PS stratification, adjustment using PS, and MSMs were used, the HRs were 1.09 [95%CI: 0.74, 1.61], 1.07 [95% CI: 0.72, 1.60], and 0.86 [95% CI: 0.55, 1.34] for SABA, and 1.09 [95%CI: 0.74, 1.62], 1.13 [95%CI: 0.76, 1.67], 0.77 [95% CI: 0.45, 1.33] for LABA, respectively. Conclusions: Results were similar for different PS methods, but systematically higher than those of MSMs. When treatment and confounders vary during follow-up, conditioning or stratification on time-dependent PS may induce substantial collider-stratification or confounding bias. Hence, the use of methods such as MSMs is recommended