Causal models for monitoring the progress of infants with low birthweight

We study the weight (body mass) of infants born premat urely and with low birthweight during the first postnatal year. The infants are enrolled in the Casa Canguro programme in Valle de Cauca, a department (province) of Colom bia. The current weight and other physiological measurements are recorded at their vi sits to participating health- care facilities. We compare two groups of infants: those bor n at 31 weeks of gestational age or earlier (extremely preterm) and those born at 33 weeks or later (preterm). The comparisons are made using the potential outcomes framewor k, regarding the two groups as treatments and selecting from them pairs matched on an exte nsive set of covariates. Matching is accomplished by propensity scoring. The outcom es (weight and height) at a particular age are approximated by interpolation. We conclude that the average weight-handicap of the extremely preterm infants first incre ases, from about 600 grams at birth to 900 grams on average at three months, and then is re duced, so that by the first birthday they are only about 250 grams lighter on averag e

Inflated assessments of disability

A medical examination provides a key input into decisions about disability pension and other forms of income support or compensation that are justified on medical grounds. The result of examining an individual is often communicated by means of a score, and inflation of such scores is a well known problem. We estimate the extent of inflation of scores from a set of disability assessments using a model based on the discrete linear distribution. We explore some extensions within the framework of a sensitivity analysis

Estimation under model uncertainty

Model selection has had a virtual monopoly on dealing with model uncertainty ever since models were identified as important conduits for statistical inference. Model averaging alleviates some of its deficiencies, but does not offer a practical solution in all settings. We propose an alternative based on linear combinations of the candidate models’ estimators. The general proposal is elaborated for ordinary regression and is illustrated with examples. Some estimators based on invalid models contribute to efficient estimation of certain quantities

Decision theory applied to selecting the winners, ranking, and classification

We address the problem of selecting the best of a set of units based on a criterion variable, when its value is recorded for every unit subject to estimation, measurement, or another source of error. The solution is constructed in a decisiontheoretical framework, incorporating the consequences (ramifications) of the various kinds of error that can be committed. The related problems of classifying the units to a small number of groups and ranking them are solved by a similar approach. An application is presented involving retention rates in the undergraduate courses of a university

Analysis of a marker for cancer of the thyroid with a limit of detection

Limit of detection (LoD) is a common problem in the analysis of data generated by instruments that cannot detect very small concentra- tions or other quantities, resulting in left-censored measurements. Methods intended for data that are not subject to this problem are often difficult to modify for censoring. We adapt the simulation- extrapolation method, devised originally for fitting models with measurement error, to dealing with LoD in conjunction with a mix- ture analysis. The application relates the levels of thyroglobulin in individuals with cancer of the thyroid before and after treatment with radioactive iodine I–131. We conclude that the fitted mixture components correspond to levels of effectiveness of the treatment

Fitting multilevel models in complex survey data with design weights: Recommendations

Abstract Background Multilevel models (MLM) offer complex survey data analysts a unique approach to understanding individual and contextual determinants of public health. However, little summarized guidance exists with regard to fitting MLM in complex survey data with design weights. Simulation work suggests that analysts should scale design weights using two methods and fit the MLM using unweighted and scaled-weighted data. This article examines the performance of scaled-weighted and unweighted analyses across a variety of MLM and software programs. Methods Using data from the 2005–2006 National Survey of Children with Special Health Care Needs (NS-CSHCN: n = 40,723) that collected data from children clustered within states, I examine the performance of scaling methods across outcome type (categorical vs. continuous), model type (level-1, level-2, or combined), and software (Mplus, MLwiN, and GLLAMM). Results Scaled weighted estimates and standard errors differed slightly from unweighted analyses, agreeing more with each other than with unweighted analyses. However, observed differences were minimal and did not lead to different inferential conclusions. Likewise, results demonstrated minimal differences across software programs, increasing confidence in results and inferential conclusions independent of software choice. Conclusion If including design weights in MLM, analysts should scale the weights and use software that properly includes the scaled weights in the estimation.</p

Estimation of progression of multi-state chronic disease using the Markov model and prevalence pool concept

<p>Abstract</p> <p>Background</p> <p>We propose a simple new method for estimating progression of a chronic disease with multi-state properties by unifying the prevalence pool concept with the Markov process model.</p> <p>Methods</p> <p>Estimation of progression rates in the multi-state model is performed using the E-M algorithm. This approach is applied to data on Type 2 diabetes screening.</p> <p>Results</p> <p>Good convergence of estimations is demonstrated. In contrast to previous Markov models, the major advantage of our proposed method is that integrating the prevalence pool equation (that the numbers entering the prevalence pool is equal to the number leaving it) into the likelihood function not only simplifies the likelihood function but makes estimation of parameters stable.</p> <p>Conclusion</p> <p>This approach may be useful in quantifying the progression of a variety of chronic diseases.</p

A Multilevel Analysis of the Impact of Socio-Structural and Environmental Influences on Condom Use Among Female Sex Workers

This study uses multilevel analysis to examine individual, organizational and community levels of influence on condom use among female commercial sex workers (FSW) in the Philippines. A randomized controlled study involving 1,382 female commercial sex workers assigned to three intervention groups consisting of peer education, managerial training, combined peer and managerial intervention and a usual care control group was conducted. The results of the multilevel analysis show that FSWs who work in establishments with condom use rules tend to have a higher level of condom use (β = .70, P < 0.01). Among the different intervention groups, the combined peer and managerial intervention had the largest effect on condom use (β = 1.30, P < 0.01) compared with the usual care group. Using a three-level hierarchical model, we found that 62% of the variation lies within individuals, whereas 24% and 14% of the variation lies between establishments, and communities, respectively. Standard errors were underestimated when clustering of the FSWs in the different establishments and communities were not taken into consideration. The results demonstrate the importance of using multilevel analysis for community-based HIV/AIDS intervention programs to examine individual, establishment and community effects