Ronald N. Forthofer

DRGs in psychiatry. An empirical evaluation.

On October 1, 1983, Medicare began paying general hospitals by a prospective payment system based on DRGs. Psychiatric settings are exempted automatically or by request. By January 1985, however, a decision is required on how to integrate psychiatric settings into this system. This article provides an empirical analysis of the current DHHS DRGs categories for mental disorders. Current mental disorder DRGs and alternate DRGs examined here explain less than 3-12% of the variation in psychiatric length of stay. This is in contrast to 30-50% explained variation for other disorders. Alternatives and policy implications are discussed.

Tests of Hypotheses

This chapter discusses tests of hypotheses and defines key terms to help succinctly communicate the ideas of hypothesis testing. Hypothesis testing is a way of organizing and presenting evidence that helps reach a decision. There are negative outcomes associated with making a wrong decision and these have to be weighed carefully. The chapter also discusses the determination of the decision rule. The decision rule specifies which values of the test statistic, or some function of it, will cause to reject the null hypothesis in favor of the alternative hypothesis. The decision rule is based on the probabilities of the type I and II errors. The probabilities of type I and type II errors are found from the consideration of the distribution of the test statistic. A two-sided test is called such because either large or small values of the test statistic cause to question the truth of the null hypothesis. A one-sided test occurs when only values in one direction cause to question the null hypothesis.

Complex Survey Data Analysis: Estimation of Standard Errors Using Pseudostrata

A proper analysis of data from complex sample surveys requires special consideration for estimating standard errors. Special techniques and software packages are available, including Taylor series linearization (delta method), balanced rexad peated replication, and jackknife. Before their use, it is often necessary to make certain modifications in original data structure, to conform to computing method requirements. The most common modifixad cation is to form pseudostrata by collapsing subxad strata or partitioning a string of geographic clusters. This paper examines the performance of the delta method when it is applied to a complex community survey data set in which sequentially drawn clusters of households are partitioned to form pseudostrata. Standard errors of rates, regression coefficients, and odds ratios are compared with those computed from the variation of replicates built into the sample dexad sign. The results demonstrate that an analysis of complex survey data should use an appropriate method for estimating standard errors, and that pseudostrata would produce reasonable estimates of standard errors for rates and regression coeffixad cients, with mixed results for odds ratios.

Effects of Smoking on Pulmonary Function and Symptomatology in Occupationally Exposed Groups

Records of 3006 employees of nine companies in southeast Texas screened by a local lung association during the years 1974-1975 were examined to assess the effect of smoking, age, and occupational and residential ambient air pollution exposure. The effects of smoking and occupational exposure were strong when tested against a respiratory morbidity response questionnaire considered to be diagnostic for early-stage bronchitis. Occupational exposure lost statistical significance after place of residence was controlled in the analysis. Smoking and age variables exhibited significant effects when the response variable was pulmonary function test results. The differences between questionnaire and pulmonary function test results may indicate early-stage vs. later-stage respiratory morbidity ascertainment. Agreement in classifying abnormal subjects with these two methods decreased with increasing levels of smoking.

Analyzing Complex Survey Data

Series Editors Introduction Acknowledgments 1. Introduction 2. Sample Design and Survey Data Types of Sampling The Nature of Survey Data A Different View of Survey Data 3. Complexity of Analyzing Survey Data Adjusting for Differential Representation: The Weight Developing the Weight by Poststratification Adjusting the Weight in a Follow-Up Survey Assessing the Loss or Gain in Precision: The Design Effect The Use of Sample Weights for Survey Data Analysis 4. Strategies for Variance Estimation Replicated Sampling: A General Approach Balanced Repeated Replication Jackknife Repeated Replication The Bootstrap Method The Taylor Series Method (Linearization) 5. Preparing for Survey Data Analysis Data Requirements for Survey Analysis Importance of Preliminary Analysis Choices of Method for Variance Estimation Available Computing Resources Creating Replicate Weights Searching for Appropriate Models for Survey Data Analysis 6. Conducting Survey Data Analysis A Strategy for Conducting Preliminary Analysis Conducting Descriptive Analysis Conducting Linear Regression Analysis Conducting Contingency Table Analysis Conducting Logistic Regression Analysis Other Logistic Regression Models Design-Based and Model-Based Analyses 7. Concluding Remarks Notes References Index About the Authors

Introduction to Biostatistics: A Guide to Design, Analysis, and Discovery

1. Introduction, 2. Data and Numbers, 3. Descriptive Tools, 4. Probability and Life Tables, 5. Probability Distributions, 6. Study Designs, 7. Interval Estimation, 8. Test of Hypotheses, 9. Test of Hypotheses Based on the Normal Distribution, 10. Nonparametric Tests, 11. Analysis of Categorical Data, 12. Analysis of Survival Data, 13. Analysis of Variance, 14. Linear Regression, 15. Logistic Regression, 16. Analysis of Survey Data, Appendix A. Statistical Tables, Appendix B. Selected Governmental Biostatistical Data, Appendix C. Solutions to Selected Exercises

Analysis of Survival Data

This chapter presents the analysis of survival data. It also presents the introduction of methods for analyzing data collected from a longitudinal study in which a group of subjects are followed for a defined period or until some specified event occurs. Such data is frequently encountered in the health field. For example, in one study, newly diagnosed cancer patients in a registry were followed annually until they died. Another example consists of smokers who completed a smoking cessation program and were then contacted every three months to find out whether they had relapsed. The focus in these studies is the length of time from a meaningful starting point until the time at which either some well-defined event happens, such as death or relapse to a certain condition or the study ends. The data from such studies are called survival data. The chapter presents a special type of life table, the follow-up life table. It discusses the collection and organization of the data. The life-table method is used for larger data sets and the product-limit method is generally used for smaller data sets.