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PubMed1987

Epidemiologic programs for computers and calculators. Inferences on odds ratios, relative risks, and risk differences based on standard regression programs.

Statistical analyses of the joint effects of several factors (covariates) on the risk of disease, death, or other dichotomous outcomes, are frequently based on a model that relates the effect of the covariates to some function of the probability of the outcome. The odds ratio, relative risk, and the difference in risks are among the simplest candidates for the outcome function. Each can be specified as a special case of the generalized linear model, but their use has been limited to researchers with access to specialized computer programs that are not yet generally available in standard computer packages. The purpose of this communication is to describe how to implement the maximum likelihood estimation procedures and hypothesis testing associated with the generalized linear model using any computer program that can perform weighted least squares analyses. The procedure is applied specifically to models for relative risks, risk differences, and odds ratios. The techniques are illustrated with SAS and SPSSx programs for data sets previously presented.

Computers↗
PubMed1999

Frequency of angina pectoris and coronary artery disease in severe isolated valvular aortic stenosis.

Angina pectoris is a frequent symptom of severe valvular aortic stenosis (AS), even in the presence of normal coronary arteries. To determine the prevalence of angiographically significant coronary artery disease (CAD) and its relation to angina pectoris and coronary risk factors in severe isolated valvular AS patients. All cases of symptomatic AS patients who underwent aortic valve replacement and preoperative cardiac catheterization at the Central Chest Hospital between January 1, 1986 and December 31, 1996 were retrospectively analyzed. Excluded were those with multiple valvular disease, aortic regurgitation of grade 2 or more, and prior coronary or valve surgery. A total of ninety consecutive patients with severe AS (64 men and 26 women, mean age 58.94 years, range 38 to 71) were studied. Significant CAD (coronary diameter stenoses > or = 50%) was found in 15 patients (16.7%). Typical angina was present in 66.7 per cent of them but it was also found in 46.7 per cent of the non-CAD patients. This symptom had low positive predictive value (22%). Of the patients without angina (n = 45) 11.1 per cent had significant CAD, The negative predictive value of angina alone was thus 89 per cent. By univariate logistic regression, the statistically significant variables to discriminate those with or without significant CAD were age, history of hypertension, positive familial history of premature CAD, and cholesterol level. However, only age and hypertension were statistically significant by multivariate logistic regression analysis. Coronary arteriography can probably be omitted in severe valvular AS, especially those without a history of hypertension and < 40 years of age in men and < 50 years in women. For all other cases, coronary arteriography is recommended. In our study, angina pectoris is not a significant predictor for associated CAD.

Adult↗
PubMed1986

Risk factors for invasive cervical cancer among Latinas and non-Latinas in Los Angeles County.

A case-control study among white women in Los Angeles County was conducted to investigate etiologic factors that might explain the high rates of invasive cervical cancer among Latinas. Two hundred patients with invasive squamous cell carcinoma of the uterine cervix and matched (age, sex, preferred language, and neighborhood) controls were interviewed, 98 pairs in English and 102 pairs in Spanish. Seven factors were found to contribute independently and significantly (P less than .01) to risk, each after adjustment for the other six: years since last Pap smear, years of education (protective), frequency-years douching, pack-years of smoking, years of barrier contraceptive use (protective), number of sexual partners before age 20, and recognized episodes of genital warts. An eighth variable, interval in years between menarche and first intercourse, was the second variable to enter the stepwise logistic regression analysis but lost its statistical significance when sexual partners before age 20 entered the model. Together, these eight variables accounted for almost 99% of the risk. There were no significant interactions between any of these variables and age, language of interview, or birth in a Latin country. There was no increased risk associated with use of oral contraceptives, either before or after adjustment for the other significant factors. Compared to English-speaking controls, Spanish-speaking controls smoked less, douched less, had fewer sexual partners before 20, and had essentially the same average interval between menarche and first intercourse and the same average number of episodes of genital warts; however, they had had a longer interval since their last Pap smear, fewer years of barrier contraceptive use, and fewer years of education. Education, presumably a correlate of an inadequately measured etiologic risk factor (possibly papillomavirus infection), was responsible for the greatest difference in risk between the Spanish- and English-speaking cases.

Adolescent↗
PubMed1989

Correlates of fertility in selected developing countries.

The impact of differentials in key socioeconomic variables on fertility levels in 32 developing countries is assessed through multiple regression analysis of aggregate-level data on 27 developing countries for 3 recent guinguennia, grouped into 4 categories according to region (Latin America vs Asia/Oceania/Africa) and stage of fertility transition (recent vs relatively prolonged fertility decline). The results demonstrate the substantial impact of differences in child survival and educational attainment on the intercountry variance of fertility rate ranges from 46-84%), while economic indicators (per capita gross national product and % labor force in agriculture) have slight net impact.

Asia↗
PubMed1988

A geographic regression model for medical statistics.

A method for modeling geographic processes using census-type data is introduced in an analysis of male and female lung cancer mortality rates. The study area comprises the counties in those states which abut the Gulf of Mexico and the southeast Atlantic Coast of the United States. A spatially autoregressive model is used to estimate the strength of the univariate relationship between both the male and female lung cancer mortality rates in a country and in the respective lung cancer rates in the first to fifth order adjacent counties. The results show that male lung cancer exhibits spatial autocorrelation while female lung cancer does not, and that the female data exhibit a spatial trend while the male data do not. These findings suggest that factors which vary at the regional scale play a greater role in the etiology of female lung cancer and that factors that vary at the neighborhood scale play a greater role in the etiology of male lung cancer.

Female↗
PubMed1978

An economic analysis of migration in Mexico.

This paper analyzes internal migration in Mexico over the 1960-70 period. A model of the determinants of migration is specified and estimated for aggregated interstate migration flows. Results show that distance serves as a significant deterrent to migration, that higher destination earning levels are attractive to migrants, and that regions with high unemployment rates experience lower rates of inmigration. An unanticipated finding is that regions with higher earning levels have greater rates of outmigration. The data are disaggregated to examine separate migration relationships for each state. The results are that distance is a lesser deterrent for those migrants with more accessible alternatives, that higher earning levels reduce the deterring effects of distance, and that regions with higher earning levels have lower associated elasticities of migration. It is concluded that economic factors have played a crucial role in internal migration and thus in the changing occupational and geographic structure of the Mexican labor force.

Americas↗
PubMed2000

Statistical methods in epidemiology. VI. Correlation and regression: the same or different?

PURPOSE: The statistical terms 'correlation' and 'regression' are frequently mistaken for each other in the scientific literature. Why this is so is unclear. This paper discusses their differences/ similarities arguing that in most circumstances regression is the most appropriate technique to use, since regression incorporates a notion of dependency of one variable on another. METHOD: Pearson's correlation coefficient (r) is introduced as a method for estimating the degree of linear association between two normally distributed variables. The problem of least squares' regression (when y depends on x) is introduced by considering the best-fitting straight line between points on a scatter plot. RESULTS: Correlation, regression analysis and residual estimation are discussed by taking examples from the author's own teaching experiences. CONCLUSIONS: Correlation and regression share some similarities. However, regression is the better technique to use because with it comes a notion of dependency of one variable upon another. Regression model checking includes residual examination. The importance of plotting and examination of residuals cannot be overemphasized. Residual examination should become as much a part of a regression analysis as the estimation of the regression coefficients themselves.

Epidemiologic Methods↗