I have a data set with two cohorts . They have outcome differences ( Length of stay, continious variables) on univariate analysis. They are however not similiar in baseline characterstics ( have different severity of illness ( continious), age ( continious) and diagnosis ( categorial variables ).
How do I adjust the outcome variable based on age, disease severity and diagnosis ?
A proper model makes all the difference! The choice is at the discretion of the researcher but it should be guided by prior knowledge, theoretical foundations, objective comparison with alternative models, and evaluation of the model assumptions.
The parameter estimates are used in a linear combination to determine the distribution parameters. They can be difficult to interpret on their own. You might click the red triangle at the top and select Profilers > Profiler. You can change factor levels and see the change in the predicted response.
I have another idea about the lack of fit. I do not see the test for over-dispersion, which is a common occurance. The Poisson distribution has a single parameter. It is the mean and the variance of the distribution. Real distributions of counts often exhibit a variance that is greater than the mean. You should find check boxes when you select Generalized Linear Models for the over-dispersion tests and intervals and for the Firth bias-adjusted estimates. I recommend selecting both of these options.
Otherwise, you might consider adding terms for potential interaction and non-linear effects.
Include the covariates as additional predictors in the multiple regression model.
Select Analyze > Fit Model. Select the response data column and click Y. Select the predictor/factor and covariate data columns and click Add. Now click Run. Use the Effect Tests to determine the significance of each term. Use the Parameter Estimates to determine the importance of each term. Click the red triangle and select Factor Profiling > Profiler to understand the contribution of changes in each predictor and covariate to the response.
See Help > Books > Fitting Linear Models for more details and examples.
You are not making sense. "On univariate comparison for the outcome variables (ICU length of stay, hospital length of stay and ventilator days) between cases and controls there is very significant difference (P value< 0.0001) . There are indeed differences in age groups, and severity of illness score" is not a univariate comparison. Also, why would you expect there to be no differences?
Aside from the issue of the assumptions of the regression model, the differences (e.g., higher severity of illness score in control group) should be modeled without difficulty. In fact the larger the difference, the more significant the effect and the more important the parameter estimates.
I have no idea what you mean by "adjust."
I have no idea what you mean by "the regression model is not taking the negative sign into account."
I am inclined to agree with your conclusion but that is only if the analysis indicates that your data meet the model and regression assumptions. This seems to be an important study. Care should be taken in the design of the study and the analysis of the data that was collected in the study. This analysis is not a simple t-test, and a t-test is not that simple.
Is it possible to share more information about the study. For example, repeat the set up for Fit Model but be sure that the Emphasis = Effect Leverage. May you include a picture of Actual by Predicted and the set of Leverage Plots? Also, the Residual by Predicted Plot or Standardized Residual plot? Those plots would go a long way towards assessing if your analysis is OK and then you can move on to conclusions with confidence.
I agree that a Poisson log-linear regression model is reasonable for counts of days. A better model will show more significance. Can you share the regression results from JMP? Some of the information is the estimates. The rest is about the quality of the model. All of the information is helpful to assess the validity of your model.
First of all, let me explain that we prefer that all exchanges happen in the discussion area, not in private messages. Why? Because other members of the community who might have similar questions or problems are deprived of part or all of the solution. I understand that sometimes the nature of the problem or the data involves privacy issues and cannot be posted publicly.
Per your message, the regression results are encouraging! Is it possible to show me the residual plot? This plot would help answer the question about one or more influential observations that are skewing the estimates and the tests. The plot also helps assess goodness of fit.
The Deviance test is highly significant, too. This test is for lack of fit. Your model is biased and will not provide accurate predictions. It usually indicates that you are either missing important variables or, more likely, are missing terms to address non-linear effects. You might consider adding cross terms (e.g., sex*age) or powers (e.g., age*age).
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