Common use of Using MEPS Data for Trend Analysis Clause in Contracts

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 2013. This effort likely resulted in improved data quality and a reduction in underreporting in 2013, but could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 2012-13), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, caution unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 20132014. This effort likely resulted in improved data quality and a reduction in underreporting in 2013, but FY 2014 and could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122013-1314), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The adjustment modification to the weight described in 3.2.3 above based on inpatient discharges hospital stays potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 2013. This effort likely resulted in improved data quality and a reduction in underreporting in 2013, but could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 2012-13), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, caution unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 20132014. This effort likely resulted in improved data quality and a reduction in underreporting in 2013, but FY 2014 and could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122011-13), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be are not attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 2013. This effort likely resulted in improved data quality and a reduction in underreporting in 2013, but could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, smooth or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 2012-13), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.. References

Using MEPS Data for Trend Analysis. MEPS began in 1996, 1996 and the utility of the survey for analyzing health care trends expands with each additional year of data. However, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be are attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, caution unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 2013. This effort likely resulted in improved data quality and a reduction in underreporting in 2013, but could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, smooth or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122004-1305), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. FinallyOf course, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking Performing numerous statistical significance hypothesis tests of to help identify existing trends increases the likelihood of inappropriately concluding there is a trend (because a test indicated statistical significance) when, in fact, there is not. Finally, it should be noted that standard errors for differences over time should be computed reflecting the correlation between MEPS samples where it exists. MEPS panels from 2007 through 2010 share the same sample PSUs and secondary sampling units. As a change has taken place result, the estimated standard error of the difference between two MEPS samples will generally be reduced due to this correlation, to the extent that there is a positive correlation between estimates over time. Failure to reflect this aspect of the MEPS sample design (i.e., treating MEPS estimates as having come from independent samples) can generally be expected to result in the estimated standard error of the difference overstating the actual standard error, thus reducing the power to detect existing differences over time. Variance estimation software packages designed for complex samples, such as SUDAAN, provide the capability to reflect the correlation between MEPS samples when one has notestimating the standard error for an estimated difference over time.

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However; however, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The adjustment modification to the weight described in 3.2.3 above based on inpatient discharges hospital stays potentially could affect some analyses of trendstrend. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 2013. This effort likely resulted in improved data quality and a reduction in underreporting in 2013, but could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 2009–2010 versus 2012-13– 2013), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. However, it should be noted that there are issues with pooling as well as comparing conditions data gathered prior to 2007 with the data collected in 2007 and beyond. Improved methods (a Priority Conditions Enumeration section and priority conditions automatically flagged by CAPI), were implemented for collecting priority conditions data for many of the conditions beginning in 2007. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.

Using MEPS Data for Trend Analysis. MEPS began in 1996, 1996 and the utility of the survey for analyzing health care trends expands with each additional year of data. However, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be are attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, caution unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 2013. This effort likely resulted in improved data quality and a reduction in underreporting in 2013, but could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, smooth or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122004-1305), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. FinallyOf course, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking Performing numerous statistical significance hypothesis tests of to help identify existing trends increases the likelihood of inappropriately concluding there is a trend (because a test indicated statistical significance) when, in fact, there is not Finally, it should be noted that standard errors for differences over time should be computed reflecting the correlation between MEPS samples where it exists. MEPS panels through 2006 (and MEPS Panel 12 in 2007) share the same sample PSUs and secondary sampling units. As a change has taken place result, the estimated standard error of the difference between two MEPS samples will generally be reduced due to this correlation, to the extent that there is a positive correlation between estimates over time. Failure to reflect this aspect of the MEPS sample design (i.e., treating MEPS estimates as having come from independent samples) can be expected to result in the estimated standard error of the difference overstating the actual standard error, thus reducing the power to detect existing differences over time. Variance estimation software packages designed for complex samples, such as SUDAAN, provide the capability to reflect the correlation between MEPS samples when one has notestimating the standard error for an estimated difference over time.

Using MEPS Data for Trend Analysis. MEPS began in 1996, 1996 and the utility of the survey for analyzing health care trends expands with each additional year of data. However, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be are attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, caution unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 2013. This effort likely resulted in improved data quality and a reduction in underreporting in 2013, but could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, smooth or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122004-1305), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. FinallyOf course, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking Performing numerous statistical significance hypothesis tests of to help identify existing trends increases the likelihood of inappropriately concluding there is a trend (because a test indicated statistical significance) when, in fact, there is not. Finally, it should be noted that standard errors for differences over time should be computed reflecting the correlation between MEPS samples where it exists. MEPS panels from 2007 through 2009 share the same sample PSUs and secondary sampling units. As a change has taken place result, the estimated standard error of the difference between two MEPS samples will generally be reduced due to this correlation, to the extent that there is a positive correlation between estimates over time. Failure to reflect this aspect of the MEPS sample design (i.e., treating MEPS estimates as having come from independent samples) can generally be expected to result in the estimated standard error of the difference overstating the actual standard error, thus reducing the power to detect existing differences over time. Variance estimation software packages designed for complex samples, such as SUDAAN, provide the capability to reflect the correlation between MEPS samples when one has notestimating the standard error for an estimated difference over time.

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However; however, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be are not attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, caution unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 2013. This effort likely resulted in improved data quality and a reduction in underreporting in 2013, but could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 19962008-97 2009 versus 20122011-1312), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. However, it should be noted that there are issues with pooling as well as comparing conditions data gathered prior to 2007 with the data collected in 2007 and beyond. Improved methods (a Priority Conditions Enumeration section and priority conditions automatically flagged by CAPI), were implemented for collecting priority conditions data for many of the conditions beginning in 2007. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 20132014. This effort likely resulted in improved data quality and a reduction in underreporting in 2013, but FY 2014 and could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122013-1314), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However; however, it is important to consider there are a variety of factors methodological and statistical considerations when examining trends over time using MEPS. Statistical Tests of statistical significance tests should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, caution unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 2014 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 20132015. This effort likely resulted in improved data quality and a reduction in underreporting starting in 2013, but FY 2015 and could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122011-1312), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However; however, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, caution unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 20132014. This effort likely resulted in improved data quality and a reduction in underreporting in 2013, but FY 2014 and could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122011-13), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. Howeverdata;.however, it is important to consider there are a variety of factors methodological and statistical considerations when examining trends over time using MEPS. Statistical Tests of statistical significance tests should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, caution unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 20132014. This effort likely resulted in improved data quality and a reduction in underreporting starting in 2013FY 2014, but and could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122011-1312), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However; however, it is important to consider there are a variety of factors methodological and statistical considerations when examining trends over time using MEPS. Statistical Tests of statistical significance tests should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, caution unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 20132014. This effort likely resulted in improved data quality and a reduction in underreporting starting in 2013, but FY 2014 and could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122011-13), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However; however, it is important to consider there are a variety of factors methodological and statistical considerations when examining trends over time using MEPS. Statistical Tests of statistical significance tests should be conducted to assess the likelihood that observed trends may be are not attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, caution unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 20132014. This effort likely resulted in improved data quality and a reduction in underreporting starting in 2013, but could the 2014 full year files and have had some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, smooth or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122011-132013), working with moving averages, averages or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.. References

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 20132014. This effort likely resulted in improved data quality and a reduction in underreporting in 2013FY 2014, but and could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122013-1314), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 20132014. This effort likely resulted in improved data quality and a reduction in underreporting in 2013, but FY 2014 and could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122011-1312), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, caution unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 2014 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 20132015. This effort likely resulted in improved data quality and a reduction in underreporting in 2013, but FY 2015 and could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122011-1312), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.